The next S-curve of growth: Online grocery to 2030

While stores remain the key channel for most grocers, online grew dramatically during the pandemic, with many retailers quickly adjusting their offerings and operations to meet consumer demand. The coming years will present new opportunities. Fueled by evolving customer expectations, increased competition, and technological advancements, online could account for up to 18 to 30 percent of the food-at-home market in some leading European countries.

Online propositions will differentiate and likely mirror today’s offline formats—for example, convenience-store visits or top-ups will be covered by instant delivery services, and discount purchases by no-frills offerings. Online shopping will also likely consolidate based on scale efficiencies and winning consumer offers. With online pure players disrupting markets, traditional grocery retailers should now determine which value propositions to focus on, define a scalable operating model, and consider partnerships to complement their historic strengths.

Tracking the trajectory of online grocery

One of the major effects of the COVID-19 pandemic has been a boost to online grocery. Consumers migrated rapidly to online channels, whose greater convenience altered consumer behaviors and expectations over time. The shift also reordered the competitive landscape as new players flooded the market, often backed by large investors.

A McKinsey survey of European consumers reveals most respondents plan to use online grocery services almost equally often as in 2021 (a net intent of –1 percent). 1 Net intent is the share of customers who indicated they want to increase use of online services minus the percentage of customers who said they want to decrease or stop the use of online services. Neutral customers who want to keep purchasing at similar levels are not included. The results vary significantly by country, however; customers plan to shop more online in advanced online markets such as the United Kingdom (a net intent of +5 percent), the Netherlands (+4 percent), and France (+2 percent). In these countries, online’s share of the grocery market stood at 8 to 12 percent in 2021, 2 Based on Europanel data. and its broader offerings have helped to increase customer satisfaction and adoption.

Our analysis segmented selected European countries into leading countries (the United Kingdom, France, the Netherlands, and Sweden) and those still catching up (Germany, Italy, Spain, and Poland). In leading countries, online grocery could make up 18 to 30 percent of the food-at-home market 3 The food-at-home market includes the grocery market (fast-moving consumer goods [FMCG], fresh) and the meal delivery market (for example, Deliveroo, Just Eat, and Uber Eats). by 2030 in our aggressive scenario (exhibit). Scheduled delivery (with the promise of same-day service) will still account for the majority of this share, while instant delivery (defined as delivery on demand, typically with a 15- to 30-minute lead time) could reach 3 to 7 percent of the total food-at-home market in leading countries. 4 The forecasts use the observed prepandemic development rates as the baseline. For countries still catching up, we assume they will either follow the development path of France and the United Kingdom (moderate) or the Netherlands (aggressive), adjusted for market fundamentals (customer adoption, latent demand, economic viability, and the grocery market’s characteristics). For leading countries, the aggressive scenario assumes market share gains from 2021 could partially endure, adjusted for market fundamentals. For scheduled online delivery, the scenarios depend primarily on the level of investment by players and potential new market entries. For instant delivery, our analysis considers the share of population living in densely populated areas, indications that consumers will shift channels, and the current level of investment for this business model.

Sources of future growth

What are the sources of growth that will determine whether online tracks more closely with the conservative or aggressive scenarios in a given country? We expect that online grocery will continue to extend its reach through 2030, with several factors poised to influence demand.

Evolving customer behavior

Online has extended its reach to new customer segments. Before the pandemic, online grocery had a more concentrated appeal, such as among young, urban, affluent families seeking the convenience of large-basket delivery to their home. Now, offerings have expanded to address more shopping missions (such as top-up shopping) and customer segments (such as younger and elder generations).

Click-and-collect models began to see rising demand during the early phases of the COVID-19 pandemic, when delivery slots were limited and customer demand was high. These models also helped grocers reach customer groups beyond urban centers in suburban areas, small cities, and even rural areas. Overall, countries that have been slower to adopt online offerings will see increasing penetration, thanks to the ability of disruptive players to change customers’ expectations and behaviors across propositions.

State of grocery Europe 2022

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Increased competition and investment.

The online market is still in the process of taking shape. Currently, a multitude of propositions are partly overlapping, but the market’s future state will likely mirror existing offline propositions and replace or improve on them:

  • Full-basket offerings are akin to supermarkets. For example, players such as Ocado, Rohlik, and Tesco typically have a very large assortment, same-day service (delivery usually within a few hours), precise delivery windows, and competitive pricing on core basket-building items. Extensions to the current offering include farm-to-table concepts (such as Crisp and Frischepost) or meal kit options.
  • Instant delivery is online’s convenience store and small supermarket. Players such as Flink, Getir, and Gorillas cater to customers who order small baskets from a more concentrated assortment. The competitive battleground is focused on speed and user experience, primarily for immediate and unplanned needs. Instant players are also increasingly adding different categories, including (warm) meals to increase their “share of stomach” and take-out options from the restaurant market or meal delivery providers.
  • No-frills offerings are the discounters of online. Companies such as Picnic offer low-minimum-order values and no delivery fees while emphasizing value for money in product pricing, but their customers must often accept trade-offs in assortment depth, delivery options, and additional services.

The growth of the online market has attracted a record level of investment. Venture capital (VC) funds and consumer-packaged-goods (CPG) companies seeking to develop their own direct-to-consumer  offering have joined the fray. Players that can secure funding for future growth will likely lead the disruption. However, other factors could shape the market’s development, including new regulations (for example, the current freeze on new dark stores in Amsterdam) that could make online grocery less attractive to investors.

Technology is in the process of disrupting several parts of the online value chain, from user experience to order preparation to the last mile. With technological advancements, business models and operations that are unprofitable today could become more sustainable in the future. For example, advanced personalization could further increase order size for large-basket delivery, and automation has the potential to transform the cost model for order preparation and last mile.

In the longer term, technological advancements could make online grocery less costly to operate than physical grocery—enabling grocers to offer lower prices online than in stores. If that were to happen, physical retail would lose a significant part of its advantage, and we could see new consumer segments—such as value seekers—shifting online and creating a boom for the online grocery market.

Implications for traditional retailers and their physical network

Incumbent retailers that are not currently playing in online might be at risk of losing market share, especially in urban areas. For example, in the aggressive market forecast for the United Kingdom, online scheduled grocery would be the largest channel in the country by 2030, overtaking supermarkets. Executives should consider several actions.

Navigating the headwinds: The State of Grocery Retail 2022: Europe

View online as a future driver of growth.

Grocers should define strategies that determine where they want to play and choose enabling investments in areas such as fulfillment, last mile, technology, and talent. This includes devising the most suitable approach, which could combine new capabilities and ways of working (for example, data-driven decision making and agile product development) with historic strengths (such as sourcing or a dense store network). When determining a new strategy, grocers’ decisions could include whether to build their own end-to-end offerings or partner with third parties to address specific parts of the value chain.

Assess the impact of online on physical stores

Online growth will have significant implications for stores, so offline incumbents with existing store networks also need to rethink their omnichannel strategies. While offline might remain a grocer’s largest channel, the role of the store will need to change beyond adding a click-and-collect offering—for example, by creating a distinctive experience that brings customers to physical locations. Overall, grocery stores might need less physical space and might need to reduce costs as offline formats lose sales volumes. The store network can also be a source of differentiation against pure players as incumbents manage their omnichannel offerings.

Chart a path to profitability

Profitability has traditionally proved elusive in online, but some companies have cracked the code. Big-basket delivery players such as nemlig.com, Ocado, Rohlik, and Tesco have achieved break-even or marginal profitability in selected geographic areas. Scale and excellence in all business areas are prerequisites, so players must be prepared to undertake targeted transformations to turn around unprofitable businesses. While online pure players have advantages—they are more flexible, agile, and not bound to parity with the offline offering—incumbent players can benefit from scale and existing brand and infrastructure, among other attributes.

Achieving profitability in the online business while continuing to grow will require management to focus on the following set of levers 5 For more on profit parity between offline and online channels, see Julia Spielvogel and Madeleine Tjon Pian Gi, “The e-grocery challenge: Moving toward profitable growth,” Disruption & uncertainty: The state of grocery retail 2021 , March 2021. :

  • Retention efforts. Build personal relationships with the customer by reestablishing a social connection and achieving daily engagement.
  • Optimized category management. Create assortment choices that allow for sourcing optimization and decoupled pricing and promotions from the offline business for omnichannel players, allowing more frequent changes and optimization.
  • Enhanced user experience during shopping. For example, use personalization to increase convenience and support cross-selling and upselling to build the basket.
  • Media monetization. Generate a new income stream in an advanced ecosystem play.
  • Automation of order preparation. Reduce costs and increase order quality through microfulfillment and other solutions.
  • New last-mile models. Use models such as autonomous vehicles and technology-enabled logistics optimization to lower costs while offering excellent service levels.
  • An agile operating model. Allow for fast product development and constant testing of improvements in the customer proposition and user experience.
  • Partnerships. Create partnerships in many business areas, from assortment and range extension to automation and last mile, to manage costs and capabilities. Access to customer data and exclusivity will largely determine the attractiveness of a partnership for different players.

Despite the rapid growth of online grocery and the sector’s increased number of players, European markets are still in the early stages of development. Retailers that take decisive action and make strategic investments today will be well positioned to carve out a winning and sustainable market position in the future.

Virginia Simmons is a senior partner in McKinsey’s London office, where Björn Timelin is a senior partner; Julia Spielvogel is a partner in the Vienna office; and Madeleine Tjon Pian Gi is a partner in the Amsterdam office.

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AmazonFresh: Rekindling the Online Grocery Market

By: Rory McDonald, Clayton M. Christensen, Robin Yang, Ty Hollingsworth

More than a decade after the high-profile failures of several early online grocers, grocery remains the largest single U.S. retail category and one of the few that has not yet migrated online. Amazon…

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More than a decade after the high-profile failures of several early online grocers, grocery remains the largest single U.S. retail category and one of the few that has not yet migrated online. Amazon began testing its grocery-delivery service, AmazonFresh, in Seattle, in 2007; five years later, the company has made significant progress. The case traces the evolution of AmazonFresh's business model and describes the operating capabilities necessary to compete with brick-and-mortar supermarkets like Wal-Mart and Safeway and with new digital grocery startups. Now Amazon needs to decide on AmazonFresh's next step. Should the company continue refining its business model in Seattle or expand to another city? What factors should it take into account when planning its next move?

