covid 19 supply chain case study

The ongoing impact of COVID-19 on global supply chains

Companies need to invest in supply chain resilience. Image:  REUTERS/Mohamed Azakir

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Mattias Hedwall

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• Companies should analyze supply chains now to mitigate against future disruptions.
• Trade wars, global politics and national policies will influence the future of supply chain structures.
• Investment in technology and considerations on sustainability in the supply chain will be key.

The COVID-19 pandemic has changed the business environment for many organizations around the globe, and has highlighted the importance of being able to react, adapt and set up crisis management mechanisms in order to weather situations of uncertainty. As the acute restrictions and lockdowns created many urgent situations that required immediate attention in the early days of the pandemic, many companies have now begun to move to a "recovery mode" and have started planning for the longer term. As companies seek to strengthen operations and business resilience, the importance of supply chain resilience and risk management is more apparent than ever.

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Discovering the real impact of covid-19 on entrepreneurship, how are companies responding to the coronavirus crisis, this is how to distribute a coronavirus vaccine to everyone.

What have we seen happen?

Around the world, many companies are hugely reliant on production and supplies in China, Southeast Asia and other low-cost jurisdictions. In recent years, broad global developments have forced these companies to rethink their supply chains and their stability and reliability for an uncertain future. This is not just in relation to COVID-19 but many other externalities and government actions through the world, which have begun impacting supply chains, such as the increased risk of trade wars, trends of nationalism and protectionism, issues of sustainability and human rights considerations.

The overall impact of the outbreak and the resulting emergency measures on international trade resulting from COVID-19 remain to be seen. However, it is clear companies have been faced with substantial business and operational disruptions, which has included everything from mitigating the effects of reduced supply, to managing disruptions to logistics suppliers, and indeed hurdles in meeting their own contractual obligations to customers. Baker McKenzie recently produced a podcast on shock-proofing supply chains , which covers many of these hurdles in detail.

While many businesses have been nimble and ready to adapt to change, companies that have not already done so should prioritize analyzing their supply chains now, to understand where they might need to make changes or take action to mitigate against future disruptions. Considerations should include reviewing contractual obligations, assessing force majeure clauses, tax and employment implications of changes, relocation costs, entry and visa issues for staff, exit possibilities, as well as the option of swiftly reversing changes if the situation stabilizes or if new developments require the supply chain to adapt quickly.

COVID-19 has presented a unique situation in which to observe how these various systems and processes respond to acute severe stress and change. It has also shone a spotlight on the importance of investing in supply chain resilience to build stronger long-term operations. As we move into the future, it is vital to use what has been learned from recent events to prepare for the future.

What can we expect to see?

Over the past decades, the discussion around optimizing supply chains has focused primarily on cost efficiency and commercial best outcomes. However, as recent history has demonstrated, future supply chains will need to begin factoring resilience and adaptability into their calculations. Before the COVID-19 pandemic, some companies began anticipating this next evolution, but this crisis has exposed those weaknesses in the modern supply chain, such that many are looking at what to do next. Such decisions should of course not only focus on the supply side patterns, but must also consider that demand patterns may look different going forward – the key here is to have a holistic approach and ensure that many different perspectives are considered.

Trade wars, global politics and national policies will influence the future of supply chain structures

The global supply chain had begun responding to US-China tensions and we can expect the disruption caused by COVID-19 to accelerate the pace of this response. Trade analytics show China lost global export market share at an accelerated pace in 2019, as companies moved to other countries. We have seen low-cost production moving mainly to Mexico and Vietnam. Together, the two countries have grown their market across the consumer goods and technology, media, and telecoms (TMT) sectors to 12% and 9% by 2019, largely at the expense of China. Vietnam’s clothing and smartphone exports, as well as Mexico’s automobile parts and computer exports, all gained as well.

There is no simple substitute for China. The country accounts for 60% of global consumer goods exports and 41% of global TMT exports. However, we expect companies will be increasingly considering China +1 strategies. Where other countries will benefit from supply chain investments will depend largely on their own investments to boost manufacturing capability as well as provide attractive offerings for land, labour and logistics. At the same time, continued efforts to conclude free trade agreements (FTAs) could further impact where and how businesses seek to restructure their supply chains.

Investment in technology and considerations on sustainability in the supply chain will be key

As supply chains are reviewed, developments in technology and sustainability should also be considered. The COVID-19 pandemic has shown the many different ways business can continue to effectively communicate and manage within a remote working environment, which many companies are likely to leverage going forward. Indeed, those operations with stronger digital infrastructure have fared better in the COVID-19 pandemic than those without.

Meanwhile, advances in artificial intelligence and new technologies, such as blockchain, may present opportunities for further supply chain innovation. Furthermore, as supply chains are arguably a mechanism by which businesses can create positive impact in the world, those looking to change their supply chains should consider how to integrate elements and practices around human rights including labour rights, environmental protection, product sustainability, inclusive economic growth and ethical business practices.

The first global pandemic in more than 100 years, COVID-19 has spread throughout the world at an unprecedented speed. At the time of writing, 4.5 million cases have been confirmed and more than 300,000 people have died due to the virus.

As countries seek to recover, some of the more long-term economic, business, environmental, societal and technological challenges and opportunities are just beginning to become visible.

To help all stakeholders – communities, governments, businesses and individuals understand the emerging risks and follow-on effects generated by the impact of the coronavirus pandemic, the World Economic Forum, in collaboration with Marsh and McLennan and Zurich Insurance Group, has launched its COVID-19 Risks Outlook: A Preliminary Mapping and its Implications - a companion for decision-makers, building on the Forum’s annual Global Risks Report.

Companies are invited to join the Forum’s work to help manage the identified emerging risks of COVID-19 across industries to shape a better future. Read the full COVID-19 Risks Outlook: A Preliminary Mapping and its Implications report here , and our impact story with further information.

Radically changing an existing supply chain is not as easy as it may sound, as creating a robust and secure supply chain will still need to balance the demands for cost efficiency. At the same time, new logistics considerations may also have an impact on supply chains and the changes thereto. In the near term, it is expected that companies will begin seeking out a more diversified supplier base, while looking to develop a flexible, but cost efficient, supply chain.

For the longer term, however, companies will need to undertake a more holistic analysis, which may lead to more drastic changes, such as moving supply chains nearby, or to different countries, as well as increasing the digitalizing of supply chains, with a view of creating a more sustainable operation for the future. A holistic analysis should be based on facts and include the modelling and testing of different scenarios. Change scenarios should also include a contingency plan that provides for the possibilities of having to quickly revert and adjust elements of the supply chain.

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COVID Tested Global Supply Chains. Here’s How They’ve Adapted

Global supply chains took some heat during the COVID-19 pandemic, with consumers waiting months for goods and politicians wringing their hands over trade policy. “Reshoring” is one of the hottest new corporate buzzwords, as many companies look to move some manufacturing operations back to the United States.

But appearances can be deceiving. What may look to more casual observers like the end of globalization—or, at least, a major step back—is anything but, suggests a new working paper . Call it instead the “great reallocation,” says Harvard Business School Professor Laura Alfaro, whose work documents how and why companies are moving operations to low-cost countries like Vietnam, Mexico, and Costa Rica.

“They moved into a world where they thought there were no more shocks. Now, they realize there are shocks of many kinds.”

It’s unclear what impact this massive reshuffling of global supply chains will have on US businesses and consumers, with worrying signs that it could result in higher costs and prices, say Alfaro and her coauthor Davin Chor, an associate professor at Dartmouth College’s Tuck School of Business. Yet, there are also advantages to diversification that could help companies weather the next global shock or crisis when it inevitably happens.

“Some geographic diversity may pay off,” Alfaro says. “Firms used to know this, then they moved into a world where they thought there were no more shocks. Now, they realize there are shocks of many kinds.”

A new sense of vulnerability

A series of major crises and surprises over the past few years raised concerns of the potential fragility of sprawling supply chains for both companies and governments.

The rethinking extends beyond the COVID-19 pandemic and the war in Ukraine to include extreme weather events such as the 2011 Tohoku earthquake and tsunami in Japan, the paper notes.

Government policy has played a leading role. The Biden Administration has continued former President Trump’s tariff policies aimed at Chinese goods, while also offering incentives that encourage American companies to bring manufacturing operations back from China.

The authors examine the effects of these variables between 2017 and 2022, a tumultuous period that put stress on the global economic system. They crunch product-level trade statistics from United Nations Comtrade, as well as information on US manufacturing, multinational activity, and foreign direct investment from multiple sources, including corporate earnings calls.

Less business in China, but trade remains global

One major takeaway from the analysis: The share of Chinese goods and services fell to 16.5 percent of all US imports in 2022, down from a peak of 21.6 percent in 2017, Alfaro notes.

Yet, the step back from business with China did not represent a retreat from global trade; US good imports hit an all-time high of $3.2 trillion in 2022, according to the US Census Bureau. That indicates that global supply chains were more resilient than commentators believed.

While the share of US imports from China fell, they were replaced by imports from other locations. For instance, the share of imports from Vietnam and Mexico, the paper notes, increased 2 percentage points and close to .7 percentage points, respectively, over the same five-year period.

In addition, there has also been some very preliminary evidence of reshoring amid plans to boost semiconductor manufacturing in the US, says Alfaro, whose research shows that worker headcounts in that space rose by 1.9 percent in the US between 2017 and 2022, but not in other sectors.

The Intel example of diversification

Companies are also starting to recognize—and rediscover, as in the case of Intel Corp.—the benefits of diversifying their global production and supply chains, Alfaro says.

The chip manufacturer downsized its plant in Costa Rica in 2014 and relocated many operations to Asia, where costs were lower, Alfaro explains. Now, with the US looking to shift its reliance on China for semiconductor chips and other critical technologies, Intel is spending more than a billion dollars to rebuild its operations in Costa Rica, which is a closer flight to the US.

One lesson leaders can learn from Intel’s story: Geographic diversification can provide a backstop against some of the inherent risks that come with relying on global supply chains and operations, says Alfaro.

Where do we go from here?

Despite all the upheaval, it’s not clear to what degree the US will succeed in reducing its dependence on supply chains that originate in China. As American companies relocate operations from China to Vietnam and Mexico, China, in turn, is increasing both trade with and investment in those two US trading partners, Alfaro says.

“This means that the US could well remain indirectly connected to China through its trade and [global-value chain] links with these third-party countries,” the paper notes.

“The global supply chains actually helped us during COVID-19.”

Moreover, there are growing concerns that the reshuffling of global manufacturing and logistics systems could prove more expensive than anticipated at a time when concern over inflation remains high. Government trade policy may in fact have pushed companies to move faster than they would have liked, raising the cost of relocating manufacturing operations and supply chains, Alfaro says.

The authors suggest further evaluation may be warranted. Alfaro suggests that what is missing amid current debates is the need to evaluate these welfare tradeoffs.

While there is a popular perception that the pandemic highlighted the problems with far-flung global supply chains, the reality is far different, Alfaro says. If anything, the global supply chain stood up under intense pressure as federal monetary policy and pandemic relief programs stoked a huge surge in consumer demand.

“The global supply chains actually helped us during COVID-19,” Alfaro says. “[Demand] would have been very hard to fill just by domestic supply chains.”

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• Published: 03 June 2020

Global supply-chain effects of COVID-19 control measures

• Dabo Guan   ORCID: orcid.org/0000-0003-3773-3403 1 , 2   na1 ,
• Daoping Wang   ORCID: orcid.org/0000-0001-5221-4965 3   na1 ,
• Stephane Hallegatte   ORCID: orcid.org/0000-0002-1781-4268 4 ,
• Steven J. Davis   ORCID: orcid.org/0000-0002-9338-0844 5 ,
• Jingwen Huo   ORCID: orcid.org/0000-0003-3082-8370 1 ,
• Shuping Li 6 ,
• Yangchun Bai 6 ,
• Tianyang Lei 1 ,
• Qianyu Xue 6 ,
• D’Maris Coffman 2 ,
• Danyang Cheng 1 ,
• Peipei Chen   ORCID: orcid.org/0000-0002-4430-1791 7 ,
• Xi Liang   ORCID: orcid.org/0000-0002-0674-2246 8 ,
• Bing Xu 1 , 9 ,
• Xiaosheng Lu 10 ,
• Shouyang Wang   ORCID: orcid.org/0000-0001-5773-998X 11 ,
• Klaus Hubacek   ORCID: orcid.org/0000-0003-2561-6090 12 &
• Peng Gong 1 , 9  

Nature Human Behaviour volume  4 ,  pages 577–587 ( 2020 ) Cite this article

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Matters Arising to this article was published on 25 February 2021

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.

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COVID-19, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in late December 2019, but quickly spread to other countries 1 in Asia, Europe and North America and was declared a pandemic by the World Health Organization (WHO) on 11 March 2020 (ref. 2 ). There are now confirmed individuals with COVID-19 in nearly every country of the world, and the WHO has urged affected countries to slow the spread of the virus by imposing containment and suppression measures 3 , 4 ranging from strict controls on travel, social gatherings and commercial activities aimed at ‘flattening the curve’ (that is, decreasing the rate of new infections to avoid overwhelming healthcare systems) to less strict measures designed to shield immunologically compromised individuals, treat victims and achieve ‘herd immunity’ (that is, a sufficiently large number of recovered and, therefore, immune individuals to prevent the effective spread of the virus) 5 . Differences in the strictness of such policies and the rapidity with which jurisdictions have imposed and relaxed the policies reflect divergent (and perhaps hasty) assessments of both the public health risk of COVID-19 and the social and economic impacts of the different policies 6 , 7 . Using a newly developed economic disaster model 8 , 9 , 10 , we quantitatively assess the short-run supply-chain effects of different containment strategies across countries and industry sectors to inform ongoing efforts to contain COVID-19 and to reveal more generally how pandemic-related economic losses will be distributed along global supply chains.

Details of our analytical approach are provided in the Methods . In summary, we modelled the short-term economic shocks of different COVID-19 response scenarios as sector-specific transportation and labour supply constraints. The model operates in weekly time steps using the latest available global input–output data 11 and taking into account interactions throughout complex global supply chains and the contexts of scarcity and imbalance that prevail in most markets 9 . Our enhanced adaptive regional input–output (ARIO) model incorporates substitutability of inputs and dynamic choices of supply-chain linkages (Supplementary Fig. 1 ), which contribute to a more realistic representation of bottlenecks along global supply chains. By applying our model to the simulation of control policies during a pandemic, we can assess the potential impact of different policies on the supply chains and examine the externalities of control measures. Note that our model is distinct from computable general equilibrium (CGE) models in that it is specifically designed to assess economic impacts in response to disasters that unfold over weeks or months, before production structures and trade networks have time to adjust to new production patterns. Moreover, the goal of this study is not to predict the true cost of the COVID-19 pandemic, but to identify the most important aspects of disease control (such as strictness, duration and recurrence of lockdowns) and test the sensitivity of these factors as their impacts ripple through global supply chains, supported by several sets of scenarios for containment measures. Thus, in addition to showing how overall damages might change under different policy scenarios, the incidence of damages across sectors and countries may inform the allocation of international aid and economic stimulus.

We modelled four different sets of pandemic scenarios, three of which (36 scenarios in total) represent different spread extents and containment responses to the COVID-19 pandemic (Fig. 1 , Table 1 and Supplementary Fig. 2 ), and the last of which (3 scenarios in total) assesses both the damages of sustaining some restrictions over a longer period as well as the losses if lockdowns are imposed again next autumn or winter. Spatial spread refers to the global extent of the pandemic—the number of countries affected. Duration refers to the number of months that lockdown measures are in place. Strictness is measured by the percentage by which labour availability and transportation capacity 12 are reduced relative to pre-pandemic levels. Given that the impacts of lockdown measures on labour availability depend on the characteristics of production, we developed specific impact-to-labour ‘multipliers’ for each sector on the basis of three factors: the level of exposure to the virus (that is, the degree and proximity of in-person interactions), essential or lifeline sectors (such as electricity), and the option of performing work from home (for example, education). Sector-specific constraints on labour availability are therefore determined by both the strictness of lockdown measures represented in the scenario (for example, 80% strictness will reduce overall transportation capacity by 80%) and the sector-specific multipliers (for example, 0.5 for wheat production as the level of exposure is low and 0.1 for electricity and gas supply as essential activities; see Methods ). Each of the 39 scenarios is based on a different combination of spatial spread, duration and strictness; the results are presented in terms of economic supply-chain effects, measured in absolute terms of loss in value added (for example, billions of US dollars) or relative terms (as a percentage of pre-pandemic value added).

a – i , Maps of the results from 9 scenarios out of the first three sets of 36 modelled scenarios, with different combinations of spatial spread (China only ( a , d , g ), Europe and the United States ( b , e , h ), and global ( c , f , i )), lockdown duration and strictness (80%–2 month ( a – c ), 60%–4 month ( d – f ) and 60%–6 month ( g – i ); see Methods ; Table 1 ). Strictness represents the level of reductions in transportation capacity and labour availability relative to pre-pandemic levels. The percentages at the bottom left of each map indicate the global value-added losses for each scenario; shading denotes the regional distributions of these losses. j – l , A summary of all 36 scenarios, showing the sensitivity of global value-added losses to duration (different stacks) and strictness (shading of stacked bars) for the China only ( j ), Europe and the United States ( k ), and the global scenarios ( l ).

Figure 1 summarizes the results of several representative pandemic scenarios. The panels in the left column (Fig. 1a,d,g,j ) show the supply-chain effects if COVID-19 had been successfully contained to only China; the panels in the middle column (Fig. 1b,e,h,k ) show the impact if COVID-19 had spread from China to Europe and the United States, which had implemented lockdowns, but no further; and the panels in the right column (Fig. 1c,f,i,l ) show the impact when the virus spreads globally and all of the remaining countries implemented containment measures. Although some of these results are outdated given the reality of the global spread of the disease, it may nonetheless be useful to examine the differences in impacts as a function of spatial spread ( Supplementary Information ). For each of the different spatial spreads (Fig. 1 , columns), Fig. 1 also shows the results of 3 different lockdown strictness–duration combinations: from 80% restriction for 2 months (Fig. 1a–c ) to 60% restriction for 6 months (Fig. 1g–i ). Note that China’s lockdown is consistently modelled as an 80% restriction for the 2 months of January and February 13 in the scenarios of greater spatial spread, with restrictions in Europe and the United States beginning in March, and restrictions in the remaining countries (in the global scenario) beginning in April (see Methods ; Supplementary Fig. 2 ).

The first insight from the model is that the global cost of the pandemic depends foremost on the number of affected countries, and then on the required duration of lockdown policies; by contrast, the strictness of these policies is comparatively less important. The spatial extent of the pandemic is the most important driver of the global cost. If only China had been affected, our results suggest that the global supply-chain effects (measured by value added) would have been 3.5% of global gross domestic product (GDP; Fig. 1a ). With the spread to highly developed western countries and the containment measures placed in Europe and the United States, we found that the global supply-chain effects increase almost fourfold to 12.6% (Fig. 1b ). Finally, the modelled impacts of global lockdowns in response to COVID-19 are greater still—26.8% of global GDP (Fig. 1c ). The magnitude of lockdown duration is shown in Fig. 1f,i , which shows the effects of global spread and relatively strict (60%) lockdowns for 4 and 6 months. In this case, global value-added losses increase slightly more than 4% (from 26.3% to 30.8%; Fig. 1f,i ).

Figure 1j–l further emphasizes the rapid increase in global losses with the duration of lockdowns. For example, in the strictest lockdown scenarios (that is, 80%) with global spread, the global supply-chain effects rise from US$20.0 trillion under a 2-month duration (Fig. 1l , blue bars) to US$22.7 trillion under a 4-month duration (Fig. 1l , green bars) and US$30.1 trillion (equivalent to 40.3% of global value added) under a 6-month duration (Fig. 1l , red bars). However, the same bar charts show that global losses are relatively less sensitive to the strictness of lockdown measures compared with the extent of pandemic or duration of the lockdown. For example, if only China had been affected (China only scenario; Supplementary Fig. 3 ), doubling the strictness would lead to an almost linear impact under a 2-month duration. As the duration increases, the economic damage is less sensitive to changes in strictness. In the global scenario, the global impacts of 2 months of lockdown are only 7.2% greater under a strictness of 80% compared with a strictness of 20% (Fig. 1l , dark and light blue bars). Although both duration and strictness determine the domestic production (through labour supply) and transportation capacity—which links upstream suppliers to downstream consumers—the economic damages through supply-chain linkages are much more sensitive to the duration of the measures.

The second insight from the model is the importance of propagation through global supply chains—even countries that are not directly affected by the virus experience large losses, and low- and middle-income countries are more vulnerable to indirect effects. Figure 2 shows direct effects (that is, due to domestic containment measures, such as lockdown or suppression; Fig. 2 , hashed areas of the bars) and propagation effects through international supply chains across the three scenarios sets (Fig. 2 , solid areas of the bars). In the scenarios in which an outbreak is contained in China, direct losses, by definition, occur only in China, but are nonetheless substantial—16.7% of China’s annual GDP (Fig. 2a ). However, even if the virus had been confined to China, its economic disruption would not have been. Forward and backward propagations along supply chains within China and with other countries add another 4.8% to China’s losses to cause overall impacts of 21.5% of annual value added. For example, although the United States and New Zealand are not directly affected by COVID-19 in this scenario, they would still suffer 0.6% and 2.2% value-added losses, respectively, during a lockdown with 80% strictness for 2 months in China due to the decline in China’s output (that is, negative forward effects) as well as a decrease in China’s final demand for their products (negative backward effect). Under the same scenario, countries such as Vietnam, Malaysia and Nigeria, which are closely linked to China’s supply chains, would experience decreases of 5.2%, 3.6% and 3.1% in their GDP, respectively. Interestingly, specialized economies such as Kazakhstan (energy), Mongolia (livestock) and Jamaica (tourism) experience even larger losses, with 6.1%, 4.2% and 11.4% decreases in their annual GDP, respectively (Fig. 2d–i ). Similarly, countries where the virus has been controlled can be continuously affected by imported losses. Assuming the virus is controlled in China over two months but spreads globally, China nonetheless suffers ongoing economic disruption due to propagations—US$5.77 trillion in the global scenario in which lockdowns are 40% strict for 6 months (Supplementary Fig. 2 ).

a – i , Economic loss (measured by the percentage of value-added losses) in the following nine selected countries: China ( a ), New Zealand ( b ), United States ( c ), Vietnam ( d ), Nigeria ( e ), Malaysia ( f ), Kazakhstan ( g ), Jamaica ( h ) and Mongolia ( i ). The top row country includes China (affected in the China only scenario), developed countries such as the United States (affected in the Europe and US scenario) and New Zealand (only affected in the global scenario). The middle row includes countries (affected in the global scenario) that have close supply-chain relationships with China to assess propagation effects. The bottom row includes countries with a dominant economic sector. Each plot contains three selected scenarios from the three scenario sets (12 per figure as indicated). The shades of blue, green and red indicate a duration of 2, 4 and 6 months, respectively. The hashed area in the bars represents direct losses due to containments and the solid area represents the propagation. The results of other selected countries are provided in Supplementary Figs. 4 – 11 .

Lockdown losses are propagated through supply-chain networks 14 . In particular, unaffected countries benefit enormously from effective containment measures in affected countries. For example, if only China had been affected, most of the supply-chain effects in other countries would have been delayed by weeks or months (depending on which country; Supplementary Fig. 2 ), as firms used their inventories to smoothen the shock. Specifically, with 2 months of the strictest lockdown measures in China but with no spread of the virus beyond China (that is, China only, 80%–2-month scenario; Fig. 2 , top blue bars), our results indicate that 21.6% of China’s value added is lost, while the impacts in other countries are much smaller compared with scenarios in which those countries are also directly affected (that is, the global scenario; Fig. 2 ).

Similarly, if the virus had been contained in those highly developed western countries by a strict 2-month containment (that is, the Europe and the United States 80%–2-month scenario; Fig. 2 , centre blue bars), Europe and the United States would suffer much larger direct losses of 15–20% of their GDP. The economic damage in countries that are not directly affected increases with the duration of lockdowns in affected countries. For example, the loss in Ethiopia will increase from 2.5% under the Europe and the US 80%–2-month lockdown scenario to 9.8% under a 6-month lockdown (Supplementary Fig. 2 ). But this is still much less than the 27.9% losses in Ethiopia under the scenario of global spread and 6 months of 40% strict lockdowns. Although these findings are too late to affect public health policies for the first round of the COVID-19 pandemic, they demonstrate that containment has both substantial positive externalities, in that all countries benefited considerably when China placed the strictest measures, and negative externalities, in that all countries suffer from containment in other countries due to reduced demand in global markets. However, our estimates show that the positive externality of containments dominates.

The third insight is that specific country sectors are quite vulnerable to impacts that are propagated through global supply chains, even in scenarios in which COVID-19 does not spread globally. Figures 3 and 4 show the upstream and downstream impacts related to the Chinese electronics and German automotive sectors, respectively. Each of these sectors are important to the respective economies of China and Germany and each are also dependent on extensive international supply chains.

a – f , The supply-chain impacts to China’s electronic-manufacturing industries under three scenario sets (China only, 80%–2 month ( a , b ); Europe and the United States, 60%–4 month ( c , d ); and global, 40%–6 month ( e , f )). The economic impacts to China’s electronics industry’s upstream supply chain ( a , c , e ) and the economic impacts from the perspective of downstream supply chains ( b , d , f ) are shown. The different colours of each bar represent the strength of linkage between industries and China’s electronics industry (blue to red). In the upstream supply chains, bars coloured towards the red end of the colour scale indicate that the suppliers are more important for China’s electronics industry; downstream, the bars coloured towards the red end of the colour scale indicate that these sectors are the main clients of China’s electronics industry. The length of the bars in a – f shows the industries’ relative production losses compared with the original capacity under different scenario sets. The colours of the bars represent the cohesion level of the particular sector to the Chinese electronics sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the Chinese electronics sector. The results of some other industries in different countries are provided in Supplementary Figs. 12 – 14 .

a – f , The economic impacts to supply chain upstream of German automobile industries ( a , c , e ) and the economic impacts from the perspective of downstream supply chain ( b , d , f ) under three scenario sets (China only, 80%–2 month ( a , b ); Europe and the United States, 60%–4 month ( c , d ); and global, 40%–6 month ( e , f )). The colour and bar area are as described in Fig. 3 . The length of bars in a – f show the industries’ relative production losses compared with the original capacity under different scenario sets. The colours of the bars represent the cohesion level of the particular sector to the German automotive sector from blue (weak) to red (strong), which is measured by the trade volume between the particular sector and the German automotive sector.

China’s electronics supply chain is labour intensive and has a ‘scale-free’ property 7 , that is, there is a clustered hub in China with connections to a large number of firms in electronics, chemical and metal production in countries throughout Asia 15 . In scenarios in which COVID-19 is confined to China by a strict 2-month lockdown (that is, the China only, 80%–2-month scenario), the global value added related to China’s electronics sector would have been reduced by 27.3% (including 20.8% in direct losses; Supplementary Fig. 15 ). However, the impacts to China’s electronics sector trigger substantial upstream decreases in production in the South Korean electronics, Japanese electronics and Australian metals sectors (in each case by roughly 21%; Fig. 3a ). Although electronic products are largely substitutable, major production lines are centralized in China 15 , such that there are also large downstream impacts as reduced output limits final consumption, particularly in the United States, Japan, Mexico and France (where reductions are >28%; Fig. 3b ). In the scenario of global spread and 6 months of lockdowns (that is, the global 40%–6-month scenario), the recovery of China’s labour supply and transportation capacity to pre-disaster levels does not prevent ongoing impacts to its electronics sector through global supply chains (largely forward effects from upstream Asian countries), which further increases the reduction in the sector’s output from 29.9% to 32.8% (Fig. 3e , Supplementary Fig. 15 ). In this global scenario, downstream consumption in countries such as the United States, Japan, Mexico and France is reduced by a total of 40% (Fig. 3f ).

Automotive sectors are similarly international 16 , with highly specialized suppliers that make short-term substitution difficult 17 . In the scenario in which only China imposes lockdown measures (that is, the China only 80%–2-month scenario), supply-chain effects to the German automobile are modest—losses of 1.8% of value added as China’s demand for German motor parts and vehicles fall by around 20% (Fig. 4b , Supplementary Fig. 16 ) and reductions in the output of various Chinese sectors (such as electronics, metals, and rubber and plastics) constrain upstream production of motor parts in the United States and the United Kingdom and electronics in Germany. However, with the spread of COVID-19 to highly developed western countries (that is, the Europe and the United States 60%–4-month scenario), labour and transportation constraints in Germany and many of the countries that supply auto parts and raw materials (Supplementary Fig. 16 ) cause a decrease in production by the German automotive sector of 28.8% (24.8% directly due to local containment and 4.0% due to effects upstream; Fig. 4c ). Such decreases in German production ripple upstream to suppliers in Hungary, Spain, Italy and the United States, and downstream demand for German cars decreases in the United States, China and Austria by 29.1%, 37.6% and 22.3%, respectively (Fig. 4d ). In the case of global spread and more widespread and longer-term lockdowns (that is, the global 40%–6-month scenario; Fig. 4e ), the output of the German automobile industries decreases by a further 0.9%. A decrease in supplies from low- and middle-income countries to Germany (Supplementary Fig. 16 ) leads German producers to look for new suppliers (substitution effect). By contrast, the production of motor parts in the United States rebounds slightly in this scenario, but the overall impacts of such global spread remain strongly negative everywhere. Consumption of German cars in the United States and Austria falls by 29.5% and—although Chinese demand for German cars in this scenario returns to pre-pandemic levels in April—supply-chain and transportation constraints nonetheless reduce Chinese consumption of German cars by 37.5%.

Our results also highlight the vulnerability of sectors such as catering and tourism to pandemic lockdowns 18 , which are exposed to both very large decreases in demand and the propagation of losses from upstream suppliers such as food and business sectors 19 . For example, in scenarios of a global pandemic (for example, the global 40%–6-month scenario), very large reductions in domestic and international travel and tourism (Supplementary Fig. 17 ) cause tourism in Jamaica to decrease by 56.3%, in turn reducing imports of beverages and tobacco products from the United States, which fall to 46.7% of pre-pandemic levels (Supplementary Fig. 17 ).

As a final analysis, we modelled three different scenarios of recovery from the global spread of the COVID-19 pandemic: (1) a ‘new normal’ scenario in which each country’s lockdown (China, 80%–2-month scenario; Europe and the United States, 60%–4-month scenario; all other countries, 40%–6-month scenario) was first relaxed to 20% strictness and then back to 0% over a period of 12 months. (2) A ‘recurrent with global cooperation’ scenario, in which (round 1) each country’s lockdown (that is, China, 80%–2 month; Europe and the United States, 60%–4 month; all other countries 40%–6 month) was first relaxed to 0% strictness over a period of 2 months, followed by a 3-month period of no restrictions, and then (round 2) all countries acted together by placing the strictest (80%), 2-month global lockdown to minimize virus spreading. (3) A ‘recurrent without global cooperation’ scenario, in which (round 1) each country’s lockdown (that is, China, 80%–2 month; Europe and the United States, 60%–4 month; all other countries, 40%–6 month) was first relaxed to 0% strictness over a period of 2 months, followed by a 3 month period of no restrictions, and then (round 2) all countries placed the same less strict but longer lockdowns as the first round.

These recovery scenarios led to a fourth and final insight—relaxing lockdown restrictions gradually over a long period of time (in our ‘new normal’ scenario, 12 months) results in substantially lower supply-chain effects than lifting restrictions quickly if it means avoiding another round of strict lockdowns in the coming year. Globally, we estimate that the overall value-added losses in the new normal scenario are 39.5%, compared with 49.5% and 61.5% in the recurrent with global cooperation and recurrent without global cooperation scenarios, respectively (Fig. 5a–c ). The differences are particularly striking in the United States, where losses related to recurrent lockdowns are 24.6–54.8% greater compared with the losses in the scenario in which restrictions are slowly relaxed (Fig. 2d–f , light-blue shading). As shown in our scenarios of initial lockdowns, if the pandemic does recur, stricter and shorter lockdowns (which may depend on global coordination) also greatly reduce losses (11% globally) in our estimates (Fig. 5b,c ). The implications of these different recovery trajectories for selected sectors are shown in Fig. 5g–i ; similar to the losses globally or in specific countries, recurrent lockdowns are considerably worse for the selected sectors (for example, 33.1–90.8% worse in the sectors depicted).

a – c , The results from three post-pandemic recovery scenarios (new normal ( a , d , g ); recurrent with cooperation ( b , e , h ); recurrent without cooperation ( c , f , i )), with different potential recovery strategies. The percentages in the bottom of each map indicate the global value-added losses. The colour shades represent the severity of economic impact by countries. d – f , The stacked area plots show the dynamics of value-added loss in different countries or regions. ROW, rest of the world. g – i , The value-added loss in ten selected sectors under different recovery trajectories.

