case studies in operations research pdf

To read this content please select one of the options below:

Please note you do not have access to teaching notes, case research in operations management.

International Journal of Operations & Production Management

ISSN : 0144-3577

Article publication date: 1 February 2002

This paper reviews the use of case study research in operations management for theory development and testing. It draws on the literature on case research in a number of disciplines and uses examples drawn from operations management research. It provides guidelines and a roadmap for operations management researchers wishing to design, develop and conduct case‐based research.

• Operations management
• Methodology
• Case studies

Voss, C. , Tsikriktsis, N. and Frohlich, M. (2002), "Case research in operations management", International Journal of Operations & Production Management , Vol. 22 No. 2, pp. 195-219. https://doi.org/10.1108/01443570210414329

Copyright © 2002, MCB UP Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Effective case research in operations management: a process perspective

2002, Journal of Operations Management

Related Papers

By: Voss etal.

Journal of Operations Management

Mark Barratt

Manufacturing & Service Operations Management

Alan Scheller-wolf

K. Amoako-gyampah

An agenda for operations management (OM) research was first proposed by Miller et al. in 1981. However, until Hill et al. examined recent Ph.D. dissertations in 1987, no other work on the agenda had been done. We take another look at the status of the OM research ...

Kimberly Bates

Gopesh Anand

Journal of Business Case Studies (JBCS)

Victoria Figiel

This case study is conducted within the context of the Theory of Constraints. The field research reported in this document contains information specific to the telecommunications industry. An examination of the history, organization design, problems and solutions for one telecommunications company are undertaken from the perspective of academic work in the Theory of Constraints. The information included in this document was developed through interviews with four senior managers including the President, the Chief Technology Officer, a Vice President and a department manager. Their responses were the basis of identifying problems and undesirable effects. The undesirable effects were diagramed in six UDE clouds dealing with the following issues: 1- unclear vision from management to employees; 2- supplier; 3- market; 4- the price and regulation environment; 5- production; and 6- bureaucracy. These undesirable effects were logically examined until a single cloud depicting the core confli...

Operations Management Research

A Georges L Romme

Amitabh Raturi

RELATED PAPERS

Yennipher Figuera

IAEME PUBLICATION

IAEME Publication

Proceedings of the American Society for Information Science and Technology

France Bouthillier

Jose Cuellar Cardozo

Journal of Testing and Evaluation

Nao-Aki NODA

Cuadernos de antropología social

Lucía Caisso

Research Letters in Physics

Sushil Kumar

The 2007 Workshop on the Economics of …

Shivendu Shivendu

International Review of Victimology

Rebeca Carrias

Proceedings of the International Joint Conference on Arts and Humanities 2021 (IJCAH 2021)

ahmad bashri

E3S Web of Conferences

Ali CHAOUNI

Social, Humanities, and Educational Studies (SHEs): Conference Series

Puput Widodo

Histoire Sociale/Social History

Tim Stanley

Mária Kotorová

Yusuf Karakaş

I Putu Bagus Eliezer

Journal of Optimization Theory and Applications

Crop and Pasture Science

maja manojlovic

Wound Repair and Regeneration

Jennifer Gonzaga

Annals of economics and statistics

Jean Gabszewicz

Abstract and Applied Analysis

Junesang Choi

Todor Petrov

Applied Optics

Sergi Gallego i Rico

Tassolimo Lenita

Sérgio Castigo

See More Documents Like This

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• Browse All Articles
• Newsletter Sign-Up

Operations →

• 01 Apr 2024
• In Practice

Navigating the Mood of Customers Weary of Price Hikes

Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.

• 22 Mar 2024
• Research & Ideas

Open Source Software: The $9 Trillion Resource Companies Take for Granted

Many companies build their businesses on open source software, code that would cost firms $8.8 trillion to create from scratch if it weren't freely available. Research by Frank Nagle and colleagues puts a value on an economic necessity that will require investment to meet demand.

• 27 Feb 2024
• Cold Call Podcast

How Could Harvard Decarbonize Its Supply Chain?

Harvard University aims to be fossil-fuel neutral by 2026 and totally free of fossil fuels by 2050. As part of this goal, the university is trying to decarbonize its supply chain and considers replacing cement with a low-carbon substitute called Pozzotive®, made with post-consumer recycled glass. A successful pilot project could jump start Harvard’s initiative to reduce embodied carbon emissions, but it first needs credible information about the magnitude and validity of potential carbon reductions. Harvard Business School professor emeritus Robert Kaplan and assistant professor Shirley Lu discuss the flow of emissions along the supply chain of Harvard University’s construction projects, the different methods of measuring carbon emissions, including the E-liability approach, and the opportunity to leverage blockchain technology to facilitate the flow of comparable and reliable emissions information in the case, “Harvard University and Urban Mining Industries: Decarbonizing the Supply Chain.”

• 02 Jan 2024

10 Trends to Watch in 2024

Employees may seek new approaches to balance, even as leaders consider whether to bring more teams back to offices or make hybrid work even more flexible. These are just a few trends that Harvard Business School faculty members will be following during a year when staffing, climate, and inclusion will likely remain top of mind.