Jul 9, 2014 (Revised: Aug 15, 2014)

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AmazonFresh: Rekindling the Online Grocery Market

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online grocery case study

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  • AmazonFresh: Rekindling the Online Grocery Market  By: Rory McDonald, Clayton Christensen, Robin Yang and Ty Hollingsworth

Tesco Case Study: How an Online Grocery Goliath Was Born

Tesco case study

Tesco boasts an impressive history in the UK and abroad. Over the years, the grocery goliath has achieved continued success by remaining at the forefront of retail trends, including everything from self-service shopping to international expansion. More recently, Tesco has made its mark with a sophisticated online grocery strategy that enables seamless digital shopping. There’s a lot that can be gleaned from Tesco’s eCommerce efforts. In this Tesco case study, we highlight the retailer’s long-term emphasis on customer service, which can be seen not only in its physical locations but also in its eCommerce strategy.

Table of Contents – Summary

A Brief History of Tesco

Tesco’s and world’s first virtual store, tesco and scandals, how tesco became a retail case study favorite, tesco’s ecommerce website, interesting technologies that tesco’s uk site uses, impressive tesco stats you may not know, faq on tesco.

  • The Tesco Success

To understand current growth and successes and why they warrant a Tesco case study, it helps to understand the retailer’s history. Founded in 1919, the company initially consisted of a group of high-performing market stalls. Founder Jack Cohen conceived the idea shortly after leaving the Royal Flying Corps as World War I drew to a close. He used demobilization funds known as “demob money” to purchase surpluses of fish paste and golden syrup.

First Tesco store

Tesco’s initial success could largely be attributed to Cohen’s understanding of mass-market sales. In a time of strict austerity, he employed a rigid business model of “stack ’em high, sell ’em low.” The brand also set itself apart by embracing a self-service approach, which, at the time, was rare in the UK. Following the introduction of its first supermarket in 1956, the retailer entered an era of rapid growth.

After emerging as the UK’s preeminent grocery chain, Tesco released the revolutionary Clubcard. During the 1990s, the chain expanded to include thousands of international locations. This was quickly followed by investments in internet retailing, which led to the chain’s current status as a top eCommerce grocer, netting  £1.3 billion in pre-tax profits  for the year ending in February 2018.

In 2011 Tesco was the first-ever retailer building the world’s 1st virtual grocery store in South Korea. The experiment took place in a subway station and the results were tremendous: the number of new registered members rose by +76%, online sales increased by +130% and Tesco became South Korea’s no1 online grocery retailer, outranking its rivals e-mart, so this experiment was one of the first key steps towards Tesco’s digital transformation.. After this phenomenal success, Tesco opened its first European virtual grocery shop in Gatwick Airport, UK. See how they did it in this brilliant video:

Tesco has occasionally suffered controversy in the last several decades, with 2 shocking moments that everyone remembers:

  • The Horse Meat Scandal: Back in February 2013, several products believed to consist entirely of beef were found to contain horse meat. The Food Safety Authority of Ireland tested a range of cheap frozen beefburgers and it found that Tesco’s sample contained 29% horse instead of beef .  The retailer made every effort to appease concerned customers. One of which included a notable promise to tighten up its supply chain and purchase a more significant share of its meat from the UK. Such efforts have likely played into the grocery chain’s recent logistics successes.
  • The Accounting scandal: It was 2014 when the news dropped like a bomb: an FTSE 100 firm could get away with “cooking the books”. The company admitted submitting overstated profits by £250 million . The results? £2 billion off the supermarket’s share price in one day.

How Tesco thrived in the COVID-19 area

During Q1 2021, Tesco reported that the sales from its online store were “remarkably higher” than before the Covid-19 crisis. As Internet Retailing mentions , Tesco’s sales increased by +22% in 2020, even though the physical stores and hospitality re-opened at some point. It is believed that this success was a result of Tesco’s recent delivery enhancements and doers mentality, implemented during the first lockdown. 

It’s revenue analysis shows that 1.3m online orders were conducted only in spring 2021. This means that the total number of transactions was 81.6% higher than the same period in 2019 (a before Covid-19 year), proving that Tesco actually turned COVID-19 into an opportunity for its business, achieving memorable results by quickly adjusting its business model to the pandemic’s needs.

Despite the horsemeat scandal, Tesco remains a customer favorite throughout the United Kingdom. The Tesco case study has become a common phenomenon, as the chain boasts several unique strengths worth emulating on a broad scale.

Over the years, the retailer has shifted its original “stack ’em high, sell ’em low” approach. While affordability remains a priority, Tesco did not pursue it to the detriment of quality. Instead, it combines reasonable prices with exceptional convenience and customer service. This can be seen in physical stores and eCommerce alike.

Tesco Express store in London

Excellent Customer Service

Strong customer service lies at the heart of Tesco’s sustained success. The retailer employs a variety of initiatives to keep consumers happy. Customer-oriented product development, for example, ensures that all stores are stocked with the items visitors actually want. This development process includes rigorous consumer testing to ensure that new products and services are well-received. Customized stores lend further appeal; each is designed based on carefully analyzed demographics.

Quality customer service means making accommodations for all consumers—including those with special needs. Tesco accomplishes this through the use of sunflower lanyards, which allow customers with hidden disabilities to secure additional assistance discreetly. The chain also provides induction loops for hard-of-hearing customers, as well as helpful visual guides for consumers with autism.

Ultimately, Tesco’s impressive customer service derives from its top-down approach, in which a commitment to customer satisfaction permeates every element of the company’s culture. Insight Traction’s Jeremy Garlick tells The Grocer that the key to large-scale retail success lies in “ understanding your customers, anticipating their needs, and giving them what they will value.” Tesco checks off all these boxes. This is true both in stores and with its website, which uses an intuitive layout to ensure that customers can quickly access the products and services they desire.

Product Diversification

Tesco may be best known as a grocery chain, but the retailer provides a surprising array of products and services. It aims to serve as the ultimate one-stop-shop for those who prioritize convenience and quality above all else. Customers can expect to find a collection of produce, dry goods, frozen products, and more. Toiletries, household products, pet food, and even apparel can also be located within Tesco stores and on the retailer’s eCommerce website.

Beyond its many product offerings, Tesco also provides a few key services to enhance customer convenience. Tesco Bank, for example, offers everything from credit cards to pet insurance. These digital offerings play largely into Tesco’s eCommerce strategy, with banking customers capable of accessing their account information online.

Fine-Tuned Logistics

Quality customer service is not possible without an effective logistics and supply chain strategy. Strong relationships with suppliers are essential, especially as Tesco seeks to diversify its already vast product collection further. Efficient routes ensure that produce and other time-sensitive products arrive promptly in stores—and are quickly distributed to customers taking advantage of the chain’s affordable home delivery program.

Ongoing investments in telematics promise to further improve Tesco’s already fine-tuned supply chain. New monitoring tools offer greater insight into the trip status and real-time decision-making—and how these elements play into both profit margins and long-term customer satisfaction.

Digital customers, in particular, appreciate Tesco’s tight supply chain. When they order items online, they can rest assured, knowing that their favorite products will consistently be in stock. What’s more, online customers feel confident that delivered items will be fresh and of exceptional quality.

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Insane International Expansion

Tesco may currently dominate the UK grocery market, but it’s also an international force. While the retailer pulled out of the United States in 2014, it has enjoyed sustained growth in Eastern Europe and Thailand.

Tesco international

Just as Tesco targets its international in-store efforts to reflect local populations, it designs its global eCommerce strategy around a diverse consumer base. Different websites are offered in each target country, with text provided in both English and the respective region’s primary language.

Customer Loyalty

Brands such as Costco and Amazon prove that customer loyalty can pay dividends for a company’s bottom line. Tesco demonstrated this long ago with the Clubcard, which encourages customers to prioritize the chain over competitors.

Today, the Clubcard continues to play a crucial role in Tesco’s success. Further transformation is in store, as Tesco recently unveiled a £7.99 per month subscription service called Clubcard Plus . Subscribers will receive significant discounts above and beyond those offered through the traditional Clubcard, including a permanent 10 percent off many of the store’s most beloved brands. Given the current popularity of subscription services, this could prove an excellent opportunity to get existing customers even more enmeshed in the Tesco ecosystem and more responsive to eCommerce marketing automation efforts.

Tesco’s eCommerce strategy reflects the brand’s commitment to value and convenience. These priorities are evident in everything from the logo to the images and even the general layout. Website visits are just as efficient and orderly as in-person purchases at Tesco’s physical locations. Tesco’s website, like its stores, may not be fancy—but it gets the job done. In this Tesco case study, we’ve analyzed several of the key eCommerce strategies that help Tesco’s page stand out in a competitive digital marketplace, as well as a few areas that warrant improvement.

Analyzing Tesco’s Homepage

Tesco Groceries Homepage

What We Liked

  • Easy to navigate . Today’s impatient customers demand easy-to-navigate websites that almost instantly get them from point A to point B. Tesco’s homepage appeals greatly to convenience-oriented online shoppers, who can quickly find desired products via a simple search tool. Headings highlight main categories, including groceries, clothing, banking, and even recipes.
  • Visually-appealing fullscreen displays . Rather than distract website visitors with several separate visuals, Tesco’s website maintains a single, but decidedly bold display. This impactful background stretches across the entire screen and is layered behind text and customer prompts. The homepage, featuring fresh produce, has eye-catching graphics that reflect the commitment to quality that emerges in every Tesco case study
  • Minimalist, but not dull . Minimalist displays dominate modern web design. Sometimes, however, white space feels excessive. Tesco strikes an ideal balance by keeping clutter to a minimum without relying on a bare-bones approach.
  • Easy logo identification . Customers can always spot the Tesco logo in the upper left-hand corner, surrounded by just enough white space to ensure that it stands out.