Our modelling of COVID-19 lockdowns demonstrates the potential for enormous economic losses in affected countries, using idealized scenarios in which the number of countries, the duration and the strictness of lockdowns, as well as the manner in which restrictions are relaxed as the pandemic abates, were varied. In each scenario, we used factors that are influenced or determined by public health policy choices across the globe 20 , 21 . Our model was designed to identify the most important containment factors and measure the magnitude of propagation effects through global supply chains. The analytical framework settings are fundamentally different from those used in other macroeconomic analyses 22 , 23 , 24 that aim to estimate the costs of COVID-19. Our model is limited by taking no consideration of technological changes and assuming that production and consumption patterns are kept the same as before the crisis. Our model focuses on the short-term scenarios and situations after a shock and, therefore, those changes are rather unlikely. Our model is further constrained by the trade relationship at the sectoral level among countries, and has no ability to capture the complexity of supply-chain networks at the firm level and may therefore underestimate the total effects. Finally, it is not our purpose, nor our approach, to model the dynamic general equilibrium effects, or health-related implications, such as mortality, quality-adjusted life year and disability-adjusted life year. This paper therefore cannot compare the costs and benefits of various strategies, but focuses only on their economic implications.

We have enumerated several insights on the basis of our results, which together suggest that economic losses will be minimized by stricter initial lockdowns, provided that such strictness reduces the duration of the measures. Indeed, emerging results of related research seems to support exactly this relationship 13 . However, our modelling of recovery scenarios suggests that an extended period of some restrictions (for example, 20% reductions in labour and transportation capacity in our new normal scenario) is nonetheless economically preferable to a more rapid return to pre-pandemic activities followed by another round of global lockdowns. This is a critical, albeit perhaps inconvenient, finding for policymakers who are eager to lift restrictions and stimulate economic recovery.

Our results also illustrate the substantial and heterogenous impacts propagated through global supply chains, which affect the level of economic loss to a country or sector in ways that are not always intuitive. Moreover, just as individuals who stay at home protect others as well as themselves, countries that impose strict lockdowns also provide a public good to other countries 25 , 26 . For example, we estimate that a strict lockdown that contained the COVID-19 outbreak to China would have reduced global GDP by 3.5% while costing China 21% of its GDP. In theory, disease control measures should be increased up to the point at which the marginal social costs of control are just offset by the marginal social benefits. Our results imply that a large part of the problem that we currently face is that individual countries are making disease control choices without sufficient consideration of the effect of their actions on global supply chains. The positive externalities of public health measures to prevent a pandemic may lead to market failures, leading to underinvestment and delayed action from the perspective of global optimization. In preparing for the next emerging disease, a global cost-sharing instrument could ensure that the costs of monitoring, containing and suppressing are fairly distributed, removing some of the disincentives for early action and providing global health and economic benefits over the long term.

Disaster impact model to measure supply-chain effects

Our impact model is an extension of the ARIO model 25 , 26 , which is widely used in the literature to simulate the propagation of negative shocks throughout the economy 27 , 28 , 29 . Our model improves the ARIO model in two ways. The first improvement is related to the substitutability of products from the same sector sourced from different regions. Second, in our model, clients will choose their suppliers across regions on the basis of their capacity. These two improvements contribute to a more realistic representation of bottlenecks along global supply chains. Note that CGE models, which are often used for economic assessment, can also handle the above two points well, and there are some pioneering studies on improving the CGE model to make it better suited for short-run simulations by adjusting behavioural parameters within the model. For example, Rose and Guha 30 proposed a CGE model for assessing electric utility lifeline losses from earthquakes. Some recent research also suggested that, if we have enough parameters, the CGE model can perform well in short-run simulations 31 . Considering that the globally validated parameter set—as well as the simulation of short-run consequences after COVID-19, in which adjustment through prices appears to be unlikely due to socioeconomic inertia, transaction costs and antigouging legislation—has not yet been estimated, here we adopted the improved ARIO model.

Our disaster impact model includes four main modules, that is, a production module, an allocation module, a demand module and a simulation module. The production module is designed for characterizing the firm’s production activities. The allocation module is used to describe how firms allocate output to their clients, including downstream firms (intermediate demand) and households (final demand); the demand module is used to describe how clients place orders to their suppliers; and the simulation module is designed for executing the whole simulation procedure.

Production module

The production module is used to characterize production processes. Firms rent capital and employ labour to process natural resources and intermediate inputs produced by other firms into a specific product (Supplementary Fig. 1 ). The production process for firm i can be expressed as follows:

where x i denotes the output of the firm in monetary values; p denotes the type of intermediate products; \(z_i^p\) denotes intermediate products used in production processes; va i denotes the primary inputs to production, such as labour ( L ), capital ( K ) and natural resources (NR). f (∙) is the production function for firms. There are a wide range of functional forms, such as Leontief 32 , Cobb–Douglas and constant elasticity of substitution production function 33 . Different functional forms reflect the possibility for firms to substitute an input for another. Considering that epidemics often cause large-scale economic fluctuations in the short term, during which economic agents do not have enough time to adjust other inputs to substitute temporary shortages, we used the Leontief production function, which does not allow substitution between inputs.

where \(a_i^p\) and b i are the input coefficients calculated as

where the overbar indicates the value of that variable in the equilibrium state. In an equilibrium state, producers use intermediate products and primary inputs to produce goods and services to satisfy demand from their clients. After a disaster, output will decrease. From a production perspective, there are mainly the constraints described below.

Labour supply constraints

Labour constraints after a disaster may impose severe knock-on effects on the rest of the economy 34 , 35 , 36 . This makes labour constraints a key factor to consider in disaster impact analysis. For example, in the case of a pandemic, these constraints can arise from the inability of employees to work as a result of illness or death, or from the inability to go to work and the requirement to work at home (if possible). In this model, the proportion of surviving productive capacity from the constrained labour productive capacity ( \(x_i^L\) ) after a shock is defined as 37 , 38 , 39 :

Where \(\gamma _i^L(t)\) is the proportion of labour that is unavailable at each time step t during containment. \((1 - \gamma _i^L(t))\) contains the available proportion of employment at time t .

The proportion of the available productive capacity of labour is therefore a function of the losses from the sectoral labour forces and its pre-disaster employment level. Following the assumption of fixed input–output relationships, the productive capacity of labour in each region after a disaster ( \(x_i^L\) ) will represent a linear proportion of the available labour capacity at each time step 40 , 41 . Take COVID-19 as an example, during an outbreak of an infectious disease, authorities often adopt social distancing and other measures to reduce the risk of infection. This imposes an exogenous negative shock on the economic network.

Supply constraints

Firms will purchase intermediate products from their supplier in each period. Insufficient inventory of a firm’s intermediate products will create a bottleneck for production activities. The potential production level that the inventory of the p th intermediate product can support is

where \(S_i^p(t - 1)\) refers to the amount of p th intermediate products held by firm i at the end of time step t  − 1.

Considering all of the limitations mentioned above, the maximum supply capacity of firm i can be expressed as

The actual production of firm i , \(x_i^a(t)\) , depends on both its maximum supply capacity and the total orders the firm received from its clients, TD i ( t  − 1), (see the ‘Demand module’ section),

The inventory held by firm i will be consumed during the production process,

Allocation module

The allocation module mainly describes how suppliers allocate products to their clients. When some firms in the economic system suffer a negative shock, their production will be constrained by a shortage to primary inputs, such as a shortage of labour supply in the outbreak of COVID-19. In this case, the output of a firm will not be able to fulfil all of the orders of its clients. A rationing scheme that reflects a mechanism on the basis of which a firm allocates an insufficient amount of products to its clients is needed 25 , 42 . For this case study, we applied a proportional rationing scheme according to which a firm allocates its output in proportion to its orders. Under the proportional rationing scheme, the amount of products of firm i allocated to firm j , \({\mathrm{FRC}}_j^i\left( t \right)\) and household h , \({\mathrm{HRC}}_h^i\left( t \right)\) , is as follows,

where \({\mathrm{FOD}}_i^j(t - 1)\) refers to the order issued by firm j to its supplier i in time step t  − 1, and \({\mathrm{HOD}}_i^h(t - 1)\) refers to the order issued by household h to its supplier j . Firm j received intermediates to restore its inventories,

Therefore, the amount of intermediate p held by firm i at the end of period t is

Demand module

The demand module represents a characterization of how firms and households issue orders to their suppliers at the end of each period. A firm orders from its supplier owing to the need to restore its intermediate product inventory. We assume that each firm has a specific target inventory level, equal to a given number of days, \(n_i^p\) , of intermidiate consumption 9 , on the basis of its maximum supply capacity in each time step,

Then, the order issued by firm i to its supplier j is

Households issue orders to their suppliers on the basis of their demand and the supply capacity of their suppliers. In this study, the demand of household h to final products q , \(HD_h^q\left( t \right)\) , is given exogenously at each time step. Then, the order issued by household h to its supplier j is

The total order received by firm j is

Simulation module

At each time step, the actions of firms and households are as follows:

Firms plan and execute their production based on three factors: (1) inventories of intermediate products they have, (2) supply of primary inputs and (3) orders from their clients. Firms will maximize their output under these constraints.

Product allocation: firms allocate outputs to clients based on their orders. In equilibrium, the output of firms just meets all orders. When production is constrained by exogenous negative shocks, outputs may not cover all orders. In this case, we use a proportional rationing scheme that was proposed previously 25 , 42 (see the ‘Allocation module’ section) to allocate products of firms.

Firms and households issue orders to their suppliers for the next time step. Firms place orders with their suppliers based on the gaps in their inventories (target inventory level minus existing inventory level). Households place orders with their suppliers based on their demand. When a product comes from multiple suppliers, the allocation of orders is adjusted according to the production capacity of each supplier.

This discrete-time dynamic procedure can reproduce the equilibrium of the economic system, and can simulate the propagation of exogenous shocks, both from the firm and household side or transportation disruptions, in the economic network. From the firm side, if the supply of a firm’s primary inputs is constrained, it will have two effects. On the one hand, the decrease in output at this firm means that the orders of its clients cannot be fulfilled. This will result in a decrease in inventory of these clients, which will constrain their production. This is the so-called forward or downstream effect. By contrast, less output in this firm also means less use of intermediate products from its suppliers. This will reduce the number of orders it places with its suppliers, which will further reduce the production level of its suppliers. This is the so-called backward or upstream effect. Similarly, these two effects can also occur if the transport of a firm to its clients or suppliers is restricted. For example, during the outbreak of COVID-19 in China, the authorities adopted strict isolation measures. These measures have placed constraints on the supply of labour and the transportation of products. This resulted in a decrease in China’s output and also triggered the forward and backward effect, leading to the propagation of the shock through the global economic production web. From the household side, the fluctuation of household demand caused by exogenous shocks will also trigger the aforementioned backward effect. For example, in the case of tourism, during the outbreak of COVID-19 in China, the demand for visiting China from tourists all over the world will decrease substantially. This influence will further propagate to the accommodation and catering industry as well as their suppliers through supplier–client links.

Supply-chain effects and economic losses

We define the value-added decrease of all firms in a network caused by an exogenous negative shock as the disaster impacts of the shock. Note that, in our estimates, whereas we considered economic impacts of the lockdowns, we were not looking at dynamic general equilibrium effects, mortality, quality-adjusted life year and disability-adjusted life year. For the firm directly affected by exogenous negative shocks, its loss includes two parts: (1) the value-added decrease caused by exogenous constraints and (2) the value-added decrease caused by propagation. The former is the direct loss, while the latter is the indirect loss. A negative shock’s total economic impacts (TEI i,r ), direct impacts (DEI i,r ) and propagated impacts (PEI i,r ) on a supply chain for firm i in region r are

Global supply-chain network

We built a global supply-chain network on the basis of the Global Trade Analysis Project (GTAP) database 11 version 10. GTAP 10 provides a multiregional input–output (MRIO) table for the year 2014. This MRIO table divides the world into 141 economies, each of which contains 65 production sectors. If we treat each sector as a firm (producer) and assume that each region has a representative household, we can obtain the following information from the MRIO table: (1) suppliers and clients of each firm; (2) suppliers for each household and (3) the flow of each supplier–client connection under the equilibrium state. This provides a benchmark for our model. It should be noted that the MRIO table provided by GTAP is only a sectoral level network, it cannot capture the complexity of supply-chain networks at the firm level. Thus, this study only serves as approximation of the actual effect. However, detailed data are rarely available, particularly those for supply chains in developing countries and for global supply chains across countries.

When applying such an aggregated network in the disaster impact model, we need to consider the substitutability of intermediate products supplied by suppliers from the same sector in different regions. The substitution between some intermediate products is fairly straightforward. For example, for a firm that extracts spices from bananas, it does not make much of a difference whether the bananas are sourced from the Philippines or Thailand. However, for a car manufacturing firm in Japan that uses screws from Chinese auto parts suppliers and engines from German auto parts suppliers to assemble cars, the products of the suppliers in these two regions cannot be substituted. If we assume that all goods cannot be substituted as in the traditional IO model, then we will overestimate the loss of producers, such as the fragrance extraction firm. If we assume that products from suppliers in the same sector can be completely substitutable, then we will substantially underestimate the losses of producers such as Japanese car manufacturing firm. To alleviate the shortcomings of the evaluation deviation under the two assumptions, we set the possibility of substitution for each firm on the basis of the region and sector of supplier supply (see the ‘Allocation module’ section).

Spread and containment scenarios

The number of affected countries, the duration of the containment and the strictness of the containment are the three important factors that influence the loss caused by the epidemic. Using these three indicators as dimensions and then referring to the actual epidemic situation, we designed three sets of scenarios, that is, China only, Europe and the United States, and global. Different sets of scenarios represent different areas of influence of COVID-19, while scenarios in the same scenario set have different assumptions about the duration and strictness of the containment.

Our first scenario set, China only, assumes that the outbreak of COVID-2019 is only in mainland China. In this scenario set, labour supply and transportation in mainland China was restricted owing to the need for epidemic control from the fourth week of 2020 (that is, 22 January 2020). To examine the impact of policy strictness and duration of the outbreak on the world economic system, we set four levels of strictness (that is, 20%, 40%, 60% and 80%) and three durations (that is, 2, 4 and 6 months) (Table 1 ). For example, the scenario ‘China only 20%–2 month’ means that the epidemic lasts for two months with labour supply and transportation restrictions of 20%.

Isolation measures have different effects on labour supply in different sectors. We set a specific multiplier for each sector on the basis of three factors, that is, the exposure level of the sector’s work, whether it is the lifeline and whether it is possible to work at home. If a sector’s work exposure level is low, it is the lifeline sector or it is easy to work at home, the sector’s multiplier will be small, and vice versa.

Then, the constraints on labour supply in each sector are determined by two parts, that is, the benchmark constraint in the scenario and multipliers for the sector. For example, we assume that the multiplier for the wheat production sector is 0.5 because the level of exposure to its production activities is relatively low. Then, in the China only 20%–2-month scenario, the labour supply in the wheat production sector will fall by 10%, that is, 20% multiplied by 0.5. At the same time, in the scenario set, transportation between mainland China and other regions will also fall by 50% throughout the duration of the epidemic.

The epidemic not only affects the global economic system from the supply side, but also affects economic output through its impact on consumer demand. Tourism demand for the region with COVID-2019 outbreaks will drop considerably. Owing to a lack of data, we assumed that the final demand for the two sectors—recreation and other services, and accommodation, food and service activities—in the outbreaking area fell by 99% during the duration of the outbreak.

In the second set of scenarios (Europe and the United States), we assumed that regions with the current severe epidemic situation have taken measures from the eleventh week (11 March 2020) to control their epidemic. These countries include the United States, France, Germany, Italy, the Netherlands, the United Kingdom, Switzerland, Spain and Iran. The labour and transportation restrictions are consistent with the settings of the scenario set China only, and take the China only 80%–2-month scenario as the default in mainland China, which matches with the reality shown in the Baidu big data.

In the final set of scenarios (global), we assumed that, in addition to mainland China and the economies in the scenario set Europe and the United States, other economies in the world also began to take measures to control the epidemic in the fifteenth week (8 April 2020). The labour and transportation restrictions are consistent with the settings of the scenario sets China only and Europe and the United States, and take the China only 80%–2-month scenario as default for mainland China and the Europe and the United States 60%–4-month scenario as default in economies in the scenario set Europe and the United States.

Finally, we designed and modelled three post-pandemic scenarios of recovery as follows:

Pandemic as a new normal scenario: starting in January 2020, China only placed 80% strictness for 2 months, which was then reduced to 20% for 12 months. EU and the United States placed 60% strictness for 4 months, which was then reduced to 20% strictness for 12 months. Global placed 40% strictness for 6 months, which was then reduced to 20% and gradually relaxed to 0% over a period of 12 months.

Recurrent pandemic scenario with global cooperation: starting in January 2020, each country’s lockdown (that is, China, 80%–2 month; Europe and the United States, 60%–4 month; all other countries, 40%–6 month) was first relaxed to 0% strictness over a period of 2 months, followed by a 3-month period of no restrictions and then another round of strict (80%), 2-month global lockdown starting in January 2021.

Recurrent pandemic scenario without global cooperation: starting in January 2020, each country’s lockdown (that is, China, 80%–2 month; Europe and the United States, 60%–4 month; all other countries, 40%–6 month) was first relaxed to 0% strictness over a period of 2 months, followed by a 3-month period of no restrictions, and then another round of the same less strict, longer lockdowns starting in January 2021, as the first round.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The global trade dataset used to stimulate the presented results are licensed by from the Global Trade Analysis Project at the Center for Global Trade Analysis in Purdue University’s Department of Agricultural Economics. The GTAP version 10 can be obtained for a fee from its official website: https://www.gtap.agecon.purdue.edu/databases/v10/index.aspx . Owing to the restriction in the licensing agreement with GTAP, the authors have no right to disclose the original dataset publicly.

Code availability

The simulation code can be accessed at https://github.com/DaopingW/economic-impact-model . The minimal input for the code is multiregional input–output table. The sample code and test data for the minimal inputs are also provided.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos. 71988101, 91846301, 41629501 and 41921005), National Key R&D Program of China (grant no. 2016YFA0600104), the Czech Science Foundation under the project VEENEX (GA ČR no. 16-17978S). We acknowledge supports from the World Bank Group and Tsinghua University Initiative Scientific Research Program and donations from Delos Living LLC and the Cyrus Tang Foundation to Tsinghua University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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These authors contributed equally: Dabo Guan, Daoping Wang.

Authors and Affiliations

Department of Earth System Sciences, Tsinghua University, Beijing, China

Dabo Guan, Jingwen Huo, Tianyang Lei, Danyang Cheng, Bing Xu & Peng Gong

The Bartlett School of Construction and Project Management, University College London, London, UK

Dabo Guan & D’Maris Coffman

School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, China

Daoping Wang

The World Bank, Washington, DC, USA

Stephane Hallegatte

Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

Steven J. Davis

Institute of Blue and Green Development, Weihai Institute of Interdisciplinary Research, Shandong University, Weihai, China

Shuping Li, Yangchun Bai & Qianyu Xue

Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China

Peipei Chen

Business School, University of Edinburgh, Edinburgh, UK

Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing, China

Bing Xu & Peng Gong

Spark Ventures, London, UK

Xiaosheng Lu

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

Shouyang Wang

Integrated Research on Energy, Environment and Society (IREES), University of Groningen, Groningen, the Netherlands

Klaus Hubacek

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Contributions

D.G. and S.H. designed the study. D.W. and D.G. performed the analysis. D.G., D.W., S.H., S.J.D., J.H. and K.H. prepared the manuscript. D.W., S.H., S.J.D. X. Liang and S.W. interpreted supply-chain data and results. X.Lu compiled the international transportation data. Q.X., D.Cheng. and P.C. prepared the Supplementary Information . S.L, Y.B. and T.L. prepared the figures. D.G. coordinated and S.H. supervised the project. D.Coffman, B.X. and P.G. participated in the writing of the manuscript.

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Correspondence to Dabo Guan .

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Guan, D., Wang, D., Hallegatte, S. et al. Global supply-chain effects of COVID-19 control measures. Nat Hum Behav 4 , 577–587 (2020). https://doi.org/10.1038/s41562-020-0896-8

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Published : 03 June 2020

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DOI : https://doi.org/10.1038/s41562-020-0896-8

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Lessons from the COVID-19 Pandemic Impact on Supply Chains webinar

In March 2022, the MIT Center for Transportation and Logistics (CTL) convened participants from industry, non-profits, and academia, for a webinar hosted by the MIT Global SCALE Network, to discuss how the ongoing Covid-19 pandemic, in all its variants, has rattled global supply chains with shockwave disruptions up, down, and sideways along the chains in a phenomenon known as the Bullwhip Effect.

Is it time to upend the textbook prescriptions? In this MIT SCALE-wide panel, the directors of the five network centers worldwide discussed their views and experiences on the evolution of global supply chains, while addressing questions such as: Is it time to shore up and shorten supply chains? Are global supply chains decoupling? Is this the end of lean and the start of just-in-case?

Gain access to complete roundtable reports and join future roundtable discovery.  Learn more about becoming a Partner .

Takeaways From the Pandemic’s Impact on Supply Chains

The Covid-19 pandemic changed the way companies respond to global disruptions. Nowhere is this more evident than in the supply chain field which was – and still is – on the front lines of the effort to manage the pandemic’s fallout.

An MIT Global SCALE Network webinar gave the network’s five member centers an opportunity to explore early supply chain responses to the crises. Here are some key takeaways.

Culture shapes Covid responses

A propensity for risk-taking distinguishes Chinese firms, a characteristic that colored their responses to the Covid-19 pandemic. For example, they embraced automation and rail transportation in their efforts to manage the pandemic’s uncertainty. Latin American firms are well versed in managing uncertainty, which is part of the commercial landscape in the region, and the pandemic reinforced this capability.

Global commonalities

But there were more similarities between how companies reacted to the pandemic than differences, due to the impact of globalization. For instance, supply chain practitioners were already used to working in teams across time zones before the pandemic erupted. Many European multinationals have headquarters in Asia, and faced similar challenges across the region when Covid-19 debilitated supply chains.

Lean called into question

The pandemic raised questions about certain well-established supply chain practices. Just-in-time/lean manufacturing came under fire as a cause of product shortages. However, this was something of a knee-jerk reaction, and JIT/Lean is still alive and well and will continue to be a key part of the supply chain management toolbox.

Reshoring doubts

Reshoring is another established supply chain practice that came under scrutiny at the height of the pandemic. The disruptions that rippled through supply chains prompted many companies to revisit their global footprints. For example, Eastern European countries such as Romania and Serbia attracted interest from European companies as alternative locations for distribution centers.

New thinking emerged

The pandemic also gave rise to fresh approaches to managing supply chains. In Latin America collaboration became more important; many Chinese firms turned to automation to help them manage the crisis. Responsiveness gained favor over precise planning, and established practices such as optimizing freight container utilization were revisited.

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Original research article, the impact of the coronavirus pandemic on supply chains and their sustainability: a text mining approach.

• Chair of Supply Chain Management, University of Kassel, Kassel, Germany

The coronavirus pandemic is an unprecedented event, putting global supply chains (SCs) into the focus of a wider public. Yet it is unclear what is communicated about this and how and what consequences SC management (SCM) would take away. This research aims at analyzing how text mining can provide insights on the impact of the coronavirus pandemic on SCs, focusing on the implications of the pandemic for the SC constructs related to risk, resilience, and sustainability. A method applies text mining of general newspapers as well as SC and logistic newspaper articles employing the open-source software R. This paper shows that certain SC topics like risk, resilience, disruption, and sustainability vary in their news coverage on the type of newspaper and the number of coronavirus disease 2019 (COVID-19) infections. It reassures trends and observations from individual experiences on a broader and global picture and discusses the limitations and possibilities of using text mining in this field. The time period was split into three phases regarding the course of the number of infections and differences in the news coverage of the phases that can be distinguished already: (1) the onset of the crisis, (2) the peak and lockdown, and (3) managing SCs during the crisis. As this pandemic is highly dynamic, is new, and has not yet ended, research implications are too early to make. This research rather serves as a base for further, more detailed research into certain topics and identifies limitations and improvements of the applied method. Due to the method chosen and the timeliness of data, this empirical research is unique and can be of great help to see trends and patterns to further deep dive into specific areas.

Introduction

The coronavirus disease 2019 (COVID-19) is an infectious disease. Since its first appearance in China in December 2019, it spread globally, resulting in an ongoing pandemic ( Statista, 2020 ). The pandemic has provoked serious social and economic disruption globally, including strict social distancing, travel restrictions, and one of the largest global recessions since the Great Depression ( Wheelock, 2020 ).

During the beginning of the global outbreak in March, supply chain (SC) management (SCM) has had major problems to cope with an unpredicted demand for certain products when simultaneous restrictions for travel and production have been enforced and is still struggling to recover from this ( Mazareanu, 2020 ). Business operations are trying to adapt to the new situation and will probably face changes that will remain even after the pandemic might be over. In the news, SCs in relation to the pandemic are widely discussed, and scientific research on the implications of the crisis has already started ( Lopes de Sousa Jabbour et al., 2020 ; Queiroz et al., 2020 ; Schmidt, 2020 ). However, traditional research paradigms fail to keep up with the pace of the current epidemic and economic developments, and thus there is still little empirical evidence on how the coronavirus pandemic impacts SC. Therefore, text mining of newspaper articles on this matter allows analyzing a timely and larger-scale dataset.

This seems evident addressing risk and resilience-related SC constructs that quickly moved into the center of attention, which seems somewhat straightforward ( Queiroz et al., 2020 ). As a second focus, this paper also addresses sustainability-related aspects. This is justified, as the need for more sustainability in SC is evident far beyond the coronavirus pandemic and the related impact is only starting to get explored ( Majumdar et al., 2020 ; Sharma, 2020 ).

The induced rethinking of SCM might offer an opportunity for improved resilience, risk mitigation, and sustainability. Therefore, this paper analyzes the following research questions:

- How can text mining provide insights on the impact of the coronavirus pandemic on SCs?

- What are the implications of the pandemic for the SCM constructs risk, resilience, and sustainability (RRS)?

Text mining allows deriving high-quality information from large text documents, to automatically filter out irrelevant information and to analyze text for topics of interest, word cluster, and sentiments toward certain ideas. To obtain a structured dataset, the Google News feed is scraped for articles in the English language with the terms “corona/COVID-19” in combination with “supply chain” and structure the input text. With this dataset, frequency and sentiment analysis are conducted on articles from different time frames to analyze text for topics of interest and the point of view in the media toward SCM constructs during the coronavirus crisis.

In the Literature Review section, a brief literature introduction into SC, RRS is given, and current research related to the coronavirus pandemic in the field is reviewed.

Research Method explains the research method applied, how datasets were obtained via the Google News feed, and the newspaper articles mined with R.

In the Findings section, the results of the text mining are presented in word clouds, frequency counts, and sentiment analysis.

In the Discussion , the output is interpreted regarding the named research questions. By using inductive reasoning, specific observations and measures of the crisis are explored, and patterns and regularities from the mined news articles are detected to formulate some tentative hypotheses that finally end up developing some general conclusions or theories of the impact of the corona crisis on SC. We further discuss if the text mining analysis is insightful and relevant to further leverage this method for SCM research, as in an information-rich environment, it is essential to efficiently identify trends of big sets of data.

Literature Review

A brief theoretical foundation on SC and management practices for the subsequent research is outlined. The second part of this section offers insights into current research in the field.

Supply Chain Management and Supply Chain Management Concepts

According to Mentzer et al. ( 2001 , p. 4) “Supply Chains are a set of three or more entities (organizations or individuals) directly involved in the upstream and downstream flows of products, services, finances, and/or information from a source to a customer.” SCM encompasses the active management of such activities and relationships with the aim of obtaining a sustainable competitive advantage and maximizing customer value through optimizing SC in the most effective and efficient ways. In order to be successful, organizations are required to carefully manage their operations by planning, scheduling, and controlling SC activities ( Bozarth and Handfield, 2016 , p. 19–23). The SCM literature focuses on three practices that are of great importance for the success and future of SC to prevent SC disruptions and ensure risk mitigation: RRS. In the following, when SCM practices and concepts are mentioned, they relate to those three practices.

Seuring and Müller define sustainable SCM as “the management of material, information and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of sustainable development, i.e., economic, environmental, and social, into account which are derived from customer and stakeholder requirements” ( and Müller , 2008 , p. 1700). Firms emphasizing sustainable SCM typically have aligned financial and environmental goals, which lead to the incorporation of sustainability into every aspect of their business, SC, and partnerships, which once more protects the entire SC from commodity traps, improving financial value to the focal firm and suppliers ( Pagell and Wu, 2009 , p. 54).

Most companies have outsourced and extended many productions and SC activities resulting in a great dependency on global suppliers and complexity, which makes them especially vulnerable to SC disruptions ( Bozarth and Handfield, 2016 , p. 226). SC disruptions can be caused by different external events that are outside of the firm's control such as natural disasters (like the COVID-19 pandemic) and internal events, for example, missing contingencies or mismanagement that are within the firm's control.

Risk can be seen as the “expected outcome of an uncertain event.” Important dimensions of risk in global SC are probability and impact of losses, speed, and frequency ( Manuj and Mentzer, 2008 , p. 5). Two of the common external risks associated with SC disruptions are supply and demand uncertainties. Supply uncertainty on the upstream/supplier end refers to the “risk of interruptions in the flow of components they need for their internal operations” ( Bozarth and Handfield, 2016 , p. 347f.). The quality of purchased goods is of great importance as much as the reliability of estimated delivery times as well as the dependency of goods on unpredicted shortages or rise in prices. The risk of a significant and incalculable fluctuation in the demand of goods is called demand uncertainty, which organizations are facing on the customer side ( Bozarth and Handfield, 2016 , p. 347f.). As a consequence of greater uncertainties in supply and demand, globalization of markets, and shorter product and technology life cycles, managing risks has become more challenging ( Rao and Goldsby, 2009 , p. 98; Handfield et al., 2020 ).

In the literature, global SC risk management (SCRM) often entails the identification, assessment, controlling, and monitoring of SC risks ( Wieland and Wallenburg, 2012 , p. 2) and implementation of appropriate strategies with the aim of reducing one or more of the risk dimensions ( Manuj and Mentzer, 2008 , p. 14).

To ensure that all incidents are covered in complex SC, the cause-focused SCRM concept is often combined with the practice of SC resilience, which is orientated on overcoming risks regardless of the cause. SC resilience possesses the “adaptive capability to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function” ( Ponomarov and Holcomb, 2009 , p. 131). According to a framework developed by Pettit et al. ( 2010 , p. 11), SC resilience is “the desired balance between vulnerabilities and capabilities, where it is proposed that firms will be the most profitable in the long term.”

It is argued that sustainable SCs are more resilient and less exposed to risk in the case of disruptive events ( Namdar et al., 2017 , p. 2345). As part of this research, we will investigate if the coronavirus crisis is driving sustainable SC or drop due to (short visioned) cost-saving opportunities.

Current Research in the Field

As the coronavirus pandemic was the first pandemic with this dimension we experienced and was not expected in the common society, there are no respective comparisons and mostly recent and ongoing research in this field. Many researchers are currently analyzing different topics that are influenced by the pandemic, and there will be many publications dealing with the impacts of the virus in the months to come. It has to be mentioned that the literature review is inevitably provisional and selective due to the highly dynamic development at the time this paper has been written.