• 12 Dec 2023

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

A global supply chain reshuffling is underway as companies seek to diversify their distribution networks in response to pandemic-related shocks, says research by Laura Alfaro. What do these shifts mean for American businesses and buyers?

• 25 Apr 2023

How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model

The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.

• 21 Apr 2023

The $15 Billion Question: Have Loot Boxes Turned Video Gaming into Gambling?

Critics say loot boxes—major revenue streams for video game companies—entice young players to overspend. Can regulators protect consumers without dampening the thrill of the game? Research by Tomomichi Amano and colleague.

• 11 Apr 2023

A Rose by Any Other Name: Supply Chains and Carbon Emissions in the Flower Industry

Headquartered in Kitengela, Kenya, Sian Flowers exports roses to Europe. Because cut flowers have a limited shelf life and consumers want them to retain their appearance for as long as possible, Sian and its distributors used international air cargo to transport them to Amsterdam, where they were sold at auction and trucked to markets across Europe. But when the Covid-19 pandemic caused huge increases in shipping costs, Sian launched experiments to ship roses by ocean using refrigerated containers. The company reduced its costs and cut its carbon emissions, but is a flower that travels halfway around the world truly a “low-carbon rose”? Harvard Business School professors Willy Shih and Mike Toffel debate these questions and more in their case, “Sian Flowers: Fresher by Sea?”

• 28 Mar 2023

The FDA’s Speedy Drug Approvals Are Safe: A Win-Win for Patients and Pharma Innovation

Expediting so-called breakthrough therapies has saved millions of dollars in research time without compromising drug safety or efficacy, says research by Ariel Stern, Amitabh Chandra, and colleagues. Could policymakers harness the approach to bring life-saving treatments to the market faster?

• 31 Jan 2023

Addressing Racial Discrimination on Airbnb

For years, Airbnb gave hosts extensive discretion to accept or reject a guest after seeing little more than a name and a picture, believing that eliminating anonymity was the best way for the company to build trust. However, the apartment rental platform failed to track or account for the possibility that this could facilitate discrimination. After research published by Professor Michael Luca and others provided evidence that Black hosts received less in rent than hosts of other races and showed signs of discrimination against guests with African American sounding names, the company had to decide what to do. In the case, “Racial Discrimination on Airbnb,” Luca discusses his research and explores the implication for Airbnb and other platform companies. Should they change the design of the platform to reduce discrimination? And what’s the best way to measure the success of any changes?

• 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

• 18 Oct 2022

Chewy.com’s Make-or-Break Logistics Dilemma

In late 2013, Ryan Cohen, cofounder and then-CEO of online pet products retailer Chewy.com, was facing a decision that could determine his company’s future. Should he stay with a third-party logistics provider (3PL) for all of Chewy.com’s e-commerce fulfillment or take that function in house? Cohen was convinced that achieving scale would be essential to making the business work and he worried that the company’s current 3PL may not be able to scale with Chewy.com’s projected growth or maintain the company’s performance standards for service quality and fulfillment. But neither he nor his cofounders had any experience managing logistics, and the company’s board members were pressuring him to leave order fulfillment to the 3PL. They worried that any changes could destabilize the existing 3PL relationship and endanger the viability of the fast-growing business. What should Cohen do? Senior Lecturer Jeffrey Rayport discusses the options in his case, “Chewy.com (A).”

• 12 Oct 2022

When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb

Airbnb bookings dropped 12 percent more for hosts with Asian names than other hosts during the early months of the COVID-19 pandemic, says research by Michael Luca. Could better design deter bias, particularly during times of crisis?

• 22 Aug 2022

Can Amazon Remake Health Care?

Amazon has disrupted everything from grocery shopping to cloud computing, but can it transform health care with its One Medical acquisition? Amitabh Chandra discusses company's track record in health care and the challenges it might face.

• 12 Jul 2022

Can the Foodservice Distribution Industry Recover from the Pandemic?

At the height of the pandemic in 2020, US Foods struggled, as restaurant and school closures reduced demand for foodservice distribution. The situation improved after the return of indoor dining and in-person learning, but an industry-wide shortage of truck drivers and warehouse staff hampered the foodservice distributor’s post-pandemic recovery. That left CEO Pietro Satriano to determine the best strategy to attract and retain essential workers, even as he was tasked with expanding the wholesale grocery store chain (CHEF’STORE) that US Foods launched during the pandemic lockdown. Harvard Business School Professor David E. Bell explores how post-pandemic supply chain challenges continue to affect the foodservice distribution industry in his case, “US Foods: Driving Post-Pandemic Success?”

• 05 Jul 2022
• What Do You Think?

Have We Seen the Peak of Just-in-Time Inventory Management?

Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments.

• 05 May 2022

Why Companies Raise Their Prices: Because They Can

Markups on household items started climbing years before the COVID-19 pandemic. Companies have realized just how much consumers will pay for the brands they love, says research by Alexander MacKay. Closed for comment; 0 Comments.