What We Didn’t Like

  • Customer testimonials . Reviews from happy customers may prove desirable in some contexts, but there is a time and a place. These particular testimonials take up the page’s most prominent space, which could be better served by showcasing exciting deals or products.
  • Tabs that open into new pages . Ideally, when clicking on a link that appears to be a tab (such as the Delivery Saver tab), the new content should open in the same page, instead of loading an entirely new page.

Analyzing Tesco’s Category Page

Tesco category page

  • Sticky cart functionality . As shoppers browse the website and add items to their carts, they can keep track of these intended purchases on the right side of the screen. This intuitive design allows for a seamless Tesco checkout process , thereby increasing the likelihood of conversion.
  • Variety of filters . A wide array of filters are provided to allow customers to browse through products based on brands and categories. Furthermore, customers can customize their browsing according to specific dietary filters such as vegan or Halal. This plays into Tesco’s overarching emphasis on personalized shopping.
  • Usually bought next . Situated at the bottom of each category page, this helpful section makes it easy to pair similar grocery items. This increases customer convenience while also helping to improve sales and final revenue on Tesco’s end.

What We Didn’t

  • Difficult filter navigation . There’s a lot to be said for the variety of filters at customers’ disposal, but the actual process of navigating them can prove complicated, particularly compared to competitor websites.
  • Navigating to different items within categories . Navigation can prove surprisingly difficult for those browsing various items within categories. The constant need to return to the homepage could quickly grate on otherwise amenable customers.
  • Lack of search functionality within categories . Items cannot be sought via keywords within specific category pages. All searches must be completed using the main search bar on the top of each page. For many users, this may represent the website’s greatest weakness, as keyword category searches are an expected feature among competitors.

Analyzing Tesco’s Product Page

Tesco product page

  • Time-limited delivery notice . Produce delivery is inherently time-sensitive, as are several other services that Tesco provides via its website. The retailer harnesses the power of time-limited delivery notices to ensure that consumers use products when they’re freshest and most appealing.
  • A wealth of product information . Product pages contain a wealth of relevant information, including everything consumers could possibly want to know about each item’s nutritional content, country of origin, and even preparation instructions.
  • Customer reviews . Shoppers on the fence about a particular product can read customer reviews to get a better idea of whether they actually want to invest in said item. With a wealth of alternatives available, they can take solace in knowing that other options are always on hand.
  • Nondescript Add to Cart button . Tesco’s approach for adding options to its carts may get the job done, but this could be an excellent opportunity for adding a bit of visual flair without detracting from the website’s minimalist approach.
  • Too much text combined with too small product images . Many shoppers regularly purchase items without actually knowing their names. Rather, they focus on packaging. Tesco’s small pictures make it difficult for these shoppers to identify the elusive products they want. Some may end up with unexpected and unwelcome surprises upon delivery.
  • Too much information . While it’s useful to know the origin of each item, including the exact address may seem like overkill to some users. This detailed information detracts from Tesco’s otherwise streamlined product pages.

Analyzing Tesco’s Checkout Process

Tesco checkout page

  • Numerous delivery slots are available . A variety of helpful slots for receiving grocery deliveries are provided on an hourly basis throughout the day. This dramatically improves customer convenience, particularly for those who work long hours and might not be available for the limited delivery times provided by some of Tesco’s key competitors.
  • Automatic Click+Collect locations . Those who opt to collect deliveries at Tesco stores can look to this feature to automatically display a variety of nearby locations. This makes in-person delivery collection nearly as convenient as Tesco’s impressive delivery setup.
  • Several Delivery plans are available . Shoppers who aren’t in a big hurry can elect to have their orders delivered mid-week for a reduced charge. Meanwhile, demanding customers are asked to pay extra for same-day delivery. Customers love options, particularly when they believe those options prompt significant savings.
  • Oddly unavailable Click+Collect hours . Shoppers who plan their grocery pickup several days out will be surprised to find that some collection times up to a week out are unavailable. Hence, while Click+Collect provides exceptional functionality for last-minute pickups, it’s not always ideal for those who prefer to schedule in advance.

Eager to learn more about Tesco’s strategy and the technologic functionalities that make Tesco’s website so easy to use, we harnessed the power of BuiltWith to scan the website. A few of the notable technologies we spotted include:

  • Omniture SiteCatalyst . Tesco’s web analytics are provided by Adobe’s Omniture SiteCatalyst — an expensive, complex system when compared to its main competition (Google Analytics). If set up correctly, however, Omniture SiteCatalyst provides excellent customer support.
  • Hotjar . One of the world’s most famous screen recording and heatmaps tools, Hotjar offers a range of behavior analytic services ideal for businesses such as Tesco, which aim for a targeted approach based on actual customer behavior.
  • Optimizely . This top experimentation platform plays significantly into modern web innovation. Despite its name, however, Optimizely may increase page load times throughout the Tesco site.
  • OpinionLab . OpinionLab does an admirable job of collecting customer feedback on every aspect of Tesco’s webpage. This allows Tesco to customize better its web offerings based on actual customer opinions
  • SendinBlue . User experience is a huge point of contention for SaaS provider Sendinblue. Clients regularly struggle with forms, automation, and APIs. ContactPigeon may prove a more customer-oriented alternative.

Some of these eCommerce tools are also used by John Lewis, UK’s homeware giant , so we do realize that these technologies play also an important part in a retailer’s business model and online success.

  • As of 2019, Tesco boasted over 6,800 shops worldwide.
  • Tesco currently employs over 450,000 employees around the world.
  • Tesco had a 26.9 percent market share in the UK in 2019.
  • Of the UK shoppers who primarily visit Aldi, 45 percent highlight Tesco as their main secondary store.

Tesco financials

Breaking Tesco News:

  • Tesco changes bonus rules after Ocado success hits pay – Read more here
  • Coronavirus: The weekly shop is back in fashion, says Tesco boss – Read more here
  • Tesco launches half price clothing sale – but some slam the company as ‘irresponsible’ – Read more here
  • Tesco, Sainsbury’s, Asda and Aldi put restrictions on items amid stockpiling –  Read more here
  • Tesco sells its Thai and Malaysian operations to CP Group.   Learn more here
  • In September 2021 Tesco launched a zero-waste shopping service, providing customers with containers. – Learn more here.

When did Tesco begin?

Tesco technically began in 1919 but did not receive its current name until 1924. The company originally consisted of market stalls, with the first shop that might be recognizable to modern consumers not opening until 1931.

What made Tesco successful?

Tesco is popular in the UK and abroad due to its combined emphasis on quality, convenience, and affordability. The Clubcard plays a huge role in the retail chain’s continued popularity, as it keeps customers coming back for deals.  So why is Tesco so successful? It is because of its customer-centric approach, that it gradually helped Tesco to develop a very loyal customer base and equity and a very powerful multinational brand.

Who is Tesco’s owner?

Tesco is currently experiencing a shakeup in leadership. After serving as CEO for several years, Dave Lewis announced his resignation in 2019. He will be replaced by Ken Murphy in 2020. John Allan currently serves as the chain’s non-executive chairman.

What is Tesco industry sector?

Tesco PLC is a retail company. Its core business is grocery retail but they also are in retail banking and assurance industries as well, as part of their product diversification strategy.

How many stores Tesco has?

Tesco has 6993 stores in 12 countries

How profitable is Tesco?

Tesco’s revenue grew by +12% YoY in 2019 hitting  £63.91 billion.

Is Tesco in the public or private sector?

While Tesco was initially a privately-held company, it became a public limited company (PLC) in 1947 and has continued to operate under this approach. However, despite Tesco’s status as a PLC, it remains firmly part of the private sector.

Discover more resources about FMCG retailers

  • Sainsbury’s Marketing Strategy: Becoming the Second-Largest Supermarket Chain in the UK
  • ASDA’s marketing strategy: How the British supermarket chain reached the top
  • The Marks and Spencer eCommerce Case Study: 3 Growth Lessons for Retailers
  • The Ocado marketing strategy: How it reached the UK TOP50 retailers list
  • ALDI’s marketing strategy: The key growth ingredients of the FMCG titan
  • Walmart Marketing Strategy: Decoding the Success of the US Multinational Retailer
  • Analyzing Lidl’s Marketing Strategy: How the Discount Supermarket Leader Scaled
  • FMCG Marketing Strategies to Increase YOY Revenue

The Tesco Case Study: An overnight Success?

As our analysis showed, a variety of factors play into Tesco’s success. The retailer has a long history of using cutting-edge practices (like the virtual store mentioned above) to set itself apart from the competition. Much of its current success, however, relies on its perception as a convenient and affordable chain.

Tesco’s success is not a matter of luck. On its website and in its stores, the retailer emphasizes customer-oriented practices designed to make every shopping experience as seamless and as enjoyable as possible. This simple yet effective approach promises to keep the retailer at the forefront of the grocery industry in years to come.

If you’re looking to emulate the qualities evident in this Tesco case study, don’t hesitate to get in touch. Contact us today to book a free marketing automation consultation.

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Modelling and analysing supply chain disruption: a case of online grocery retailer

  • Open access
  • Published: 25 August 2023
  • Volume 16 , pages 1901–1924, ( 2023 )

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  • D. G. Mogale   ORCID: orcid.org/0000-0002-7977-0360 1 ,
  • Xun Wang 1 ,
  • Emrah Demir 1 &
  • Vasco Sanchez Rodrigues 1  

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Supply Chains (SCs) are becoming more vulnerable to disruption risks because of globalisation, competitiveness, and uncertainties. This study is motivated by an online grocery retailer in the UK that experienced multiple disruption risks, such as demand and supply shocks, facility closures, and disruption propagation simultaneously in 2020. The main purpose of this study is to model and perform quantitative analyses of a range of SC disruption risks affecting the UK online retailer. We have attempted to study how UK retailers responded to the first and second waves of the pandemic and the effect on multiple products. Six scenarios are developed based on SC disruption risks and their impacts on SC performance are analysed. The quantitative analysis of two strategies used by grocery retailers during the pandemic, namely vulnerable priority delivery slots and rationing of products, illustrates that rationing of products had a greater SC impact than the use of priority delivery slots. The effects of two resilience strategies, backup supplier and ramping up distribution centre capacity, are also quantified and discussed. Novel managerial insights and theoretical implications are discussed to make online grocery SC more resilient and robust during future disruptions.