Regarding SCM, the corona outbreak represents one of the major disruptions encountered during the last decades and is “breaking many global supply chains” ( Araz et al., 2020 ; Ivanov, 2020 , p. 1; Queiroz et al., 2020 ); however, it was not the first crisis that the industry faced. Other examples of disruption risks are the Tsunami in Japan in 2011 and its impact on SC worldwide or other epidemic outbreaks like severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), or Ebola. Research regarding SC and global logistics during those previous epidemics is numerous (e.g., Chou et al., 2004 ; Tan and Enderwick, 2006 ; Lee et al., 2009 ; Dasaklis et al., 2012 ; Green, 2012 ; Calnan et al., 2018 ). Tan and Enderwick ( 2006 , p. 16) suggest companies “to reexamine their supply chains to identify potential problems and bottlenecks and allow for enough slack to accommodate delays and potential problems that can arise. Such readjustments may include keeping buffer inventory and safety stock to hedge against uncertainties.” Queiroz et al. (2020) conducted a comprehensive literature review on the impact of epidemic outbreaks on SC and with those findings proposed a framework for SCM during the coronavirus pandemic. Queiroz et al. (2020) suggest sustainability as one of the main research agendas in terms of SCM under epidemic outbreaks. Besides the focus on sustainability, it seems important to use digital and technical ways like data analytics or digital manufacturing as ways to improve operations and SCM under epidemic outbreaks and pandemics. The suggested research agenda is dominated by aspects of SC resilience, such as recovery, ripple effect control, preparedness, and adaption.

In terms of resilience, current research focuses on the diversity of suppliers and transparency, so that one can rapidly switch to alternative supply sources if a region is affected by a crisis ( Alicke et al., 2020 ; Schmidt, 2020 ; Todo et al., 2020 ).

In the field of sustainable SC during and after the coronavirus pandemic, studies mainly deal with the question of the localization of SC, general behavioral changes, and the possibility of a transition toward more sustainability ( Bodenheimer and Leidenberger, 2020 ; Fischedick and Schneidewind, 2020 ; Lopes de Sousa Jabbour et al., 2020 ; Sarkis et al., 2020 , p. 3, 4), but also social sustainability issues ( Majumdar et al., 2020 ). This posits further research questions regarding the future of just-in-time practices, industrial structures, and storage and connected to this energy and waste losses from excess inventory. The rebuilding of SC and production can be “an opportunity to marry the needs of equitable prosperity and climate protection” ( Sarkis et al., 2020 , p. 5).

Text mining based on news articles in the field of SCM is not widespread yet, even less in the context of the coronavirus. Sharma et al. (2020) used text mining techniques to analyze Twitter data from NASDAQ 100 firms and thus came across four major themes, (i) demand supply challenges during the coronavirus pandemic, (ii) technological challenges during the coronavirus pandemic, (iii) building a resilient supply chain, and (iv) sustainable supply chain challenges, continuing the line of research already mentioned.

The topic addressed here is a young and emerging one. This paper strives to add insights on the impact of the coronavirus pandemic on SC and the implications for sustainability with the usage of text mining of news articles. It is an attempt to apply a fairly new analysis tool to SCM research and thus adds further content to the current research on the coronavirus pandemic and its impact on SC.

Research Method

In 1982 in In Search of Excellence , Peters and Waterman (2006) firstly coined the term DRIP with their observation that companies were “data rich and information poor” (DRIP). During this so-called “information age,” an era suffering from information overload, as big amounts of data are collected on a daily basis, the need to analyze this data arose. Slowly developing into the “data age,” data mining is seen as an attractive method to tackle this problem, where analyzing news feeds emerges as a promising research approach (e.g., Handfield et al., 2020 ).

Data mining is defined as the process of discovering interesting patterns and knowledge from large amounts of data ( Han et al., 2011 ). Functionalities needed in the data industry include the collection of data and database creation, the management of data (from storage to database transaction processing), and data analysis, where data mining and warehousing belong to.

Text mining aims at solving the problem of information overload by combining theoretical approaches and methods from data mining, machine learning, natural language processing, information retrieval, and knowledge management ( Feldman and Sanger, 2006 ).

The main common denominator in text mining is that text is the input information ( Feinerer et al., 2008 ). While some define this new research method simply as an extension of classical applications in data mining, Hearst understands text mining as “the use of large online text collections to discover new facts and trends about the world itself” ( Hearst, 1999 , p. 5).

Text mining involves the preprocessing of document collections (text categorization, information extraction, and term extraction), the storage of the intermediate representations, the techniques to analyze these intermediate representations (such as distribution analysis, clustering, trend analysis, and association rules), and visualization of the results. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns ( Feldman and Sanger, 2006 ).

The general goal of information extraction is to discover structured information from the unstructured or semi-structured text ( Aggarwal and Zhai, 2012 ), as opposed to formalized database records used in data mining.

Text mining is mostly applied to commercial activities such as corporate finance and areas like patent research or life sciences to analyze trends and industries ( Feldman and Sanger, 2006 ). Due to the named characteristics, it appears applicable in the context of the coronavirus pandemic and its impact on SCM, as the research field is highly dynamic with high and fast information flow, where a manual literature review is not feasible.

Data Collection

As described above, the first step in text mining is the extraction of a structured dataset. In order to obtain the dataset of our empirical research, an independent and unbiased global database for online newspapers that has the option to search for specific keywords and date ranges is required. This research is limited to newspapers in the English language to be able to easily compare content and use text mining techniques without having to rely on potentially poor translations.

Datasets: General Newspapers and Supply Chain Newspapers

This analysis is conducted with two datasets, one with general news sites (GN) and one with SC and logistics news sites (SCN) to compare the results with each other predicting that SCN will give us deeper insights into our research since GN seem to mainly cover relevant SC topics superficially.

To identify the most common English-speaking newspapers (NP), unique visitors and pageviews from the established traffic estimator providers Alexa ( Alexa Internet Inc, 2020 ) and SimilarWeb ( SimilarWeb LTD, 2020 ) as well as Techworm ( Sharma, 2020 ) have been taken into consideration. Those sources are traffic based, and the largest English-speaking population lives in the USA, this can lead to a built-in bias toward US media. Therefore, the findings and discussion have a focus on the USA. For the SCN dataset, a general research on the most popular news sites was done and advised with leading professionals in the field (see Annex 1_3.1 . Data Collection → Annex 1_3.1.1 . Supply Chain News Sites), as no established list has been available.

For content scraping purposes, a unique Cascading Style Sheets (CSS) pattern for each newspaper needs to be identified. CSS is a computer language designed to enable the separation of presentation and content ( Meyer, 2000 ). The CSS content scraping was not possible for some newspapers due to general data protection regulations (GDPRs), subscriptions, or company policies. Therefore, such sites have been excluded. For the extant research, 14 GN and five SCN have been used and are listed in Table 3 in the Dataset Summary section. In the Annex 1_3.1 ._Data_Collection; Annex 1_3.1.1 . General_News_Sites, all newspapers before filtering are listed. Due to a lack of qualitative SC online newspapers outside the USA and partly as a result of this limitation, all but one SCN are from the USA.

For the data collection, Google News ( news.google.com ) was selected as the most suitable platform to collect articles from the selected newspapers. Google News does have the limitations that only a “past × time” option for selecting the time frame instead of a date range option is available, and the platform only shows up to 100 results per search. Other free options considered were the Europe Media Monitor and the news option in the Google search ( google.com > news). However, they lacked the availability of all newspapers and the conversion into an Rich Site Summary (RSS) feed, which is a requirement for data mining with R. Given the above limitations, the platform Google News with a selection of the past 300 days as a time frame has been chosen.

For the GN dataset, the search term “corona” or “COVID-19” and “supply chain” and “[ Respective NP homepage ]” have been used. For the SCN dataset “supply chain” is left out, as the newspapers themselves should predominantly cover SC topics.

Rich Data Summary Feeds

Google News results need to be converted into RSS feeds, in order for the data mining software to access updates to websites in a standardized, computer-readable format ( Hammersley, 2005 ). These RSS feed URLs have been compiled individually for each newspaper in order to later collect and scrape their content with data mining software.

An exemplary Google News RSS feed for CNN with keywords and time frame can be found here: https://news.google.com/rss/search?q=corona%20%22supplychaindigital%22%20when%3A150d&hl=en-US&gl=US&ceid=US%3Aen .

Unfortunately, Google News RSS feeds have a limit of 100 articles only, which means that in case that there were more than 100 articles published with the selected keywords in the last 300 days for a respective newspaper, Google's algorithm focused on the most recent and most relevant articles. While this is a limitation, it offers a broad dataset of sufficient depth. There will hardly have been more relevant articles in general newspapers as a limited time span is analyzed.

The fellow analysis has been done with the open-source software R (R Version 4.0.2) that is mostly used for statistical computing and graphics. R has been chosen as our preferred text mining software due to its visualization capabilities, but also because it encompasses a large number of statistics as well as natural language processing libraries, and it is free, well-documented, and user-friendly.

Content Scraping

In the next step, on August 19, 2020, for both datasets, the articles for each newspaper have been collected through their respective RSS feed and their content scraped with the individual CSS patterns and the rCrawler function in R.

The explicit code used in this step can be viewed in Annex 2_3.1 . R Code_Data Collection.

Time Frames

In terms of time frames, in order to compare the results with each other depending on infection trends, the obtained datasets have been split into three periods based on the number of infection in the USA ( Google LLC, 2020 ; Johns Hopkins University, 2020 ), as most newspapers of the datasets are US-American. Table 1 shows the time frames of the different phases and explains the division into an onset phase, wave 1 and wave 2.

Table 1 . Time frames and number of coronavirus disease 2019 (COVID-19) infections per day ( Google LLC, 2020 ).

Text Cleaning

To structure the input text, there are certain steps used to filter out relevant information. This process includes the removal of blank text and duplicated, irrelevant symbols, stop words, and morphological conversion ( Zhao et al., 2019 ). Stop words are words that have low discrimination power and include general words like “a, an, the, etc.” that are not relevant for quantitative analysis of the text ( Lo et al., 2005 ). In this analysis, we used a prepared list of stop words by the tidytext package of R, which is derived from the snowball set ( Porter and Boulton, 2001 ), and we manually added irrelevant words (see Annex 4_3.3._4 . Data Summaries Main and Newer Dataset). These words are referring to keywords used and therefore overrepresented, numbers and times that were irrelevant, or words not belonging to the article's content due to poor CSS coding from individual newspapers. To reduce subjectivity, the manually removed irrelevant words only focused on the most obvious insignificant words such as “image,” “week,” or “follow”.

Dataset Summary

Concluding the data collection part of this research, the summary in Table 2 shows the number of articles per dataset, time period, and newspaper. In total, 1,258 articles and 712,614 words (1,523,896 incl. stop words) have been analyzed.

Table 2 . Summary of the number of articles per dataset, time frame, and newspaper.

Text Mining

After putting the articles in a structured dataset, what follows are the pattern and trend analysis. For the text mining part of this research, the book Text mining with R from the developers of the tidytext package of R ( Silge and Robinson, 2017 ) served as orientation.

Word Clouds and Frequency Plots

The first analysis done on the structured data looks for frequent terms and associations. Looking for frequent terms means counting the usage of words and displaying the most used words over all articles and the number of documents that include specific terms. To visualize this, word clouds that show the words in different font sizes according to their frequency are shown.

To derive valuable clues to the research questions, the proportion that documents regarding RRS have compared with the entire collection of news is calculated, and the frequency of different words in the different time frames and context is analyzed.

Sentiment Analysis

The results of the Word Clouds and Frequency Plots section help with the sentiment analysis , which follows the common text mining visualizations. According to Kwartler ( 2017 , p. 85), “sentiment analysis is the process of extracting an author's emotional intent from text.” In sentiment analysis, one analyses the words associated with certain topics and the general tone of a document. This can be done with a polarity function that calculates the result based on a subjectivity lexicon. Generally, in newspaper articles, authors usually try to speak about general aspects and to not express their opinion ( Balahur et al., 2010 , p. 1). With this being the case, the sentiments of an article are from a certain newspaper itself, the public opinion, or the company's side of the story. In this paper, it is not interesting to know about the personal beliefs of an author but to learn about the general attitude of the public, the firms themselves, and the change in SC with the coronavirus.

To evaluate the opinion or emotion in text programmatically with the tools of text mining, in this research, mainly the Bing lexicon has been used; however, for comparison purposes, AFINN and NRC have also been taken into consideration. All three lexicons are based on unigrams, i.e., single words. These lexica contain many English words that are assigned scores for a positive or negative sentiment and partially emotions like joy, anger, or sadness ( Silge and Robinson, 2017 ).

To answer the research question, it is essential to know whether there is positive news associated with this topic or rather negative news articles. Therefore, word clouds and lists with the most frequent positive and negative words have been plotted, as well as a general overview of the sentiments by date and source. In order to know whether the impact on SC RRS is seen rather positively or negatively, the sentiment of those articles is analyzed separately.

Validity and Reliability

In terms of validity, we made sure to obtain a dataset that is big enough for comparison purposes. By comparing the same time frames, which were set based on COVID-19 infection numbers, with the different datasets, internal validity is given, and results are reliable. However, the unequal amount of articles published per time period, newspaper, and dataset should be kept in mind when comparing results in the Findings section.

As the research is based on English articles and due to the dataset selection with a focus on the USA, in the context of external validity, results from this research cannot be generalized for the whole world, but for the USA and partially the United Kingdom and India, where a few newspapers are part of the analysis.

Objectivity is ensured, as general newspapers are not preselected by personal taste but by popularity, and in the sentiment analysis, different lexica have been compared in the Sentiment Analysis section and differences were explained. The stop words that have been deleted from the results are based on a set of lexica by the tidytext package, and manually added stop words were non-sentimental and have been listed for transparency purposes in the Text Cleaning section. For the frequency analysis, no validation is needed, as no models are used, but only words counted.

Repeatability is ensured, as the approach chosen is clearly documented. The code provided in Annexes 2, 3 can be run, edited, and amplified by any other researcher. This makes this research reliable in the sense that by collecting the dataset at the same time, the same answers can be obtained. However, as the Google News feed has the limitation that news articles of the past X days are collected, selecting the exact same dataset is difficult, if done at a later stage. To measure the reliability of this research, the code was run 1 month later on September 18, 2020, and still the same main trends, patterns, and topics remained (see Annex 6_3.3 . Findings Newer Dataset). Of course, findings are dependent on the article's content, which by nature vary on events happening in the future. If the search date could be specified, the results could be repeated in the exact same manner.

With the obtained datasets, it is possible to conduct a multitude of analyses by comparing specific dates, words, or newspapers with each other by applying a variety of text mining techniques. In this Findings section, trends, observations, and limitations of the text mining analysis in respect to the implications of the coronavirus pandemic on SC and SCM constructs are shared with an emphasis on sustainability topics.

In the basic word clouds, the 50 most frequent words for each dataset are demonstrated. The bigger the word, the more was the word mentioned in the respective newspaper articles. Meanwhile, the frequency plots in Figure 1 show the 15 most frequent words in a bar chart. Even without knowing any context of or not having experienced the coronavirus pandemic itself, which facilitates associating these words with historical events, the illustrations in Figure 2 give an insightful overview of the impact of this crisis: a global pandemic with issues around people, companies, health and logistics, food and demand, workers, and some special role of China. Already in the first plots ( Figure 2 ), a difference between GN and SCN is visible. The GN content centers more on health-and people-related issues, while the SCN content puts business into the focus.

Figure 1 . Frequency plots ( n = amount of words mentioned).

Figure 2 . Comparing word clouds for GN (left) and SCN (right) across the three phases.

When looking at the three different time frames, both illustrations nicely show how the high frequency of the word “China,” which was at that time the epicenter and country of origin of the COVID-19 outbreak ( Statista, 2020 ) is very dominant in the onset phase, when reported infections where high, and gets smaller over time, when the epicenters changed to other parts of the world (see Figure 2 ).

On the opposite side, while the word “pandemic” is not even mentioned in the SCN_1 dataset, as the corona disease spreads globally and is declared a pandemic by the World Health Organization on March 11 [ World Health Organization (WHO), 2020 ], the use of the word gets more and more frequent over time.

When comparing GN with SCN, it is not unexpected that in the general articles words like “people” (3,802 times) and “health” (3,102 times) are predominant, while for the SC articles, “company” and “companies” (1,066 times), “business” and “businesses” (804 times), “demand” (604 times), and “logistics” (397) are more frequently mentioned. Mostly in the onset phase, one can see that in GN, SC disruptions were a subject of discussion, as “limited,” “supplies,” “shortage,” “disruption,” and “demand” were frequently used in the articles.

Important terms like “risk” and “disruptions” are dominant in the SCN articles, while “resilience” and “sustainability” are not represented among the top 50 words, giving hints that a focus on optimizing SC to become more sustainable has taken a backseat as dealing with the risk and disrupted SC was more urgent. However, it can be seen that words like “impact” or “disruption” are used less frequently over time, and instead words like “operations” or “plan” appear in the word clouds. This indicates that the debate moved from overserving risk and disruption to looking for solutions and dealing with the situation.

In the sentiment analysis, the sentiment of the articles is given a positive or negative score by scanning the content with specific lexicons. Comparing the general-purpose sentiment lexicons AFINN, Bing, and NRC.png (see Annex 5_4.2 . Sentiment Analysis → Sentiment Comparison GN dataset between dictionaries), the sentiment assessment of AFINN and Bing across all time frames is fairly similar, showing a rather negative sentiment until the middle of May, following a phase of rather neutral news coverage and finishing up from July with both strong positive and negative sentiments. Meanwhile, NRC is not showing a negative sentiment at all. The reason for this is not fully clear; according to the authors of the text mining book, the NRC lexicon has a lower ratio of negative to positive words and is high in sentiment, which typically leads to a more positive sentiment as compared with the other two lexicons ( Silge and Robinson, 2017 ). This demonstrates that sentiment analysis is not fully unbiased and depends on the lexicon used. Kim (2018) recommends focusing on one lexicon and points out some malfunctions of the NRC lexicon. For the following sentiment analysis, the Bing dictionary has been used.

The below sentiment comparison of the GN and SCN dataset across all phases ( Figure 3 ) shows that the sentiment scale of the GN dataset ranges between +154 (19/08/2020) and −484 (07/08/2020), while the ranges in the SCN dataset are only between +90 (06/04/2020) and −136 (17/08/2020).

Figure 3 . Bing dictionary sentiment analysis (dark blue, onset; blue, wave 1; light blue, wave 2).

An explanation can be that the articles of the SCN datasets as specialized literature are more professional, while articles in the GN dataset are mainstream and likely to be more emotional. Especially tabloid formats, a largely sensationalist journalism style, like the Daily Mail, can cause this discrepancy ( Gossel, 2017 ). In fact, the Daily Mail has by far the highest negative sentiment with an average of −1,171 compared with the remaining broadsheet formats with an average of −71, statistically supporting this explanation (see Figure 4 ).

Figure 4 . Bing dictionary sentiment GN and SCN across time frames (for more breakdowns per Newspaper, see Annex 5_4.2 _Additional Material).

When looking at the sentiment of the five SC newspapers using the Bing dictionary, a trend across all sources can be identified (see Figure 4 ). While articles in the onset phase have been rather negative, in wave 1, they were the most positive, slightly decreasing for most papers, but still positive in wave 2. The reasons for this can be the sudden panic buying and stockpiling that happened mostly in the first half of March ( Arafat et al., 2020 ), while at the same time, some production sites and travel restrictions have been in place, which have led to a severe supply crisis, which relaxed in waves 1 and 2. The lower values in wave 2 might be explained by the infection numbers staying high.

Word Clouds and Frequency Count With Sentiment and Bigrams

Derived from the sentiment analysis, the charts in Figure 5 demonstrate the 50 most frequent words that have a positive (bottom) or negative (top) connotation.

Figure 5 . GN word cloud with sentiment (negative, top; positive, bottom).

What catches the viewer's attention is the dominance of “trump” (1,670 times) in the positive associated words as seen in Figure 6 . While the word trump in a wide meaning stands for the outrank or defeat of someone or something ( Cambridge Dictionary, 2020 ) and is therefore considered “positive,” in the context of our analysis, however, “trump” is referring to the president of the USA Donald Trump and therefore should rather have a neutral sentiment, as names are usually left out of sentiment analysis ( Kim, 2018 ). The word “positive,” which is categorized as positive (902 times), is probably referring to being tested positive with COVID-19 and should in this context rather be listed as a negative sentiment. However, the used text mining algorithms for these unigrams are not capable of analyzing the whole context of a sentence yet and instead only look at the sentiment of a single word. This is a clear limitation of the method. The GN word cloud with sentiments in Figure 5 , compared with the same figure for SCN ( Annex 5_4.3 ._Figure 18_Word cloud with sentiment SCN), or with the SCN word clouds with sentiments per timeframe as seen in Figure 7 shows again how the mainstream newspapers mainly focus on general topics like people, health, employment, and the virus itself. As already mentioned, it can be questioned whether “Trump” and “positive” should really appear on the positive side.

Figure 6 . GN frequency contribution to sentiment negative/positive.

Figure 7 . Word cloud with sentiment SCN.

So far, words have been considered as individual units. To examine which words tend to follow others immediately, bigrams can be used to give better insights toward the context of frequent terms. The most frequent bigrams from GN can be accessed through Annex 5_4.3 . Word Clouds and Frequency Count with Sentiment and Bigrams, and one can see that “tested positive” (376 times), “donald trump” (272 times), and “president donald” (210 times) is listed in the top 15, as previously assumed.

In general, these word clouds, frequency counts, and bigrams visualize and summarize some common associations with the coronavirus pandemic like “outbreak” and “crisis” but give little insights to gain new knowledge. Potentially a positive negative word cloud with subjects only and or bigrams could be more meaningful, as insignificant adjectives are left out and words put into their context.

The SC word clouds demonstrate common terms from SCM theory like “resilient” in SCN1, “sustainability” in SCN3, and “risk” (399 times) and “disruption” (232 times) in all time frames. However, the word “risk” in the context of the coronavirus pandemic cannot be distinguished from SC risk as described in the Supply Chain Management and Supply Chain Management Concepts section and is therefore not very significant.

Sustainability is firstly represented in SCN2: wave 1 word cloud with sentiment of 100 words (see Annex 5_4.3 ._Figure 10_Extension 100 words_Word cloud with sentiment SCN2) and is then listed in SCN3 word cloud with sentiment 50 words, while it is not a frequent word for the GN word clouds nor the onset phase. This leads to the potential assumption that sustainability for SC articles was firstly overshadowed by the SC disruption in the onset phase and the risk associated with this emergency, while the focus on sustainability slowly reemerges as the recovery of the crisis is discussed more frequently. Despite the pandemic continuing, the topic is treated in a wider, more reflected manner. The size of the negative associated words backs this point, as many words like disruption, emergency, or critical become less frequent and the positive associated words gain attention.

To get a better understanding of the context of the popular words and to find hints toward the impact of the coronavirus pandemic on SC, associated words have been analyzed through the bigrams in Figure 8 .

Figure 8 . Bigram SCN.

Seeing that “risk” is associated with “management,” “restrictions” with “travel,” “supplies” with “alternative,” and “medical” or “paper” with “toilet” give further insights; however, it also demonstrates that conclusions by only looking at monograms should be drawn very carefully, as they might represent something completely different.

Risk, Resilience, and Sustainability

In order to gain additional insights on the impact on the SCM practices of the coronavirus pandemic, a deep dive into articles with the terms risk, resilience, and sustainability has been conducted. In Figure 9 , the word clouds with sentiment demonstrate articles that respectively contain the word “resilience,” “risk,” and “sustainability.” It seems that especially the plots containing “resilience” or “risk” are fairly similar, which could conclude that both terms are often used together.

Figure 9 . Word cloud with sentiment containing supply chain management (SCM) constructs.

In fact, of all GN articles containing “resilience,” every second article also contains “risk,” and for SCN, two out of three articles containing resilience also name “risk.” However, when looking at the contribution of the terms RRS, as seen in Figure 10 , it becomes obvious that “risk” is clearly dominating, by being mentioned in more than 40% of all articles. As the source of the two datasets let expect, in the SCN dataset, the terms risk, resilience, and sustainability are higher represented as in the GN dataset.

Figure 10 . Risk, resilience, and sustainability (RRS) contribution per dataset (green, GN; orange, SCN).

Risk is represented almost equally often in both datasets, but when looking at the bigrams in Figure 11 , slightly different topics are revealed. While “risk” in the GN dataset is focused more on health and people, in the SCN dataset, “risk” is more frequently surrounded by management and suppliers and mainly concerns risk mitigation. One can notice that “risk” is used in articles mentioning “disruption,” “supply,” and “demand,” which are words associated with the construct of external risks mentioned in the Supply Chain Management and Supply Chain Management Concepts section.

Figure 11 . Bigram “risk”.

Frequent words of the articles containing “resilience” show that while in the onset time frame (see Annex 5_4.4 _Word Frequency Resilience_SCN1) “disruption” and “logistics” are named more often, in wave 1 (see Annex 5_4.4 _Word Frequency Resilience_SCN2), “organizational” becomes more important; and later, “production” is combined with those articles (see Annex 5_4.4 _Word Frequency Resilience_SCN3).

In order to get a better understanding of the timing when SCM practices were mostly mentioned, the chart in Figure 12 demonstrates the distribution of topics of the SCN dataset per time frame.

Figure 12 . Distribution RRS per time frame of SCN in % for original (23/01/2020–18/08/2020) and new dataset (23/01/2020–18/09/2020).

While in the onset phase “risk” is mentioned the most, wave 1 and wave 2 phases are rather concerned with “resilience,” while especially wave 2 brings “sustainability” back into the focus of discussion. The highest distribution of articles concerning two or all three of the mentioned SCM practices (RRS) in the last time frame supports the thesis of the previous section that the topics are treated after the initial “chaos” phase in the first time frame, and partly in wave 1, rather together in a wider, more reflected manner in wave 2.

To further validate this finding, the newer dataset used for data validation as described in the Validity and Reliability section has been evaluated, showing that the frequency of RRS in wave 2 increased by 57–6.4%.

Sustainability

As part of our research, the question remains whether any conclusion can be drawn on the impact of the coronavirus pandemic on sustainability for SC. Therefore, all articles that contain the word “sustainab” have been collected, as this includes the words sustainability and sustainable, to conduct a deep dive into their content.

In the SCN dataset, 31 articles (9.9%) mentioned the word “sustainab.” The focus on sustainability increased in each time frame. However, when going through each individual article, we identified that 19 articles were using the term “sustainable” from a rather economic perspective, referring to profitability, price, and growth than truly sustainable, environmental, and social measures. Only six articles, which are all environmentally focused, mentioned “sustainb” more than twice. Similarly, in the GN dataset, 55 articles mentioned “sustainab;” again the frequency increased in wave 1 and wave 2 compared with the onset. Only 11 articles, which are all environmentally focused, mentioned “sustainb” more than two times.

Table 3 shows an overview and a list of bigrams of these words for the SCN and GN datasets.

Table 3 . “sustainab” bigrams.

Even though the bigrams demonstrate in which context sustainability is discussed in the articles, they do not give enough information to answer our research question.

For further investigation, a deep dive of each article can be manually done to identify the core statements of these articles.

This section explains the contribution of the paper. Avoiding overlap, there are also some aspects, where the previous findings are aggregated.

Through text mining, insights on the impact of the coronavirus pandemic on SC can be drawn, and these results reflect trends and observations on a broader and global picture when linked with infection numbers of individual regions and historical events like the “toilet paper crisis” (see e.g., Paul and Chowdhury, 2020 ).

The first contribution relates to the method applied, which we address first here, as this has a great impact on the content-related issues. Text mining allows detailed insights on the impact of the coronavirus pandemic on SC. While similar tools start to emerge in related literature (e.g., Handfield et al., 2020 ), this is still a rarely applied method and has not yet been used in the COVID-19 SC-related literature. So the review paper by Queiroz et al. (2020) does not provide evidence of empirical material being analyzed. While analyzing a news feed implies that data created for other purposes (i.e., not primary empirical data) are carried out, it allows getting access to real-world data in a fast and timely manner. This is a great strength of the method. At the same time, this might be seen as a limitation, as the data are rather unspecified and interpreted. We will briefly return to further limitations after the more content-related aspects have been covered.

The next contribution is made by providing evidence that the initial separation of three phases proved highly valuable in analyzing the material. We would not be aware of a similar approach so far (e.g., Majumdar et al., 2020 ; Queiroz et al., 2020 ), so related papers reviewed on the topic did not yet offer such detailed coverage. Yet for the management and as discussed below, this seems to have clear implications.

The offered insights from the sentiment analysis showed that both general and SCN newspaper articles changed from a rather negative tone to positive coverage, indicating that at the onset, reporting was focused on the problems caused by the disruption of SC, leading to a focus on solutions and positive news in the later time frames regarding fixed SC and positive trends out of the crisis. The words “impact” or “disruption” lessen in frequency over time in the SCN dataset, and the words “operations” or “plan” appear in the 50 most frequent words, supporting this thesis. The word clouds of positive and negative words strengthen this idea, as negative words seem to become less relevant over time, meaning they were used less often. In the positive word cloud, words become bigger and therefore are used more often, and topics like “sustainability,” “improve,” or “recovery” become visible.

In the GN dataset, the sentiments turn more positive after many negative articles in the onset and beginning of wave 1 time frame. However, there are not as many days with a positive score compared with the SCN dataset. The reason for this is probably that the GN articles report much more, and all the time, about deaths of corona patients than the SCN articles. This can be seen well in the diagrams and word clouds of the most positive and negative words. Even though GN is filtered for articles including “supply chain,” the topics of the articles seem to be mostly focused on people, health, employment, and the virus itself. Words like “limited,” “supplies,” “shortage,” “disruption,” and “demand” were prominent only in the onset when the SC disruptions were affecting people directly. As SC issues are not substantially represented, with the GN dataset, few conclusions on the impact of the coronavirus pandemic on SC can be drawn. This confirmed the initial assumption that general newspapers treat SC topics rather superficially.

This is a mix of content as well as method-related contribution. It is possible to show differences among GN and SCN as well as the phases onset, wave 1, and wave 2.

When analyzing the implications of the pandemic for the SCM constructs risk, resilience, and especially sustainability, our analysis shows that the mentioned constructs vary in their news coverage over different time frames depending on the type of newspaper and the number of COVID-19 infections. This provides a more detailed analysis of the topics than the either quite generic ( Ivanov, 2020 ) or socially sustainable-focused papers (e.g., Majumdar et al., 2020 ) available so far.

When looking at RRS, it is important to distinguish between the three concepts and words. “Risk” is used in many articles but with different meanings, but the SCN dataset is mostly combined with management, mitigation, and other SCM topics, linking well into the typical SC risk literature ( Manuj and Mentzer, 2008 ). “Resilience” is often combined with risk, and the word clouds of those words are quite similar. In Figure 12 , it is noticeable that “sustainability” is not often used in combination with the two other words. This confirms what was mentioned when the three concepts are explained, that SCRM and SC resilience are often combined ( Pettit et al., 2010 ). The findings also show that the common external risks associated with SC disruptions, supply and demand uncertainties, were the main topics of newspaper articles regarding risk in all time frames.

When interpreting the frequency counts of SCN articles mentioning “resilience,” the onset phase focuses on the disruption and inventory; in wave 1, logistics and general organization play a more important role; and in wave 2, time frame production becomes a more frequent topic.

In the literature review, the research focus in terms of resilience during the pandemic lays on the diversity of suppliers and transparency ( Alicke et al., 2020 ; Schmidt, 2020 ), but with this text mining approach, no such conclusion can be made. While most research on this new topic is based on a literature review with a focus on detailed results in a specific area, our findings are rather general and on a broader picture. This is an interesting deviation from existing academic literature and might ask for more subsequent research analyzing this in more detail.

In the field of sustainable SC, this is similar; but some analogy can be drawn between the current research and the findings here. The studies analyze the question of localization, general behavioral changes, and the rebuilding of SC as an opportunity for sustainability ( Fischedick and Schneidewind, 2020 ; Lopes de Sousa Jabbour et al., 2020 ; Sarkis et al., 2020 ). Even though localization does not come up in this research, we can see that sustainability gained more attention in wave 1 and 2 time frames ( Figure 12 ), while aspects of risk and resilience were almost equally discussed over all time frames. This leads to the conclusion that sustainability was firstly left out of the picture when looking at SC in times of corona but soon became a topic of interest, supporting the thesis of Sarkis et al. (2020) , Fischedick and Schneidewind (2020) , and Bodenheimer and Leidenberger (2020) that the crisis can be a possibility of a transition toward more sustainability.