• 31 Mar 2022

Navigating the ‘Bermuda Triangle’ in Professional Services

Not all companies need to scale. Ashish Nanda explores a crucial choice that leaders of professional services firms face as their organizations grow. Open for comment; 0 Comments.

• 28 Feb 2022

How Racial Bias Taints Customer Service: Evidence from 6,000 Hotels

Hotel concierges provide better service to white customers than Black and Asian customers, says research by Alexandra Feldberg and colleague. They offer three strategies to help companies detect bias on the front line. Open for comment; 0 Comments.

• 10 Feb 2022

Why Are Prices So High Right Now—and Will They Ever Return to Normal?

And when will sold-out products return to store shelves? The answers aren't so straightforward. Research by Alberto Cavallo probes the complex interplay of product shortages, prices, and inflation. Open for comment; 0 Comments.

Operations Research Case Study Papers for Africa: A Bibliometric Review

• First Online: 10 November 2022

Cite this chapter

• Majdi Argoubi 2 &
• Hatem Masri 3  

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

113 Accesses

Despite repeated requests for Operations Research to shift away from theoretical works and toward case studies, problem-oriented work, and real-world applications, theoretical papers still make up a sizable fraction of Operations Research publications. In order to accomplish two main goals: firstly, to investigate the scope of Operations Research case study in Africa (OR-CSA); and, secondly, to identify major areas of case study, evolutionary stages of the major techniques involved, and intellectual milestones in the development of key techniques. This work presents a systematic review of the literature on major aspects of OR-CSA. By using a generic search strategy, a representative dataset of OR-CSA bibliographic records is established. Next, we progressively synthesize empirical results. Results suggest that case studies are cited less often than other types of publication that deficit is more marked for African researchers where the case study has been effectively marginalized. It is suggested that, although a reduction in the proportion of publications in Operations Research made up of theoretical works may be desirable and would be an indication of maturity of the field, well-directed theoretical works will continue to play a role, albeit a diminishing one, in advancing the discipline. On the other hand, the evolution of the OR-CSA involves the development of several interconnected disciplines. As a final step, co-citation networks are constructed and visualized to assist with visual analysis of the OR-CSA’s structural and dynamic relationships and developments. Fourteen major techniques are discussed in detail. For the purpose of demonstrating the analytical potential of the systematic method, the trajectory of citations made by specific categories of African authors and references is shown. Major milestones in key techniques are also investigated.

• Operations Research
• Case studies
• intellectual structure
• Co-citation analysis
• literature mapping

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Al-Fawzan, M. A., & Haouari, M. (2005). A bi-objective model for robust resource constrained project scheduling. International Journal of Production Economics, 96 (2), 175–187.

Article   Google Scholar  

Argoubi, M., Ammari, E., & Masri, H. (2021). A scientometric analysis of Operations Research and Management Science research in Africa. Operational Research: An International Journal, 21 , 1827–1843.

Barlow, R. E., & Proschan, F. (1996). Mathematical theory of reliability . Society for Industrial and Applied Mathematics.

Book   Google Scholar  

Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101 , 5303–5310.

Chen, C. (2012). Turning points: The nature of creativity . Springer-Verlag.

Google Scholar  

Chen, C., & Leydesdorff, L. (2013). Patterns of connections and movements in dual map overlays: A new method of publication portfolio analysis. Journal of the American Society for Information Science and Technology, 65 (2), 334–351.

Dantzig, G. B., Fulkerson, D. R., & Johnson, S. M. (1954). Solution of a large-scale traveling-salesman problem . RAND Corporation.

Eck, N. J. V., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523–561.

Finkelstein, M. (2011). On the ‘rate of aging’ in heterogeneous populations. Mathematical Biosciences, 232 (1), 20–23.

Govindan, K. (2016). Evolutionary algorithms for supply chain management. Annals of Operations Research, 242 , 195–206.

Guan, J., Manikas, J., & Boyd, L. H. (2019). The International Journal of Production Research at 55: A content-driven review and analysis. International Journal of Production Research, 57 , 4654–4666.

Hossain, M., Patras, A., Barry-Ryan, C., Martin-Diana, A., & Brunton, N. (2011). Application of principal component and hierarchical cluster analysis to classify different spices based on in vitro antioxidant activity and individual polyphenolic antioxidant compounds. Journal of Functional Food, 3 (3), 179–189.

Johnson, D. S. (1993). Random starts for local optimization. In DIMACS workshop on randomized algorithms for combinatorial optimization .

Kao, C. (2008). The authorship and country spread of operation research journals. Scientometrics, 78 , 397–397.

Katz, J. S., & Martin, B. R. (1997). What is research collaboration. Research Policy, 26 , 1–18.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis . Wiley.

Kim, M. C., Zhu, Y., & Chen, C. (2000). How are they different? A quantitative domain comparison of information visualization and data visualization. Scientometrics, 107 (1), 123–123.

