Knowledge that Transforms

Problem definition: Blockchain is a form of distributed ledger technology. While it has grown in prominence, its full potential and possible downsides are not fully understood yet, especially with respect to operations management (OM). Academic/practical relevance: This article fills this gap. Methodology: After briefly reviewing the technical foundations, we explore multiple business and policy aspects. Results: We identify five key strengths, the corresponding five main weaknesses, and three research themes of applying blockchain technology to OM. The key strengths are (1) visibility, (2) aggregation, (3) validation, (4) automation, and (5) resiliency. The corresponding weaknesses are (1) lack of privacy, (2) lack of standardization, (3) garbage in, garbage out, (4) black box effect, and (5) inefficiency. The three research themes are (1) information, (2) automation, and (3) tokenization. Managerial implications: We illustrate these research themes with multiple promising research problems, ranging from classical inventory management, to new areas of ethical OM, and to questions of industrial organization.

Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management in three major areas—supply chain management, revenue management, and healthcare operations—and highlight some exciting directions for the future.

The number of people affected by disasters has increased over the past decades, whereas funding has declined. The need for effective humanitarian aid is, therefore, larger than ever. Humanitarian organizations have recognized the critical role of supply chain management in reaching beneficiaries, and they have introduced commercial routines and best practices. Academics realized that humanitarian operations constitute a fruitful new research area and adapted solution techniques developed for commercial operations to disaster situations with mitigated success. Meanwhile, the problems that humanitarian practitioners face quickly evolve. In this paper, we highlight challenges in matching practitioner needs with academic publications and outline the great opportunities for impactful and relevant research.

The past three decades have witnessed tremendous growth of supply chain activities around the globe thanks to the lowered trade barriers and free trade agreements among countries. The global supply chain management literature developed during this time provided theoretical frameworks and decision support tools for operational and supply chain decisions in the global context. Recent development in trade barrier debates as well as increasing uncertainties in countries’ trade policies have forced companies to rethink their global operational strategies. In this short paper, we draw insights from the existing global supply chain literature and in particular, focus on interpreting recent research contributions published in Manufacturing & Service Operations Management (M&SOM) on the topic to gain understanding of the implications of trade policies, especially tariffs, for firms’ global facility network design decisions. We also provide a discussion of important dimensions that were absent from the existing literature but are pertinent to providing a richer understanding of implications of trade policies for today’s interconnected supply chains. Future research that takes into consideration those previously less explored dimensions has the potential to offer insights not only to industry practitioners but also, to the policy makers. Developing an appreciation of potential reactions from complex connected supply chains in various industries would help policy makers better anticipate the impact of trade policy change.

Problem definition: This study examines the impact of disability diversity on the productivity of apparel manufacturing teams in the context of a work integration social enterprise. Two measures of disability diversity are examined: the number of disability categories employed in a production line and the evenness of disability category dispersion among workers employed in an apparel production line. Academic/practical relevance: The problem has both academic and practical relevance. From an academic standpoint, the current literature does not study the implications of employing individuals with disabilities and their impact on productivity with microdata. The issue also has practical relevance because it ties into recent managerial interest to employ individuals with disabilities in organizations. According to statistics, only 35% of workers with disabilities have some form of significant full-time employment. Methodology: The study uses panel regression analyses to test the impact of disability diversity (number of disability categories and evenness of disability category dispersion) of workers in a production line using detailed multiyear data on the productivity of apparel manufacturing cells. Results: Two key insights emerged from the analyses. First, productivity can be enhanced by increasing the diversity of workers with disabilities in the workforce within a garment-manufacturing cell. Specifically, productivity is best at moderate levels of disability categories in a team. Second, team productivity is higher at greater levels of evenness of disability category dispersion. Managerial implications: The analysis in this paper sheds light on the potential benefits of integrating individuals with disabilities into organizations and its implications on productivity. Specifically, the study finds evidence that having moderate levels of disability categories on a team with higher levels of evenness in disability category dispersion is associated with better productivity rather than having a concentrated team focused on a specific disability. The implications of the results and limitations for the study as well as its potential insights into the context of social enterprises that employ individuals with disabilities are discussed.

Economic growth in many countries is increasingly driven by successful startups that operate as online platforms. These success stories have motivated us to define and classify various online platforms according to their business models. This study discusses strategic and operational issues arising from five types of online platforms (resource sharing, matching, crowdsourcing, review, and crowdfunding) and presents some research opportunities for operations management scholars to explore.