While text mining allows insights on the impact of the coronavirus pandemic on SC, there are a couple of limitations that have been already indicated in the Research Method and Findings sections. The dataset has a focus on the USA, as only English-speaking newspapers with the most views have been used, since a text mining analysis with multiple languages is very difficult to keep unbiased and valid.

The unequal amount of articles per time period, newspaper, country, and dataset is difficult to influence, but important to highlight when conclusions and comparisons are drawn. Also, if other newspapers or date ranges would have been chosen, the results will likely be quite different.

Additionally, content scraping is at the current stage not completely tidy and depends on the quality of the CSS code by the individual newspaper developers. Filtering out stop words is highly important to clean results by unnecessary words; however, this is also risky for an independent analysis, as words that in one context seem unnecessary could be filtered out erroneously and distort the results of this analysis.

Similarly, as we have seen, the lexica used for sentiment analysis can be limited and biased, as these algorithms are unable to understand and analyze whole sentences. A focus on monograms can lead to wrong assumptions being made, as they are lacking the individual context of each word. It also has to be mentioned that the interpretations made to explain the findings in this paper are based on personal experience and knowledge over this crisis; while they make sense to us, these findings could also be interpreted in different ways leading to other explanations.

As this recent pandemic is highly dynamic and has not yet ended, research implications are still too early to make. Our findings relate to other research in the field, as similar keywords, topics, and trends are identified. For further research in the field, this work serves as a solid base to obtain an unbiased and complete dataset, which we suggest might be used for a manual literature analysis that can specifically look into articles containing RRS in order to answer our research question. The code can also be used to analyze other SCM concepts or time frames.

In order to analyze the impact of the coronavirus crisis on SC globally, we recommend a similar analysis with newspapers from more countries in the respective official language of each country and later combine each analysis to see similarities and differences on a global picture. It could be beneficial to analyze outlier articles that show a significant positive or negative sentiment to interpret the results better and to validate the algorithm.

Additionally, further research is needed on text mining methods, specifically if similar results can be obtained with different software like Python or different platforms such as the Europe Media Monitor and the News option in the Google Search or social media platforms like Twitter.

It is hard to offer clear managerial implications based on such a study. The SCN dataset shows that a professional approach is already coming forward over time, so the profession seems to turn to the related challenges of risks, resilience, and sustainability already.

In this research, the impact of the coronavirus pandemic on SC has been analyzed by using text mining techniques on newspaper articles from general and supply and logistic press. Analyzing general newspapers does not lead to a conclusive answer to the research questions; however, the results on SCN offer some indications. As far as the impact on the SC of the coronavirus pandemic goes, the disruption, food, companies, and businesses are the main focus of general newspapers besides people and health issues. In SCN, the focus changes from trade, demand, logistics, and manufacturing in combination with disruption, impact, and risk toward technology, increase, and commerce and from being problem-focused toward solution-focused.

The importance of sustainable SC in both general and SCN press has been overshadowed at the beginning of the coronavirus outbreak by the supply shortage and supply disruption, while after the supply recovery, sustainability is more discussed and in SCN more often discussed in combination with the SCM practices, risk, and resilience.

Some of the current research in the field of sustainable SC during COVID-19 is in line with the results of this study.

Given the discussed limitations of the text mining method, based on our analysis, it is not possible to say whether sustainable SCs are more resilient and prepared for risk management. Even though through the approach applied in this research a first trend and a good overview is given, a thorough manual analysis of relevant articles would be necessary to give a more detailed answer to the second research question.

Looking at future research in the field, this work serves as a foundation to obtain an unbiased and complete dataset to be used for a manual literature analysis that can specifically look into articles containing RRS.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author.

Author Contributions

AM, WW, and SS developed the paper idea together. AM did the programming in R. AM and WW jointly did the data collection, data analysis, and wrote the first draft of the paper. SS supervised and guided the work and contributed to improving and finalizing the text. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsus.2021.631182/full#supplementary-material

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Keywords: COVID-19, corona pandemic, supply chain, supply chain management, risk, resilience, sustainability, text mining

Citation: Meyer A, Walter W and Seuring S (2021) The Impact of the Coronavirus Pandemic on Supply Chains and Their Sustainability: A Text Mining Approach. Front. Sustain. 2:631182. doi: 10.3389/frsus.2021.631182

Received: 20 November 2020; Accepted: 19 January 2021; Published: 11 March 2021.

Reviewed by:

Copyright © 2021 Meyer, Walter and Seuring. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stefan Seuring, seuring@uni-kassel.de

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Sustainability and Resiliency in Organizations during Times of Crises: Addressing the Challenges of COVID-19

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When the UK stood in the face of a rapidly escalating COVID-19 outbreak and thousands of people were being admitted to hospitals daily, the British Government feared an impending shortage of machines vital for treating critically ill patients. So, the UK Prime Minister called on industry to manufacture new ventilators to help meet the unprecedented demand.

Responding to the call for assistance, the High Value Manufacturing Catapult CEO, Dick Elsy, pulled numerous leading aerospace, automotive and engineering businesses - including Smiths Group, Penlon, Rolls-Royce, GKN Aerospace, McLaren, DHL and Accenture – together to form the VentilatorChallengeUK consortium.

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Given our long-standing relationship with Rolls-Royce and close collaboration with companies involved with UK Made Smarter, Accenture was asked to oversee and support execution of the supply chain for the Smiths Group ventilators.

Managing the ordering process, our teams are helping to source & stock the right amount of product at the various assembly locations. Rolls-Royce identified more than 100 suppliers, providing the 292 unique parts, and our role is to precisely coordinate the order and shipment of some 3.4million parts.

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Just two days after being asked for assistance, Accenture started issuing new purchase orders. Within ten days, Accenture and Avanade had designed the supply chain processes, establishing protocols for the flow of information, product and payments across more than 100 organisations globally. And after five weeks, we had 100% of the parts needed to build the first batch of ventilators.

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Supply Chains and the COVID‐19 Pandemic: A Comprehensive Framework

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• Magableh G 1

European Management Review , 01 Jan 2021 , 18(3): 363-382 PMCID: PMC8014293

Abstract 

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Ghazi M. Magableh

1 Industrial Engineering Department, Yarmouk University, Irbid Jordan,

The Coronavirus pandemic affected activities worldwide, among which the supply chain (SC) disruptions is significant. The impact is expected to affect businesses indefinitely; thus, the SC is unlikely to resume its pre‐COVID‐19 status. This study examines the impact of the COVID‐19 pandemic on SCs regarding its disruptions, associated challenges, and trend. It conducts an analysis of SCs stages, phases, and manifestations regarding the consequences, opportunities, and developments induced by the pandemic. A framework for the SC with COVID‐19 is presented towards a future global value chain and continuous improvements. It explores and connects the relevant elements to address the relations of SC‐COVID19 (SCC19). This study is novel as it identifies, categorises, and frames the essential factors and their interrelationships in a comprehensive framework. The SCC19 framework can be of value for decision‐makers and researchers, and can be generalised to other industries. Furthermore, study limitations and future research directions are discussed.

• Introduction

Global supply chain (GSC) disruptions started after the World Health Oganization (WHO) declared the coronavirus disease outbreak to be global health emergency at the end of January 2020. During the first half of 2020, the virus spread to almost all the countries in partial or total lockdown (McKenzie,  2020 ). Such a crisis affects the supply network at the source and destination, has extreme effects on GSC, and interrupts production process (Choudhury,  2020 ). According to Institute of Supply Management (ISM), about 75% of the companies reported supply chain (SC) disruptions, 80% expected some kind of disruptions in the near future, 62% reported delays in receiving goods, and 53% of firms reported difficulties in getting information from China (McCrea,  2020a , 2020b ). More than five million companies with Tier 2 supplies were impacted by the pandemic (Dun and Bradstreet, 2020). It is estimated that among the 450 million persons working in GSCs, many have faced reduced income or even job loss due to COVID‐19 (Kippenberg, 2020). Globally, organisations have been shutting down shops, deleting orders, and suspending production. Some sectors like garment, mining, jewelry, and automobiles have been suffering as the employees in these sectors are among the most vulnerable and being affected by the pandemic (Kippenberg, 2020).

Traditionally, research has focused on resource allocation and distribution during the pandemic using different approaches such as optimisation (Queiroz et al.,  2020 ). Ivanov and Dolgui ( 2020b ) examined the available literature to conceptualise the ripple effect of the pandemic, focusing on SCs structural dynamics, and derived the managerial implications. Some researchers study and proposed reconfigurable SC by comprehensively merging components taken from resilient, digital, lean, agile, and sustainable SC to adjust to sudden changes (Battaïa et al.,  2020 ; Dolgui et al .,  2020 ). A viable supply chain that balances sustainability, agility and resilience can support the organisation on recovery decisions and rebuild their SC after long standing crises like COVID‐19 pandemic (Ivanov,  2020b ). Some studies have introduced models to solve specific problems with specific applications (Mehrotra et al .,  2020 ). Researchers in the field of operations and management can also contribute to critical economic activities (Gao and Su,  2017 ). Choi ( 2020 ) introduced an analytical model to discover whether technology based logistics management can support the continuity of businesses, while (Gao and Su,  2017 ) proposed a hybrid forecasting technique dependent on nearest supplier and clusters to estimate COVID‐19 growth to help decision‐makers and SC executives to make decisions during the current crisis and future pandemics. Modelling, technology, and organisation raises questions and provides opportunities as the impact of the COVID‐19 pandemic on SCs continues to be increasingly destructive (Queiroz et al .,  2020 ).

Previous research largely analysed the impact of COVID‐19 on SC performance (Ivanov,  2020a ), focusing on solutions (Rowan and Laffey,  2020 ; Staal,  2020 ) to enhance the decision‐making process and viability (Ivanov and Dolgui,  2020c ), and utilising different tools, like simulation and ISN viability, to resist the pandemic and reach future resilience (Cheng and Lu, ; Golan et al .,  2020 ; Kahiluoto et al .,  2020 ; Ivanov,  2020a ; Ivanov and Dolgui,  2020c ). Studies have also investigated challenges (BESSON,  2020 ), explored SC disruptions (Cheema‐Fox et al .,  2020 ; Craighead et al .,  2020 ; Hobbs,  2020 ), examined SC security and safety issues (Liu et al .,  2020 ), estimated SC losses (Guan et al .,  2020 ), studied SC sustainability (Mari et al .,  2014 ; Hakovirta and Denuwara,  2020 ), evaluated localisation of the supply (de Sousa Jabbour et al .,  2020 ; McCrea,  2020b ), measured for critical goods (Lopes de Sousa Jabbour et al .,  2020 ), and investigated unusual SC events associated with COVID‐19 (Anner,  2020 ; Ivanov,  2020a ).

The future directions for research involve focusing and deepening investigation on SC and the innovation and development accompanying it. Studies should engage with sustainable supply and economic aspects to explore the impact of and to deal with COVID‐19 (Hensel et al .,  2020 ). They can also uncover several future SC trends (Liu et al .,  2020 ), explore the opportunities arising from the pandemic (Staal,  2020 ), focus on the complicated solutions for the shortage of critical products (Rowan and Laffey,  2020 ), and conduct an evaluation of economic effects, the delivery and supply system, as well as the monitoring and improvement policy to combat the possible threats (Gray,  2020 ) and sustain SC with the rapid propagation of the virus and the economic shock (Cheema‐Fox et al .,  2020 ), and understand the SC phenomenon to make the system more prepared for the future crises and risks (Craighead et al .,  2020 ).

The motivation of this study came from the instability and rapid changes in the business environment caused by the disruptions of global businesses and trades, which have been greatly affecting SCs. Such a pandemic requires making rapid decisions in a complex and challenging environment, considering many factors like SC responsiveness, costs and quality. This necessitates companies to set up tools to estimate, assess, respond to, and measure the effect of the pandemic on demand, supply, and, thus, SC. These tools should consider the critical element and different factors related to the SC‐COVID‐19 (SC and COVID‐19 pandemic) relationship to assess and respond to crises and consider solutions that guarantee the continuation of supply and delivery. The proposed framework introduces these components and their interrelationships to help organisations in building their tools and to support their decision‐making process.

This study investigates 75 articles dealing with the impact of the pandemic, its manifestation, and factors related to SC‐COVID‐19 and seek to contribute to SC recovery. It streamlines the literature and explores several new tensions and novel interconnections among various SC‐COVID‐19 factors. Therefore, this study seeks to address key research questions, namely: What are the factors that affect the performance of the supply chain, cause supply disruption, interrupt SC flows, and impact strategies to build end‐to‐end SC capability? What are the recurring, infrequent, and unique phenomena accompanying the COVID‐19 pandemic? How can decision‐makers employ the main interconnected SC‐COVID‐19 factors to overcome this ordeal and face similar crises in the future?

The literature review allows recognising combinations of factors related to SC‐COVID‐19 and could help decision‐makers enable recovery from the ongoing and future similar crises. For the purpose of this study, nine factors and relevant 58 elements are considered in structuring the comprehensive framework. Although many of these elements have evolved from growing topics in the literature as main factors in SC management and operations, the focus has been on their individual contributions to organisational performance and efficiency. This study differs from its predecessors as it tries to arrange, map out, and combine these factors in a comprehensive framework that can be used by researchers in future developments and provides a chance for decision‐makers to contemporise their SCs. This study, thus, pursues the following contributions:

It seeks to extend the investigation of the pandemic's impact on end‐to‐end GSC, considering the main causes of the disruption, the challenges associated with the pandemic, and the trend of the crises. Moreover, it examines the main interconnected factors leading to the disruption of SC operations: change in supply, fluctuation in demand, and the reaction of governments and countries to confront the pandemic.

It explores the main SC stages, phases, and manifestations of the crises against its consequences, opportunities, and developments to clarify their interrelationships based on cumulative experience, new businesses, and the economic environment.

It extends the analysis to comprehend the COVID‐19 consequences, measures, and solutions related to existing SCs. It describes the capability building method and crisis aspects, areas of developments and the principal steps relating to the future global value chain (GVC) and links the cost and continuous enhancements to resist the effect of COVID‐19 and possible future crises.

Additional contributions comprise the continuous developments methodology to allow SC managers and executives to express requirements, decision‐making process and inputs. The study introduces the triangular pedestal loop of continuous improvements: solutions, decision requirements, and evaluation tool.

The main structure of the framework interlinks these factors to assure analysis, improvements, and responsiveness to help decision‐makers endorse SCs future flexibility and competition. Other contributions involve an identification of several open research questions to be explored in the future.

The study outcomes and results can be used by logisticians, researchers, policymakers, and specialists in the field of supply chain to assist their organisations in decision support systems during the period of this pandemic and to ensure resistance to future crises. The framework can be extended to other industries and other related sectors.

In particular, this study finds that the outcomes are structured into nine factors: disruptions, cost control, capability building, aspects, facts, phenomena, areas of enhancement, steps towards SC stability, and continuous development. The rationality and practicality of the proposed model components was checked with a few SC and logistics specialists. The study can be used by both researchers and the industry to improve decision support systems and enable readjustment and restoration during the ongoing pandemic and future crises. Further future research directions are introduced and discussed as well.

The rest of the paper is organised as follows. The following section presents the SC and COVID‐19 related literature. The third section discusses the impact of the pandemic on the SCs. The fourth section analyses SC manifestations associated with the pandemic. The fifth section proposes a framework to understand SC systems with COVID‐19 and related solutions. The final section concludes and presents future research suggestions.

• Literature review

The complex networks of recent SCs can be exposed to disruptions due to shutdowns, which can be directly or indirectly caused by risk factors from natural, social, political, and economic phenomena (Scheibe and Blackhurst,  2018 ). Pandemic outbreaks are special cases of SC risks with indefinite disruptions, propagation, and considerable uncertainty (Ivanov,  2020a ). Such a crisis is not new, as SC vulnerabilities were exposed during the H1N1 and Ebola outbreaks in 2009 and 2014, respectively, thereby creating a critical demand for necessities. Thus, firms realised that they must quickly respond to current SC disruptions regarding what to produce, for whom, and how it should be produced (Armani et al .,  2020 ).

Today, Chinese SCs are under significant pressure (Chatterjee,  2020 ). Given, the rising global concern for the shortage of critical products like personal protective equipment (PPE), SCs can help supply crucial health care items and help reduce infections. The health care SC is global and requires urgent actions to ensure support to the health care systems during times of crises (Mirchandani,  2020 ). Contingency plans for critical product shortage are, thus, vital (Rowan and Laffey,  2020 ).

The COVID‐19 pandemic has placed the spotlight on SCs and challenged more than 30 years of progress towards globalisation. Moreover, the pandemic coincided with the current trade wars between the USA and China, thus uncovering the instability of GSCs and trading systems. It highlights the need to build more resilient SC operations (Feinmann,  2020 ).

The COVID‐19 outbreak introduced an unprecedented and extraordinary situation for SC resilience (Ivanov and Dolgui,  2020c ), during which SC survivability requires large scale resilience. SC network resilience and its ripple effect give scope for decision‐making. It grants access to short‐ and long‐term performance and behaviour of the network after the disruptions; supports design suitable for sourcing, production, and marketing in preparation for potential disruptions; and maintain competitive advantages in risky surroundings (Li and Zobel,  2020 ; Ivanov and Dolgui,  2020b ).

Companies should consider the ripple effect when investigating their resilience practices. Resilience can mitigate disruptions when considering SC complexity (Birkie and Trucco,  2020 ). SC resilience depends on the capability of rearranging resources to control disruptions (de Sá et al .,  2019 ). Collaborative activities, such as information sharing and communication, enhance SC resilience by improving visibility, quickness, and flexibility (Scholten and Schilder,  2015 ). External and internal SC network can improve SC resilience and customer‐related performance (Asamoah et al .,  2020 ). Increasing redundancy associated with operational flexibility ultimately enhances network resilience; moreover, simulations offer a holistic methodology to evaluate SC resilience (Childerhouse et al ., ).

As the pandemic is expected to progress indefinitely, much will be learned about how SCs respond to the crisis, modify strategies to improve resilience, and maintain customer trust (Hobbs,  2020 ). SC monitoring, proactive development strategies, assessment of economic impacts and delivery systems are necessary to address potential SC threats (Gray,  2020 ). Well known and emerging theories can reduce challenges and provide potential solutions. These theories help scholars study the pandemic, its impact on SC, and support decision‐makers to formulate responses (Craighead et al .,  2020 ).

Instability and rapid changes in the business environment require strategic alliances and inter‐organisational collaboration (Cravens et al .,  1996 ). Multi sourcing, quality, and flexibility can help achieve efficient coordination throughout the network and lead to different levels of collaboration to improve SC integration and sustainability (Kumar et al .,  2020 ; Vlachos and Dyra,  2020 ).

SC policymakers should respond to the spread of COVID‐19 while minimising its impact and maintaining the SCs (Guerin et al .,  2020 ). Decision‐making can be based on available data, demand, and past decisions (Mehrotra et al .,  2020 ). Applications of big data and analytics, machine learning, and digitisation can be applied to crises management activities and efforts (Cheema‐Fox et al .,  2020 ). Lean management practices and sustainability innovations enable SMEs to improve sustainability performance and economic efficiency (Dey et al .,  2020 ). Given that the digital transformation is rapidly and significantly changing business processes (He et al .,  2020 ), digital SC networks must be aligned with business strategies to enable businesses to adjust and recover from future SC disruptions.

• Impact of COVID‐19 pandemic on SCs

The COVID‐19 pandemic has significantly impacted end‐to‐end GSC activities, especially medical and food supplies. Thus, the integrity and stability of the value chain (VC) have been compromised. The global media has covered other angles from threats to accusations and even intercepting shipments, where countries scrutinise anything that enters respective territories, given its right to protect people and avoid catastrophe by all means. Such events have profoundly impacted GSCs.

Figure  1 illustrates the impact of the pandemic on SCs. The study considers three main aspects: the main causes of the disruptions, the challenges associated with the pandemic, and the trend of the crises. Three main interrelated factors led to the disruption of the SC operations: change in supply, fluctuation in demand, and the reaction of governments and countries to confront the pandemic.

Impact of the COVID‐19 pandemic on SCs

The pandemic affected SC activities, operations, processes, and management due to supply disruptions, demand volatility, and government actions to combat the crisis. Factory closures, border restrictions, travel bans, ports closures, and suspended transportations interrupted the entire supply network, thus leading to shortages. GSCs connected to China, USA, and Europe were greatly affected and induced spikes in many products in response to panic buying, which, in turn, led to price fluctuations, as supplies become limited. High demand and order congestions from the delays resulted in demand vulnerability and shocks, which impacted both offline and online purchases. Furthermore, complex challenges in addressing the massive demand while maintaining quality and continuity persisted. Many countries acted proactively to protect its people and imposed strict restrictions on movements via lockdowns, social distancing, and quarantining measures in the early parts of this year. The tension and panic from such decisions affected SC operations and performance. Government measures led to the decline in production and considerable contractions in international trade flows.

The main challenges associated with the COVID‐19 pandemic include industrial, economic, inflow, on‐hold production, delivery, online shopping, and SC upsets. Components needed to assemble final products are procured from (and assembled in) several locations. They are then re‐exported, transferred, or shipped to specified locations worldwide. Thus, disruption in the SC complicates the process. Supply and demand disruptions, along with governments' responses, contribute to external and internal economic risks in the short‐ and long‐term. Given such disruptions, against the backdrop of government protection measures, several manufacturing plants and factories suspended production. Delivery and distribution faced several challenges, including direct distribution difficulties, increased online orders, re‐staffing of distribution centres (DCs) and warehouses (WHs), and changes in the allocation of inventory across the network and distribution channels to increase responsiveness. Thus, the pandemic proved to be an opportunity for online stores and businesses. Moreover, online shopping increased, especially for food and grocery products. The pandemic changed customer trends, as many of them relied on online shopping and reduced their visits to the retailer centres. Furthermore, there were freight prices spikes, a low travel interest, and increases (decreases) in demand for key products (luxury products), as well as cancelled and extra orders. Thus, organisations are pressured to lay off workers and halt productions.

Most of the world companies are affected by the pandemic; they also report disruptions in their SCs. The main trends of this effect include the following. (1) Prices fluctuate, as the extraordinary increase in prices during the pandemic negatively affect the relationship between suppliers, retailers, and customers. (2) There is a long lead time due to delays in receiving items from the source and delays in distribution. (3) There are delays in shipments, moving cargo, loading, shipping, and unloading, as well as extra delays at the borders and ports. (4) Return, profits, and income reduce, thus hurting retailers' abilities regarding their quality control. (5) Production capacity and manufacturing capability decrease and affects the sources of production. (6) Lack of disruptions plans associated with small inventory levels, single supplier, or minor diversification underestimate the possibility of severe disruptions; the focus on short‐term and costs minimisations lead to a lack of risk information and contingency plan. (7) Moreover, there are difficulties in getting information and data from partners, lack of end‐to‐end visibility, lack of integrations and coordination, and variations in technology utilisation throughout the SC.

In general, global trade and business experience abnormal interruptions, which affect the demand and supply end of the SC. Thus, organisations face significant challenges in maintaining a continuous flow of goods and services.

• SCs manifestations

It is too early to evaluate and quantify the total effect of COVID‐19 on the SC disruptions. However, the primary decline in production, business, and trade in China and other industrial nations will strongly impact many countries down the SC as most countries restrict the movement of supplies to combat the spread of the virus.

Figure  2 shows three main stages that characterised the SC: short‐, medium‐, and long‐term shocks and developments. The SCs will go through six phases: supply upset, demand upset, new ordinary, unique time, post‐pandemic, and the opportunity. COVID‐19 manifestations on SCs are briefly discussed, based on their connection with each stage, to clarify the interrelationships. The arrow at the bottom indicates the development of the SC, based on continuous improvements taken from the cumulative experience, new business, and the economic environment.

SCs stages, phases, and manifestations, given COVID‐19

The main SC stages, as shown in Figure  2 , explores the periods of GSC related to COVID‐19 pandemic. All stages are interconnected. Each stage, however, has its own characteristics. SC decision‐makers and managers must consider the disruptions, consequences, and solutions of each stage carefully.

Short‐term stage

Short‐term disruptions lead to a considerable readjustment from the usual supply sources, like China, and result in long‐term benefits to the local industry and domestic sources. The two phases of this stage are supply and demand upsets. Since the availability of raw materials, components, finished products, and items used in the factories were interrupted, organisations strived to fulfil demand requirement at the minimum level or partially. The demand shock began when people started to engage in panic‐driven hoarding of goods. Given the empty warehouses, DCs, retailers, and supply lines, rerouting and redirecting inventories and available products to newly identified priorities and bypassing supply decision systems was a challenge. Human resources are the most crucial components; nonetheless, many factory and service employees were infected. Hence, measures are required to improve continuity, cut costs, produce cash, develop agile SCs, and guarantee the safety of the employees.

Medium‐term stage

The prevalent opinion indicates that recovery from the pandemic and its consequences is here to stay. The phases associated with this stage are new normal and unique time. In this stage, it is necessary to build new supply continuity beyond COVID‐19. When the demands reach a new steady‐state value, there should be production adjustments to reflect the real new demand (whether an increase or decrease), along with the supply lines adaptations. Inventory bounce should be treated carefully to avoid the bullwhip effect. As the length and magnitude of the pandemic remain unclear, the consequences will persist, even after the outbreak is contained. For some companies, the impact will stay longer and worsen without stable GSCs, as well as vulnerable sources. Thus, SCs must review their strategies since the pandemic has introduced unique moments to SC managers, decision‐makers, planners, and regulators to reconsider the global SC model, new supply needs, and dependencies. Moreover, they can realise and develop SCs for the domestic market, SC resilience, and SC visibility. Furthermore, they can strengthen consumer relationships, buffer stock, adjust the planning system, assure continuity, adopt demand and supply changes, modularise production according to demand change, and develop risk management plans.

Long‐term stage

If the crises persist, further SC interruptions on delivery and distribution delays are expected. The phases of this stage represent the post‐pandemic and the opportunity. In the post‐pandemic phase, the crises will speed up the digital transformation of SCs and businesses to address inherent weaknesses and vulnerability. Technology will be the main player in building future SC strategies. Moreover, several methods explored can help businesses generate resilience SCs post‐COVID‐19. The opportunity from the pandemic stems from key technologies that support businesses in making them efficient and capable of handling risks after surviving the crises. Employing digital technologies help build resilient GSCs. Moreover, digital technology, visibility, integration, and collaboration with other partners are crucial to survive a future crisis. Industrial establishments that can modularise production according to demand change could be the norm in the future in supporting the SC network in communications and agility. Moving to the cloud may be necessary to ensure business continuity. Safety is one of the main factors as companies need to consider risk management in any future strategy and planning. Remote working post‐COVID‐19 must be carefully considered, as it will change tradition work processes and operating principals.

• SC and COVID‐19 (SCC19) framework

It is clear that the SC‐COVID‐19 perspectives will dominate the researchers in the industry throughout the pandemic effect and recovery era (Ivanov and Dolgui,  2020b ). There are many studies in the literature considering the different risks, impact, and solutions of the pandemic. Moreover, they investigate different tools and techniques like modelling, optimisation, and simulation. The COVID‐19 pandemic resulted in a unique environment of disruption, implications, and risks on SCs, and thus the SC‐COVID‐19 components are affected at different levels and times. With the pandemic's impact, it is difficult to apply the majority of common SC techniques such as risk mitigation, transportation, inventory, and smooth flow of material, money, and information. So, there is a necessity of framing the main component of SC‐COVID‐19 factors and elements with their interconnections to help decision‐makers combat the ongoing and future crises.

The selected literature of the latest contributions is used to explore and connect the relevant elements to address the SC‐COVID‐19 relationship. The studies relevant to SC with COVID‐19 are reviewed towards future resilience and continuous improvements. This selection was complemented with a search in common available database including journal articles, periodicals, publications of international organisations like the WHO and World Bank, and experts' reports. To ensure representativeness of studies, the following rules are used: SC and COVID‐19 is used as the basic search protocol and subsequently, one of the nine factors (disruptions, facts, phenomena, capability building, crisis aspects, steps towards resilience and new normal, areas of improvements, continuous developments, and costs) is added every time there is new search; for example: (SC, COVID‐19, and disruptions), (SC, COVID‐19, cost). The keywords are selected based on an analysis of relevant literature on SC‐COVID‐19.

After narrowing the search and filtering the articles according to the scope of the study, a list of 75 articles related to SC‐COVID‐19 are selected to be used in the study. In each reviewed paper, three factors were selected from the nine that were considered. These factors are arranged in descending order according to the extent of focus on them in the research paper being reviewed.

A detailed article‐by‐article analysis is shown in Table  1 , which shows the major outcomes of the main factors related to SC‐COVID‐19 insights. Table  1 summarises the author(s), title, journal, and the factors of focus. The study focuses on the major insights relevant to supply chains in the context of COVID‐19. The insights are generalised from every single article and classified into nine factors.

Studies related to the proposed SCC19 framework factors

As summarised in Table  2 , the analysis revealed several components that have been effectively applied to SC elements related to the main factors. It was found that each of the nine factors is aligned with several elements. The total number of elements consider in the study is 58, as shown in Table  2 . Such an organisation seemed the most reasonable and convenient manner for constituting the SCC19 framework and conducting further analysis. The SCC19 framework links the components that are composed of the factors and their relevant elements.

Components used in the SCC19 framework

These components are confined by the scope of the framework at the strategic and process levels, which bound the structure required to connect the impact and manifestation associated with SC‐COVID‐19, SC pillars, costs issues, and actions and solutions suggested to determine the areas of improvements and come up with steps towards continuous improvements. However, the model does not elaborate on details regarding other elements like flows, inventory, ordering, and collaboration process as they are accommodated at the operational level. The model comprises the major components required to support decision‐making processes and help researchers implement further analysis.

SC disruptions and recommendations for the future are mentioned in the highest number of publications, followed by the areas of enhancements, SC‐COVID‐19 facts, and SC capability building. Meanwhile, cost control got the lowest number. Based on the reviewed papers, the number of citations per factor is shown in Table  2 . Such an arrangement seemed reasonable and convenient for developing further classifications of the main SC‐COVID‐19 outcomes, and deriving better conclusions and future research guidelines.

SC and pandemic analysis is a huge area and is expected to increase over time. It is clear that there is an urgent need to determine the main factors related to SC‐COVID‐19, their interconnections, and how to utilise the outcomes from different studies to help SC managers evaluate their situation, make their decision, survive the current disruptions, recover and build new normal, and resist future crises. The analysis of SC‐COVID‐19 in the context of the connection among different factors and elements represents a novel study array. Based on the above analysis and findings, the SCC19 framework is structured.

Figure 3 presents the framework to understand the COVID‐19 effect, measures, and solutions related to existing SCs. Initially, SC curators must understand the phenomena that accompanied the spread of the virus. They should then study and realise the interruptions that negatively affected the performance of the existing SC, whether they are constraints, disruptions, and challenges. Thus, to build any short‐, medium‐, or long‐term solutions, including the necessary immediate solutions, it is essential to realise the necessary pillars to support the areas of development, future GVC, and continuous improvement. The first pillar represents capacity‐building to mitigate any future threat to the SC. The second pillar represents the crises aspects for readiness, response, and recovery of the VC, followed by an adequate appreciation of the SC in the pandemic period. These facts should be considered in any developments of plans, procedures, and policies. The next segment represents development areas, which include five main areas necessary to support continuous improvement and ensure continuity, responsiveness, and future SC competition. Furthermore, the necessary steps to ensure the performance and effectiveness of the SC include several steps towards the future SC development. This improvement is a guarantee for resilience towards unknown challenges that often disrupt the SC in crisis situations like COVID‐19. The continuous improvements approach enables SC managers and executives to define requirements, decision‐making and inputs, as well as finds appropriate solutions, associated evaluations, and measurement processes while ensuring that these procedures continue to be revised in the future. The following sectors constitute the main structures of the framework in analysing, improving, and ensuring SC future resilience and competition.

Panic buying, supply disruptions, localisation trends, raw material limitations, and transportation problems, as well as political, social, and health problems associated with the COVID‐19 are the main SC phenomena from the impact of the pandemic. Moreover, the phenomena are accompanied by delivery delays, unpredicted practices, stockpiling, hoarding goods to sell later with inflated prices, reduction in quality assurance of products, reduction in data access, banning exports, and panic‐induced buying. The panic buying phenomenon made the international headlines as it provided the opportunities for fraudsters to fill out the gaps.