Kleinberg, J. (2002). Bursty and hierarchical structure in streams. In: KDD ’02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining ((pp. 91–101). ACM.

Kumar, R., Novak, J., & Tomkins, A. (2006). Structure and evolution of online social networks. In: KDD ’06 .

Laengle, S., Merigójm, J. M., Modak, N. M., & Yang, J. B. (2020). Bibliometrics in operations research and management science: a university analysis. Annals of Operations Research, 294 , 769–813.

Liao, H., Tang, M., Li, Z., & Lev, B. (2018). Bibliometric analysis for highly cited papers in operations research and management science from 2008 to 2017 based on Essential Science Indicators. Omega, 88 , 223–236.

Liu, D., & Stewart, T. J. (2004). Object-oriented decision support system modeling for multicriteria decision making in natural resource management. Computers and Operations Research, 31 (7), 985–999.

Luxburg, V. U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17 , 395–416.

Manikas, A., Boyd, L., Pang, Q., & Guan, J. J. (2019). An analysis of research methods in IJPR since inception. International Journal of Production Research, 57 , 4667–4675.

Merigó, J., & Yangj, B. (2017). A bibliometric analysis of operations research and management science. Omega, 73 , 37–48.

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114 , 163–191.

Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45 (2), 167–256.

Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103 , 8577–8582.

Porter, M. E. (1990). The competitive advantage of nations . The Free Press.

Rau, E. P. (2005). Combat science: The emergence of operational research in World War II. Endeavour, 29 , 156–161.

Romero-Silva, R., & Marsillac, E. (2019). Trends and topics in IJPR from 1961 to 2017: A statistical history. International Journal of Production Research, 57 , 4692–4718.

Saaty, T. L. (1988). What is the analytic hierarchy process? In G. Mitra, H. J. Greenberg, F. A. Lootsma, M. J. Rijkaert, & H. J. Zimmermann (Eds.), Mathematical models for decision support (pp. 109–121). Springer.

Chapter   Google Scholar  

Shang, G., Saladin, B., Fry, T., & Donohue, J. (2015). Twenty-six years of operations management research (1985-2010): authorship patterns and research constituents in eleven top rated journals. International Journal of Production Research, 53 , 6161–6197.

Shukla, P. K., & Deb, K. (2007). On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods. European Journal of Operational Research, 181 (3), 1630–1652.

Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24 (4), 265–269.

Van Waltman, L., Eck, N., & Noyons, E. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4 (4), 629–635.

White, H., & Mccain, K. W. (1989). Bibliometrics. Annual Review of Information Science and Technology, 24 , 119–186.

Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18 (3), 429–472.

Download references

Acknowledgments

We thank Paul Randall of the ōbex project for providing language editing support.

Author information

Authors and affiliations.

Higher Institute of Management of Sousse, University of Sousse, Sousse, Tunisia

Majdi Argoubi

College of Business Administration, University of Bahrain, Sakhir, Kingdom of Bahrain

Hatem Masri

You can also search for this author in PubMed   Google Scholar

Editor information

Editors and affiliations.

College of Business Administration, University of Bahrain, Sakhir, Bahrain

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Argoubi, M., Masri, H. (2022). Operations Research Case Study Papers for Africa: A Bibliometric Review. In: Masri, H. (eds) Africa Case Studies in Operations Research. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-17008-9_8

Download citation

DOI : https://doi.org/10.1007/978-3-031-17008-9_8

Published : 10 November 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-17007-2

Online ISBN : 978-3-031-17008-9

eBook Packages : Mathematics and Statistics Mathematics and Statistics (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Community Med
• v.44(4); Oct-Dec 2019

Operational Research in Health-care Settings

Rajesh kunwar.

Department of Community Medicine, TS Misra Medical College, Department of Community Medicine, Prasad Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

V. K. Srivastava

Origin of the term operational research (OR), also known as operations research, can be traced back to World War II when a number of researches carried out during military operations helped British Forces produce better results with lesser expenditure of ammunition. The world soon realised the potential of this kind of research and many disciplines especially management sciences, started applying its principles to achieve better returns on their investments.

Following World War II in 1948, the World Health Organization (WHO) came into existence with research as one of its core functions. It emphasized the need of identifying health-related issues needing research and thereby generation, dissemination, and utilization of the newly acquired knowledge for health promotion.[ 1 ] In 1978, Alma Ata Declaration acknowledged that primary health care was well known globally but, at the same time, also noted that modalities of its implementation were likely be different in different countries depending on their socioeconomic conditions, availability of resources, development of technology, and motivation of the community. A number of issues were yet to be resolved and researched before primary health care was operationalized under local conditions.[ 2 ]

T HE D EFINITION

The kind of research that Alma Ata Declaration recommended for improvement of health-care delivery is essentially OR. Described as “the science of better,” it helps in identifying the alternative service delivery strategy which not only overcomes the problems that limit the program quality, efficiency, and effectiveness but also yields the best outcome.[ 3 ] In its report on “The Third Ten Years of the WHO,” WHO has highlighted the usefulness of OR in improvement of health-care delivery in terms of its efficiency, effectiveness, and wider coverage by testing alternative approaches even in countries with limited national resources.[ 4 ]