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1 Introduction

Recent global events such as the COVID-19 pandemic, Suez Canal blockage, Russia-Ukraine war and Brexit severely affected the global SCs, resulting in shortages of raw materials, components, and goods, as well as fuel, containers, and workers (Roh et al. 2022 ; Zhou et al. 2023 ). SCs are becoming more susceptible to disruption risks that influence the overall SC operations (Waters 2011 ). Internal, SC and external risk events impact the SC facilities and generate shortages and delays in the SC network. The effects of such disruption propagate to the upstream or downstream members of the SC, resulting in a decline in its operational and financial performance (Dolgui et al. 2020 ; Ivanov 2020 ). Most companies optimise the SC for efficiency, not for resilience (Golan et al. 2020 ; Sharma et al. 2021 ). In the traditional structure, SC resilience mainly protects from a single-point failure, but recent risk events caused multiple disruptions simultaneously, in the form of supply shortages (supply risk), facility closures and demand surges (demand risk) in the network (Ali et al. 2022 ). It is quite difficult to deal with different types of disruptions at the same time and maintain efficiency and reilience in the supply chain. Hence, researchers and practitioners are exploring more robust and resilient SCs that can sustain multiple disruptions simultaneously and quickly recover from these disruptions and return to an original or new desirable state (Queiroz et al. 2022 ; Yu et al. 2021 ). The modelling and quantitative analysis of SC disruption risks is an unexplored area in Supply Chain Risk Management (SCRM) despite established literature (Ghadge et al. 2021 ; Hosseini and Ivanov 2021 ; Ivanov 2021 ).

The retail sector plays a vital role in the UK’s economy and accounts for 5.2% of the UK’s Gross Domestic Product (GDP) in 2020. This sector employed 9.3% of all UK employees in 2019 and provided essential goods and services to customers (The Office for National Statistics (ONS) 2021 ). This sector has gone through significant transformations in the last decade with new digital technologies such Internet of Things, Artificial Intelligence, Robotics etc., rising competition, large use of the Internet and sophisticated customers. In the last 25 years, food stores have constantly been the dominant retail sector in this industry. The online retail sales share in total retail sales had 27.9% in 2020 against 3.4% in 2007 (ONS 2021 ). Millions of people turned to online shopping in recent times, and currently, more than half of UK consumers are shopping online (Statista.com). According to the Global Supply Chain Risk Report ( 2019 ), supplier criticality and global sourcing are the key risks in the retail sector ( http://wef.ch/risks2019 ). Supplier criticality denotes the significant dependency on suppliers and global sourcing illustrates the growing inclination towards sourcing from low-cost high-risk countries. The UK’s economy recently plunged into an SC crisis because of the lowest stocks at major retailers since 1983 (Partington and Partridge 2021 ). This happened because of workers shortage and transportation disruption caused by the pandemic and Brexit. Thus, we need to study how UK retail sector can effectively and efficiently manage SC risks.

The UK retail sector is facing several major challenges, including a lack of warehouse staff, a shortage of Heavy Goods Vehicles (HGV) drivers and food-grade CO 2 , high freight costs, rising fuel prices, and increased Brexit-related red tape (theguardian.com). This research study is motivated by these challenges. SCs in the UK and global SCs experienced widespread disruptions in 2021 and these disruptions hit the news headlines. Ports closures, rising waiting time of goods at ports, the US-China trade war, fire at one of the largest semiconductor makers for the automotive industry, Suez Canal blockage and increased border controls due to Brexit are some of the reasons behind the global SC crisis. The pandemic occurred during these times which made the situation worst. SC problems caused the late deliveries, higher prices, empty petrol stations and supermarket shelves. Many UK retailers struggled to get goods and materials from the EU and within the UK in recent times. Several major retailers across the UK reported these problems and emphasised the need for an SCRM approach (Garnett et al. 2020 ). The first-tier suppliers also faced some challenges while supplying products to these retailers. One of the most important elements during any crisis is food. During crises, governments try to ensure sufficient availability of food/grocery items and control food prices. Similar to other sectors in the UK, the grocery retail sector has been affected by the recent crises. The effect was generally positive (beneficial) because numerous customers over-purchased and overstocked the grocery items during crises which helped to leap the annual sales and core profitability. Major retailers’ earnings were more than their estimates. However, they need to tackle some of the major challenges mentioned above to prepare the SCs for future crises and ensure everyone gets enough food at reasonable prices. To deal with the above-discussed SC challenges and make SC more robust and resilient, this research aims to model and quantify the impact of SC disruptions on the online grocery retailer and to evaluate their response policies as well as resilience strategies that can make the grocery supply chain more responsive in future crises. The following four research questions are addressed in this study.

RQ1. What is the effect of disruption on online grocery SC?

RQ2. What is the impact of response strategies followed by online grocery retailers on their SC performance?

RQ3. Which resilience strategy is more suitable for online grocery retailers?

RQ4. How can online grocery SC resilience be improved?

There are various ways to address these above RQs such as a qualitative approach by doing interviews with experts from the industry and then trying to discuss the answers of these RQs in the subjective form. Many scholars discussed the generic answers to these RQs without considering the real-time data from the industry during the early stages of the pandemic (Carissimi et al. 2023 ). The second approach is the quantitative modelling and analysis of the pandemic as a unique disruption risk. There are limited studies available on quantitative modelling of disruption risks with real-time data hence we decided to address the RQs through a quantitative approach.

This research study makes the following four important contributions to the body of literature. First, we consider the pandemic as an SC disruption risk and model it through the mathematical modelling approach. Second, we analyse the effect of the pandemic on the grocery retail sector by considering a real-world case study from the UK. Scenarios of positive and negative SC performance dynamics are examined. The third vital contribution is related to response strategies followed by grocery retailers to fight this pandemic. Two practical policies (priority delivery slots and limiting the maximum number of products sold to each customer) are investigated in this research. Finally, a quantitative analysis of two resilience strategies is discussed to manage the disruption and make SC more resilient.

The article is structured as follows. Section  2 reviews the relevant literature on supply chain risk management and simulation-based supply chain risk modelling, with a focus on pandemics. In Section  3 , the underline problem along with details on data collection, and mathematical and simulation models are presented. The experimental setup and results are discussed in Sections  4 and 5 , respectively. Section  6 is devoted to the discussion and implications of the study, while Section  7 concludes the article and suggests avenues for future research.

2 Literature review

SCRM is a broad topic hence only relevant and recent literature on it is discussed here. The first subsection looked at the studies related to uncertainty and risk in supply chains, vulnerability, and resilience. The simulation-based supply chain risk modelling approaches are discussed in the second subsection.

2.1 Supply chain risk management (SCRM)

The SCRM literature has developed significantly in the last two decades and become a growing research area (Pournader et al. 2020 ; Fahimnia et al. 2015 ). Risk occurs due to the uncertain future and this uncertainty may bring unexpected and risky events (Waters 2011 ). When these unexpected risky events occur, they may cause damage. Supply chain vulnerability is the exposure to disturbances due to risks that affect the ability of the supply chain to meet the end customers’ demand (Jüttner 2005 ). Supply chain resilience is a supply chain’s ability to return to its original or move to a new, more desirable state after being disturbed (Christopher and Peck 2004 ; Glickman and White 2006 ). The impact of the pandemic on the dairy SC was assessed by Karwasra et al. ( 2021 ). Recently, Shi et al. ( 2021 ) and Montoya-Torres et al. ( 2023 ) discussed the present and future research topics in logistics and SCM area considering Covid-19. The SCRM is the identification, assessment, mitigation and monitoring of supply chain risks. Interested readers can refer to the review articles by Baryannis et al. ( 2019 ), Colicchia and Strozzi ( 2012 ), Fan and Stevenson ( 2018 ), Heckmann et al. ( 2015 ), Ho et al. ( 2015 ), Pournader et al. ( 2020 ), Fahimnia et al. ( 2015 ), Ribeiro and Barbosa-Povoa ( 2018 ), Rajagopal et al. ( 2017 ), Li et al. ( 2022 ), Rinaldi et al. ( 2022 ), Suryawanshi and Dutta ( 2022 ) and Ghadge et al. ( 2012 ) for other related SCRM studies.

2.2 Simulation-based supply chain risk modelling

Supply chain risk modelling is getting more attention because companies are looking for quantitative risk measures which will be helpful to assess and prioritize the risks and prepare mitigation plans (Aqlan and Lam 2016 ; Ghadge et al. 2021 ). Mathematical and optimisation modelling is the dominating research methodology in SCRM. Simulation modelling is an under-explored area and needs attention to deal with uncertainty and limited historical risk data (Ivanov 2017 ; Hosseini and Ivanov 2021 ). These are mainly used when stochastic parameters exist in a SC network. Only a few studies adopted modelling and simulation techniques to study the complexities of SCRM (Maliki et al. 2022 ; Tordecilla et al. 2021 ). Scholars used different simulation techniques, such as Discrete-event simulation (DES), Agent-Based Simulation (ABS) or Multi-agent-based Simulation, System Dynamics (SD), Monte Carlo Simulation and Petri Nets based on the nature of the problems. A summary of the key existing studies focusing on simulation-based risk modelling is given in Table 1 .

We can see from Table 1 , the DES technique was extensively used by various scholars for supply chain risk analysis (Schmitt and Singh 2012 , Schmitt et al. 2017 , Ivanov 2019 and Tan et al. 2020 ). Most of the authors addressed the manufacturing SC problems and focused on disruptions risk in their articles. Limited studies considered multiple products. Redundant suppliers and inventory buffers were the most used SC resilience strategies. Only Ivanov ( 2017 ) considered real-life data while addressing the four-stage supply chain problem. The mathematical models were also proposed by a few authors. The simulation-optimisation methods are being used by the academic community for the analysis of supply chain risks (Ojha et al. 2018 ).