SC interruptions

Every SC has its own constraints, such as productions, flows, locations, inventory, and storage constraints. In addition to the normal SC constraints, planning, decision‐making, capability, visibility, quality constraints, capacity, legal, regulation, institutions, sustainability, talent, liquidity, and responsiveness limits the ability of delivery. SC disruptions from government measures and actions significantly impact the global SC chains as they abandon exports, imports, travelling, transportations, and social isolations, in addition to many other restricted actions. SC challenges, such as trade barriers, delivery challenges, suspended operations, social distances, and border restrictions impact SC continuous improvement, strategies, and competitiveness if appropriate measures are not considered promptly with the right tools. Other parts of the segments in Figure  3 help prevent SC interruptions from affecting the SC in the short‐, medium‐, and long‐term regarding the development of future VCs.

SCC19 Framework

The first pillar represents the strategies for building SC capability, which is the main supporter of any SC process, operations, and activities during crises. The second pillar represents crisis aspects, which supports the future reliance of SC and prevent disruptions from aborting SC operations and continuity.

Ten success strategies help build SC capability to maintain SC operations, given the COVID‐19 pandemic. These strategies include planning, integration, optimisation, digital collaboration, rapid insights, quality test, empowered SC team, correct ERP system, network agility, and reliable supply. Companies should ensure a reliable and predictable supply and attain the greatest value from suppliers. Diversification of supply base and sourcing strategies must focus on returning to full production beyond the outbreak. Building for rapid generation of insights allows SCs to respond to changes and shocks. Resilience in SC operations is a key factor for the success of many strategies. It empowers SC teams and involves all staff in the supply chain management. Decentralised teams can respond quickly to insights and generate recovery capabilities to disruptions. A correct ERP system is a necessary component with many industrial systems to eliminate the use of manual systems. Moreover, if the organisation already has an ERP system, they should use an advanced system to increase the production with efficiency improvements. An adaptive, flexible, and agile SC network with quick planning and integrated implementation should be formed. Dynamic planning capability may ensure agility for SC changes and enable continuous, dynamic SC modifications and adjustments to respond to the changes quickly and efficiently. Digital collaboration increases with information sharing enhanced by cloud technology, collaborative base, and suitable tools. Optimisation of product designs and management for supply, production, and sustainability will speed up the advancement of innovations. Innovation is crucial for competition and can be reflected in the design of the product regarding price, time, and location. Embedded sustainability into SC operations is a crucial success factor. SCs should be aligned with business objectives by integrating trades and operations with business planning. In any strategy, resources planning is the key to improving SC success. Failing to plan resources requirements lead to delays in deliverables and delivery, reduction in quality, overtime, and a reduction in consumers' satisfaction.

The crisis is discussed here from four aspects, including impact, readiness, response, and recovery. Operations were affected by different levels like volume decline, transit delay, inadequate capacity, delivery delay, late payments, cancelled credit lines, inconsistent demand quantity, and increased costs. Moreover, there was a delay in communication with sources and shippers.

Organisations require the development of readiness capability while decreasing the decision uncertainty during response and recovery. Companies may build readiness through three main phases: development, assessment, and finalising. Response plans should determine actions to minimise and shorten the length of the disruption and its effect on SC. The consideration of available resources (human, financial, and assets) is required to accomplish the plans. Further, recovery comprises the prevention strategies and policies, control measurements, treatment programs, manpower management, and disruption elimination.

SCs will adapt, and businesses and the economy usually find a way to survive adverse situations. Thus, the SC must be restructured for alterative SC suppliers to initiate more redundant SCs. Current trends indicate that firms are working towards reliance on local and regional sources and have become more responsive to disruptions. The COVID‐19 pandemic is different from any other black swan event and affects both demand and supply, simultaneously. The crisis gives scope for exploring the necessity of joint working. Many countries and organisations are convinced that stabilising their SCs require nearby local or regional sources. Joint working by sharing information and understanding suppliers in the SC promotes good preparations for future crisis.

Areas of enhancement

Organisations should first focus on short‐term solutions and management to recover and enable the continuity of flows. They may hold operation and activities for a while, such as money transfer, resources exchange, cooperative applications, and systems developments and technology deployment until they recover and resume work. The long‐term plan should focus on solutions and strategies to mitigate matching future events. The fields of improvements include visibility and monitoring, adoption of emerging technology, blockchain technology, automation, and digitisation and transparency.

The visibility of the inventory in the entire SC allows organisations to integrate their efforts, database, and decision process for the necessary flexibility and agility to react to rapid changes, speed up processes, and reduces costs. Several technologies rapidly change the SC process, such as 3DP, IoT, predictive technology, cloud computing, big data and analytics, and autonomous systems. Industry 4.0 technologies, SC 4.0, and augmented reality can expedite SC digital transforming. Blockchain technology encourages suppliers to share data and ensure data confidentiality. Moreover, it should quickly be implemented since disruptions may happen anytime. Automation systems in the workflow, process, and operations increase the agility of replenishment. Automation greatly impacts the SCs and continues to reshape the costs of sourcing, especially in production lines that are suited to automation like electronics. SC digitisation should be integrated as part of its design. Digitisation enables risk management and business continuity as part of the entire business strategy.

Forward steps towards SC stability

There are six main forward steps towards SC stability. They include start‐up solutions to guarantee the recovery from the first shock, short‐ and medium‐term measures to assure the continuity of flows, and the recovery from the pandemic disruptions, as well as transforming the SC to face challenges. Moreover, the key steps for more SC resilience include reshaping the SC based on the technologies, lessons learned, best practices, and building the future SC model to combat current and future crises.

Any start‐up solution should consider the economic effect and direct impact on sources and supplies, escalations in prices, and the employees' safety and income. Start‐up solutions may include an electronic platform to connect with entities that can transport cargo across the country. An immediate business model should be built to make SCs more effective, transparent, increase logistics capacity, and reduces the travel time as much as possible. Organisations should make sure of correct procedures for a strong protection system for workers in line with international standard and social security.

Measures to respond to short‐medium change and protect SC operations include the following. Train employees about COVID‐19 symptoms, precaution, prevention, and protection. Concentrate on human resources planning and strategies with work flexibility to allow for remote work. Organisations should efficiently make sure of the IT system infrastructure, networks, and its suitability for a seamless flow of operations. Supply networks should focus on direct suppliers, reduce extended supply networks, and supply from closer sources. Organisations should employ digital methods to build supplier networks and visibility for critical SC activities.

Flexibility is necessary to prepare for smooth factory closure. Moreover, production rescheduling and agility must be emphasised to prepare for global scenario planning, and a rebound as production is moved to another location within the SC network. Experts can introduce innovative and agile solutions like mobile production entity. It is important to update the plans frequently, including supply, demand, impacted markets, and the virus controlled or spread locations.

Many organisations and businesses have been forced to transform their SC model to a flexible and responsive one. New technologies and digital SC could assist organisations to oversee their entire supply network from routing, rerouting, destination change, and bypass process to guarantee the continuations of flows.

Organisation recovery varies based on SC risk management, disruptions mitigation plans, business strategies, geographical area (close suppliers, factories, and industry in the region), reliance on multi suppliers and sources, and stock size to buffer against SC instability.

The new normal is necessary to guarantee refreshments, prevent productions and industry shutdown, and generate the ability to face shocks to be more reliable, secure, and responsive after the experience.

Three key factors make SCs more resilience: SC visibility, digitisation, and data confidentiality for suppliers. During crises, visibility via SC is vital to understand the impact on the rest of the SC network so that other partners can plan and take actions like evolving routes to other sources. Digitising SC will create more resilience to future disruptions. Organisations should initiate a digital transformation to make the process more efficient and secure. Blockchains could be the solution to satisfy confidentiality and encourages suppliers to share information, contribute to SC, and increase visibility, thus leading to a resilient SC.

Beyond COVID‐19, SC resilience is the key to recover from the global crises. It is reshaping SCs and sectoral activities via recovery lessons to long‐term resilience. GSCs will require restructuring to ensure business goals and objectives are met. GSCs must avoid prolonged economic suffering and distress. Moreover, coordination policies could be a promising path to recovery from the crisis.

Risk elements are interconnected with geopolitical, social, and regulatory uncertainties, especially with the global economy. Organisations should study the potential risks in other countries and regions to specify the actions and plans that will appropriately protect against these risks. Organisational recovery will vary based on their SC risk management. Understanding risk divers, priorities, and solutions is the best way to plan for a future risk management system. Organisations must quickly arrange for inventory and cash flow during SC crisis to mitigate the risks of SC critical disruptions.

Crucial elements for future SCs include intelligent procurements, SC control tower, SC data management with intelligent automation and analytics, supplier risk management, and SC simulation. Intelligent procurements help companies know where and when to supply based on previous procurements, commodity pricing, industrial trends, and machine learning systems. SC control tower allows for the visibility of the external SC environment, the entire SC, trading partners. SC data management with intelligent automation and analytics based on accurate real‐time data about SC transactions without redundancy is one of the keys for the future GVC. Further, SC simulation could model and optimise new strategies changes.

Cost control

Given the pandemic, SC cost control and management is vital to support business objectives amid the increases and change in demand and financial volatilities. SC costs impact most of SC decisions and business. Decision‐makers try to manage margins and improve liquidity during the crisis. Thus, it is crucial to reshape SC costs in an innovative way to confront future growth and organisational agility. All businesses and SCs are worried about sudden changes in their tariffs, currency changes, currency depreciation, taxes, drops in revenues, operations and capital strategies, and conducting trade‐offs of cost reduction in various areas of SC. Such concerns are important for SC managers, executives, and decision‐makers for the future. Organisations that invest in high flexible SCs and resilience to reduce the product development cycle can adjust faster to market changes. Moreover, their revenue will increase.

Continuous developments

SCs have no choice but to evolve, and organisations require additional innovation to prepare for the future. Continuous development is a vital way to prepare for future crisis. It requires decision inputs, solutions, and evaluation tools.

Decision‐making requires the reconsideration of the GSC strategy, the demand impact specific to businesses, and the utilisation of SC experts. Given COVID‐19, additional decision‐making requirements include open‐source COVID‐19 data, real‐time SC data, and demand change per region or country, considering the opportunities and drawbacks of each decision. Thus, developing and enhancing SC excellence requires decision‐making skills, analytics capabilities, understanding costs and constraints, and business capability to support SC continuity to become a right business partner in the SC.

Solutions amid the pandemic include sourcing diversity, online delivery, contracts revision, sustainability, government support, and continuous development. Solutions can be implemented in steps as follows. Develop contingency plans with scenarios for different demand surroundings and changes. Reduce supply shock by working closely with the current suppliers and diversifying the sources. Arrange for demand volatility by mitigating panic buying, supporting retailers, promoting a safe work environment, protecting SC employees via investments, making PPEs available, and looking ahead to facilitate the resumption of business. Solutions require communication to manage time, availability, and safety.

Organisations require the establishment of a tool to evaluate, assess, and measure the effect of COVID‐19 on demand, supply, and, thus, the SC. The challenging times call for the latest analytics solutions. Many companies use software to analyse situations during crises and make decisions. Organisations’ evaluation tools may include software, analytics programs, group meeting, expert interviews and opinions, prioritising products, and best scenarios. Furthermore, validation of the decision‐making and solutions can employ benchmarking.

The framework was discussed with several experts in logistics and supply chain systems (two procurement specialists, two academicians, and three managers from companies specialising in global trade in Jordan). Trade companies are import and export firms that usually deal with bids to supply the local market with imported products. The nine factors that were adopted in the framework were reviewed, along with their interactions, by the experts. They emphasised the framework's applicability, the importance and effectiveness of the framework in reducing the impact of the pandemic on the performance of the supply chain in the short and long run. They also emphasised the effectiveness of the continuous development steps outlined in the framework. They focused on the importance of the framework in assisting managers in making decisions and planning for the supply chain in light of the pandemic and the post‐pandemic status, until it reaches the new normal. They also stressed the need to keep pace with these changes by utilising emerging technologies that help to overcome future crises, such as 3D printers and smart systems, and to increase reliance on local and nearby sources within the global supply chains. Their comments on areas of improvement ranged from adding new areas to rearranging them according to their priority. They added that an implementation of the framework must be accompanied by supportive government measures and the cooperation of the private sector, to facilitate and support the provisioning measures. They also stressed the need to identify the main SC‐COVID‐19 solutions and arrange them according to the priority of implementation and their importance in overcoming the crisis. They mentioned that these measures could lead to the stability of the supply chain and the aforementioned factors should be taken into consideration by decision‐makers at all levels.

COVID‐19 underlined the necessity to transform traditional SC models. During a ‘black swan’ event like COVID‐19, a strong understanding of the factors that could help organisations to initiate the correct plan to face the unexpected. New forms of tools can play a critical role in assisting SC managers view the potential SC‐COVID‐19 risks and use the framework to take corrective actions to ensure better SC flows. The multi‐factor framework enables enhanced characterisation of the complexity of SC‐COVID‐19, and therefore it offers support for more cognisant professional decision‐making regarding investing in improving SC stability. The observed results could help decision‐makers and managers make better decisions regarding the current and future crises.

The COVID‐19 pandemic disrupted global business and trades, thus severely affecting SCs. The shutdown of factories and disrupted the VCs left many businesses struggling to source materials and fulfil demand. Most organisations reported SC disruptions in their capacity because of the pandemic. It is challenging for a planner to prepare for crises, such as the COVID‐19 pandemic. Such crises require rapid decisions in a complicated and challenging environment, given SC costs and quality.

The effects are unlikely to stop soon. It will reasonably continue to affect businesses and trade policies for a long time. SCs will not be the same post‐COVID‐19. The shocks of the panic and the resulting economic impact led to a disruption in most of the GSCs. Generally, the pandemic impacted SCs and their management in several critical capacities. Many companies are continually analysing and tracking new trends in the SC and procurement process. Organisations that take steps to mitigate risks are better prepared to face future challenges. Companies that diversified their suppliers are poised to overcome the impact of the pandemic on SCs.

The COVID‐19 outbreak disrupted many businesses and related SC operations. Motivated by the findings of the literature analysis, the study examines the contribution by answering fundamental questions including: the factors impacting the performance, interrupting flows, and building SC end‐to‐end capability; the recurring and unique phenomena associated with COVID‐19 and their short‐medium‐long term consequences, opportunities, and developments; and how SC managers and decision‐makers can utilise the SC‐COVID‐19 framework components to face such crises, existing and future.

This study examined 75 articles on coping with the impact, manifestation, and factors connected to SC‐COVID‐19 and those that might contribute to SC recovery. Streamlining the literature uncovered several new prospects and novel interconnections among several SC‐COVID‐19 factors. The initial outcomes revealed that each factor or element was discussed thoroughly, but there is no congregation of these factors in a single study or framework.

This study differs from its predecessors as it tries to integrate different elements and factors together in a comprehensive framework. For this purpose, we first analysed the impact of COVID‐19 on SC operations and performance in terms of its disruptions, associated challenges, and new trends, and subsequently investigated SC manifestations arranged in three phases: short, medium, and long term. Finally, we build the framework based on the nine factors to explore the elements that could help decision‐makers withstand and recover from the ongoing as well as similar crises in the future. In addition, the framework introduced the triangular pedestal of continuous improvements: solutions, decision requirements, and evaluation tool. To the best of our knowledge, this research is the first to combine and emphasise the impact, phenomena, and propose a multi‐factor multi‐step framework for SC‐COVID‐19. It also contributes to the literature by defining the components of SCC19: disruptions, facts, phenomena, capability building, crisis aspects, costs control, areas of improvements, steps, and continuous developments towards SC resilience and the new normal. It provides a road map (enhancement areas and steps) to resist the ongoing and future crisis.

SCC19 frames the essential elements, understands their structure, sequences, and captures their relations. SCC19 can assist decision‐makers to build SCs that can respond adaptively and help organisations to guide their decisions on surviving, recovering, improving, and rebuilding their SCs beyond global long‐term crises like the COVID‐19 pandemic. The initial rationality and applicability of the model was checked with several experts in the field of logistics and SC systems.

Though it is early to evaluate and measure the entire effect of COVID‐19 on SCs, the study provides several implications to help SC managers better understand the interrelation between these factors and pandemic and boost the decision‐making process. Firms' practitioners can estimate the enhancement areas and steps towards resilience based on the environment and phenomena associated with the pandemic. This allows the evaluation of the post‐pandemic behaviour to help them reassert new normal and ensure continuous improvements against a risky environment. Our results evidently identify the main factors and elements related to SC‐COVID‐19 and propose the interrelationships between the components constituting the framework to support policymakers making proactive decisions.

In conclusion, the study explores the impact of the COVID‐19 pandemic on SCs be discussing the causes of disruptions, related challenges, and the trend of the pandemic. SC stages, phases, and manifestations of COVID‐19 were analysed. Moreover, the consequences, opportunities, and developments to explore the interconnections between different elements depend on aggregate experience, benchmarking, and a new business environment. A comprehensive SCC19 framework explores the interrelationships between the three factors of SC interruptions and the resulting facts and phenomenon from the crisis. Descriptions of the capability building process and crisis aspects are presented regarding SC resilience. Areas of improvements and the main steps concerning the future GVC were presented and linked to the cost control and continuous improvements to survive the impact of COVID‐19 and potential future crises.

The continuous developments methodology allows SC managers and executives to express requirements, decision‐making process and inputs, in addition to finding the appropriate solutions, related evaluations, and measurement practices while make sure of the continuous revisions in the future. The main structures of the framework interlinked these factors to assure the analysis, improvements, and responsiveness to help decision‐makers endorse SCs future flexibility and competition.

Perhaps, COVID‐19 will lead a new era and revolution in the SCs, and the time is ripe to review the performance and evaluation of the GSCs. Among the most important results of this crisis is the increased focus on the local and regional SCs. Investment in local SCs has several benefits but no company that can operate alone. Thus, GCSs remain necessary.

Given the COVID‐19 pandemic, organisations must encourage more information sharing, communications, and visibility regarding SCs. Technologies and enabling plans and procedures will help rebuild the SC system and improve its resilience in the future. Technology, digital SC, and visibility through the VC and blockchain supply should be a priority for future SCs.

Nevertheless, there are several limitations which include narrowing the literature review and limiting the factors to those extracted from the reviewed articles. Consequently, the analysis may miss some key components influencing the SC‐COVID‐19 relationship. This research is limited by the scarcity of the number of studies related to a comprehensive analysis of SC‐COVID‐19. There are studies that may not have been published and, thus, are still outside the review. The framework may benefit from empirical studies and application of the study in real SCs as well. In general, development of logistics systems is challenging and requires a long time due to the complexity involved.

It is expected that the novel identification and categorisation of factors and elements proposed in the framework with their connections and relations will be of value for practitioners, managers, and researchers in directing SCs during and after the pandemic. The framework is constructed with a generalised view to be utilised by other industries and professionals to survive the current pandemic and to resist and recover from future crises. The framework application can be extended to other industries and related sectors.

Utilising the analysis of the SCC19 framework and literature review findings and outcomes, there are several opportunities for future research directions as follows:

Extend the literature review to include new factors and elements to support the framework comprehensiveness. Future studies may focus on developing and testing new models, frameworks, and components related to SC‐COVID‐19 to build a tool to improve DSS. Further, they may extend the knowledge of how SCC19 influences the DSS and performance of organisations.

The proposed framework can be extended by incorporating more complexity and more components; empirically testing the framework with quantitative data to validate and generalise the findings, and checking the impact on different business performance; developing SC‐pandemic plans to enhance coping strategies and achieve organisational goals; or considering the interrelationship between the framework components in the geographical context.

Investigate the emerging technologies that can lead to continuous development and help SC recovery and withstand future pandemics. Moreover, they may explore changes brought about by digital transformation and other emerging technologies like AI, 3D printers, and machine learning.

Stimulate the solutions towards future resilience and the new normal. Further, these may be ranked according to their importance and priorities of implementation. The solutions should be emphasised and ranked based on their effect and importance in resisting the ongoing and future crises. Subsequently, the tools required to implement the solutions need to be identified.

Explore the opportunities associated with the pandemic. For example, the studies may investigate the impact of localisations and supply from different suppliers and suppliers from nearby locations, on the stability of the SC. They may also study the spikes in online buying resulting from both personal safety measures and governmental measures to minimise the spread of the virus.

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Case Study: How Should We Diversify Our Supply Chain?

• Krishna G. Palepu

A Chinese appliance maker considers expanding production to Mexico.

In the wake of Covid-19’s disruptions, Kshore, a Chinese appliance maker, is thinking of realigning its supply chain. Like many other global manufacturers, it’s being pressured by its customers, which include Walmart and other large retailers, to reduce the time, expense, and environmental impact of shipping goods between countries.

On a trip to Monterrey, Mexico, Kshore’s CEO and COO tour factories that are closer to North American markets—and are impressed by their professionalism. But questions about transportation and staffing give the executives pause. Should Kshore start production in Mexico or consider other countries? Two experts weigh in.

On the sidewalk outside the airport in Mexico City, Yun Liu and Keith Smith, the CEO and COO of Kshore, a Chinese appliance maker with $150 million in annual revenues, waited for their town car. Their journey from Guangzhou, China, had been a long one.

• Krishna G. Palepu is the Ross Graham Walker Professor of Business Administration at Harvard Business School. His research focuses on globalization, emerging markets, and strategies for multinational and local companies in those markets. He cochairs the HBS executive program Leading Global Businesses.

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Blood plasma supply chain planning to respond COVID-19 pandemic: a case study

Affiliations.

• 1 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
• 2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
• 3 Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.
• PMID: 36530360
• PMCID: PMC9734997
• DOI: 10.1007/s10668-022-02793-7

The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.

Keywords: Blood supply chain network; COVID-19 pandemic; Logistics; Sustainable location-allocation analysis.

© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

• Introduction
• Conclusions
• Article Information

MIDAS indicates Multinational Integrated Data Analysis.

a Drugs were defined on the Anatomical Therapeutic Class, level-3 (ATC3)-molecule-formulation level.

b Excludes drugs with less than 6 months of information prior to assigned index date.

c A list of excluded topical and nonprescription medications can be found in eTable 2 in Supplement 1 . The 1597 matched comparison drugs were allowed to be selected multiple times over the study period for a total sample size of 7296 matched comparison drugs over the study period.

d Categories are not mutually exclusive since drugs could appear more than once in either group.

Post hoc mORs and marginal estimated probabilities were obtained from the fitted logistic regression model. Full regression output is provided in eTable 5 in Supplement 1 . Estimated probabilities represent absolute incidence outcomes. Estimated marginal outcomes were obtained by calculating the mean over drug formulation, years since US Food and Drug Administration approval, marketing category, World Health Organization essential medicine status, clinician vs self-administration, total sales, and number of manufacturers at baseline.

Post hoc mORs and marginal estimated probabilities were obtained from the fitted logistic regression model. Full regression output is provided in eTable 6 in Supplement 1 . Estimated probabilities represent absolute incidence outcomes. Estimated marginal outcomes were obtained by calculating the mean over drug formulation, years since US FDA approval, marketing category, WHO essential medicine status, clinician vs self-administration, total sales, and number of manufacturers at baseline.

eFigure 1. Conceptual Model

eTable 1. Excluded Nonprescription Products

eFigure 2. Merging Algorithm for FDA and ASHP Supply Issue Reports

eFigure 3. Examples of Medications With No Supply Decrease, Meaningful (≥33%) Shortage and Severe (≥66%) Shortage in MIDAS

eTable 2. Covariate Definitions

eTable 3. Characteristics of Drugs with Supply Chain Issue Reports, by COVID-19 Pandemic Period

eTable 4. Characteristics of Drugs with Supply Chain Issue Reports, by Report Type

eTable 5. Characteristics of Supply Chain Issue Reports and Matched Comparison Drugs, by Shortage Status

eFigure 4. Unadjusted Probabilities of Meaningful (≥33%) and Severe (≥66%) Drug Shortages Within 6 Months, by Report Type

eFigure 5. Unadjusted Proportion of Incident Supply Chain Issue Reports and Matched Comparison Drugs Associated With Severe (≥66%) Drug Shortages Within 6 Months, by Quarter, 2017-2021

eFigure 6. Marginal Odds Ratios and Estimated Probabilities of Severe (≥66%) Shortages Within 6 Months, Supply Chain Issue Reports vs Matched Comparison Drugs, Prepandemic vs During the COVID-19 Pandemic

eFigure 7. Marginal Odds Ratios and Estimated Probabilities of Severe (≥66%) Shortages Within 6 Months, Supply Chain Issues vs Matched Comparison Drugs, by Drug Characteristics

eTable 6. Odds of Meaningful (≥33%) and Severe (≥66%) Shortages for Supply Chain Issue Reports vs Matched Comparison Drugs

eFigure 8. Marginal Odds Ratios and Estimated Probabilities of Meaningful (≥33%) and Severe (≥66%) Shortages for Supply Chain Issue Reports vs Matched Comparison Drugs, Sensitivity Analyses Using Different Outcome Definitions and Inclusion Criteria

eTable 7. Odds of Meaningful (≥33%) and Severe (≥66%) Shortages for Supply Chain Issue Reports vs Matched Comparison Drugs, Sensitivity Analysis Using 9 Months to Define Shortages

eTable 8. Odds of Meaningful (≥33%) Shortages for Supply Chain Issue Reports vs Matched Comparison Drugs, Sensitivity Analyses Using Different Inclusion Criteria

eTable 9. Odds of Severe (≥66%) Shortages for Supply Chain Issue Reports vs Matched Comparison Drugs, Sensitivity Analyses Using Different Inclusion Criteria

eTable 10. Characteristics of Supply Chain Issue Reports vs Matched Comparison Drugs, 2017-2021, Sensitivity Analysis Among Propensity-Score-Matched Cohort

eTable 11. Odds of Meaningful (≥33%) and Severe (≥66%) Shortages for Supply Chain Issue Reports vs Matched Comparison Drugs, Sensitivity Analysis Among Propensity-Matched Cohort

Data Sharing Statement

• Drug Shortages—A Study in Complexity JAMA Network Open Invited Commentary April 5, 2024 Mariana P. Socal, MD, PhD; Joshua M. Sharfstein, MD

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Callaway Kim K , Rothenberger SD , Tadrous M, et al. Drug Shortages Prior to and During the COVID-19 Pandemic. JAMA Netw Open. 2024;7(4):e244246. doi:10.1001/jamanetworkopen.2024.4246

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Drug Shortages Prior to and During the COVID-19 Pandemic

• 1 Department of Medicine, Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
• 2 Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
• 3 Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
• 4 Women’s College Research Institute, Toronto, Ontario, Canada
• 5 School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla
• 6 Center of Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
• 7 Des Moines University, Department of Public Health, Des Moines, Iowa
• 8 Department of Industrial Engineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, Pennsylvania
• Invited Commentary Drug Shortages—A Study in Complexity Mariana P. Socal, MD, PhD; Joshua M. Sharfstein, MD JAMA Network Open

Question   What was the incidence of drug shortages in the US between 2017 and 2021?

Findings   In this cross-sectional study, a total of 571 drugs exposed to 731 supply chain issue reports were matched to 7296 comparison medications with no reports from 2017 to 2021. One in 7 reports (14%) were associated with drug shortages (decreases in sales of ≥33%); supply disruptions increased for drugs with and without reports at the start of the COVID-19 pandemic and returned to prepandemic levels after May 2020.

Meaning   These findings suggest that ongoing policy work is needed to protect US drug supplies from future supply shocks.

Importance   Drug shortages are a chronic and worsening issue that compromises patient safety. Despite the destabilizing impact of the COVID-19 pandemic on pharmaceutical production, it remains unclear whether issues affecting the drug supply chain were more likely to result in meaningful shortages during the pandemic.

Objective   To estimate the proportion of supply chain issue reports associated with drug shortages overall and with the COVID-19 pandemic.

Design, Setting, and Participants   This longitudinal cross-sectional study used data from the IQVIA Multinational Integrated Data Analysis database, comprising more than 85% of drug purchases by US pharmacies from wholesalers and manufacturers, from 2017 to 2021. Data were analyzed from January to May 2023.

Exposure   Presence of a supply chain issue report to the US Food and Drug Administration or the American Society of Health-Systems Pharmacists (ASHP).

Main Outcomes and Measures   The main outcome was drug shortage, defined as at least 33% decrease in units purchased within 6 months of a supply chain issue report. Random-effects logistic regression models compared the marginal odds of shortages for drugs with vs without reports. Interaction terms assessed heterogeneity prior to vs during the COVID-19 pandemic and by drug characteristics (formulation, age, essential medicine status, clinician- vs self-administered, sales volume, and number of manufacturers).

Results   A total of 571 drugs exposed to 731 supply chain issue reports were matched to 7296 comparison medications with no reports. After adjusting for drug characteristics, 13.7% (95% CI, 10.4%-17.8%) of supply chain issue reports were associated with subsequent drug shortages vs 4.1% (95% CI, 3.6%-4.8%) of comparators (marginal odds ratio [mOR], 3.7 [95% CI, 2.6-5.1]). Shortages increased among both drugs with and without reports in February to April 2020 (34.2% of drugs with supply chain issue reports and 9.5% of comparison drugs; mOR, 4.9 [95% CI, 2.1-11.6]), and then decreased after May 2020 (9.8% of drugs with reports and 3.6% of comparison drugs; mOR, 2.9 [95% CI, 1.6-5.3]). Significant associations were identified by formulation (parenteral mOR, 1.9 [95% CI, 1.1-3.2] vs oral mOR, 5.4 [95% CI, 3.3-8.8]; P for interaction = .008), WHO essential medicine status (essential mOR, 2.2 [95% CI, 1.3-5.2] vs nonessential mOR, 4.6 [95% CI, 3.2-6.7]; P  = .02), and for brand-name vs generic status (brand-name mOR, 8.1 [95% CI, 4.0-16.0] vs generic mOR, 2.4 [95% CI, 1.7-3.6]; P  = .002).

Conclusions and Relevance   In this national cross-sectional study, supply chain issues associated with drug shortages increased at the beginning of the COVID-19 pandemic. Ongoing policy work is needed to protect US drug supplies from future shocks and to prioritize clinically valuable drugs at greatest shortage risk.

Drug shortages have reached record highs. 1 The US Food and Drug Administration (FDA) defines a shortage as “a period of time when the demand…for the drug…exceeds the supply.” 2 Shortages have been associated with missed or delayed dosages, 3 , 4 medication errors, 5 , 6 increased spending, 7 , 8 and death. 9 Shortages are caused by underreimbursement, 10 quality concerns, 11 and an overreliance on single-source production. 12

Since 2012, companies must report to the FDA known issues affecting the supply chain which could result in shortages (hereafter, supply chain issue reports ). 2 These reports are also tracked by the American Society of Health-Systems Pharmacists (ASHP). 13 Examples of supply chain issue reports include quality concerns (eg, microbial contamination) and unanticipated events that may impact production (eg, natural disasters). 14 The FDA also publishes recalls and discontinuations. The COVID-19 pandemic was associated with record numbers of supply chain issue reports. 1 , 15 Despite these known risks, it is unknown how often supply chain issues were associated with subsequent shortages, ie, meaningful decreases in the national supply of a medication.

As the public health emergency ends, there is an imperative need for evidence regarding its impact on the drug supply chain to inform policies 16 - 20 that protect patients from future, unanticipated shocks. 21 The primary objective of our study was to quantify the proportion of all supply chain issue reports that were associated with drug shortages from 2017 to 2021. Our study adds to existing literature by comparing shortages during vs prior to the COVID-19 pandemic, and by drug characteristics. Our results provide evidence to inform policies to manage upstream events before they impact patients’ access to life-saving treatments.

We conducted a population-based, longitudinal cross-sectional study of US prescription drug purchases from January 2017 to December 2021. Using public information from the FDA and ASHP, we identified medications with supply chain issue reports and matched them to drugs without reports. We selected supply chain characteristics based on a conceptual framework and compared the odds of shortages for each group. The University of Pittsburgh Institutional Review Board approved the study as not human participants research; therefore, the requirements for approval and informed consent were waived. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

Drug manufacturers are legally required to report known supply chain issues which could result in shortages (eg, inability to obtain raw ingredients) to the FDA; they report voluntarily to ASHP. However, supply chain reports may be incomplete indicators for meaningful decreases in national supplies (ie, shortages). As represented in eFigure 1 in Supplement 1 , protective shields in supply chains, 22 , 23 including opening more production lines, importing drugs from other countries, lengthening expiration dates, 14 , 22 or using residual supplies, 22 may address issues prior to the depletion of supplies held by manufacturers, wholesalers, and pharmacies. Therefore, not all reported issues necessarily result in drug shortages.