OR has been variously defined. Dictionary of Epidemiology defined it as a systematic study of the working of a system with the aim of improvement.[ 5 ] From a health program perspective, OR is defined as the search for strategies and interventions that enhance the quality and effectiveness of the program.[ 6 ] A global meeting held in Geneva in April 2008 to develop the framework of OR, defined the scope of OR in context to public health as “ Any research producing practically usable knowledge (evidence, finding, information, etc.) which can improve program implementation (e.g., effectiveness, efficiency, quality, access, scale up, sustainability) regardless of the type of research (design, methodology, approach), falls within the boundaries of OR .”[ 7 ]

OR, however, is different from clinical or epidemiological research. It addresses a specific problem within a specific program. It examines a system, for example, health-care delivery system, and experiments in the environment specific to the program with alternative strategies to find the most suitable one and has an objective of improvement in the system. On the other hand, clinical or epidemiological research studies individuals and groups of individuals in search of new knowledge. In addition, ethical issues, which form an integral part of all clinical and epidemiological research, have their role poorly defined in OR, more so if it is based on secondary data.

The keyword in all the definitions is improvement, which is to be brought about by means of research in the operation of an ongoing program. Its characteristics include:

• It focuses on a specific problem in an ongoing programme
• It involves research into the problem using principles of epidemiology
• It tests more than one possible solution and provides rational basis, in the absence of complete information, for the best alternative to improve program efficiency
• It requires close interaction between program managers and researchers
• It succeeds only if the research is conducted in the existing environment and study results are implemented in true letter and spirit.

T HE P ROCESS

In health-care settings, an ongoing health program often fails to achieve its expected objective and the program managers are faced with problems factors responsible for which are not apparent. This is the stage where process of OR is initiated. In a standard OR process, planning begins with organization of a research team, which should have a mix of people with different backgrounds such as epidemiology, biostatistics, health managements, etc., The program managers may not be able to carry out the research themselves because of their work responsibility and in all probability, their biased views. However, they need to have a working relationship with the research team to ensure smooth conduct of the research and ownership of the result by all parties.

According to Fisher et al ., OR is a continuous process of problem identification, selection of a suitable strategy/intervention, experimentation of the selected strategy/intervention, dissemination of the findings, and utilization of the information so derived.[ 8 ] However, it may not always be possible to follow a step by step approach in OR since it is carried out in the existing environment, and many of the activities may be taking place simultaneously. The process involves the following steps [ Figure 1 ].

Process of operational research

Identifying problems

Like any other research, it is essential to have a research question as to the first and foremost step for beginning the process of OR. Discussion with program managers and staff, review of project reports and local documentation, discussion with experts in the field and literature search gives an insight into why the problem is occurring and what are possible solutions; and help in the identification of the research question. OR methods are useful for the systematic identification of problems and the search for potential solutions. Structured approaches to identifying options, such as the strategic choice approach or systematic creativity approaches have great potential for use in low-resource settings.[ 9 ]

Choosing interventions

Choosing appropriate interventions is clearly a crucial step. Effectiveness, safety, cost, and equity should all be considered, and researchers should be familiar with standard textbook methods for assessing these. Finding the best combinations and delivery methods is a major research exercise in its own right. Modeling different intervention strategies before rollout is now ubiquitous in many industries but is less common in healthcare.[ 10 ] Modeling work has been done on ways to reduce maternal mortality and in cervical cancer screening in low-resource settings.[ 11 ]

An appropriate intervention design, depending on available time and resources, should have a written protocol spelling out details of steps to be taken during implementation. Only valid and reliable instruments – be it quantitative or qualitative study-should be used; and wherever possible, a pilot study be carried out to further refine the conduct of the intervention. The contribution that OR and management science can make to design and delivery is not restricted to high technology. Oral rehydration therapy is a “low-tech–low-cost–high-impact” innovation, in which OR was used to explore ways it could be administered using readily available ingredients by laypeople, with an escalation pathway to treatment by health-care professionals when necessary.[ 12 ]

Small-scale projects generally need considerable modifications to work on a larger scale. Classic OR techniques such as simulation modeling can be used in locating services, managing the supply chain, and developing the health-care workforce.

Integrating into health systems

After analysis of the result, the information gathered should be disseminated to stakeholders and decision-makers. The modalities of information utilization should have been predecided and included in the research proposal. Successes in global health programs often result from synergistic interactions between individual, community and national actors rather than from any single “magic bullet.” A greater focus is needed on how interventions should be used in a complex behavioral environment, to better capture the dynamics of social networks, and to understand how complex systems can adapt positively to change. This is a task where OR and management science tools can be useful, as demonstrated by systems analysis of programs for cervical cancer prevention[ 13 ] or agent simulation modeling of spread of HIV in villages.[ 14 ]

E VALUATION

One of the greatest challenges for global health is the measurement and evaluation of performance of projects and programs. The WHO defines evaluation as “ the systematic and objective assessment of an ongoing or completed initiative, its design, implementation, and results. The aim is to determine the relevance and fulfillment of objectives, efficiency, effectiveness, impact, and sustainability .”[ 15 ] It may or may not lead to improvement.