Problem definition: The growing trend in online shopping has sparked the development of increasingly more sophisticated product recommendation systems. We construct a model that recommends a personalized discounted product bundle to an online shopper that considers the trade-off between profit maximization and inventory management, while selecting products that are relevant to the consumer’s preferences. Academic/practical relevance: We provide analytical performance guarantees that illustrate the complexity of the underlying problem, which combines assortment optimization with pricing. We implement our algorithms in two separate case studies on actual data from a large U.S. e-tailer and a premier global airline. Methodology: We focus on simultaneously balancing personalization through individualized functions of consumer propensity-to-buy, inventory management for long-run profitability, and tractability for practical business implementation. We develop two classes of approximation algorithms, multiplicative and additive, to produce a real-time output for use in an online setting. Results: Our computational results demonstrate significant lifts in expected revenues over current industry pricing strategies on the order of 2%–7% depending on the setting. We find that on average our best algorithm obtains 92% of the expected revenue of a full-knowledge clairvoyant strategy across all inventory settings, and in the best cases this improves to 98%. Managerial implications: We compare the algorithms and find that the multiplicative approach is relatively easier to implement and on average empirically obtains expected revenues within 1%–6% of the additive methods when both are compared with a full-knowledge strategy. Furthermore, we find that the greatest expected gains in revenue come from high-end consumers with lower price sensitivities, and that predicted improvements in sales volume depend on product category and are a result of providing relevant recommendations.

Problem definition: We design a combinatorial auction to clear the Turkish day-ahead electricity market, and we develop effective tabu search and genetic algorithms to solve the problem of matching bidders and maximizing social welfare within a reasonable amount of time for practical purposes. Academic/practical relevance: A double-sided blind combinatorial auction is used to determine electricity prices for day-ahead markets in Europe. Considering the integer requirements associated with market participants’ bids and the nonlinear social welfare objective, a complicated problem arises. In Turkey, the total number of bids reaches 15,000, and this large problem needs to be solved within minutes every day. Given the practical time limit, solving this problem with standard optimization packages is not guaranteed, and therefore, heuristic algorithms are needed to quickly obtain a high-quality solution. Methodology: We use nonlinear mixed-integer programming and tabu search and genetic algorithms. We analyze the performance of our algorithms by comparing them with solutions commercially available to the market operator. Results: We provide structural results to reduce the problem size and then develop customized heuristics by exploiting the problem structure in the day-ahead market. Our algorithms are guaranteed to generate a feasible solution, and Energy Exchange Istanbul has been using them since June 2016, increasing its surplus by 448,418 Turkish liras (US$128,119) per day and 163,672,570 Turkish liras (US$46,763,591) per year, on average. We also establish that genetic algorithms work better than tabu search for the Turkish day-ahead market. Managerial implications: We deliver a practical tool using innovative optimization techniques to clear the Turkish day-ahead electricity market. We also modify our model to handle similar European day-ahead markets and show that performances of our heuristics are robust under different auction designs.

This article is based on modeling studies conducted in response to requests from Yale University, the Yale New Haven Hospital, and the State of Connecticut during the early weeks of the SARS-CoV-2 outbreak. Much of this work relied on scratch modeling, that is, models created from scratch in real time. Applications included recommending event crowd-size restrictions, hospital surge planning, timing decisions (when to stop and possibly restart university activities), and scenario analyses to assess the impacts of alternative interventions, among other problems. This paper documents the problems faced, models developed, and advice offered during real-time response to the COVID-19 crisis at the local level. Results include a simple formula for the maximum size of an event that ensures no infected persons are present with 99% probability; the determination that existing intensive care unit (ICU) capacity was insufficient for COVID-19 arrivals, which led to creating a large dedicated COVID-19–negative pressure ICU; and a new epidemic model that showed the infeasibility of the university hosting normal spring and summer events, that lockdown-like stay-at-home and social distancing restrictions without additional public health action would only delay transmission and enable a rebound after restrictions are lifted, and that aggressive community screening to rapidly detect and isolate infected persons could end the outbreak.

Problem definition: This paper provides empirical evidence on the effect of the 2011 Great East Japan Earthquake (GEJE) on the financial performance of firms. Academic/practical relevance: The GEJE was characterized as the most significant disruption ever for global supply chains. In its aftermath, there was a great deal of debate about the risks and vulnerabilities of global supply chains, and there were calls to redesign and restructure supply chains. Methodology: We empirically estimate the effect of the GEJE on the stock prices of firms. Our analyses are based on a global sample of 470 firms collected from articles and announcements in the business press that identify affected firms, as well as 382 firms that are not mentioned in the business press but are in industries potentially subject to contagion or competitive effects. Results: We estimate that firms experiencing supply chain disruptions as a result of the GEJE lost on average 5.21% of their shareholder value during the one-month period after the GEJE. For Japanese firms, the effect was much more severe with an average 9.32% loss in shareholder value. Non-Japanese firms averaged a 3.73% loss in shareholder value. We also find that upstream and downstream supply chain propagation effects from the GEJE are negative, and the contagion effect on firms related to the nuclear industry is very negative. For firms in the rebuilding industries or competitors to firms affected by the GEJE, the competitive effect from the GEJE is positive. Managerial implications: The loss suffered by both Japanese firms and non-Japanese firms experiencing supply chain disruptions as a result of the GEJE is economically significant. Although the loss is more severe for firms whose operations were directly affected by the GEJE, it is also significant for firms who experienced indirect effects from their upstream and downstream supply chain partners, further confirming the importance of supply chain risk mitigation strategies.