Given these complexities and the known limitations of reporting, the purpose of our study was to quantify the proportion of supply chain issue reports that were associated with subsequent population-level shortages, both prior to and during the COVID-19 pandemic (eFigure 1 in Supplement 1 ). Because there is no legal standard for meaningful decreases in supply, we defined shortages as decreases in purchased units of at least 33% within 6 months after the issuance of a new supply chain issue report. This threshold was chosen based on previous studies 24 and is consistent with the FDA and ASHP’s definitions of shortages, ie, significant decreases in national supply 2 with the greatest potential to impact patient care. 25 We accounted for non–shortage-related supply chain factors by matching drugs exposed to reports to unexposed drugs without reports. We then compared subsequent odds of shortages (ie, ≥33% decreases in supply) for each group.

Our study population comprised a longitudinal cross-section of drugs purchased in the US from 2017 to 2021 in IQVIA’s Multinational Integrated Data Analysis (MIDAS) database. MIDAS comprises 85% of retail and 97% of nonretail purchases by pharmacies from wholesalers and manufacturers, based on unit volume. 26 Data are reported in standardized units, defined as 1 pill, capsule, or vial or 5 mL oral liquid. We conducted our analyses on the drug formulation level. We excluded products with incomplete data, radiopharmaceuticals, unapproved products, allergens, antidotes, and over-the-counter products that are not fully captured in MIDAS. We also excluded topicals, since these may reflect different dosages that are difficult to compare with systemic formulations (eTable 1 in Supplement 1 ).

Our exposed group comprised drugs with incident supply chain issue reports issued from January 2017 to September 2021 (3 months prior to data end). For each report, we defined the index date as the initial posting date by the FDA or ASHP. This included issues reported as being likely to result in shortages, 2 , 13 , 27 , 28 FDA recalls, 29 and discontinuations. Reports for the same drug-form were considered the same episode if they occurred within 90 days (eFigure 2 in Supplement 1 ). We conducted sensitivity analyses excluding recalls and discontinuations which may be indefinite. 30

We performed exposure density sampling (EDS) to match each drug with a supply chain issue report (ie, exposed) with up to 10 comparison drugs with no reports at the time of exposure (ie, nonexposed). 31 , 32 EDS is akin to sampling techniques for nested case-control studies, 32 - 34 with the exception that matching is performed at the time of an exposure instead of the time of an outcome. This approach mitigated bias due to seasonality. When appropriately combined with multivariable adjustment for confounders, EDS leads to unbiased results. 32 We did not match on baseline purchasing trends, since many reports were precipitated by abnormal demand increases. 35 Drugs could appear more than once in either group. Drugs with reports could serve as at-risk comparators during any month without an active supply issue report. 33

Motivated by FDA’s definition 2 and based on previous studies, 24 we defined meaningful shortages as at least 33% decreases in purchased units within 6 months after an incident supply chain issue report, compared with 3 months prior (eFigure 3 in Supplement 1 ). This decrease is greater than what would be expected based on seasonal trends. We conducted secondary analyses of severe shortages, defined as decreases of at least 66%, as in previous literature. 24

We used random-effects logistic regression models followed by marginal estimation to compare the odds of shortages for drugs with vs without supply chain issue reports. We included a random intercept for each matched set to account for potential dependency induced by matching. Our main estimates were the mean marginal odds of shortages for drugs with vs without reports. 34 , 35 We estimated absolute incidence by comparing the estimated probabilities of shortages in each group. A type 1 error rate of .05 was assumed, all hypothesis tests were 2-sided, and no adjustments were made for multiplicity. Analyses were conducted from January to May 2023 in SAS version 9.4 (SAS Institute), Stata version 16.1 (StataCorp), and RStudio version 4.1 (R Project for Statistical Computing).

To determine the association of the COVID-19 pandemic with US drug supply shortages, we included interaction terms to assess differential changes in the odds of shortages for drugs with vs without reports before the pandemic (January 2017 to January 2020), in the first few months after the World Health Organization (WHO) declared COVID-19 an emergency and when stockpiling occurred 36 (February 2020 to April 2020), and in the first pandemic year (May 2020 to September 2021). We grouped issues based on their issuance date.

To assess the associations of supply chain characteristics with shortage incidence, we included interaction terms by formulation. We then used marginal estimation to compare odds of shortages for drugs with vs without reports, within each form, eg, parenteral drugs with reports vs parenteral comparators. Additional interaction terms were added for other characteristics, including years since market entry and generic availability from the FDA National Drug Code Directory (August 2022), 37 WHO essential medicine status (June 2022), 38 and clinician- vs self-administration, defined using J-codes. 39 We proxied supply chain redundancy using the number of manufacturers in MIDAS in the 6 months prior to a supply chain issue report. To capture market size, we adjusted for total sales in MIDAS during the same period (eTable 2 in Supplement 1 ).

We conducted sensitivity analyses to assess the influence of methodological assumptions. Because some shortages take longer to develop (eg, due to residual supply), we redefined shortages as decreases in supply within 9 months after report issuance. We assessed 5 (vs 10) matched comparisons, FDA and ASHP reports separately, and excluded recalls and discontinuations. 30 Finally, we used nearest-neighbor propensity score methods to match drugs on month, formulation, class, clinician- vs self-administration, WHO essential-medicine status, at least 20 years since market or generic availability, at least 5 manufacturers, and at least $5 million in sales.

Prior to inclusion criteria, there were 4142 drugs. As shown in Figure 1 , 2276 drugs met exclusion criteria, resulting in a final sample of 1866 drugs. Among these, 571 drugs (31%) had 731 incident supply chain issue reports. These were EDS-matched to 1597 unique drugs without concurrent exposures; 717 reports were matched to 10 comparators and 14 reports had 9 comparators, for a total sample size of 7296 comparators.

Per the Table , generic drugs were overrepresented in supply chain issue reports (616 reports [84%] vs 4397 comparison drugs [60%]), as were products at least 20 years old (375 reports [51%] vs 2434 comparison drugs [33%]), drugs with at least 5 manufacturers (378 reports [52%] vs 2225 comparison drugs [30%]), WHO essential medicines (316 reports [43%] vs 2199 comparison drugs [30%]), and drugs with less than $5 million baseline sales (234 reports [32%] vs 2158 comparison drugs [30%]). Manufacturers did not provide a reason for 264 reports (36%); the percentage of reports with unknown or unspecified reasons increased to 42% from February to April 2020 and 47% after May 2020 (eTable 3 in Supplement 1 ). Across the entire study period, more than half of reports were recalls or discontinuations (498 reports [68%]), mostly due to quality concerns ( Table ; eTable 4 in Supplement 1 ).

We observed 15 to 71 incident supply chain issue reports per quarter from 2017 to 2021 ( Figure 2 ). Across the study period, the mean change in drug purchases was −5.1% (95% CI, −8.2% to −2.1%) in the first quarter and −7.0% (95% CI, −14.7 to 0.01%) in the second quarter after report-issuance ( Table ). Among all reports, 113 reports (16%) were associated with shortages (≥33% decrease) within 6 months vs 491 of 7290 unexposed comparison drugs (7%) (eTable 5 in Supplement 1 ). We observed an increase in comparison drugs with shortages in the first quarter of 2020 ( Figure 2 ). Severe shortages (≥66% decrease) were less common, with 63 reports (9%) and 188 comparison drugs (3%) associated with severe shortages (eTable 5, eFigure 4, and eFigure 5 in Supplement 1 ). Nonrecall and nondiscontinuation reports were more likely to be associated with meaningful (83 reports [23%]) and severe (43 reports [12%]) shortages, compared with recalls (meaningful: 13 reports [10%]; severe: 4 reports [3%]) and discontinuations (meaningful: 48 reports [13%]; severe: 33 reports [9%]) (eFigure 4 in Supplement 1 ).

Marginal odds ratios (mORs) and estimated probabilities from our logistic regression analysis are shown in Figure 3 , with full regression output in eTable 6 in Supplement 1 . Across baseline drug characteristics, the marginal estimated percentage of supply chain issue reports associated with shortages was 13.7% (95% CI, 10.4%-17.8%) of reports vs 4.1% (95% CI, 3.6%-4.8%) of comparators ( Figure 3 ). Compared with drugs without reports, drugs with reports had greater marginal odds of shortages (mOR, 3.7 [95% CI, 2.9-5.1]). Results were similar for severe shortages (mOR, 6.3 [95% CI, 3.7-10.9]), although adjusted incidences were lower (6.0% [95% CI, 3.8%-9.2%] for supply chain issue reports and 1.0% [95% CI, 0.7%-1.4%] for comparison drugs) (eFigure 6 in Supplement 1 ).

As shown in Figure 3 , the adjusted incidence of shortages for supply chain reports issued in the first 3 months of the COVID-19 pandemic were more likely to be associated with subsequent shortages than reports issued during the prepandemic period (34.2% [95% CI, 18.6%-54.1%] vs 13.7% [95% CI, 10.0%-18.5%]). However, we also observed increases in the adjusted proportion of comparison drugs without reports with shortages at or above our 33% cutoff compared with the prepandemic period (9.5% [95% CI, 7.2%-12.5%] vs 4.0% [95% CI, 3.3%-4.7%]). There were no significant interactions by COVID-19 pandemic period. The 95% CIs of the mORs comparing drugs with vs without reports issued from February to April 2020 overlapped with the prepandemic period (prepandemic mOR, 3.9 [95% CI, 2.6-5.6] vs February to April 2020 mOR, 4.9 [95% CI, 2.1-11.6]; P for interaction = .60). The mOR for drugs with vs without reports issued during or after May 2020 also did not significantly differ from the prepandemic period (mOR, 2.9 [95% CI, 1.6-5.3]; P for interaction = .38) ( Figure 3 ). The association between report status and severe shortages did not significantly differ during the prepandemic period (mOR, 6.8 [95% CI, 3.6-12.4]) vs during the early stage of the pandemic (February to April 2020: mOR, 5.9 [95% CI, 1.3-26.3]) or after May 2020 (mOR, 5.3 [95% CI, 2.4-11.8]) (eFigure 6 in Supplement 1 ).

Figure 4 summarizes our assessment of heterogeneity by drug characteristics. Across other characteristics, we observed a significant interaction for parenteral drugs with vs without reports, compared with oral drugs (parenteral mOR, 1.9 [95% CI, 1.1-3.2] vs oral mOR, 5.4 [95% CI, 3.3-8.8]; P for interaction = .008). There were also significant interactions by WHO essential medicine status (essential mOR, 2.2 [95% CI, 1.3-5.2] vs nonessential mOR, 4.6 [95% CI, 3.2-6.7]; P for interaction = .02), and for brand-name vs generic drugs (brand-name mOR, 8.1 [95% CI, 4.0-16.0] vs generic mOR, 2.4 [95% CI, 1.7-3.6]; P for interaction = .002). We observed similar heterogeneity for severe shortages (eFigure 7 in Supplement 1 ).

As shown in eFigure 8 and eTables 7 to 11 in Supplement 1 , sensitivity analyses were consistent with our main findings. Excluding recalls and discontinuations resulted in a slightly higher adjusted incidence of shortages (16.7% [95% CI, 12.9%-21.4%]); however, the marginal odds of shortage were similar (mOR, 3.9 [95% CI, 2.8-5.4]).

In this national cross-sectional study, approximately 1 in 7 supply chain issue reports (14%) were associated with drug shortages within 6 months, and 1 in 15 supply chain issue reports (6%) were associated with severe shortages. The incidence of shortages increased to 1 in 3 reports (34%) from February to April 2020 and then reduced to prepandemic levels after May 2020. However, we also observed increases in shortages in drugs with no supply chain issues during the early pandemic. WHO essential and parenteral medicines were less likely to be associated with shortages, and brand name drugs were more likely to be associated with shortages.

Our findings are mostly consistent with a similar RAND study in MIDAS from 2016 to 2019. 30 In that study, utilization decreased by a mean of 8.4% in the quarter after a supply chain issue report vs 5.1% in our study. Our smaller absolute estimate may be due to a lower incidence of shortages after May 2020. Nevertheless, our study is an important contribution to the literature because we included recalls and discontinuations, which were less likely to be associated with shortages. We also tested whether shortages changed after the onset of the COVID-19 pandemic.

In our study, drugs with and without reports from February to April 2020 were more likely to be associated with subsequent shortages, compared with prepandemic rates. These findings are consistent with the known destabilizing impact of COVID-19 on supply chains. We observed a return to prepandemic levels after May 2020. This finding may be explained by the implementation of new policies that empowered the FDA to take more direct action. 16 , 40 , 41 Despite these improvements, ongoing vulnerabilities in drug supply chains are in need of policy action, 17 including greater supply chain transparency 19 , 21 and addressing root economic causes of shortages. 42

It is also possible that issuance of an FDA or ASHP report, while consistently associated with higher odds of shortages, may have underestimated relevant upstream issues at pandemic start. Before the pandemic, 4% of drugs with no report were associated with shortages at or above our 33% decrease cutoff; this increased to 10% from February to April 2020. We also observed a greater proportion of supply chain issues with no or unspecified reasons during the pandemic vs the prepandemic period. Temporary pauses in quality assurance reviews during COVID-19 lockdowns may have been associated with underreporting. There are no penalties for failure to report. 22 Our wide 95% CIs for February to April 2020 should be interpreted with caution, given the small sample size (57 reports) and potential underreporting.

When examining variation across drugs, we observed a higher mOR for the association between having a report and subsequent shortage among branded medications. This finding is not unexpected, given that branded markets usually have 1 supplier. The higher mOR among branded medications may therefore be explained by the unavailability of alternative manufacturers to supply product. Shortages of branded drugs may also respond to different underlying reasons, such as increased demand, quotas, or manufacturer capacity. 43 , 44 In contrast, approximately 4 in 5 supply chain issue reports in our study were for generic medications, and one-third were for drugs with prereport sales less than $5 million. Generic supply chain issues likely involve insufficient incentives for manufacturers to invest in resilient supply chains for products of limited profitability. 11 , 41 In this context, recent policy proposals suggest the implementation of reimbursement models which would provide add-on payments for generics manufactured in more resilient supply chains. 42 , 45 Our findings of differing shortage risk by supply chain characteristics could be used to develop lists of high-priority generic drugs for the application of these novel reimbursement models. Our findings may also be informative for the development of shortage-critical lists to prioritize medications for other drug shortage policies, 22 , 46 like those in Canada and Europe. 47 , 48 Similar frameworks have not yet been adopted in the US but would be useful in the prioritization of national drug shortage policy. 49

Interestingly, we observed a lower mOR for the association between having a report and subsequent shortage for parenteral vs oral drugs. Changes in demand for these products, eg, cancellation of elective surgeries, may have resulted in residual supply during the pandemic. A higher proportion of parenteral drugs without supply chain reports were also associated with meaningful decreases in supply at or above our 33% cutoff, eg, 7% of parenteral vs 3% of oral drugs, suggesting that it is also possible all parenteral products were more vulnerable to disruption, lessening the difference between exposed vs unexposed drugs. 11 , 41 Generic sterile injectables have therefore been prioritized in several recent drug shortage policy proposals. 42 , 45

There are several limitations to this study. First, the observed increase in drugs without supply chain issue reports that experienced shortages from February to April 2020 suggests potential for underreporting during the COVID-19 shutdown. Our mORs comparing drugs with vs without reports from February to April 2020 should be interpreted with caution. Our dataset was also time-limited (2017-2021). Many pandemic-related policies were not yet fully implemented, and we could not assess the association of the end of the public health emergency in May 2023. 50 Second, although MIDAS is the most comprehensive dataset available, it does not fully capture over-the-counter and topical products, many of which have recently been experiencing shortages. Third, we do not know the downstream impact of the identified shortages on patients. Bedside efforts and availability of therapeutic alternatives may maintain standards of care, even when shortages occur. 22 ASHP includes information on equivalents for some drugs; however, this information was lacking in FDA databases.

This cross-sectional study found that 1 in 7 supply chain issue reports were associated with drug shortages. Although the shortages increased early in the COVID-19 pandemic, we observed a return to prepandemic levels after May 2020. As the public health emergency ends, continued policy effort is needed to mitigate shortages and to prepare pharmaceutical supply chains for future, unanticipated shocks.

Accepted for Publication: January 29, 2024.

Published: April 5, 2024. doi:10.1001/jamanetworkopen.2024.4246

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Callaway Kim K et al. JAMA Network Open .

Corresponding Author: Katie J. Suda, PharmD, MS, Department of Medicine, Division of General Internal Medicine, University of Pittsburgh School of Medicine, 3609 Forbes Ave, 2nd Floor, Pittsburgh, PA 15213 ( [email protected] ).

Author Contributions: Ms Callaway Kim and Dr Suda had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Callaway Kim, Rothenberger, Tadrous, Hernandez, Gellad, Suda.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Callaway Kim, Rothenberger.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Callaway Kim, Rothenberger.

Obtained funding: Suda.

Administrative, technical, or material support: Suda.

Supervision: Suda.

Conflict of Interest Disclosures: Dr Tadrous reported receiving personal fees from Health Canada outside the submitted work. Dr Hernandez reported receiving personal fees from Pfizer and Bristol Myers Squibb outside the submitted work. Dr Suda reported receiving grants from the National Institutes of Health, Agency for Healthcare Quality and Research (AHRQ), US Food and Drug Administration, Department of Veterans Affairs, and Centers for Disease Control and Prevention outside the submitted work. No other disclosures were reported.

Funding/Support: This project was funded through AHRQ grant No. R01 HS027985 “Clinical implications of drug shortages during COVID-19 in the US and Canada” (principal investigator: Dr Suda).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ, the Department of Veterans Affairs, the US government, or of IQVIA or any of its affiliated entities. The statements, findings, conclusions, views, and opinions contained and expressed in this article are based in part on data obtained under license from IQVIA (source: MIDAS, January 2017 to December 2021, IQVIA). All rights reserved.

Meeting Presentations: A prior version of this work was presented at the Academy Health Annual Research Meeting; June 24-27, 2023; Seattle, Washington; and the 39th International Conference on Pharmacoepidemiology & Therapeutic Risk Management of the International Society for Pharmacoepidemiology; August 23-27, 2023; Halifax, Nova Scotia, Canada.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: Tate Miner, BA, and Anna Conboy, MEd (Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine), assisted in the collection of raw data and provided project management support. They were not compensated for this work outside of their salaries.

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COVID-19 and supply chain risk mitigation: a case study from India

• School of Business Administration (Harrisburg)

Research output : Contribution to journal › Article › peer-review

Purpose: This study prioritizes the supply chain risks (SCRs) and determines risk mitigation strategies (RMSs) for the Indian apparel industry to mitigate the shock of the COVID-19 pandemic disruption. Design/methodology/approach: Initially, 23 SCRs within the apparel industry are identified through an extant literature review. Further, a fuzzy analytical hierarchy process (FAHP) is utilized to prioritize the SCRs considering the epidemic situations to understand the criticality of SCRs and determine appropriate RMSs to mitigate the shock of SCRs during COVID-19. Findings: This study prioritized and ranked the SCRs within the Indian apparel industry based on their severity during the COVID-19 disruption. Results indicate that the demand uncertainty and pandemic disruption risks are the most critical. Based on the SCRs, the present work evaluated and suggested the flexibility and postponement mitigation strategies for the case under study. Research limitations/implications: This study has novel implications to the existing literature on supply chain risk management in the form of the FAHP framework. Supply chain practitioners from the other industrial sectors can extend the proposed FAHP framework to assess the SCRs and identify suitable mitigation strategies. The results aid the practitioners working in an apparel industry to benchmark and deploy the proposed RMSs in their firm. Originality/value: The present study is a unique and earlier attempt to develop a quantitative framework using FAHP to evaluate and determine the risk mitigation strategy for managing the SCRs during the coronavirus epidemic.

All Science Journal Classification (ASJC) codes

• Business and International Management
• Transportation

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• 10.1108/IJLM-04-2021-0197

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• Link to publication in Scopus
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• Supply Chain Risk Business & Economics 100%
• Risk Mitigation Business & Economics 96%
• Supply chains Engineering & Materials Science 81%
• India Social Sciences 57%
• Apparel Industry Business & Economics 41%
• Analytical Hierarchy Process Business & Economics 40%
• Apparel Industries Social Sciences 35%
• Disruption Business & Economics 25%

T1 - COVID-19 and supply chain risk mitigation

T2 - a case study from India

AU - Dohale, Vishwas

AU - Verma, Priyanka

AU - Gunasekaran, Angappa

AU - Ambilkar, Priya

N1 - Publisher Copyright: © 2021, Emerald Publishing Limited.

PY - 2023/3/14

Y1 - 2023/3/14

N2 - Purpose: This study prioritizes the supply chain risks (SCRs) and determines risk mitigation strategies (RMSs) for the Indian apparel industry to mitigate the shock of the COVID-19 pandemic disruption. Design/methodology/approach: Initially, 23 SCRs within the apparel industry are identified through an extant literature review. Further, a fuzzy analytical hierarchy process (FAHP) is utilized to prioritize the SCRs considering the epidemic situations to understand the criticality of SCRs and determine appropriate RMSs to mitigate the shock of SCRs during COVID-19. Findings: This study prioritized and ranked the SCRs within the Indian apparel industry based on their severity during the COVID-19 disruption. Results indicate that the demand uncertainty and pandemic disruption risks are the most critical. Based on the SCRs, the present work evaluated and suggested the flexibility and postponement mitigation strategies for the case under study. Research limitations/implications: This study has novel implications to the existing literature on supply chain risk management in the form of the FAHP framework. Supply chain practitioners from the other industrial sectors can extend the proposed FAHP framework to assess the SCRs and identify suitable mitigation strategies. The results aid the practitioners working in an apparel industry to benchmark and deploy the proposed RMSs in their firm. Originality/value: The present study is a unique and earlier attempt to develop a quantitative framework using FAHP to evaluate and determine the risk mitigation strategy for managing the SCRs during the coronavirus epidemic.

AB - Purpose: This study prioritizes the supply chain risks (SCRs) and determines risk mitigation strategies (RMSs) for the Indian apparel industry to mitigate the shock of the COVID-19 pandemic disruption. Design/methodology/approach: Initially, 23 SCRs within the apparel industry are identified through an extant literature review. Further, a fuzzy analytical hierarchy process (FAHP) is utilized to prioritize the SCRs considering the epidemic situations to understand the criticality of SCRs and determine appropriate RMSs to mitigate the shock of SCRs during COVID-19. Findings: This study prioritized and ranked the SCRs within the Indian apparel industry based on their severity during the COVID-19 disruption. Results indicate that the demand uncertainty and pandemic disruption risks are the most critical. Based on the SCRs, the present work evaluated and suggested the flexibility and postponement mitigation strategies for the case under study. Research limitations/implications: This study has novel implications to the existing literature on supply chain risk management in the form of the FAHP framework. Supply chain practitioners from the other industrial sectors can extend the proposed FAHP framework to assess the SCRs and identify suitable mitigation strategies. The results aid the practitioners working in an apparel industry to benchmark and deploy the proposed RMSs in their firm. Originality/value: The present study is a unique and earlier attempt to develop a quantitative framework using FAHP to evaluate and determine the risk mitigation strategy for managing the SCRs during the coronavirus epidemic.

UR - http://www.scopus.com/inward/record.url?scp=85118543218&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85118543218&partnerID=8YFLogxK

U2 - 10.1108/IJLM-04-2021-0197

DO - 10.1108/IJLM-04-2021-0197

M3 - Article

AN - SCOPUS:85118543218

SN - 0957-4093

JO - International Journal of Logistics Management

JF - International Journal of Logistics Management

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How did COVID-19 affect logistics and supply chain processes? Immediate, short and medium-term evidence from some industrial fields of Italy

Marta rinaldi.

a Department of Engineering, University of Campania “Luigi Vanvitelli”, via Roma 29, 81031, Aversa, Italy

Eleonora Bottani

b Department of Engineering and Architecture, University of Parma, viale delle Scienze 181/A, 43124, Parma, Italy

Associated Data

Data will be made available on request.

This paper provides empirical evidence on the impact of the Covid-19 pandemic on logistics and supply chain processes of five industrial sectors of Italy, namely food & beverage, machine manufacturing, metal mechanical industry, logistics & transport, and textile & fashion. A questionnaire survey, with 82 useful responses, was conducted to investigate various effects of Covid-19 on these businesses, such as the volumes handled and the service performance in the immediate-, short- and medium-term, the countermeasures implemented by companies and the future decision-making strategies. The period of analysis spans from January 2020 to June 2021. Results show that the impact of Covid-19 on volumes and service performance varied across the sectors: the food & beverage and logistics & transport were poorly affected by the pandemic and experienced a general increase in the demand and volumes, while mechanical or textile & fashion industries were mostly affected by a decrease in demand. The positive/negative impacts were particularly evident at the beginning of the pandemics, but, depending on the sector, the effects could cease quite quickly or last in the short-term. The countermeasures adopted against the Covid-19 emergency differ again across sectors; in general, industry fields that were particularly impacted by the pandemic emergency have applied more countermeasures. Typical strategies for risk management (e.g., the diversification in transport modes or the stock increase) turned out to be applied as immediate countermeasures or in plan for the future in few industries only. Differences across sectors were also observed about the sourcing strategies already in use, implemented to counteract the pandemics or expected to be maintained in time. Empirical outcomes offered are expected to help researchers gain a deep understanding of Covid-19 related phenomena, thus inspiring further research activities.

1. Introduction

The Covid-19 pandemic has changed the global economy by increasing the uncertainty of all the markets, sectors, and businesses, and largely disrupting supply chains (SCs) and logistics systems worldwide.

Pandemic is a special case of disruption, characterized by long-term persistence, global propagation, and high unpredictability ( Ivanov, 2020 ). The duration of the outbreak is crucial for companies which must react during the pandemic and face both short and long-term impacts ( Baghersad and Zobel, 2021 ). In addition, disruptions usually affect one supply side, while pandemic impacts on both supply and demand dynamics ( Kwon, 2020 ). This new aspect complicates the management of the ripple effect and leads to both forward and backward disruptive propagation ( Ivanov and Dolgui, 2021 ). Furthermore, the three Covid-19 waves, which occurred before the distribution of the vaccine, have forced many countries to rapidly change restriction and obligation measures in different periods, and many companies to adopt practical actions and adapt their business to quickly face the new challenges ( Rothengatter et al., 2021 ). All these differences from the other disruptions make the study of the impact of the Covid-19 pandemic a promising area of research.

Since the beginning of 2020, studies have been conducted on the effects of the pandemic on SCs, logistics systems and operations management worldwide, and different strategies and mitigation measures have been explored ( Ivanov, 2021a ). Researchers have presented early analyses, whose aim was to provide an initial or general overview of the Covid-19 impact on SCs or logistics systems (e.g., Umaña-Hermosilla et al., 2020 ). Some of these studies have been in empirical form, such as interviews to capture the general aspects on the new phenomenon ( Remko, 2020 ), surveys to investigate the role of SC risk management to mitigate the effects of the disruption ( El Baz and Ruel, 2021 ; Sharma et al., 2022a ), or case studies ( Hohenstein, 2022 ; Butt, 2021 ; Rinaldi et al., 2021 ). Nonetheless, authors in general agree that empirical research on Covid-19 is still limited ( Ivanov, 2020 ; El Baz and Ruel, 2021 ; Hohenstein, 2022 ) and additional investigations are needed to clarify the impact of the pandemic on SCs, in terms of how (and to what extent) companies have been affected by the Covid-19 crisis, and how to learn from this kind of exogenous disturbances ( Dolgui & Ivanov, 2021 ; Ivanov and Dolgui, 2021 ), with the ultimate goal to delineate strategies for enhancing SC robustness and resilience and be more prompt in the future. Chowdhury et al. (2021) have expressively noted that the lack of empirical studies also limits the generalizability of the findings available in literature.

To date, literature has analyzed the adaptive behaviours of SCs during the pandemic, while few researchers have examined the after-shock risks and the pandemic's effects in the post-emergency period ( Ivanov, 2021b ), or its medium- and long-term effects ( Zhao and Chen, 2022 ), which instead must be explored further ( Pujawan and Bah, 2022 ). Studies on the effects of Covid-19 in different time horizons, with a focus on the short-, medium- and long-term are therefore needed ( Montoya-Torres et al., 2021 ; Ardolino et al., 2022 ).

In addition, despite the pandemic has strongly affected the flow of materials because of unexpected restrictions and interruptions, a research gap still exists about the impacts of Covid-19 on logistics and SC processes ( Queiroz et al., 2022 ), intended as the set of processes relating to the management of inventory, service level, materials flow, warehousing, transport and relating information ( Christopher, 1987 ). As a matter of fact, what has received significant attention are actually “humanitarian” logistics activities, which have been extensively studied in literature ( Chiappetta Jabbour et al., 2019 ; Queiroz et al., 2022 ); however, analysing the impact of Covid-19 on (“industrial”) logistics and SC processes needs a different perspective and should be extended ( Xu et al., 2021 ).

As a further point, it is well acknowledged that the Covid-19 outbreak caused a global economic catastrophe, with severe consequences on every type of SC and every sector across the globe. However, it is also evident that there have been differences in the level of severity of impact, the duration of the effect and the extent of recovery across the sectors ( Xu et al., 2020 ; De Vet et al., 2021 ). Al-Hyari (2020) has suggested the tourism, trade, health, manufacturing, education, and transportation as the sectors most severely affected by the Covid-19 pandemic. However, differences between sectors have been less discussed in the available literature, as well as the medium- and long-term strategies of related companies in response to the impact observed; these aspects are relevant for developing knowledge on how to build more resilient systems in the future ( Queiroz et al., 2022 ).

This paper merges the research gaps mentioned above and tries to enrich the literature in various ways. First, it contributes to the (still limited) empirical debate about the impact of the Covid-19 pandemic on logistics and SC processes. Second, the specific facet of this study is its multi-sectorial perspective, which is expected to highlight the differences or similarities in the impact across various industry fields. As such, this cross-sectoral study is among the first works that provide present and future insights on the impact of the Covid-19 pandemic on logistics and SC processes in different businesses. A questionnaire has been designed to this end and a survey was conducted involving companies from five main industrial sectors of Italy. Third, the horizon of analysis set in this study covers the three Covid-19 pandemic waves, to investigate the changes in the companies’ behavior as the pandemic progressed, as well as its modified effects in time. Overall, the study aims at answering the following research questions (RQs):

What are the effects of the Covid-19 pandemic (in the immediate- and short-term) on logistics and supply chain processes across different industry fields?

What countermeasures have been implemented by the different industry fields? What decision-making strategies will be maintained in the medium-term to improve the post-pandemic business?

The contribution of this paper can also inspire further research since it offers reliable and real data useful for developing quantitative models which need a deep understanding of the involved phenomena and its stages before modelling the system ( Rinaldi et al., 2022 ; Bottani et al., 2022b ).

The rest of the paper is structured as follows. The next section first reviews the available empirical literature about Covid-19 and SCs, then provides the theoretical background for the two RQs of the study. Section 3 details the research methodology, Section 4 presents the results of the survey, and Section 5 discusses the key findings for answering the RQs, showing the theoretical/practical contributions, and suggesting future research directions. Section 6 delineates the limitations of the study, summarises and concludes.

2. Literature analysis

2.1. scs and covid-19: empirical studies.

In recent years, empirical studies have been conducted to explore the impact of the pandemic on SCs ( Taqi et al., 2020 ). These studies take different perspectives in terms of the specific facet of the Covid-19 impact they explore, of the supply chain player analyzed (e.g., focal companies, suppliers, logistic service providers or citizens), the industrial sector(s) targeted for the analysis, as well as the research methodology (case study/interviews vs. questionnaire surveys).

Among the first studies, Al-Hyari (2020) has investigated the effects of the Covid-19 pandemic on 45 small and medium-sized enterprises (SMEs) of the Jordanian manufacturing sector, using semi-structured interviews. The focus of the analysis was on how companies of different size have managed the disruptions caused by Covid-19; some prospects for the future are also delineated, so as to suggest political actions to the governments. An investigation of the consumers’ perception of Covid-19 impact on the local economy (in particular, SMEs) of Chile has also been made by Umaña-Hermosilla et al. (2020) . By reaching 313 citizens, the authors found a general concern about the capability of SMEs to ensure employment and job security. These results are confirmed by a similar, smaller scale, empirical investigation that the authors made on 51 companies of the country.