Accelerated Child Survival and Development (ACSD) program, an initiative of UNICEF, was implemented in eleven West African countries from 2001 to 2005 with an objective of reducing mortality among under-fives by at least 25% by the end of 2006. Retrospective evaluation of the program was carried out in Benin, Ghana, and Mali by comparing data of ACSD focus districts with those of remainder districts. It showed that the difference in coverage of preventive interventions in ACSD focus areas before and after program implementation was not significant in Benin and Mali. This probably resulted in failure of ACSD program to accelerate survival of under-fives in-focus areas of Benin and Mali as compared to comparison areas. The inputs obtained from the evaluation of the program if translated into policy or national program would have delivered the desired result of ACSD program implementation.[ 16 ] Evaluation, thus, is fundamental to good management and is an essential part of the process of developing effective public policy. It is a complex enterprise, requiring researchers to balance the rigors of their research strategies with the relevance of their work for managers and policymakers.[ 17 ]

Standard control trial approaches to evaluation are sometimes feasible and appropriate but often a more flexible systems-oriented approach is required, together with modeling to help assess the effectiveness of preventive interventions.[ 18 ] Decision tree modeling can give rapid insights into the operational effectiveness and cost-effectiveness of procedures[ 19 ] and programs.[ 20 ]

O PERATIONAL R ESEARCH IN H EALTH-CARE S ETTINGS : E XAMPLES

The relevance of OR in health-care settings cannot be overemphasized. It has been successfully used all over the world in various health programs such as family planning, HIV, tuberculosis (TB), and malaria control programs to name a few. Its role in causing improvement in various health programs and the development of policies has been acknowledged globally. Sustained OR efforts of several decades helped in developing the Global strategy for control of TB. India and Malawi provide the most successful example of OR in this field.[ 21 ] In India, it was demonstrated by OR that successful implementation of DOTS strategy throughout the country led to reduction in the prevalence of TB, reduction in fatality due to TB and release of hospital beds occupied by TB patients; and thereby a potential gain to the Indian economy.[ 22 ]

For the treatment of TB, about half of TB patients in India rely on the private sector. In spite of it being a notifiable disease, TB notification from private sector has been a challenge. In 2014, Delhi state, by adopting direct “one to one” sensitization of private practitioners by TB notification committee, was able to accelerate notification of TB cases from the private sector.[ 23 ]

In view of the growing burden of multidrug-resistance TB (MDR-TB), an OR was conducted in the setting of Revised National Tuberculosis Programme on patients with presumptive MDR-TB in North and Central Chennai, in 2014 to determine prediagnosis attrition and pre-treatment attrition, and factors associated with it. Prediagnosis and pretreatment attrition were found 11% and 38%, respectively. The study showed that patients with smear-negative TB were less likely to undergo drug susceptibility testing (DST) and more attention was required to be paid to this group for improving DST.[ 24 ]

One of the most successful examples of OR in India is the experimental study carried out in Gadchiroli district of Maharashtra from 1993 to 1998. In their path-breaking field trial, Bang et al . trained village level workers in neonatal care who subsequently made home visits at scheduled intervals and managed premature birth/low birthweight, birth asphyxia, hypothermia, neonatal sepsis, and breastfeeding problems. This led to a significant reduction in neonatal mortality rates in intervention villages.[ 25 ] Encouraged by the success of this field trial, Home-Based Newborn Care has been adopted by many districts in India to combat neonatal mortality.

In leprosy case detection campaign (LCDC), introduced under National Leprosy Eradication Programme of India in 2016, false-positive diagnosis is a major issue. A study carried out in four districts of Bihar found 30% false-positive cases during LCDC. Using “appreciative inquiry” as a tool, Wagh et al . were able to achieve a decline in false-positive diagnosis.[ 26 ]

OR has been successfully used in hospital settings too. In Latin America, unsafe abortions used to be one of the most common causes of high maternal mortality. Billings and Bensons reviewed ten completed OR projects conducted in public sector hospitals of seven Latin American countries. Their findings indicated that sharp curettage replaced by manual vacuum aspiration for conducting abortion reduced the requirement of resources for postabortion care, reduced cost, and length of hospital stay and reduced maternal mortality.[ 27 ]

C ONCLUSION

Following Alma Ata declaration and Millennium Development Goals, all countries of the world have instituted their own National Health Programmes in a bid to improve health of their countrymen. Although health programs are in place, Governments are committed, guidance from the WHO is available, support from NGOs have been garnered, still many countries have not been able to achieve their desired goals. Operational Research is now being used as a key instrument, especially in resource-poor countries, to tap the untapped information. Administrators are using it as a searchlight for discovering what is still in the dark. It is there to stay. It is high time that the scientific community working in health-care settings gets acquainted with the nuances of OR and uses it more often for improving the outcome of health programs and for making them more efficient and effective.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

R EFERENCES

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

Herbert Chanoch Kelman, 94

Everett irwin mendelsohn, 91.