2.3 Research gaps

We can easily observe from Table 1 that most of the scholars analysed the 2 and 3 stages of the SC network and considered various types of disruptions. The impact of disruptions is mainly analysed on the manufacturing, automotive and electronics industries using DES (Aqlan and Lam 2016 ; Ivanov 2018 ). Among the considered papers, a lack of scholars focused on grocery supply chains and tried to analyse the influence of SC disruptions. The authors who used the simulation method did not use realistic data (Dolgui et al. 2020 ; Ivanov 2019 ). Mathematical models were seen in limited studies (Burgos and Ivanov 2021 ; Ghadge et al. 2021 ). In the current work, we have analysed the impact of disruptions on an online grocery retailer with the help of realistic data. To the best of our knowledge, the quantitative analysis of the impact of response strategies followed by online retailers on SC performance is not discussed in the previous studies (Singh et al. 2021 ). Regarding the impact of resilience strategies on SC, almost all previous authors theoretically discussed existing resilience strategies but their impacts on SC performance have not been quantified (Sodhi et al. 2021 ; Sharma et al. 2020 ).

3 Problem overview, data and model development

3.1 problem overview.

The grocery retailer considered in this study manages its SC operations with a limited number of large and centralised warehouses located across the UK. Its network includes suppliers, Customer Fulfilment Centres (CFCs), spokes and customers. Numerous suppliers located across the UK and Europe supply the products to various CFCs which store products and assort the customer orders. The CFCs supply the different products to several spokes as per the customer’s orders received online. Spoke plays the role of cross-dock in this supply chain and does not store the products. At spoke, the outbound vehicles coming from CFCs are unloaded and then products are re-loaded onto small delivery vans which fulfil customers’ orders. The customers who are nearby to CFCs are directly served by CFCs. Different types of capacitated vehicles including single and double-deck trailers, delivery vans and trucks are used to transfer products from one point to another point. The overview of this supply chain network is shown in Fig.  1 below.

figure 1

Multi-stage supply chain network of e-grocery distribution company

Currently, the company has limited storage and logistics capacity which became the major hurdle to satisfy the growing demand of customers. In addition to the regular customers, millions of new customers moved to this retailer during the first wave of the pandemic in 2020. The retailer struggled to accommodate all new customers because of supply shortages, the spike in demand from regular customers, limited storage, and logistics capacity. The surge in demand and limited logistics capacity resulted in the delivery slots shortage problem and many customers did not get slots for their delivery. To help the elderly and vulnerable people, this retailer introduced priority delivery slots which helped to tackle the societal challenge also. Some other policies such as limiting the maximum number of products sold to each customer had helped to provide access to products to all sections of society. Overall, the company’s profit significantly increased during the first and second waves of the pandemic. The company was interested to study the effect of the pandemic on their performance, the impact of response and resilience strategies followed by them and some ways to make their SC more robust and resilient to deal with future pandemics.

3.2 Data and model specification

This online retailer sells numerous grocery products to millions of customers. We have considered five products (i.e., Eggs, Pasta, Plain Bagels, Self-rising flour and Chicken Breast Fillets) whose demand is significantly affected in the first and second waves of the pandemic. The SC network comprising one supplier, one CFC, two spokes and ten customers is considered here for simplicity purposes. The data for aggregate weekly demand and prices of products, capacity of warehouses, lead time, order received, and order picked is obtained from the online retailer for the whole year 2020 (Jan to Dec 2020). We have considered the following assumptions. The transportation cost from CFC to spoke and spoke to customers is product and distance based, i.e., one unit per ton per unit distance. The fixed delivery cost between suppliers and CFC is 500 units per trip. The inventory carrying cost at CFCs is 0.001 unit per kg per day. Suppliers supply these products in trailers (capacity 28 tons) to CFC. The CFC keeps the stock of these products and transfers them to spokes. The CFC also fulfils the orders of nearby customers. Trucks with a capacity of 20 tons are used to transfer products to spokes and delivery vans having a capacity of 3.5 tons to meet the demand of nearby customers from CFC and spokes. In order to consider a large geographical area, we use the demand of one single customer to represent the total demand of 100 real customers. The summary of the real data related to different products is given in Table 7 (Appendix). CFC follows the Min–Max inventory policy whereas spokes follow the cross-dock policy. These policies closely resemble the actual ordering policies used in the company. The summary of inventory control parameters for CFC and spokes is given in Tables  8  and 9  (Appendix). The values in this table are produced following the customers' demand and other real data.

3.3 Mathematical formulation

The mathematical model is developed to study the company’s existing network. The objective function of the model is to maximise the profit that is obtained by subtracting the total cost from the revenue.

Assumptions:

Each customer point represents the aggregate demand of many customers.

Demand and capacity of CFCs are deterministic and well-known in advance.

Each spoke works as a cross-dock.

Multiple products and time periods are considered.

Transportation costs are considered with travelled distances.

Set/Indices

Set of suppliers indexed by \(i \in I\)

Set of customer fulfilment centres (CFCs) indexed by \(j \in J\)

Set of spokes indexed by \(k \in K\)

Set of customers who are supplied from CFCs indexed by \(l \in L\)

Set of customers who are supplied from spokes indexed by \(m \in M\)

Set of products indexed by \(p \in P\)

Set of vehicle types indexed by \(v_{1} ,v_{2} ,v_{3}\)

Set of time periods \(t \in T\)

Distance between supplier i and CFC j in km

Distance between CFC j and spoke k in km

Distance between CFC j and customer l in km

Distance between spoke k and customer m in km

Fixed delivery cost from supplier i to CFC j in $

Unit transportation cost per unit distance from CFC j to Spoke k in $

Unit transportation cost per unit distance from CFC j to customer l in $

Unit transportation cost per unit distance from spoke k to customer M in $

Unit inventory carrying cost per day in $

Unit purchasing cost of product p in $

Unit selling price of product p in $

Weekly demand of product p from customers l in Kgs

Weekly demand of product p from customers m in Kgs

Storage capacity of CFC j in Kgs

Capacity of vehicle type v in Kgs

Decision variables

Shipment quantity of product type p between supplier i and CFC j in Kgs

Shipment quantity of product type p between CFC j and spoke k in Kgs

Shipment quantity of product type p between CFC j and customer l in Kgs

Shipment quantity of product type p between spoke k and customer m in Kgs

Number of vehicle type \(v_{1}\) used between supplier i and CFC j

Number of vehicle type \(v_{2}\) used between CFC j and spoke k

Number of vehicle type \(v_{3}\) used between CFC j and customer l

Number of vehicle type \(v_{3}\) used between spoke k and customer m

Inventory of product type p available at CFC j in Kgs

Purchased quantity of product type p from supplier i

Objective function

The objective function ( 1 ) maximises profit by subtracting the sum of total costs from revenue.

Revenue = Selling price of the product *demand of the product

The revenue is calculated using Eq. ( 1.1 ).

Total cost = Total purchasing cost + Total transportation cost + Total inventory carrying cost

Total purchasing cost = Purchasing cost of product type p * purchased quantity of product type p

Total transportation cost = Transportation cost between supplier and CFCs + Transportation cost between CFCs and spokes + Transportation cost between CFCs and customers + Transportation cost between spokes and customers

Total inventory carrying cost = Inventory carrying cost at CFCs

The second term of objective function consists of three types of cost i.e., purchasing, transportation and inventory carrying cost (Eq.  1.2 ). The first and last term in Eq. ( 1.2 ) illustrates the purchasing and inventory carrying cost, respectively. The remaining terms (second to fifth) shows transportation cost in between the suppliers, CFCs, spokes and customers.

Maximise Profit = Revenue – Total cost

The mathematical form of objective function is represented using Eq. ( 1.3 ).

The constraint ( 2 ) and ( 3 ) are demand satisfaction constraints.

Constraint ( 4 ) makes sure that the inventory available at each CFC should be less that its capacity.

The vehicle capacity constraint between supplier and CFC is shown by Eq. ( 5 ).

Similarly, the vehicle capacity constraint between CFCs and spoke, CFCs and customers, and spokes and customers are defined by constraint set ( 6 )–( 8 ).

Finally, constraint ( 9 ) and ( 10 ) depicts the non-negativity and integer constraints, respectively.

3.4 Digital twin and simulation model

Simulation and digital twins are important components of Industry 4.0 (Machado et al. 2020 ). Digital SC twins are computerised models for physical networks at any given time (Ivanov and Dolgui 2021 ). The digital twins involve more flows, networks and operational parameters hence they are more complicated than simulation models. The real-time data is updated in digital twins through external systems and databases. The digital twins help to improve the SC visibility and resilience with this real-time transparency. We can analyse the impact of disruption, develop alternative SC networks and conduct KPI analysis using real-time data. A multi-stage supply chain network of the online grocery retailer in the UK is modelled using anyLogistix SC simulation and optimisation software. The application of this software for SC resilience analysis has been growing recently because of its integrated features of optimisation and simulation (Ivanov  2020 ; Singh et al. 2021 ; Burgos and Ivanov 2021 ). Figure  2 depicts the simulation model developed in Personal Learning Edition of anyLogistix software and this model was run on the 8 GB RAM with Intel Core i7 1.8 GHz processor and 64-bit Operating System of Windows 8. The customer places an online order on the retailer’s platform and then receives products either from nearby CFCs or spokes. Suppliers dispatch products to CFCs as per their orders received from them.

figure 2

Supply chain network in “anyLogistix” software

4 Experimental set-up

Initially, the mathematical model is solved without considering any disruptions and obtained the optimal results of the model. Next, the disruption is modelled in anyLogistix using various risk events like demand variations, supplier closures, CFCs and spokes closures. The impact of the disruption is analysed based on the following Key Performance Indicators (KPIs). The disruption mainly affects the financial and operational performance of the organisation hence we considered the financial indicators, product-related indicators, expected lead time and fulfilment-related indicators.

4.1 COVID-19 pandemic scenarios

The first national lockdown was imposed on 23rd March 2020 due to the continuous spread of the virus and the UK government ordered people to stay at home and leave home only for limited reasons. The lockdown was further extended for ‘at least’ three weeks on 16th April 2020 and eased on 10th May 2020 when the UK passed the peak of the first wave of the pandemic. The second wave of COVID-19 hit the country in early September which led to the imposition of new restrictions on 22nd September 2020. The second national lockdown was imposed on 31st October 2020 following the soaring cases and death toll across the country. The new tougher restrictions were enforced on 19th December after finding a new variant of the virus and continued till the end of the year. This overall timeline of the pandemic in 2020 in the UK is shown in Fig.  3 . The simulation scenarios are developed based on this timeline and are outlined in Table 2 .

figure 3

(Source: Institute for Government analysis)

The COVID-19 timeline in the UK

4.1.1 Scenario 0: baseline scenario

This scenario analyses the performance of the online grocery retailer without disruptions and will be used to compare the performance with disruptive events.