Sułkowski et al. (2022) have analyzed the relationships between the innovations introduced by Poland logistic companies (courier-express-parcel in particular) in response to the Covid-19 pandemics, for increasing the service level delivered to customers. To this end, they have carried out an empirical analysis targeting the final consumers (citizens) and submitted it as an online survey. The logistics context has been analyzed also by Ketudat and Jeenanunta (2021) , who have investigated the response of three companies, taken as case studies, to the pandemic emergency. They found that the impact of Covid-19 resulted sometimes in an increase and sometimes in a decrease in the volumes handled by the companies. A similar conclusion has been reached by Perkumiene et al. (2022), whose study has again targeted one company in the logistics and transport industry. Hohenstein (2022) has carried out a multiple case study-based research focusing on the strategies implemented by logistics service providers to counteract the Covid-19 pandemics, deriving eight key success factors critical to companies in this field.

Compared to the logistic sector, empirical studies focusing on the impact of Covid-19 in other industry fields are significantly less numerous. To the best of the authors’ knowledge, the only empirical study targeting the agri-food supply chain has been by Mishra et al. (2022) , who have investigated the impact of Covid-19 on a fruit and vegetable online retailer in India, using a case study approach. The final goal of the analysis was to identify the key disruptions generated by the Covid-19 pandemic and to determine the corresponding countermeasures. Similarly, two empirical studies only have been carried out in the retail supply chain ( Butt 2021 ; 2022 ), using a multiple case-study approach and with the specific aim to identify the countermeasures adopted against Covid-19. Two case studies in the fashion footwear context have been analyzed by Braglia et al. (2022) , with the aim to investigate how companies in this field reacted to the disruption caused by the Covid-19 pandemic, so as to increase resilience in case of future disruptions. The papers by Tamtam and Tourabi (2021) and Ghadir et al. (2022) have instead analyzed the effects of Covid-19 in the automotive industry. The former authors have focused on the agile capability of automotive companies in the pandemic period, while the latter authors have proposed an approach for prioritizing risks induced by Covid-19 in the automotive supply chain. Finally, Ando and Hayakawa (2022) have used secondary data (export data) from the machinery industry as an example of global SC, for analyzing the supply-side impact of Covid-19 on global logistics systems.

Looking at cross-sectorial studies, El Baz and Ruel (2021) have investigated the role of supply chain risk management practices in mitigating the effects of disruptions in the context of Covid-19, by means of an empirical survey targeting French companies operating in various sectors (manufacturing, energy, transport, chemical, retail and service & humanitarian). They evaluated the risk management practices applied in the Covid-19 period, along with the four typical stages of risk management (i.e., risk identification, risk assessment, risk mitigation and risk control) plus some complementary aspects, namely SC robustness, resilience, and control. A cross-sectorial study has also been performed by Anakpo and Mishi (2021) , covering nine industrial fields. The focus of the analysis was on the financial performance of the companies, in terms of turnover, as well as on the responses to counteract the effect of Covid-19. Belhadi et al. (2021) have carried out an empirical study involving 145 companies from the automotive and airline industries, to investigate the short-term strategies implemented by those companies in response to Covid-19; findings are analyzed distinctly for the two sectors.

Some specific facets of the Covid-19 impact on SCs have also been analyzed in literature. Wissuwa et al. (2022) have examined the topic of supplier “complexity” and its relationships with buyer disruptions, which were particularly evident in the Covid-19 period. By analyzing 59 buyer-supplier dyads, the authors found that supplier complexity is actually harmful for the buyer, and thus suggest its introduction as an important criterion for effective supplier selection. The study by Nader et al. (2022) has targeted three industrial sectors, such as the food, pharmaceutical and medical manufacturing, which are taken as representative of the production of essential goods. These authors have focused on the role of pandemic emergency planning (i.e., the practices implemented by companies to counteract the pandemic emergency) and of sustainability practices in enabling risk mitigation and enhancing company's resilience and performance.

2.2. Theoretical background

2.2.1. effects of covid-19 on logistics and sc processes.

One of the first and most influenced aspects of Covid-19 impact on SCs concerns the changes in global trade volumes ( Aday and Aday, 2020 ). Early analyses carried out during the first stage of the pandemic have discussed demand-side shocks and the sudden changes in consumption patterns in food SCs ( Hobbs, 2020 ), but also in the healthcare, medical tools, or personal protective equipment (PPE) production ( Kraus et al., 2020 ; Milzam et al., 2020 ).

Looking at the food SC, the pandemic has adversely affected this context by causing both demand and supply uncertainty. A decrease in demand has been observed because of consumers' less frequent store visits and restrictions/limitations to people movements ( Ramakumar, 2020 ; Montenegro and Young, 2020 ); at the same time, however, various studies have shown that the early lockdown measures have changed the people's purchasing behavior (the so-called “panic buying”), causing unexpected peaks of demand that have strongly increased the food consumption ( Borsellino et al., 2020 ; Loske, 2020 ). Sgroi and Modica (2022) have also demonstrated some changed consumers' habits related to food consumption in the Covid-19 period.

In general terms, it could be conjectured that in crisis situations, people tend to buy essential goods (e.g., food or medical products), while avoiding purchasing non-essential ones ( Nader et al., 2022 ). In this respect, Shafiee et al. (2022) have noted that a further challenge in the food or pharmaceutical SCs, exacerbated by the Covid-19, is the perishability of the items handled, which involves additional risks. Rinaldi et al. (2021) have in fact shown that the impact on food volumes also depends on the type of food category and distribution channel. Thus, despite the demand of some goods has experienced a significant growth, the pandemic has slowed down the sale of other products ( Nandi et al., 2021 ). Overall, these considerations suggest that the consumer's behavior and purchasing pattern, relating variations, and demand shocks ( Chowdhury et al., 2021 ; Nandi et al., 2021 ) have played an important role in determining changes (either positive or negative) in the volumes handled by the SCs. Evidence in literature suggests that this could also be the case for the logistics sector, for which, indeed, the available empirical studies report controversial effects of Covid-19 on volumes ( Ketudat and Jeenanunta, 2021 ; Perkumiene et al., 2021 ).

Other sectors, whose activity falls into the production of non-essential goods (like the automotive, textile and electronics industry), have typically experienced a sudden decrease in the sales volumes ( Cai and Luo, 2020 ). Indeed, in response to the measures taken by some governments (such as stores closure, borders closure, and lockdown - Chowdhury et al., 2021 ; Khan et al., 2021 ), companies in various sectors were forced to shut down the production of finished goods, because the pandemic has stopped the demand for them ( Korankye, 2020 ; Kraus et al., 2020 ; Cai and Luo, 2020 ), or severely limited the import/export activities ( Khan et al., 2021 ). Anakpo and Mishi (2021) have observed that some industry fields (namely agriculture, hunting, forestry, fishing, and electricity/gas/water supply) were likely to operate at their normal level during the Covid-19 period, thus showing more resilience compared to other sectors that relied more on exports-driven businesses. In a similar way, export-driven businesses with local market potential or alternative transaction were more likely to withstand the pandemic shock and remain within the normal operating conditions. In terms of markets, a new challenge introduced by the Covid-19 pandemic is the necessity of being “online”, which was the only way to reach the customers; during the pandemic, sales and purchases through the e-commerce channel have increased ( Sułkowski et al., 2022 ). From the supply side, instead, production stops in some sectors could further prevent other companies get the supplies they need to continue their own production, thus causing a forced reduction of activities in other sectors as well ( Korankye, 2020 ; Aday and Aday, 2020 ; Cai and Luo, 2020 ). Some studies have observed that a further determinant of the impact of Covid-19 on volumes is the company's size ( Fitriasari, 2020 ; Qamruzzaman, 2020 ); regardless of the sector, the underlying assumption is that SMEs are more susceptible to disturbances, because of the lower availability of resources, and thus the effects of the pandemic could be particularly severe. The above evidence indicates that the Covid-19 has significantly changed the sales volumes, with effects that could be different depending on industrial business, network, and sector ( De Vet et al., 2021 ).

A second key effect of Covid-19 on SCs is that the distortion of demand and supply has caused long lead times, because of delays in production and distribution processes ( Magableh, 2021 ), ultimately resulting in poor service performance. At the beginning of the Covid-19 pandemic, consumers frequently experienced shortages of (even essential) goods, often because of disaggregated and globalized SCs with long lead times and lengthy recovery ( Ivanov and Dolgui, 2020 ; Li et al., 2021 ; Paul and Chowdhury, 2021 , Chowdhury et al., 2021 ; Sharma et al., 2022b ). In general terms, accurate and timely deliveries are the result of good performance of all SC processes, encompassing production, logistics and distribution. As far as production is concerned, restrictions imposed by governments worldwide had a negative effect on operations, as they have delayed the production of the necessary inputs to the next nodes of the system, thus delaying the flow of products ( Pu and Zhong, 2020 ). Looking at logistics/distribution activities, travel restrictions or blocking of transport activities, reduced available workforce and, in the case of essential goods, an increase in demand, contributed to delays in production and distribution, preventing the possibility of completely fulfilling orders ( Montenegro and Young, 2020 ) and undermining the related completeness ( Ivanov, 2020 ). This is why researchers have analyzed the quality of the service level provided during the pandemic, considering cancelled orders ( Zhang et al., 2020 ), unfilled orders ( Altig et al., 2020 ), shipping delays ( Gereffi, 2020 ), and out of stocks ( Pantano et al., 2020 ). Sathyanarayana et al. (2020) have also evaluated the impact of the pandemic on the service level and discussed the performance of a SC when varying the distance between supplier and customer. As the role of logistics service is crucial for competitiveness, Choi (2021) and Hohenstein (2022) have renewed the need for empirical research on how SCs will be able to maintain adequate service performance in the pandemic and post-pandemic period. Sharma et al. (2020) have argued that digital tools could be somehow useful to companies for maintaining a satisfactory service level, in terms of on-time order fulfilment, as they allow enhancing collaboration mechanisms between SC players. The rise of the e-commerce and electronic payment are good examples of digitalization involved by the Covid-19 pandemic ( Perkumiene et al., 2021 ); at the same time, e-commerce causes high demand for logistics service, being in direct contact with the customer.

2.2.2. Countermeasures and decision-making strategies against Covid-19

It is widely known that the Covid-19 has a very high potential of being transmitted from one individual to another, causing dangerous health consequences; this forced many countries (including Italy) to implement specific containment measures. Physical/social distancing was among the most applied countermeasures, also recommended by the World Health Organization to reduce the transmission of the Covid-19 virus ( Qian and Jiang, 2022 ). That policy was basically implemented in any company, regardless of the size and sector, in various ways depending on the activity carried out. Social distancing has obviously been complemented by other physical measures, all intended to reduce the spread of the virus by decreasing the number of workers in common spaces; examples of these measures are work re-organization ( Narayanamurthy and Tortorella, 2021 ), re-layout of offices and workspaces ( Ardjmand et al., 2021 ), sometimes coupled with the installation of protective barriers between workstations ( Bottani et al., 2022b ), and the definition of new working procedures ( Agba et al., 2020 ; Narayanamurthy and Tortorella, 2021 ). Other common measures are the usage of PPE, e.g., masks ( Cai and Luo, 2020 ), or the sanitization of equipment between work shifts ( Bottani et al., 2022a ), which were recommended by governments in almost all businesses. Digital technologies also have a role in avoiding or reducing the physical contacts ( Ye et al., 2022 ), as demonstrated by the wide diffusion of home/smart working practices ( Green et al., 2020 ) and of online meetings ( Pratama et al., 2020 ). Other practices, which have been observed in logistics companies, include opportunities for revisiting the allocation of items in warehouses ( Butt, 2021 ) or introducing rules for loading/unloading of goods at warehouses ( Rinaldi et al., 2021 ), always with the aim to avoid the simultaneous presence of more workers. Changes in the production activities or the employees' tasks have instead been suggested in manufacturing industries ( Lutfi et al., 2020 ; Telukdarie et al., 2020 ). Empirical analyses have demonstrated that these practices have strongly affected the economic performance of SMEs ( Aday and Aday, 2020 ), leading companies to implement structural changes that are needed to adapt the business to these new protocols ( Lutfi et al., 2020 ). The available literature on the effects of the practices implemented to counteract Covid-19 has mainly targeted the governmental measures ( Anakpo and Mishi, 2021 ), given their widespread diffusion; on the contrary, the effects of internal or specific measures on the system's performance have instead been explored to a lower extent.

As a final general effect, the pandemic as an unexpected disruption has increased the attention towards the resilience of production and distribution systems. Most of the researchers have analyzed the impact of different strategies, which could be adopted to face SC disruptions and mitigate their negative effects ( Raj et al., 2022 ). Literature suggests various practices which have been commonly implemented for building resilient SCs and reacting to general disturbances; well-known strategies are sourcing decisions and (safety) stock policies ( Christopher and Peck, 2004 ). The relevance of sourcing strategies has been particularly emphasized by the Covid-19 pandemic, which made some electronic components (e.g., semiconductors used in the automotive or computer industries) completely unavailable ( Sułkowski et al., 2022 ). Consequently, multiple, or local sourcing strategies started being implemented as opposed to single sourcing practices, for guaranteeing the supply of raw materials and critical components ( Zhu et al., 2020 ; Belhadi et al., 2021 ). These practices are often associated to increased inventory levels, for mitigating supply disruption risks ( Chopra and Sodhi, 2004 ). Additional, more specific (and more structured), adaptation strategies have emerged during the pandemic period, and besides being implemented as immediate countermeasures, have the potential of being maintained in the future. These strategies include revising the production activity ( Bottani et al., 2022a ) or reorganizing the workplace and workforce to minimize the probability of infection among workers ( Telukdarie et al., 2020 ).

Unexpected and unplanned lockdowns imposed by the governments in response to Covid-19 have also severely affected the transport activity (Mashud et al., 2022; Koerber and Schiele, 2022 ). Nonetheless, global SCs cannot do without these activities, as they must ensure a seamless flow of goods between partners and countries; therefore, outsourcing of logistics functions to third-party logistics services providers or diversification of transport activities through alternative channels have been proposed as additional countermeasures against Covid-19 ( Twinn et al., 2020 ). Digital technologies have also been suggested as a leverage to increase the resilience of companies ( Chowdhury et al., 2021 ), especially in the logistics and transport sector ( Sułkowski et al., 2022 ; Klein et al., 2022 ) and in the automotive industry ( Balakrishnan and Ramanathan, 2021 ). Looking at the internal processes, sharing of data and usage of digital technologies can improve process visibility and enhance automation ( Ye et al., 2022 ); in terms of SC processes, these tools have been indicated as leverages for developing stronger and more robust networks of relationships with upstream and downstream players ( Chowdhury et al., 2021 ), thus helping companies be quicker in restoring their operations, and ultimately increasing resilience.

3. The research methodology

3.1. questionnaire development, 3.1.1. structure.

The questionnaire used in this study was designed for capturing the key impact (in the immediate-, short- and medium-term) of Covid-19 on logistics and SC processes. Based on the evidence emerged from the literature, four main impacts have been targeted for the analysis: 1) the impact of Covid-19 on the sales volumes, 2) the impact of Covid-19 on the service level; 3) the impact of Covid-19 on work organization; 4) the impact of Covid-19 on present and future decision-making strategies.

The questionnaire consists of five sections, as illustrated in the scheme in Table A- 1 ( Appendix 1 ). Section 1 includes questions intended to delineate the company's profile and respondent's role. Companies were analyzed in terms of their size, according to the European Commission's (2003) recommendations, and industrial sector; for this latter, a list was proposed to the respondents including the five sectors previously mentioned, plus an additional option (“other”), for specifying a different field (in case).

The next four sections of the questionnaire address, instead, the four aspects of the Covid-19 impact on logistics and SC processes, in line with the previous list. Respondents were asked to indicate their opinion on the impact of Covid-19 using two different 5-point scales ( Taherdoost, 2019 ), depending on the question and on the scope of the analysis (cf. Table A- 1 ):

• • Scale#1 (unipolar Likert scale): from 1 = “not at all” (or “no changes”) to 5 = “completely” (or “significant changes”);
• • Scale#2 (bipolar Likert scale): from 1 = “significant worsening” to 5 = “significant improvement”. In this scale a score of 3 means that “no changes” were observed.

In detail, Section 2 contains questions intended to evaluate the impact of Covid-19 on the sales volumes handled by the company at the various nodes of the SC. Respondents were asked about the positive and negative changes in volumes they observed in the whole period of analysis (cf. section 3.1.2 ). That change was to be evaluated in comparison with the pre-Covid activity of the company, represented, for simplicity, by year 2019. The causes that determined the observed change (either positive or negative) in the volumes handled were also investigated. A predefined list of nine possible factors, derived from the literature as well as from the direct observation of the Italian scenario, was proposed to the respondent to this end.

Section 3 includes questions relating to the impact of Covid-19 on the quality of the service offered by the company to its customers. Three service factors, typically used by companies to measure the quality of their service were targeted for the analysis; respondents were asked to evaluate whether the service performance against these factors experienced a change (either positive or negative) because of the pandemic emergency. The subsequent question aims at investigating the causes of the worsening or improvement in the quality of the service offered; the analysis targeted the role of three key dynamics involved by the Covid-19 pandemic (blocking of transport activities, production delays and supplier delays) as factors which could have affected the service performance. The extent to which each factor influenced the service performance was evaluated.

Section 4 investigates the countermeasures adopted by companies to deal with the early Covid-19 infection. A predefined list of 11 mitigation strategies, summarized from the literature, was investigated in terms of the implementation level. The next question investigates the impact these precautionary measures could have on some key company's performance, such as costs, productivity, and service.

Finally, Section 5 investigates various strategies implemented for reducing risks in the SC, including more traditional measures as well as additional solutions enabled by digital technologies. As far as the traditional strategies, questions in this section aim at evaluating the extent to which they were implemented for counteracting the Covid-19 pandemics, as well as the willingness of companies to maintain each strategy in the future. To this end, respondents were asked to state the degree of usage on an ad hoc 4-point scale, according to which a strategy could be: 1-already used in the company; 2-temporary adopted to face the Covid-19 emergency; 3-adopted to face the emergency and expected to be maintained in the future; 4-not adopted and not in plan for the future. Looking instead at the digital solutions, respondents were asked to judge whether their company would be willing to favor the usage of these strategies in the future, and thus, if an increase in the usage of digital tools is to be expected.

For enhancing effectiveness in collecting the responses, the questionnaire was elaborated online using Google Forms.

3.1.2. Period of analysis

Recalling that one of the goals of the study is to evaluate the immediate-, short- and medium-term impact of Covid-19, a timespan of six quarters (Q1 …,Q6, from January 2020 to June 2021) was taken into account. This timespan was selected as the “period of analysis” (and referred to in that way from now on) for being able to capture possible variations in the behavior and performance of companies during the various phases of the pandemic. As a matter of fact:

• - Q1 (January 2020–March 2020) corresponds to the spread of the virus, first in China and then globally, with the first outbreaks, and then the lockdowns measures implemented by the governments;
• - Q2 (April 2020–June 2020) reflects the beginning of what was called the “phase 2” by the Italian government and involved some initial post-lockdown re-openings and resumption of travelling activities;
• - Q3 (July 2020–September 2020) was overall characterized by a slow economic recovery;
• - Q4 (October 2020–December 2020) was characterized by the outbreak of the second wave of the virus, with the consequent return to social distancing policies, albeit with an easing in the Christmas holiday period;
• - Q5 (January 2021–March 2021) involved a new period of closure for some activities because of the beginning of the third wave, depending on the sanitary condition of the region, and with the beginning of the vaccination campaign;
• - Q6 (April 2021–June 2021) involved a gradual reopening of activities, following the improvement in the sanitary conditions and the positive effects of the vaccination campaign.

To consider the immediate- and short-term impact of Covid-19 on the various industry field and answer RQ1 , the six quarters were taken into account, where appropriate, for questions included in sections 2 and 3 of the questionnaire; answers had to be provided by the respondent for each quarter. Instead, questions of sections 4 aim at capturing the immediate-term changes caused by the pandemic, focusing on the early adopted strategies to counteract to the emergency during the first three trimesters. Then, questions included in sections 5 investigate the past, present and future adoption of mitigation strategies across the different sectors; “past” refers to the pre-Covid period, “present” captures both the immediate- (from Q1 to Q3) and short-term (from Q4 to Q6) effects, and “future” refers to the medium-term response (from July 2021 onwards). These last two sections of the questionnaire have been designed to answer to RQ2 .

3.2. Sample construction

An appropriate sample of companies was created by querying the Kompass database ( www.kompass.com ), which provides various pieces of information about the companies indexed and lists companies from more than 70 countries in the world. We targeted companies headquarted in Italy, possibly known to the authors of the paper (e.g., because of previous collaborations for research purposes), in the attempt to increase the likelihood to get a reply from the interviewees. The sample was built to be representative of various industry fields and to include companies of different size, with the aim to investigate the effects of Covid-19 on different industrial sectors and as a function of the company's characteristics. Five sectors were taken into consideration, namely: food & beverage (F&B); plant and machinery manufacturing (MACH); logistics & transport (L&T); metal mechanical industry (MEM), which includes the producers of metal goods that supply parts to industries such as manufacturing, construction, automotive, agriculture and many other sectors; and textile & fashion (T&F). These sectors are among the top producers of the Italian economy ( Italian Institute of Statistics, 2021a ; 2021b ) and are therefore expected to well depict the Italian scenario. Overall, the sample consisted in 288 companies which were involved in the study. The sample construction took from March to May 2021 approximately.

3.3. Pre-testing, data collection and elaboration

A preliminary version of the questionnaire was discussed with three companies of to the sample, for pre-testing purpose ( Malhotra and Grover, 1998 ). These companies belong to three different industry fields, i.e., F&B, MACH, and MEM, to check whether the proposed questions were appropriate to the various industrial contexts. During the interviews, the answers to the questionnaire were collected and further specific aspects - which could not be captured by the questionnaire itself - were discussed. Evidence from these interviews were used to substantiate the research outcomes (cf. section 5 ). A personal email was then sent to the remaining 285 companies, with a link to the questionnaire on Google Form, in its final version. A cover letter was included in the email for briefly illustrating the research aim and scope and asking for the company's participation. The data collection phase took from October to November 2021 approximately.

All items of the questionnaire were elaborated using statistical techniques, supported by Statistical Package for the Social Sciences (SPSS) release 28 for Windows software package (IBM® Corp., https://www.ibm.com/it-it/analytics/spss-statistics-software ). The overall validity and reliability of the responses gathered was preliminary evaluated using the Cronbach's alpha parameter ( Nunnally, 1978 ), which returned a value of 0.872 significantly higher than the threshold of 0.6 recommended for conducting effective survey research ( Malhotra and Grover, 1998 ).

Questions in Section 1 were primarily used to provide an overview of the sample of respondent companies, through descriptive statistics in the form of frequencies or contingency tables. Items of Sections 2 and 3 were again elaborated using descriptive statistics; the trends in time of the answers provided was also derived for each industry field under examination. The average value of the responses obtained by companies belonging to the different industry fields was computed to this end; contingency tables were instead used to show the average responses as a function of the industrial context. Similar considerations can be made for items in Sections 4 and 5, for which, besides an overview of the responses collected, the average value of the answers obtained for the different industrial fields was computed and used for comparison purpose. Analysis of variance (ANOVA) with F-test ( Lomax, 2007 ) was used, whenever appropriate, to check the statistical differences of the items tested across the industry fields. For the sake of brevity, the full ANOVA analyses are reported in Appendix 2 ( Table A- 2 ); significant outcomes are mentioned while presenting the results from the survey.

4. Results from the survey

Out of the 288 companies involved in the study, a total of 79 responses was obtained, which become 82 including the three companies involved in the pre-testing of the questionnaire; the overall response rate was therefore 82/288 = 28.5%. That value is in general appropriate in survey research, as it is above the suggested threshold of 20% ( Yu and Cooper, 1983 ; Malhotra and Grover, 1998 ). The sample of companies itself is in line with empirical studies in similar fields (cf. Henkel et al., 2022 ; Wissuwa et al., 2022 ).

4.1. Overview of the sample

The descriptive results presented in this section outline the characteristics of the sample of companies involved in the study. The main features of the respondent companies are shown in Table 1 .

Characteristics of the final sample (note: F&B = food & beverage; MACH = plant and machinery manufacturing; L&T = logistics & transport; MEM = metal mechanical industry, T&F = textile & fashion).

Overall, the sample is sufficiently heterogeneous and as such, it is representative of various industries. More than half of the sample is represented by SMEs (57.3%); this is not surprising as the vast majority (approx. 95%) of the Italian manufacturing system consists of these companies, which also have a primary role in workforce employment ( Italian Institute of Statistics, 2019 ; Fortis and Sartori, 2016 ). At the same time, 32.9% of the respondents are large companies and the rest of the sample consists of micro-sized organizations (9.8%). A sufficient heterogeneity has also been reached looking at the industrial sectors involved in the study; indeed, the responses reflect the targeted industrial sectors, with four companies only belonging to a context different from those targeted in the analysis. 1 As far as the area of expertise, most of the respondents (24.4%) have a managerial role in the company and therefore they are expected to know quite well the internal business process and functions; otherwise, respondents were SC (20.7%), operations (14.6%) and marketing (15.9%) managers. The remaining quota of respondents (24.4% in total) belong to other business functions. Overall, top managers provided the 49% or responses, followed by directors (30%) and employees (21%).

4.2. Statistical analyses

This section describes the key results of the survey and following the structure of the questionnaire, consists of four sub-sections.

4.2.1. Impact of Covid-19 on volumes

The first analysis aims at identifying the variations in volumes during the six quarters considered; outcomes from questions relating to the increase and/or decrease of volumes observed by companies were elaborated to this end and results are depicted in Fig. 1 (a and b). According to Scale#1, a score of 1 in Fig. 1 (a) means that the respondent has not observed an increase in the volumes, but this does not necessarily mean that he/she has observed a decrease; conclusions can be reached by comparing the two graphs (a) and (b).

Average increase (a) and decrease (b) in sales volume per sector during the period of analysis (note: scale#1 is used).

The first evidence from Fig. 1 is the peak of volumes observed for F&B, followed by MACH and L&T, during the first wave of the Covid-19 compared to the other sectors (MEM and T&F); statistically significant differences among sectors are also observed in Q2 and Q3. Then, during the following quarters, all the sectors investigated show a constant growth up to Q6, with no statistical differences. The second outcome involves again F&B, which maintains a high level of increase in volumes in the whole period of analysis. L&T has faced a trend similar to F&B during the first three quarters, which could suggest a correlation between the two sectors. Instead, the peak of the last three quarters is probably due to the increasing volumes observed in all the sectors. MACH is the only field that maintained a middle-high increase and experienced a slow but constant growth moving from Q1 to Q6. The remaining sectors (MEM and T&F) exhibit a similar trend: at first, they have been negatively affected by the pandemic outbreak, facing a decrease in volumes, with the worst situation during Q2 (the lockdown period). Significant differences against the decrease in volumes are also evident in Q2, which again confirms that the immediate effect of lockdown varied across the sectors. Then, during the following four quarters, T&F has faced a slight increase in volumes, while MEM has strongly increased its volumes, registering the maximum growth compared to the remaining sectors.

The next analysis investigated the impact of nine selected factors on the increase/decrease of sales volume faced by the companies, as previously presented. Results are proposed in Fig. 2 .

Average impact of factors that led to the variation of sales volume (note: scale#1 is used).

From Fig. 2 it is evident that the main factors that have affected the F&B volumes are related to the downstream part of the SC and linked to the consumer behavior (factors 1 and 2) or to the interruption or limitations downstream the SC (factors 4 and 8) - these latter, presumably, with negative effects. A very similar result has been obtained for L&T, for which lower scores have been registered but with the same relative importance assigned to the nine factors. This outcome reinforces the previous consideration about a possible correlation between the results of the two sectors, and more in general, about the relationships between them. Compared to these sectors, MACH shows a similar result with an increasing importance, however, of the interruptions upstream the SC, further highlighted by MEM, that has assigned a moderate importance to all the factors. Indeed, in MEM and MACH, the variation in volumes is also due to the interruption of suppliers’ activities (factors 6 and 7). These latter factors were also observed to have a statistically different impact across the sectors investigated. Finally, T&F has been strongly affected by the limitations to people mobility (factor 4), and by the closure of shops and shopping centers (factor 8); compared to the remaining sectors, the variation in demand seems instead a secondary factor, while the interruption of production activities (factors 5, 6 and 7) achieves a noteworthy importance.

4.2.2. Impact of Covid-19 on service level

Items of section 3 of the questionnaire were used to evaluate the impact of the pandemic on three selected performance indexes typically used to express the service level of companies throughout the period of analysis. Results are shown in Fig. 3 (a–c).

Average variation of on-time delivery (a), order quality (b), delivery lead time (c) per sector during the period of analysis (note: scale#2 is used).

From the outcomes in Fig. 3 , it appears that L&T differs from the remaining fields; in fact, that sector did not experience negative variations, and it exhibits a slight improvement in the last quarters. The remaining sectors, instead, have all observed a slight worsening of delivery performance during the early period of emergency, with a minimum reached in Q2; then, a gradual recovery was observed for all sectors in the last quarters, up to approximately restoring their pre-pandemic state. Once again, T&F seems to be the most affected sector; however, no significant differences across the sectors are observed in on-time delivery and order quality. Looking at the delivery lead time, it emerges that MACH experienced a worsening of its performance in the two quarters of 2021 (Q5 and Q6). This highlights a kind of return to a critical situation with the advent of the third wave of Covid-19 for that sector. On the contrary, the remaining sectors experienced quite negative performance at the beginning of the pandemic, with a gradual recovery registered during the last quarter; indeed, some significant differences across sectors emerge in Q5 and Q6.

The possible causes for the variations in the service performance experienced by the companies were then investigated, focusing on three factors expressing some key problems involved by the Covid-19 and presented in Fig. 4 (a–c). As can be seen from this figure, the perception about the changing role of the three factors in time is similar across the industry fields under examination. Indeed, all sectors have rated as particularly relevant the impact of all three factors analyzed during Q2, corresponding to the months of lockdowns and closures imposed to cope with the first wave of the pandemic, with a trend towards the decrease in their importance when reaching the last months of the analysis. Overall, this indicates that these factors were more problematic at the beginning of the pandemic period but have gradually gone back to normal.

Effect of the block in transport activities (a), production delays (b), and supply delays (c) on service performance (note: scale#1 is used).

However, differences emerge in the role of these factors across the various industries. The impact of blocks in transport shows significant differences across sectors in Q2 and Q3, suggesting that the effect was particularly relevant in some industries (T&F and MEM in particular). As for production delays, the perception of its effects varies as well across the industries, highlighting statistical differences in Q2, Q3, Q4 and Q6. Similarly, for supply delays a significant difference is observed in Q6, in which the perception of the companies from the T&F sector is particularly low (suggesting an almost negligible effect), while the remaining sectors still perceive an effect of that factor.

4.2.3. Impact of Covid-19 on work organization

The next analysis focuses on the impact of the pandemic on the early (re-)organization of work and workplaces. To this end, two different analyses were made, focusing on the strategies adopted to counteract the emergency, and the impact of these measures on the company's performance, in terms of costs, productivity and service level. Fig. 5 shows the results of the first analysis.

Average usage of the preventive strategies adopted by companies (note: scale#1 is used).

In general, the main strategies adopted across the industrial sectors turn out to be the use of PPE (strategy 9), the re-layout of spaces (strategy 1) combined with the use of protective barriers (strategy 10) to limit the direct contact and the physical proximity of workers, the introduction of new rules intended for guaranteeing the social distancing during the loading/unloading activities (strategy 11), and finally the smart working (strategy 2) and the adoption of digital systems (strategy 8), again with the purpose of avoiding physical contacts. These last two strategies exhibit significant differences across the various sectors; the former strategy indeed is more common in the L&T and MEM fields, compared to the remaining industries; MEM also shows a peak in the usage of digital systems, significantly higher than other sectors. Reinforced sanitization practices have instead been equally adopted in all the sectors excluding L&T; this result could be due to the manual production activities that are required in most of the sectors, as well as to the consequent need of disinfecting shared workstations.