Anticipate, accommodate, empower

Exploring generative ai at harvard.

Jessica McCann

Harvard Correspondent

Leaders weigh in on where we are and what’s next

The explosion of generative AI technology over the past year and a half is raising big questions about how these tools will impact higher education. Across Harvard, members of the community have been exploring how GenAI will change the ways we teach, learn, research, and work.

As part of this effort, the Office of the Provost has convened three working groups . They will discuss questions, share innovations, and evolve guidance and community resources. They are:

• The Teaching and Learning Group , chaired by Bharat Anand , vice provost for advances in learning and the Henry R. Byers Professor of Business Administration at Harvard Business School. This group seeks to share resources, identify emerging best practices, guide policies, and support the development of tools to address common challenges among faculty and students.
• The Research and Scholarship Group , chaired by John Shaw , vice provost for research, Harry C. Dudley Professor of Structural and Economic Geology in the Earth and Planetary Sciences Department, and professor of environmental science and engineering in the Paulson School of Engineering and Applied Science. It focuses on how to enable, and support the integrity of, scholarly activities with generative AI tools.
• T he Administration and Operations Group , chaired by Klara Jelinkova , vice president and University chief information officer. It is charged with addressing information security, data privacy, procurement, and administration and organizational efficiencies.

Klara Jelinkova, Bharat Anand, and John Shaw.

Photos by Kris Snibbe/Harvard Staff Photographer; Evgenia Eliseeva; and courtesy of John Shaw

The Gazette spoke with Anand, Shaw, and Jelinkova to understand more about the work of these groups and what’s next in generative AI at Harvard.

When generative AI tools first emerged, we saw universities respond in a variety of ways — from encouraging experimentation to prohibiting their use. What was Harvard’s overall approach?

Shaw: From the outset, Harvard has embraced the prospective benefits that GenAI offers to teaching, research, and administration across the University, while being mindful of the potential pitfalls. As a University, our mission is to help enable discovery and innovation, so we had a mandate to actively engage. We set some initial, broad policies that helped guide us, and have worked directly with groups across the institution to provide tools and resources to inspire exploration.

Jelinkova: The rapid emergence of these tools meant the University needed to react quickly, to provide both tools for innovation and experimentation and guidelines to ensure their responsible use. We rapidly built an AI Sandbox to enable faculty, students, and staff to experiment with multiple large language models in a secure environment. We also worked with external vendors to acquire enterprise licenses for a variety of tools to meet many different use cases. Through working groups, we were able to learn, aggregate and collate use cases for AI in teaching, learning, administration, and research. This coordinated, collective, and strategic approach has put Harvard ahead of many peers in higher education.

Anand: Teaching and learning are fundamentally decentralized activities. So our approach was to ask: First, how can we ensure that local experimentation by faculty and staff is enabled as much as possible; and second, how can we ensure that it’s consistent with University policies on IP, copyright, and security? We also wanted to ensure that novel emerging practices were shared across Schools, rather than remaining siloed.

What do these tools mean for faculty, in terms of the challenges they pose or the opportunities they offer? Is there anything you’re particularly excited about?

Anand: Let’s start with some salient challenges. How do we first sift through the hype that’s accompanied GenAI? How can we make it easy for faculty to use GenAI tools in their classrooms without overburdening them with yet another technology? How can one address real concerns about GenAI’s impact?

While we’re still early in this journey, many compelling opportunities — and more importantly, some systematic ways of thinking about them — are emerging. Various Harvard faculty have leaned into experimenting with LLMs in their classrooms. Our team has now interviewed over 30 colleagues across Harvard and curated short videos that capture their learnings. I encourage everyone to view these materials on the new GenAI site; they are remarkable in their depth and breadth of insight.

Here’s a sample: While LLMs are commonly used for Q&A, our faculty have creatively used them for a broader variety of tasks, such as simulating tutors that guide learning by asking questions, simulating instructional designers to provide active learning tips, and simulating student voices to predict how a class discussion might flow, thus aiding in lesson preparation. Others demonstrate how more sophisticated prompts or “prompt engineering” are often necessary to yield more sophisticated LLM responses, and how LLMs can extend well beyond text-based responses to visuals, simulations, coding, and games. And several faculty show how LLMs can help overcome subtle yet important learning frictions like skill gaps in coding, language literacy, or math.

Do these tools offer students an opportunity to support or expand upon their learning?

Anand: Yes. GenAI represents a unique area of innovation where students and faculty are working together. Many colleagues are incorporating student feedback into the GenAI portions of their curriculum or making their own GenAI tools available to students. Since GenAI is new, the pedagogical path is not yet well defined; students have an opportunity to make their voices heard, as co-creators, on what they think the future of their learning should look like.