4.1.2 Scenario 1: demand variations

The whole year 2020 is categorised into five distinct periods mentioned in Table 3 following the timeline described in Fig.  4 . The baseline is considered a business-as-usual scenario and base demand (100%) corresponds to this scenario. Among the considered five products, the demand for Self-rising flour (SRF) and pasta increased by almost 407% and 168% respectively during the first national lockdown. Eggs, Plain Bagels (PB) and Chicken Breast Fillets (CBF) also experienced growth in demand, but their magnitude was lower compared with SRF and pasta. The next period is the time after the lifting of the first lockdown (10th May) till the start of the second lockdown (30th Oct). The demand for products varied (dropped compared to the first lockdown) because of no strict lockdown and social distancing measures. The fourth period corresponds to the second national lockdown in the UK (31st October to 2nd December 2020). The variations in demand for different products are given in the below table. The last and fifth period from 3rd to 31st Dec 2020 was the period after the second national lockdown. The online retailer received many orders from new customers who joined this retailer during the first lockdown. To replicate this case of additional customers, three new customers are included in addition to the existing 10 customers. Among these three new customers, one will be supplied by CFC, the second will be by spoke 1 and the last by spoke 2. It means that this scenario consists of additional demands from existing customers and orders from new customers.

figure 4

Simulation results for baseline (disruption free) scenario

The details about supplier (scenario 2), CFC (scenario 3) and spokes (scenario 4) disruptions are given in Table 2 .

4.1.3 Scenario 5: ripple effect

The “ripple effect” is a phenomenon of disruption propagation and its impact on SC performance (Ivanov 2017 ). If the supplier stops supplying the products to the CFC, then after some time CFC and spokes would struggle to meet the customers' demand because of lack of inventory. CFC and Spokes remain closed for some days until they receive enough stock. This overall scenario of ripple effect is replicated in simulation software by creating some delays after the occurrence of disruption.

4.1.4 Scenario 6: COVID-19 scenario

This scenario simulates the effect of disruption on the online grocery retailer by combining scenarios 1, 2, 3, and 4 together. The overall performance of the online grocery retailer considering two pandemic lockdowns in 2020 is analysed in this scenario.

5 Results and analysis

The results of all six scenarios are discussed in this section by focusing on RQ1 what the impact of disruption on online grocery retailer is. Furthermore, cross-comparison analysis of scenarios provides additional insights into the effect of disruption on the retailer.

5.1 Scenario 0: baseline scenario

The ELT service level indicates the ratio of on-time orders to the total ongoing deliveries and late orders negatively influence this service level. Service level 1 (Fig.  4 ) portrays all customers receiving their orders within the ELT and without any delays. The lead time of each product ordered is shown by the histogram in Fig.  4 . The X-axis depicts the lead time in days and the Y-axis indicates the occurrence of orders with a specific lead time. The lead time for most orders is between 0 and 0.34 days. The maximum lead time (1.247 days) (within the ELT) is obtained for a few orders. Regarding shipped vehicles, the delivery vans made maximum trips to transfer products to customers from CFC and spokes. The capacity of trailers and trucks are higher compared to van hence their number of trips are smaller. The simulation results show the on-hand inventory levels at the CFC for each product category. The Min–Max inventory policy (s, S) is used in this study that allows ordering products up to maximum level (S) when the inventory level reaches minimum point (s). No abrupt trends or patterns in inventory levels are observed during the whole year.

5.2 Scenario 1: demand variations

The simulation outcome of demand variations in terms of KPIs is presented in Fig.  5 . The impact of the pandemic on all products is not the same. The simulation outcome reveals that the demand variations positively affect the revenues because of the drastic increase in demand for maximum products due to the customers' panic buying and stockpiling behaviour. The new customers' entry is also one of the major reasons behind the sale of a higher number of products during the first lockdown. The inventory levels were reduced when demand increased which led to an increase of lead time and late orders. The ELT service level fell below 1 because of late orders during the first lockdown and never came back to normal till the end of the year. Most of the orders are delivered between 0 and 0.37 days, but a few orders reached 2.26 days. The number of vehicle trips is higher compared to the baseline scenario because of increased orders. After the first lockdown, the demand for all products except CBF was still higher than the base demand but lower than the first lockdown. Hence, the on-inventory level for CBF was slightly increased whereas other product levels were decreased. Overall, we observed that the inventory level reduced when demand increased and vice versa.

figure 5

Simulation results: Demand variations scenario

5.3 Scenario 2: supplier disruption

Figure  6 depicts the simulation results after the supplier shut down for 20 days in the first lockdown and 15 days in the second lockdown. According to these results, the profit of SC was slightly reduced because of the rise in transportation costs during the lockdowns. The on-hand inventory levels of all products were reduced when the supplier stopped supplying to CFC. Thus, the inventory carrying cost is not substantially reduced. The late orders started escalating when the supplier disrupted and continued till the end of the year. These late orders negatively affected the service level which falls below one when suppliers disrupted for the first time in March 2020. Regarding lead time, maximum orders are delivered within 2.8 days however few customers received their orders in 21.247 days because of a lack of stock at CFC. The number of trailers trips between the supplier and CFC was reduced due to the supplier’s disruptions in the first and second lockdowns. More trips of trucks and vans compared to the baseline scenario happened to transfer products due to less than truckload and lower inventory levels.

figure 6

Simulation results: Supplier disruptions scenario

5.4 Scenario 3: CFC disruption

The profit is significantly reduced when CFC is closed for a few days during two lockdowns. The CFC was unable to fulfil customers' demand in closure time hence revenue generated by selling the products is decreased. The late orders started increasing around 100 days when CFC closed on the 10th of April 2020 due to the spread of COVID-19 in it (Fig.  7 ). These late orders continue to rise a few days after the opening of CFC because of the huge backlog generated during closure time. The number of late orders remain constant till the second lockdown but again started rising when CFC closed on the 10th of Nov for 15 days in the second lockdown. The second lockdown affected the progress of the service level when it was improving after the impact of the first lockdown. Although maximum orders need an average of 4.6 days to reach customers, few orders are delivered in 25.256 days. The number of trips of the trailer from supplier to CFC is reduced compared with the baseline because of 35 days of closure during the entire year. The on-hand inventory at CFC remained constant during lockdowns because CFC was not able to transfer products to spokes and meet the demands of nearby customers.

figure 7

Simulation results: CFC disruption scenario

5.5 Scenario 4: spokes disruption

Figure  8 presents the results of the simulation model when spokes were closed for several days. The profit of the online grocery retailer decreased significantly due to a drop in the number of products sold when the spokes became inactive. The on-hand inventory at the CFC had little impact on the closure because the CFC uses a min–max policy for inventory management. During the first lockdown, many customers did not receive their products on time after the spoke closure, leading to a significant increase in late orders. A similar pattern of late orders was observed during the second lockdown. The service level dropped below one due to these late orders and never returned to normal afterwards.

figure 8

Simulation results: Spokes disruption scenario

5.6 Scenario 5: ripple effect

Scenario 5 demonstrates the impact of disruption propagation on the performance of the online grocery food retailer using Fig.  9 . According to the results, the ripple effect significantly impacted the profit performance as the number of products sold decreased when the impact of supplier disruption propagates to CFC and spokes. The inventory levels for all products considerably reduced during lockdowns which led to an increase in late orders. The magnitude of the rise of late orders and drop-in service level in this scenario is higher because of the simultaneous closure of facilities for a limited period compared with scenarios 2, 3 and 4.

figure 9

Simulation results: Ripple effect scenario

5.7 Scenario 6: simultaneous disruptions

Figure  10 shows the impact of demand variations and facility disruptions on the SC performance of the online grocery retailer. Despite the closure of facilities during two lockdowns, the retailer's profit significantly improved due to higher demand from existing and new customers. The inventory levels for all products remained consistent until the reopening of all facilities, after which the inventory level for CBF increased drastically due to a collapse in customer demand. The graph of late orders clearly shows an increasing trend after the facilities were disrupted, remaining constant from the first lockdown to the second lockdown. The ELT service level decreased significantly to 86.7% by the end of the year due to a growing number of late orders and the simultaneous occurrence of different disruptive events. The CFC was able to deliver most orders within 5.8 days, with some exceptions taking up to 32.25 days to reach customers. This scenario, which includes demand variations and facility closures, resulted in lower profit than the demand variation scenario.

figure 10

Simulation results: COVID-19 scenario

5.8 Cross-comparison analysis

The cross-comparison analysis of all scenarios was conducted here to generate more insights and see the overall impacts. Table 4 depicts the results in terms of various KPIs for this cross-comparison analysis and helps to address some of the major issues encountered by the retailer. This table clearly shows the revenues, total costs and profits for each scenario as a financial performance. The profit is calculated by subtracting the total costs from revenues. The baseline scenario shows good profitability. The total cost ($6.8Mn) is almost 77% of the revenue ($8.8Mn). The purchasing cost contributes significantly to the total cost because the retailer does not produce anything but purchases all products. The inventory carrying cost has a very limited contribution to total costs because maximum grocery products are perishable in nature that need to be replenished regularly.

Scenario 1, which involved a demand spike from existing customers and the entry of new customers, had the most positive impact on the supply chain performance. The revenue increased by $2.92 million compared to the baseline scenario, thanks to the growth in sales. However, the transportation costs increased by almost 65% compared to the baseline, as many additional trips were required to meet the increased demand from customers.

Similarly, the online retailer purchased more products which led to a rise in purchasing costs by 32% compared with the baseline scenario. Mean lead time and late orders are also increased due to demand variations and the entry of new customers. The 11 dropped orders depict the number of orders that CFC could not fulfil due to insufficient stock.