The remaining strategies have received less attention and they have been less applied; no trends emerge. Despite the lack of significant differences, outcomes in Fig. 5 seem to suggest that some strategies are somehow industry-specific. This is the case for strategy 3, quite common in L&T, probably because of the kind of business carried out by logistics companies, or of strategies 4–6, more diffused in the F&B sector compared to the remaining fields.

The impact of the above-mentioned measures on the company's performance has then been evaluated, with outcomes presented in Table 2 , Table 3 , Table 4 .

Impact of the preventive strategies on costs (note: scale#2 is used).

Impact of the preventive strategies on productivity (note: scale#2 is used).

Impact of the preventive strategies on service level (note: scale#2 is used).

From the outcomes in those tables, it is evident that the adoption of the preventive strategies has strongly impacted on costs, generating a negative effect (i.e., a cost increase) in all sectors involved in the study, without notable differences across sectors. Instead, the protective measures seem to have a slightly negative effect on productivity, with the main consequences observed in MEM, MACH, and T&F sectors. No impacts on service level are observed.

4.2.4. Impact of Covid-19 on present and future decision-making strategies

The last set of elaborations focuses on the implementation of typical risk mitigation strategies and investigates their adoption before the pandemic emergency (“past”), as opposed to their introduction in response to the pandemic itself (“present”), as well as their removal or usage in the medium-term (“future”). Fig. 6 (a–e) shows the level of implementation of three sourcing strategies (i.e., multiple, global, and local sourcing) across the sectors investigated, as a response to SC disruptions caused by the pandemic. The y-axis of this figure reflects the percentage of companies that have chosen a particular answer, while the x-axis depicts the corresponding answer, in line with the description in section 3.1.1 .

(a–e): past, present, and future adoption of mitigation strategies across the different industry field - sourcing strategies.

A first consideration from the outcomes in Fig. 6 (a–c) is that most of the companies belonging to F&B, MACH and MEM were adopting all three sourcing strategies investigated even before the pandemic emergency, with the general aim to improve the performance of their business and make the supply side more resilient. It is also interesting to note that the three sectors mentioned privileged the application of one specific sourcing strategy before the pandemic, and in particular, the most implemented practice was multiple sourcing in F&B, local sourcing in MACH, and global sourcing for MEM. Fig. 6 (b and c) also show that MACH and MEM have instead preferred a different (sometimes even opposite) strategy as the countermeasure to be implemented in the pandemic period; in particular, multiple, and local sourcing, respectively, became the preferred strategies temporarily used by companies of the two fields to counteract the pandemic emergency. The perception of companies towards the future adoption of multiple sourcing also exhibits statistically significant differences across the industrial sectors. On the contrary, 31% of companies belonging to F&B has adopted the global sourcing strategy as the ad hoc measure to counteract the emergency and is willing to maintain it in the future to enhance the robustness of the network ( Fig. 6 (a)). It is also evident from Fig. 6 (d) that the L&T has basically not changed its sourcing strategy, nor it has adopted additional measures to counteract the emergency. Finally, a relevant quota of companies belonging to T&F were not implementing any specific sourcing strategy before the pandemic, but have adopted all of them during the pandemic, being willing to maintain the chosen strategy after the end of the pandemic emergency.

Besides the sourcing strategies, two additional well-known countermeasures to (general) SC risks have been investigated; relating results are shown in Fig. 7 (a and b) as a function of the industry field.

(a–b): past, present and future adoption of mitigation strategies across the different industry field – increase in the stock level and diversification of transport modes (note: scale#1 is used).

The outcomes show that, without remarkable differences across the industrial fields, most of the companies have replied negatively to the possibility of increasing the stock level to counteract the pandemic emergency, being unwilling to implement that practice not now nor ever ( Fig. 7 (b)). This is particularly the case for companies belonging to L&T (60%) and T&F (67%) sectors. Nonetheless, the remaining quota of T&F companies (33%) has chosen to increase the inventory level during the pandemic emergency and is willing to maintain this practice to cope with unexpected interruptions along the SC in the future. Some MACH and MEM companies have temporary implemented the same strategy, but, in general, they do not seem to be motivated in maintaining a high stock level after the pandemic.

Similar considerations can be made for the other strategy ( Fig. 7 (a)). Outcomes confirm that, without significant differences across the industry fields, in general the companies surveyed have not adopted the diversification of transport modes. Nonetheless, outcomes reveal that T&F companies are somehow predisposed to implement diversified transport modes: in fact, 33% of respondents have used this strategy to react to the pandemic and would like to maintain it in the future.

A last analysis was made for understanding the willingness of companies to favor (and thus increase) the future adoption of additional mitigation strategies, always suitable for counteracting the pandemic emergency, and linked to digitalization, Industry 4.0, and automation of processes. Results ( Fig. 8 ) show that there is not a difference among the responses of the various sectors, since all respondents confirm the importance of adopting these new strategies to rapidly react to future emergencies and mitigate the relating negative effects; the absence of statistically significant differences across the sectors also emerges from the ANOVA analysis. The main need for improving cooperation (strategy 1) and information sharing (strategy 2) have been observed in MEM, while F&B returned the highest score against process automation (strategy 3). This latter strategy is overall less considered than the remaining ones, and many companies seem not to be particularly interested in enhancing the implementation of Industry 4.0 technologies for improving process automation.

Expected increase in the adoption of digital strategies in the future (note: scale#1 is used).

5. Discussion and implications

5.1. answer to the research questions.

Starting from the outcomes from the survey, the following considerations can be formulated for answering the RQs of this study.

Moving from a general evaluation of the impact of the Covid-19 on logistics and SC processes, results of this study allow formulating additional considerations along the horizon of analysis, focusing on difference in severity, duration and appearance of the impact across different industry fields. Our empirical analysis confirms a strong impact of Covid-19 on the volumes handled in the immediate term ( Kraus et al., 2020 ), with positive or negative changes mainly depending on the peculiarity of goods (respectively essential vs. non-essential goods) ( De Vet et al., 2021 ). However, although authors have suggested that the effects of Covid-19 have been different across the industrial sectors ( Xu et al., 2020 ; De Vet et al., 2021 ), the available studies have provided evidence on some sectors only. While L&T, in its various segments, has been somehow discussed (e.g., Perkumiene et al. 2022; Hohenstein, 2022 ; Ketudat and Jeenanunta, 2021 ), other sectors (e.g., MACH or MEM) have been almost neglected in literature.

Also, the available knowledge about the impact of the pandemic has mainly focused on the early effects seen during the first pandemic wave ( Hobbs, 2020 ; Ketudat and Jeenanunta, 2021 ), while the duration in time of these effects has not been so explored. The results of this work show that all sectors have restored the pre-Covid conditions at the end of the period of emergency and underline a gradual recovery with an important increase of sales in the short-term across all the sectors. In addition, although theory has so far considered the negative impact in service performance caused by disruptions ( Magableh, 2021 ), the (very few) cross-sectorial studies about Covid-19 have not deepened the effect of the pandemics on the company's service performance, nor its duration in time, while focusing, more in general, on the company's financial performance (cf. Anakpo and Mishi, 2021 ). In this regard, results of this research provide evidence of the severity of that impact on service, highlighting that the worsening of service performance was in general light, particularly evident at the beginning of the pandemic emergency, and affected almost all industry fields. Instead, results from the short-term period generally show no significant variations in the service level, but also highlight the return to a critical situation for some sectors only during the third wave of Covid-19. This result is significant since it calls the attention to the strong difference in the propagation of disruptions among different business.

Starting from the F&B sector, the outcomes of this work generally confirm an increase in the volumes handled across the period considered ( Borsellino et al., 2020 ; Loske, 2020 ). This is in line with the outcomes by Anakpo and Mishi (2021) , who have found that food-related industry fields were likely to operate at their normal level during the Covid-19 period. Furthermore, this study also confirms that results for the F&B sector mainly depend on consumer-related factors, in line with the “impulse” or “panic” buying behavior against to food products ( Hobbs, 2020 ); at the same time, we also show that this behavior was evident at the beginning of the pandemic emergency, but also had short-term effects, thus supporting the conclusion of generally changed food consumption habits by customers ( Sgroi and Modica, 2022 ). Despite the increased requests, results also show that F&B was able to guarantee good service performance to customers, seeing only a slightly worse performance during the lockdown period, probably due to interruptions or limitations downstream the SC, which as well emerged as relevant causes of the change in the volumes handed.

Similarly, MACH seems not to have been significantly impacted by the Covid-19 pandemic in terms of sales volumes, which tended to increase in the whole period of analysis, albeit with a lower trend compared to F&B. Our results thus highlight that, even in the Italian context, the impact of Covid-19 on the MACH industry was particularly relevant at the very beginning of the pandemic emergency, as observed by Ando and Hayakawa (2022) , with no effects in the short-term. This study also provides additional findings on the activity of the MACH sector, which was explored to a very limited extent in the literature. In particular, it is shown that this sector suffered because of interruptions downstream and upstream in the SC after the end of the early emergency period; this is in line with the type of business, since MACH companies usually work on an engineer-to-order basis, with very long delivery times and quite high dependency on their suppliers ( Ando and Hayakawa, 2022 ). This is also reflected by the worsened service performance during the third wave, thus highlighting a delayed negative impact of the pandemic on the MACH sector.

The T&F and MEM sectors, instead, have been more severely affected by the pandemic outbreak which caused a significant decrease in volumes in the whole 2020, with a gradual recovery in 2021. While the available literature has typically indicated these sectors as the most affected by the pandemic ( Cai and Luo, 2020 ), outcomes of this study better depict the effect of the Covid-19 in time and analyze the causes of variation. One of the key characteristics of T&F is that models and items offered on the market for sale try to capture the trends of the moment, with a very limited possibility to sell past collections in the future ( Braglia et al., 2022 ). This makes T&F particularly exposed to risk of losing sales compared to other sectors and makes it essential for T&F companies to simultaneously ensure short lead times and low costs of items, driven also by the “fast fashion” business model ( Shen and Chen, 2019 ); this is true in normal situations and exacerbated in times of uncertainty, such as during the Covid-19 pandemic. In addition, as opposed to F&B, T&F is non-essential business, which led governments to order the closure of shops during the lockdown period. Our outcomes confirm that shop closure, coupled with limitation to people's mobility, was the key cause of the observed decrease in volume in the T&F sector ( Braglia et al., 2022 ). This study also adds considerations on interruptions in the SC, both at the supplier and production level, which emerged as important aspects as well, showing that T&F companies are particularly exposed to supply and production risks. Indeed, to enhance cost efficiency, fashion SCs typically spread worldwide, with production located in low-cost countries, such as China or India ( McMaster et al., 2020 ); unfortunately, China was also the first country that had to face the Covid-19 pandemic, which caused a drastic reduction of supply availability. Moreover, causes of poor service performance have been investigated for extending the current knowledge. Our findings show that for T&F companies, supply delay or unavailability had consequences on the service performance, with stronger effects in 2020. Production delays and blocks of transport activities also had an important role in early worsening of the service performance, again in line with the global spread of the fashion SCs.

Focusing instead on the MEM industry, the available literature has provided some evidence on the automotive industry ( Tamtam and Tourabi, 2021 ; Ghadir et al., 2022 ), which is a very specific segment of this sector; hence, results of these previous studies are not always suitable for a direct comparison with the present paper. Having said that, it is first to be observed that the drop in the volumes handled for the MEM industry appears as less relevant compared to T&F. Similar considerations hold true for the service level delivered to customers: MEM companies suffered from poor service performance mainly at the very beginning of the pandemic period, corresponding to the lockdown of many countries, but the pre-Covid conditions were easily restored. Findings of this study, therefore, indicate that no long-term effects of Covid-19 on volumes or service performance are to be expected in the future for MEM industries. Issues in the procurement phase have impacted on that sector, which reflects, once again, the characteristics of global MEM SCs, in which low-cost countries are usually identified as the main suppliers of raw materials and semi-finished products. Again, this adds knowledge to the results provided by Ghadir et al. (2022) , who (more generically) reported “suppliers” and “suppliers’ temporary closure” among the top-10 risks associated to the automotive industry during the Covid-19 outbreak.

Finally, outcomes of this study show that the L&T field is somehow unique compared to the remaining sectors. Results from the current literature show that Covid-19 impacted on L&T, with some companies experiencing a negative impact and others being relatively less affected by the pandemic ( Ketudat and Jeenanunta, 2021 ; Perkumiene et al., 2021 ). However, the analysis of different time horizons and the multi-sectorial perspective allows this study to provide additional findings. Overall, we can conclude that the trend in the volumes handled by L&T companies exhibits a prevalent increase, and even after six quarters, these volumes remain greater that those handled in the pre-Covid period. A similar outcome was suggested by Ketudat & Jeenanunta (2021) : these authors found that one of the companies they analyzed experienced a decreased in volume at the beginning of the pandemics, then a slow recovery from the volume loss, and finally (September 2021), the volume handled exceeded the loss.

At first glance, the prevalent increase experienced by the L&T sector could be justified on the basis of a possible correlation with the F&B industry, as results from this study show similarities between these sectors in terms of immediate-term trend and causes of the variation in the volumes handled; Ketudat & Jeenanunta (2021) have reported a similar consideration, observing that logistics companies handling food or medical products benefited from increased volume. Nonetheless, it is evident that L&T companies do not work with F&B companies only, but with companies belonging to various industrial sectors, which also experienced a decrease in the volumes handled during the first months of the pandemics. Moreover, L&T differs from the remaining sectors in various aspects. In terms of service, it emerged as the only sector that did not experience variations during the lockdown period, and that exhibited generally good performance; this enhances the available literature, which lacks considerations about service performance of L&T companies. An explanation for this outcome is that logistics companies typically embrace various transport activities (e.g., rail, road, sea, or air freight services), as well as additional services (e.g., warehousing and distribution), but, most importantly, L&T is a highly creative sector, with great development potential and great ability to apply new/modern solutions and innovate itself in case of issues, such as disruptions ( Klein et al., 2022 ).

The first evidence of this study on mitigation strategies is in line with the current literature: different strategies were used by the surveyed companies to counteract the Covid-19 emergency, either as temporary solutions or as definitive ones ( Cai and Luo, 2020 ; Raj et al., 2022 ). In addition, our study captures differences across the sectors investigated and depending on the specific characteristics of the business, such as the SC structure or the production process. In general, our empirical findings confirm that immediate protective measures have been implemented by companies of any industrial field ( Anakpo and Mishi, 2021 ), but apart from the (popular) usage of PPE, other measures are somehow industry-specific and were implemented in some sectors only. Also, the decision of maintaining some of them strongly depends on the kind of business and the impact the Covid-19 had on the specific sector. In general, industry fields that were particularly impacted by the pandemic emergency (e.g., T&F) are more interested in applying almost all the possible measures to mitigate SC disruptions, as well as to maintain these measures in the future, with the aim to make their business more resilient. A change in these business fields can thus be forecasted in the future. On the contrary, less affected sectors (e.g., L&T) have no interest in adopting new strategies to face the pandemic and are not expected to implement them to address post-Covid SC disruptions; in that case, no changes in the business are expected. Some typical mitigation strategies (unexpectedly) resulted to be not adopted in any sector, probably because of the unique behavior that the progress of Covid-19 had in time, which made it strongly different from other disruptions ( Rinaldi et al., 2022 ). Regardless of the specific measures and concerning their general effects, we confirm a negative impact on the system's cost, in line with Aday and Aday (2020) . Moreover, our empirical results add significant considerations to the literature by providing further evidence on the slightly negative impact of the pandemic on the productivity, and on the (again unexpected) not appreciable effects on the service level provided by the companies.

Starting from the F&B sector, food companies involved in this study have highlighted the need to increase the usage of global sourcing strategy in the future. This measure is also indicated by the available literature as implemented strategy for guaranteeing the supply of raw materials and critical components ( Zhu et al., 2020 ). This result is probably linked to the structure of food SCs, which consist of many players, and, depending on the final product, raw materials can be also numerous ( Bottani et al., 2019 ). The lack of a critical raw material could compromise the production, thus preventing the possibility of reaching the final customer with finished products. In case of supply disruption, the food SC must be able to reconfigure its structure by resorting to alternative suppliers, possibly not involved in the disruption. Hence, these outcomes indicate that, in a short while, food companies could include in the procurement network suppliers located outside the own country or continent, increasing the likelihood of having the supplier available ( Bottani et al., 2019 ; Koerber and Schiele, 2022 ). However, our empirical results add new information, by indicating that addressing new suppliers during a disruption is not feasible in F&B. This aspect could have encouraged F&B companies to develop substitute products with the available raw materials, as suggested by the food company interviewed in the pre-testing phase. Similar considerations hold true for the diversification of transport modes, which is not adopted nor in plan for most of the F&B companies involved. If not already in place in the pre-Covid period, it is in general hard to think that a food company could introduce new transport modes during the pandemic period, as F&B requires specific transport conditions for guaranteeing the food preservation and transport tariffs have significantly increased in various countries worldwide ( Mogaji, 2020 ). In addition, results show that the characteristics of the F&B sector have led companies to face the first stage of the pandemic without revising the production processes. Indeed, F&B production processes already have a good level of automation, and employees typically act as supervisors of these processes; this is expected to favor (by itself) the distance among workers. At the same time, however, food production processes are hard to change, because of the greater level of complexity compared to the remaining sectors, and the strict constraints and protocols about product quality and conformity. Empirical findings from this study also underline that digitalization within the food SC is an ongoing process, which is expected to be accelerated by the pandemic. This outcome is in line with the current literature, which highlights that any technology that facilitates social distancing, reduces business travels, and possibly increases food security (e.g., by providing reliable traceability data) is welcome in that sector ( Hobbs, 2021a , 2021b ).

A well-known trend in the MACH industry is the implementation of just in time and/or lean strategies, whose general aim is to reduce the inventory level to a minimum ( Sanci et al., 2021 ). However, it is interesting to note that this is the only sector in which several companies have considered to temporarily increase the stock level to counteract the emergency (although they are not willing to maintain it in the future). Just in time and/or lean strategies are often coupled with single sourcing policies with one partner supplier, to take advantage of economic benefits of the partnership (lower transaction costs, higher quality, or specialization) and of opportunities for better coordinating the supplier's deliveries with the production schedule ( Sanci et al., 2021 ). In line with these considerations, it is not surprising that MACH companies investigated in this study have indicated multiple sourcing as the most frequently adopted strategy in response to the Covid-19 emergency, for enabling the purchase of components from more suppliers and thus enhancing the robustness of the SC (also suggested by Ando and Hayakawa, 2022 ). Our results also confirm that MACH companies are fully convinced about the importance of global/transcontinental sourcing and do not plan to abandon this strategy despite the difficulties in transport due to the Covid-19 ( Koerber and Schiele, 2022 ). Furthermore, contrary to F&B, some companies have started to introduce new transport modes during the pandemic, moving from road to rail to improve the on-time delivery and avoid issues linked to the limitation of movements between countries. Digitalization of the MACH industry, although could be accelerated by Covid-19 ( Roosefert Mohan et al., 2022 ), was already at a quite good level in the pre-pandemic period ( Yang et al., 2019 ). In addition, the pandemic has boosted firm investments in digital technologies and automation in many sectors, by increasing the requests and sales to MACH industries. Finally, it is also interesting to note that immediate protective measures focusing on reorganizing the employee's tasks have been judged as poorly adopted by respondents from the MACH sector; the peculiarities of the production activities, usually carried out by high skilled and not interchangeable workforce, prevented the possibility of adopting that strategy.

About the T&F field, our outcomes indicate that this is almost the only sector in which a relevant quota of respondents has indicated the diversification of transport modes as a strategy adopted to counteract the pandemic emergency and to be maintained in the future. That strategy, instead, is not so popular (and probably underestimated in its effectiveness) in the remaining industrial fields, while it appears as an effective response of the fashion SCs to the block of transport activities, which has particularly affected that sector. A further interesting outcome from this study is that none of the T&F companies surveyed mentioned the increase in the stock level as an existing (pre-Covid) risk management strategy. The presence of high stock levels, indeed, contradicts the fact that many T&F companies work on a make-to-order basis ( Braglia et al., 2022 ), as well as the characteristics of volatility, velocity and variety typical of the T&F context ( McMaster et al., 2020 ). Another outcome of this study concerns the positive inclination towards investing in data and information sharing, which confirms the expected increase in the usage of information tools by T&F companies, for enhancing their online presence (also discussed by Achille and Zipser, 2020 ). The current literature indicates these results as an obvious effect of the Covid-19 and specifically of the closure of shops, which accelerated the shift towards a more digital world and triggered changes in consumer's behavior, shifting towards online shopping ( Alderighi, 2021 ). Finally, the empirical findings of this study also underline that the adopted early preventive measures are in line with the pre-Covid low level of automation and the need of skilled workers which characterize the T&F sector.

Our findings about the MEM sector indicate that multiple sourcing and global sourcing were quite diffused among companies, which, coupled with the previous consideration, reinforces the idea that suppliers were located in low-cost countries and thus were particularly affected by the pandemic emergency. It is not surprising that the present study indicates the local sourcing as provisional countermeasure adopted by MEM companies to face the pandemic, coupled with the temporary increase in the stock level, which obviously enhances the resilience of the system. The trend for the future, instead, is to maintain past strategies; this confirms the previous consideration about the primary role of global/transcontinental sourcing for various industrial fields and the willingness to keep this strategy unchanged in the future ( Koerber and Schiele, 2022 ). Our study has also shown that a quite relevant (compared to the remaining sectors) quota of MEM companies already makes use of different transport modes. Although the specific types of transport have not been investigated in the survey, from the interview carried out in the pre-testing it emerged that sea transport is frequently used when importing components from global suppliers or exporting finished products worldwide; intermodal transport can be used to the same extent, while road transport is mainly used for deliveries in Italy. Because of the problems in exchanging goods from/to China by sea, rail has started being used as the primary transport mode for long distances, to increase punctuality. A last outcome concerns the future adoption of data and information sharing tools, which was judged very positively by the companies surveyed. By the way, the current literature has already debated on the crucial role of digitalization for MEM, where it is expected to positively impact, among others, on traceability of assets (e.g., between the company's sites or facilities), production monitoring and communication among SC partners ( Granillo-Macías et al., 2020 ). This result is also consonant with the usage of digital solutions as immediate countermeasure adopted to face the spread of the pandemic, grounding on the wide automation and digitalization started by this sector even before the pandemic itself. Similarly, the need for specialized manpower has limited the possibility of changing the operators' tasks and reorganizing the work shifts to guarantee the social distancing.

Once again, results of this study highlight some traits of uniqueness of the L&T field with respect to the adoption of mitigation strategies. L&T emerged the only sector that has no interest in applying any strategy among those typically used to counteract risks and disturbances in the SC. This result, although surprising at first glance, can be justified if thinking about the kind of activity carried out by L&T companies, as most of these companies will probably act as third-party logistics service providers. This outcome confirms the current literature, which identify the possibility to even increase the logistics services during the pandemic as more companies outsource SC and logistics activities ( Perkumiene et al., 2021 ). Hence, the choice of increasing the stock level or to implement a given sourcing strategy does not apply to this sector. However, findings from this study reveal that some industry-specific countermeasures against Covid-19 were nonetheless adopted by L&T companies, such as the revision of the allocation of items in the company's warehouse, or the definition of new procedures for loading/unloading of goods. These practices thus fit particularly L&T companies, because of the peculiarities of the logistics activities, while they are not popular in the remaining contexts. Instead, the only mitigation strategy of interest to L&T companies for a future implementation is the usage of data and information sharing tools ( Twinn et al., 2020 ); however, the expected increase in the usage of these tools is not so striking.

5.2. Managerial implications

The present study also provides several practical implications and valuable suggestions for companies on how to manage disruption and post-disruption periods within their organizations and overall SCs. The primary lesson that emerged is the possibility for firms to exploit the acquired experience to convert the Covid-related disruptions into opportunities for improvement. As we found that mitigation measures typically did not involve consequences on service performance and productivity of companies, some of these measures can be maintained and even reinforced in the future. As far as their cost, this needs to be converted into an investment for the future for maintaining these measures. Hence, two key areas of strategic intervention arise from this study:

• a) Internal (Re-)organization: the strong variations in volumes and SC conditions resulting from this study have attested the importance of agility and digitalization; by the way it has long been known that “resilience implies agility” ( Christopher and Peck, 2004 ). The promotion of agile work and agile processes needs a change in the vision of the company and the awareness that a new concept of work must be developed. The findings of this study reveal that smart working has been adopted to face the pandemic and guarantee the social distancing. However, once restored the pre-Covid conditions, this measure should be adapted to the industrial context, since it should no longer be conceived as the same work, simply made from home ( Brown, 2008 ; Clack et al., 2019 ). Also, the implementation of this practice should be designed with the aim of capturing the peculiarities of the specific sector. New practices, new objectives, and new system to measure workers' performance should be also designed. Agile work also needs the revision of social relations and workspaces, reducing the space for individual offices (individual work can be performed at home), and increasing the space dedicated to social interaction and brainstorming (meeting, workshop, training, mentorship) ( Holeman and Kane, 2020 ). At the same time, agile processes need the implementation of Industry 4.0 tools to provide digital and automated solutions to adopt in global and volatile markets. All the companies involved in this study have confirmed the importance of adopting these tools to rapidly react to pandemic and mitigate the relating negative effects. Hence, companies should consider the need to re-design the workspaces and revise the working processes to allow for smart technologies to support more and more the human work. Technology should be pulled and not pushed by the manufacturing system ( Tortorella et al., 2023 ), and the organizational conditions should be prepared for automation and digitalization before their adoption.
• b) External (Re-)organization: if our findings indicate that the implemented countermeasures strongly depend on the industrial sector, it is equally clear that all the sectors attribute to other SC players the cause of the change experienced in volumes and service performance. The only difference is observed in the origin of the perturbance, which mainly comes from the upstream (MACH, and MEM) or downstream (F&B, L&T, and T&F) the SC. This result should encourage companies to adopt or maintain in the future all the measures aiming to enhance the robustness of the whole network. It is interesting to note from this study that, regardless of the industry field, the diversification of the sourcing strategy has been considered as a winning practice to manage unexpected interruptions along the SC. On the contrary, outcomes reveal that only sectors mainly structured in global SCs (T&F, MEM, and MACH) have considered the diversification of transport modes to overcome the difficulties in transport due to the Covid-19. Some authors have reported that among the future effects of the Covid-19, a decrease in transport activities could be expected, as global SCs will become less global, being distant destinations perceived as less reliable than closer ones ( Alderighi, 2021 ); other authors, instead, claim that global/transcontinental transport will not be affected by medium-/long-term effects of the pandemic, as various industrial fields cannot do without global sourcing strategies ( Koerber and Schiele, 2022 ). Looking at the outcomes of this study, most of the companies surveyed declared not to be predisposed to change their transport modes; however, the evidence on the L&T field seems to support the idea that Covid-19 will not involve a decrease in transport activities in the future; this should lead companies to reflect on the possibility to revise the organization of transport to increase resilience of its business.

5.3. Future research directions

This study is expected to fuel various lines of research, by offering, in the meanwhile, reliable data that could help researchers gain a deep understanding of Covid-19 related phenomena. The following research directions are suggested for future studies:

• • the future role of L&T companies and the medium-/long-term effect of Covid-19 on L&T activities need further analysis. Outcomes of our study suggest that Covid-19 will not involve a decrease in the demand of L&T services in the short-/medium-term, but the fact that some industry fields investigated experienced a decrease in the volumes handled could somehow contradict that conclusion. Repeating an analysis similar to that made in this paper in a suitable time (e.g., one year) or carrying out a dedicated empirical study targeting L&T companies would allow to derive additional insights on this topic;
• • more in general, dedicated analyses, in the form of surveys or case studies, would be appropriate for each of the sector investigated in this paper, to increase the level of detail of the results obtained;
• • similar behaviours were observed for some sectors (e.g., F&B and L&T), which seems to suggest correlations and interdependences across industries. Analysing these aspects was out of scope for the present study, but could represent an interesting future research step;
• • to enhance the generalization of the outcomes, other sectors could be taken into consideration for expanding the present analysis and gaining further insights. Similarly, an enlarged sample of companies could be analyzed in the near future to further substantiate the outcomes observed.

6. Conclusions

This paper has proposed an empirical survey whose aim was to investigate the impact of Covid-19 pandemic on logistics processes, looking at five different industry fields and considering a time span of 18-month (from January 2020 to June 2021). As such, this study contributes to the literature in various ways. First, it fuels the debate about the relationships between Covid-19 and logistics and supply chain activities across different sectors and periods, which, despite the wide literature on Covid-19, is not so investigated. Second, it reports real (empirical) data on various industry fields; this is an important point, as some industrial sectors have been widely investigated as far as the impact of Covid-19 (e.g., F&B), while other (e.g., MEM or MACH) have been significantly less explored. Also, even if looking at well-debated industry fields, empirical studies on the impact of Covid-19 are still limited and the available studies often refer to contexts outside Europe, with no studies carried out in Italy.

At the same time, the fact that our research sample consists of companies from Italy only could be seen as a limitation. Actually, focusing the analysis on a specific country is a common procedure in empirical studies targeting industry fields, and is often an effective approach, since the country delimitation offers a homogeneous context among the firms investigated. In this specific case, also restrictions imposed by the Italian government across the considered periods are homogeneous among and into sectors; this makes the sample suitable for the proposed analysis. Unfortunately, however, a country-specific study could reduce the potential for generalizing our findings. Hence, it is recommended to conduct future studies in other international contexts, in which different scenarios could have affected the same sectors in different ways.

Authors contribution

Conceptualization (Ideas; formulation or evolution of overarching research goals and aims) Marta Rinaldi; Eleonora Bottani. Methodology (Development or design of methodology; creation of models) Eleonora Bottani. Software (Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components) Not applicable. Validation (Verification, whether as a part of the activity or separate, of the overall replication/reproducibility of results/experiments and other research outputs) Not applicable. Formal analysis (Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data) Marta Rinaldi; Eleonora Bottani, Investigation (Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection) Marta Rinaldi; Eleonora Bottani, Resources (Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other analysis tools) Not applicable, Data Curation (Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where it is necessary for interpreting the data itself) for initial use and later reuse) Marta Rinaldi, Writing - Original Draft (Preparation, creation and/or presentation of the published work, specifically writing the initial draft, including substantive translation) Marta Rinaldi; Eleonora Bottani. Writing - Review & Editing (Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision – including pre-or postpublication stages) Eleonora Bottani, Marta Rinaldi, Visualization (Preparation, creation and/or presentation of the published work, specifically visualization/data presentation)Marta Rinaldi; Eleonora Bottani. Supervision (Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team) Eleonora Bottani. Project administration (Management and coordination responsibility for the research activity planning and execution) Not applicable. Funding acquisition (Acquisition of the financial support for the project leading to this publication) Not applicable.

Acknowledgment

The authors wish to express their thanks to all the companies for their participation in this work.

Biographies

Marta Rinaldi has a Ph.D. in Industrial Engineering and she graduated cum laude at the University of Parma. Currently, she is an Assistant Professor and researcher in the Department of Engineering at the University of Campania “Luigi Vanvitelli”. She teaches and conducts research in the field of industrial system design and management, supply network mapping, modelling and simulation, inventory management and sustainability. She published more than 50 scientific papers, published in International ISI journals or in proceeding of international conferences Peer Reviewed. She is an Editorial Board Member of Healthcare Analytics and Supply Chain Analytics Journals.

Eleonora Bottani is Full professor of Industrial Logistics at the Department of Engineering and Architecture of the University of Parma since November 2019. She graduated cum laude in Industrial Engineering and Management in 2002 and got her Ph.D. in Industrial Engineering in 2006, both at the University of Parma. Her research activities concern logistics and supply chain management issues. She is author (or co-author) of >200 scientific papers (citations on Scopus>3400; H-index = 31), referee for more than 60 international journals, editorial board member and associate editor of various journals, and editor-in-chief of a scientific journal.

1 Those companies are included in the general overview of the sample, for completeness, but they will not always be included in future elaborations; in particular, they will not be included in elaborations focusing the specific industrial fields.

Appendix 1. 

Scheme of the questionnaire used in the survey

Details of the statistical tests (Note: statistically significant outcomes at p < 0.05 are highlighted)

Data availability

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