Beyond this, we’re starting to see other learning benefits. Importantly, GenAI can reach beyond a lecture hall. Thoughtful prompt engineering can turn even publicly available GenAI tools into tutorbots that generate interactive practice problems, act as expert conversational aids for material review, or increase TA teams’ capacity. That means both that the classroom is expanding and that more of it is in students’ hands. There’s also evidence that these bots field more questions than teaching teams can normally address and can be more comfortable and accessible for some students.

Of course, we need to identify and counter harmful patterns. There is a risk, in this early and enthusiastic period, of sparking over-reliance on GenAI. Students must critically evaluate how and where they use it, given its possibility of inaccurate or inappropriate responses, and should heed the areas where their style of cognition outperforms AI. One other thing to watch out for is user divide: Some students will graduate with vastly better prompt engineering skills than others, an inequality that will only magnify in the workforce.

What are the main questions your group has been tackling?

Anand: Our group divided its work into three subgroups focused on policy, tools, and resources. We’ve helped guide initial policies to ensure safe and responsible use; begun curating resources for faculty in a One Harvard repository ; and are exploring which tools the University should invest in or develop to ensure that educators and researchers can continue to advance their work.

In the fall, we focused on supporting and guiding HUIT’s development of the AI Sandbox. The Harvard Initiative for Learning and Teaching’s annual conference , which focused exclusively on GenAI, had its highest participation in 10 years. Recently, we’ve been working with the research group to inform the development of tools that promise broad, generalizable use for faculty (e.g., tutorbots).

What has your group focused on in discussions so far about generative AI tools’ use in research?

Shaw: Our group has some incredible strength in researchers who are at the cutting edge of GenAI development and applications, but also includes voices that help us understand the real barriers to faculty and students starting to use these tools in their own research and scholarship. Working with the other teams, we have focused on supporting development and use of the GenAI sandbox, examining IP and security issues, and learning from different groups across campus how they are using these tools to innovate.

Are there key areas of focus for your group in the coming months?

Shaw: We are focused on establishing programs — such as the new GenAI Milton Fund track — to help support innovation in the application of these tools across the wide range of scholarship on our campus. We are also working with the College to develop new programs to help support students who wish to engage with faculty on GenAI-enabled projects. We aim to find ways to convene students and scholars to share their experiences and build a stronger community of practitioners across campus.

What types of administration and operations questions are your group is exploring, and what type of opportunities do you see in this space?

Jelinkova: By using the group to share learnings from across Schools and units, we can better provide technologies to meet the community’s needs while ensuring the most responsible and sustainable use of the University’s financial resources. The connections within this group also inform the guidelines that we provide; by learning how generative AI is being used in different contexts, we can develop best practices and stay alert to emerging risks. There are new tools becoming available almost every day, and many exciting experiments and pilots happening across Harvard, so it’s important to regularly review and update the guidance we provide to our community.

Can you talk a bit about what has come out of these discussions, or other exciting things to come?

Jelinkova: Because this technology is rapidly evolving, we are continually tracking the release of new tools and working with our vendors as well as open-source efforts to ensure we are best supporting the University’s needs. We’re developing more guidance and hosting information sessions on helping people to understand the AI landscape and how to choose the right tool for their task. Beyond tools, we’re also working to build connections across Harvard to support collaboration, including a recently launched AI community of practice . We are capturing valuable findings from emerging technology pilot programs in HUIT , the EVP area , and across Schools. And we are now thinking about how those findings can inform guiding principles and best practices to better support staff.

While the GenAI groups are investigating these questions, Harvard faculty and scholars are also on the forefront of research in this space. Can you talk a bit about some of the interesting research happening across the University in AI more broadly ?

Shaw: Harvard has made deep investments in the development and application of AI across our campus, in our Schools, initiatives, and institutes — such as the Kempner Institute and Harvard Data Science Initiative. In addition, there is a critical role for us to play in examining and guiding the ethics of AI applications — and our strengths in the Safra and Berkman Klein centers, as examples, can be leading voices in this area.

What would be your advice for members of our community who are interested in learning more about generative AI tools?

Anand: I’d encourage our community to view the resources available on the new Generative AI @ Harvard website , to better understand how GenAI tools might benefit you.

There’s also no substitute for experimentation with these tools to learn what works, what does not, and how to tailor them for maximal benefit for your particular needs. And of course, please know and respect University policies around copyright and security.

We’re in the early stages of this journey at Harvard, but it’s exciting.

Share this article

You might like.

Memorial Minute — Faculty of Arts and Sciences

How to ensure students with disabilities have an equal chance to succeed?

Yes, it’s exciting. Just don’t look at the sun.

Lab, telescope specialist details Harvard eclipse-viewing party, offers safety tips

Forget ‘doomers.’ Warming can be stopped, top climate scientist says

Michael Mann points to prehistoric catastrophes, modern environmental victories

Navigating Harvard with a non-apparent disability

4 students with conditions ranging from diabetes to narcolepsy describe daily challenges that may not be obvious to their classmates and professors