Scenario 2 illustrates that supply shortages for certain periods do not have a strong effect on financial performance. CFC can manage the customers’ demand with additional lead time despite the supplier’s two times closure. Hence, the mean lead time is increased by nearly 127% compared with the baseline. The slight reduction (less than 1%) in profit is obtained because of the Min–Max policy that helped to manage small supply variations and avoid inventory shortages. ELT service level reached 95.1% because CFC was not able to meet demands on time when supply stopped.

The effect of the pandemic on online grocery SC performance is worst in scenario 3 when CFC stopped working for certain periods in two lockdowns. The worst effect shows that the CFC is a vital member of the company’s supply chain. The revenue generated shrunk by approximately 2% as compared to the baseline scenario due to lower sales. The total number of trips made by all vehicles is reduced a little because no products moved from CFC to spokes and customers. The spokes cannot keep stock of inventory and work as a cross-dock. Hence, they cannot meet the demands of customers on their own when CFC became inactive. Due to this inability, the late orders are higher than in the previous two scenarios. These late orders influenced the service level which experienced a significant drop compared with the previous two scenarios. The 50 dropped orders show the number of orders that customers could not place because CFC was closed for certain periods. Like the last scenario, scenario 4 shows the negative impact on SC performance when the spokes stopped working two times. The number of products sold to customers reduced hence revenues (decreased by almost 3.79%) and profit (reduced by 4%) compared to the baseline scenario. Product movement between CFC and spokes stopped completely during spokes closures hence transportation costs were reduced. Spoke directly delivers orders to customers hence dropped orders increased compared with the CFC disruption scenario.

The revenue generated and total costs of scenario 5 are lower than baseline as well as the previous three scenarios because two facilities remained closed simultaneously for a certain period due to the ripple effect. The simultaneous closures of facilities increased the mean lead time and dropped orders against all previous scenarios. The additional lead time is the primary reason behind the late orders that escalated considerably as compared with previous scenarios. The negative effect of late orders appeared on the service level that reached to lowest (86.2%).

The SC of the online grocery retailer experienced a very positive effect of COVID-19 in scenario 6. Due to the demand growth of major products and the entry of new customers, revenue generated by selling products drastically increased by approximately 25% compared with the baseline scenario but lower than scenario 1. Scenario 1 does not have the combination of all scenarios hence their revenue and profit are higher than the COVID-19 scenario. The profit is nearly 17% higher than the baseline scenario and higher than all scenarios except scenario 1. Many orders, new customers, and limited storage and logistics capacity contributed to the rise in mean lead time and dropped orders. All vehicles made a total number of 1448 trips lower than scenario 1 to dispatch products to customers. The late and dropped orders are highest in this scenario because of the simultaneous consideration of disruptive events. The service level is somewhat better than scenario 5 and worse than all other scenarios.

Among all scenarios, scenario 1 (demand variations) has a very significant positive impact on the SC performance of the online grocery retailer followed by scenario 6. In case of negative effect, scenario 3 (CFC disruption) made the worst effect on profit followed by scenario 5 (ripple effect). The mean lead time of scenario 5 is the highest hence its ELT service level is the worst among all scenarios. All vehicles made the highest trips in scenario 1 and late and dropped orders reached the maximum level in scenario 6.

6 Mitigation strategies used by grocery retail sector

Many food retailers and giant supermarkets across the UK implemented various strategies to deal with the panic buying and stockpiling behaviour of customers throughout the pandemic (Sky News 2020 ). People with pre-existing conditions are most vulnerable to catching the virus if they visit supermarkets for grocery shopping. Buying more than needed means that these vulnerable may face shortages of items like pasta, toilet rolls, and self-rising flour. Product prices may increase strongly hitting the vulnerable and poor people of the society. All major retailers worked with the UK government to develop various measures and strategies to deal with panic buying and avoid artificial shortages. Online retailers and supermarkets introduced many measures such as rationing the number of each item customers can buy, prioritizing home delivery slots for vulnerable people, and prioritising passes for in-store shopping and e-gift cards. In this study, we have analysed the effect of some of these measures followed by the online retailer.

6.1 Vulnerable priority delivery slots

The online grocery retailer had kept some priority delivery slots for vulnerable people. This subsection presents the results of the simulation model after embedding priority delivery slots in scenario 6. The online retailer normally supplies orders to customers from 5.30 am to 11.30 pm. Customers select the slots and then get orders from the retailer during that slot. The online retailer kept three hours (5.30 am to 8.30 am) every day in two lockdowns as priority delivery slots during which it delivers orders to vulnerable customers only. Normal customers can get orders after 8.30 am. This scenario has been replicated in the simulation software while defining the start and end times of shipping orders. As per the results, the online retailer experienced a negligible impact on financial performance compared with the Covid-19 scenario because of the similar number of customer orders received and CFC was able to deliver those orders with some additional lead time. The mean lead is slightly increased (by more than 1%) because of priority delivery slots. The rest of the KPIs like service level, shipped vehicles, and dropped and late orders are identical to scenario 6. These results (Table 5 ) demonstrate that the online retailer can deliver priority orders to vulnerable people without affecting its financial performance with slightly more lead time.

6.2 Limit on maximum products sold to customers

This scenario put the limit on the maximum number of products sold to each customer per order. The impact of the first lockdown was more severe than the second lockdown. The first lockdown was completely new, and people were worried about catching the virus and shop closures. Thus, panic buying, and stockpiling started by them. Following this new pattern of shopping and supplier shortages, supermarkets and online retailers put restrictions on maximum products, especially dried pasta, self-rising flour, toilet rolls, hand sanitiser and many others sold to each customer. To mimic this situation, we have considered that each customer can buy up to 30% more products compared with their normal demands (base demand) in the first lockdown and up to 50% more products in the second lockdown. The second lockdown was not very hard hence customers have flexibility of buying products up to 50% compared with base demands. The results (Table 5 ) show a slight reduction (-0.66%) in revenue generated and profit (-0.91%) compared with the scenario of Covid-19 because of restrictions put on customers' orders for each product. Suppliers struggled to supply all requested products on time to CFC. The lower number of product movements from supplier to CFC, CFC to spokes and customers decreases the transportation cost as well. The mean lead time is 1.537 days which is slightly lower than scenario 6 hence the late orders are also a little lower compared with scenario 6. The ELT service level is somewhat better than it. The cap on the maximum number of products bought does not have any effect on dropped orders but late orders decreased by almost 3% compared to the vulnerability slot scenario. The shipped vehicles also increased by approximately 3% compared with scenario 6 because vehicles travelled with less than truckload capacity.

By comparing the vulnerable delivery slots and rationing goods scenario, we find that minor negative effect of the latter on the SC performance of the retailer than the former. The rationing avoided the panic buying and stockpiling behaviour of customers that’s why profit is slightly reduced compared with the Covid-19 scenario. The mean lead time of the rationing scenario is lower than the vulnerable delivery slot which led to a reduction of late orders.

7 Supply chain resilience strategies

The supply, demand, production, transportation and logistics stages collapsed due to the lockdowns and border closures. Many resilience strategies could have been used in different stages like preparedness for risky events, response and recovery to deal with these problems. The supply disruptions, supply shocks and shortages could be managed effectively using multiple and varied suppliers along with keeping backup suppliers at different locations (Chowdhury et al. 2021 ; Moosavi and Hosseini 2021 ). Building temporary capacity, increasing production in early stages, balancing between local and domestic production, and distributing manufacturing systems are some of the strategies that may solve the production-related problems that occurred during the pandemic. In this study, we use the backup supplier and ramp-up CFC capacity strategies to curb the impact of the pandemic on the retailer because of their popularity and relevance in the retail sector.

7.1 Backup supplier

The online grocery retailer considered in this study buys different products from multiple suppliers located in the UK and Europe. Scenario 2 shows the impact of supplier disruptions on the SC performance of the retailer. It is observed from Table 4 that the profit slightly decreased after supplier disruptions. We want to depict how the multi-supplier or backup supplier strategy helps to maintain similar profit performance during disruption as well. The backup supplier provides supply to distribution centres when the main supplier stopped working due to some unexpected risky events. Scenario 2 represents that the main supplier stops working for 20 days in the first lockdown and 15 days in the second lockdown. One backup supplier is added into scenario 2 that supplies products to CFC when the main supplier stops supplying products to CFC. Figure  11 a depicts that the main supplier is active and supplying products to CFC. When the main supplier stopped working the backup supplier started supplying products to CFC which helped it to reduce the late orders completely and improve the profit to the level of the baseline scenario. This backup supplier supplied almost 10% of the total products supplied to CFC throughout the year.

figure 11

a Main supplier supplying products to CFC and b Back up supplier supplying products to CFC

7.2 Ramping up CFC capacity

The retailer has very limited large warehouses located across the UK to keep products and supply mainly to several spokes and nearby customers. The limited storage capacity of CFC is one of its major constraints to accept new customers during the first lockdown and satisfy the growing demand of existing customers. Furthermore, this limited capacity of CFC was the primary reason behind the spike in late orders during two lockdowns. Hence, the impact of the storage capacity of CFC on the SC performance especially on late orders and dropped orders indicators is analysed in this section. The storage capacity of CFC was 45,000 kg in all scenarios considered in Table 4 . Scenario 6 (COVID-19) is considered for comparison of the results of this strategy. The storage capacity is increased by 25%, 50%0.75% and 100% of its current capacity to see the effect on SC performance. The rest of the parameters and events of scenario 6 remained the same. The results of this resilience strategy are given in below Table 6 . There is no effect on revenue generated because the number of products sold to customers remained the same after the increments of CFC capacity.

Similarly, the purchasing cost also has not changed because the number of products purchased from suppliers is similar. As the capacity of CFC increases the inventory cost also increases by minimum value (less than 1%) because CFC can store additional products to meet customers’ orders on time and avoid any lost orders. In scenario 6, sometimes vehicles between suppliers and CFCs move with less than truckload capacity because of limited storage capacity at CFC. Also, the limited inventory available at CFC made less than truckload trips between spokes and customers. The sufficient CFC capacity allows the vehicles to move with full truckload capacity. Hence, the number of shipped vehicles is reduced when CFC capacity increases which led to the reduction of transportation costs by almost 10% compared with scenario 6. The reduction in total expenses helped to enhance the profit marginally after a gradual increment of CFC capacity. The mean lead time started decreasing when CFC capacity increased and reached 1.237 (days) when capacity increased by 100%. The sufficient availability of products at CFC prevented the LTL deliveries which resulted in the fall in mean lead time and late orders as well. Reducing late orders improved the ELT service level, reaching 89.3% when capacity increased by 75% and 100%. It is observed from Table 6 that many of the performance indicators are not significantly changed after the increment of CFC capacity by 100% as compared with the 75% increment. Therefore, the 75% rise in CFC capacity is optimal to reduce the number of late orders and improve the mean lead time and ELT service level.

8 Discussion and implications

This section focuses on how the SC resilience of retailers can be improved and what measures companies should consider for future disruptions. The decision-makers, online grocery retailers and other stakeholders involved in the grocery SC can find several managerial insights from the current study. The developed simulation model helps to analyse the impact of future disruptions on SC performance. The food retailers can visualise the effect of facilities disruptions like supplier, facility, distribution centres and cross docks on the supply chain and take necessary measures to manage this effect. The impact of the demand variation scenario can give evidence about how to deal with the sudden demand spike and collapse. The decision-makers can explore the ripple effect scenario to understand how the disruption propagates downstream from upstream and affects the entire supply chain. The last scenario explains what happens when all risky events occur simultaneously and affect all supply chain operations. The simulation analysis of the strategies used by supermarkets and retailers to manage panic buying and stockpiling behaviour can be used to see which strategy is beneficial for both customers and food retailers. The food retailer and policymakers can use this model to see the benefits of resilience strategies like backup suppliers and ramping up the DC capacity during disruptions.

We found that when disruption comes from the demand side (scenario 1), then it is a good thing for the company and when it comes from the supply side (scenarios 2, 3, 4 and 5), then it is a bad thing. The inventory is mostly kept in the warehouse in the retail sector hence warehouse plays a crucial role in the retail sector and its disruption severely affects SC performance. The CFC disruption has the worst impact on the SC performance of grocery retailers among all disruptions. Thus, retailers need to use effective and efficient mitigation strategies to avoid the disruption of warehouses (CFCs) and if disrupted, it needs to be quickly restored using resilience strategies. Grocery retailers can fulfil the priority orders of vulnerable people without affecting their financial performance. The mean lead time will slightly increase while delivering these priority orders. The priority orders help to protect and support vulnerable people during crises. Rationing of goods was dominantly used by grocery retailers to tackle the panic buying and stockpiling behaviour of customers and maintain the proper flow of goods across the supply chain. This strategy can be used for future disruptions also because in this strategy profit reduces by less than 1% compared with the pandemic scenario. Grocery retailers can reduce the mean lead time and late orders with the help of this strategy. Regarding resilience strategies, grocery retailers can use a backup supplier strategy during crises to come back to their original performance (baseline scenario). The CFC plays an important role in the grocery supply chain thus its capacity augmentation helps to enhance the SC performance. Results reveal that grocery retailers can increase CFC capacity by 75% to improve profit and reduce mean lead time and late orders.

9 Conclusions

This study has been motivated by the disruptions faced by the grocery retail sector in the UK. This sector experienced various disruptive events like supply shortages, demand spike and collapse, and distribution centres closures when the national lockdown was imposed in the UK. The case company considered here was interested to analyse the effect of these events and their strategies on the SC performance and explore some resilience strategies to manage future disruptions. We have made the following four academic contributions to the previous literature. The disruption risks have been modelled mathematically as well through simulation modelling (Singh et al. 2021 ). The effect of these disruptions on the grocery retail sector is investigated by taking the case study from the UK. The grocery retail sector followed two important mitigation strategies including vulnerable delivery slots and rationing of goods to manage the disruption (Hobbs 2021 ). The impact of these two strategies on the grocery retail sector’s performance is quantified and explored (Burgos and Ivanov 2021 ). The quantification of two resilience strategies is another major contribution of this study (Chowdhury et al. 2021 ; Ghadge et al. 2021 ).

The exciting findings of this study are mentioned below.

The demand variation (spike) has the strongest positive impact on SC profit followed by the Covid-19 scenario.

With regards to negative effects, the profit of the online grocery retailer reached to lowest when CFC was disrupted (scenario 3) followed by scenario 5 when disruption propagated.

There was negligible impact on the financial performance of the grocery retailer compared with the Covid-19 scenario when priority delivery slots were used. The mean lead time was slightly (more than 1%) increased because of priority slots.

The restrictions put on the customers buying slightly affected the profit and reduced the mean time and late orders compared with the Covid-19 scenario.

Backup supplier strategy helped to bring the profit of the grocery retailer to its original level.

The capacity of CFC should be enhanced by 75% to improve profit and decrease mean time and late orders.

The effect of vulnerable priority delivery slots and rationing products followed by the online grocery retailer on SC performance is also discussed in detail. Finally, two important resilience strategies (backup supplier and capacity augmentation) and their impacts on the grocery supply chain are quantified. Multiple managerial insights and academic implications based on the results are also suggested.

Similar to other research studies, this study has some limitations which could open the doors for future research. The simulation model can be extended further by including environmental impact into it. The logistics capacity limitation can make the mathematical formulation more realistic. The other type of costs like warehousing cost, facility closure and reopening costs could add further value to the mathematical formulation. The formulation of a multi-objective model is another future research avenue. This study considers only two measures followed by grocery retailers and two resilience strategies to see the impact of them on the supply chain. In future, scholars could explore more measures and resilience strategies for deeper analysis.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

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D. G. Mogale, Xun Wang, Emrah Demir & Vasco Sanchez Rodrigues

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Mogale, D.G., Wang, X., Demir, E. et al. Modelling and analysing supply chain disruption: a case of online grocery retailer. Oper Manag Res 16 , 1901–1924 (2023). https://doi.org/10.1007/s12063-023-00405-9

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Received : 19 February 2023

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Published : 25 August 2023

Issue Date : December 2023

DOI : https://doi.org/10.1007/s12063-023-00405-9

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AmazonFresh: Online Grocery Experience Case Study

While online retailing keeps growing its revenues, online grocery appears to be one of the least successful spheres of online shopping business (Ramus and Nielsen 335). Several companies have managed to achieve considerable success in online grocery business despite numerous challenges it presents. AmazonFresh is one of the examples of organizing a profitable online grocery business. However, it remains a local experiment in Seattle (Martinez par. 5), as certain challenges put a threat to the success of the expansion of the company. The analysis of customers’ perception of problems and needs related to online grocery experience helps to suggest potential solutions to existing problems in the online grocery business. Such solutions are able to provide the sustainability of AmazonFresh.

The interviews were conducted to find out which attitudes to online grocery prevail among consumers and what they define as the main problems of such form of shopping. The analysis of the interviews reveals that most consumers share the same views on online groceries while experience in online shopping of other products appears to be the factor directly influencing the disposition to use online groceries. According to the results of the interviews, the main problems of online groceries include the absence of possibility to check the quality of products personally, worries about the freshness of products, high prices for delivery, and absence of recreational or entertaining aspect related to conventional shopping. The consumers demonstrate high interest in online grocery shopping as it is considered a convenient and time-saving activity. However, lack of experience lowers their willingness to change the usual way of shopping. Some factors promote the willingness of customers to use online groceries. Such factors include the positive experience of shopping other categories of products, good feedbacks received from other consumers, low delivery fares, and quickness of delivery.

The discussed findings received by analyzing the results of the interviews might be of interest to online grocers, as they show which aspects of online grocery shopping should be eliminated or improved. The results of the interviews apply to the challenges faced by AmazonFresh. The relation between previous experience in online shopping and positive attitude to online groceries is supported by the study conducted by Hansen (135). Therefore, AmazonFresh can promote the interest in its services by providing proper advertising at websites used for shopping other categories of products. McDonald et al. suggest a switch to the yearly delivery fee as an effective way of attracting more customers to AmazonFresh (9).

Such method addresses the problem of high delivery prices identified through the interviews. Neighborhood effect is considered an influential factor that promotes trust to the online grocery (Bell and Song 364). Therefore, AmazonFresh should put much effort in promoting the positive feedback on its services shared by the consumers during social interactions with other people. In such way, the company can boost its popularity as positive feedbacks were identified as important factors for raising the interest to shop grocery online. Ensuring the quickness of delivery also plays an important role in attracting new customers. Therefore, by advertising and ensuring that the purchase process is quick for a first-time buyer, the company can “increase customer acquisition” (Wilson-Jeanselme and Reynolds 538).

Current in-store experience appears to be successful due to its recreational aspect and the willingness of customers to be able to see and touch the products they buy personally. Online grocery experience has positive sides related to the advantages of pricing policy and convenience while lacking interpersonal interactions and entertaining factors. Current in-store experience significantly prevails over online grocery experience. That is why numerous online groceries have failed to support their sustainability and left the market.

The analysis of the findings based on the results of the interviews helps to suggest solutions to potential problems related to the expansion of AmazonFresh.

Works Cited

Bell, David, and Sangyoung Song. “Neighborhood Effects and Trial on the Internet: Evidence from Online Grocery Retailing.” Quantitative Marketing and Economics 5.4 (2007): 361-400. Print.

Hansen, Torben. “Consumer Values, the Theory of Planned Behavior and Online Grocery Shopping.” International Journal of Consumer Studies 32 (2008): 128-137. Print.

Martinez, Amy. AmazonFresh Set to Expand? 2013. Web.

McDonald, Rory, Clayton Christensen, Robin Yang, and Ty Hollingsworth. AmazonFresh: Rekindling the Online Grocery Market . 2014. Web.

Ramus, Kim, and Niels Asger Nielsen. “Online Grocery Retailing: What Do Consumers Think?” Internet Research 15.3 (2005): 335-352. Print.

Wilson-Jeanselme, Muriel, and Jonathan Reynolds. “Understanding Shoppers’ Expectations of Online Grocery Retailing.” International Journal of Retail & Distribution Management 34.7 (2006): 529-540. Print.

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