Knowledge that Transforms

The continued success of Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take their precious time to provide careful and constructive reviews of the manuscripts submitted to the journal in a timely manner. On behalf of M&SOM, Editor-in-Chief Georgia Perakis expresses her deepest gratitude to all those who served as reviewers for the journal in 2021. Among all reviewers, some individuals have distinguished themselves by reviewing several manuscripts and, with each manuscript, by writing a fair, critical, and constructive review in a timely fashion. In recognition of their outstanding service provided to support the journal’s scholarly mission, M&SOM grants the 2021 Meritorious Service Award to…

Problem definition: We study profit allocation for a sourcing network, in which a buyer sources from a set of differentiated suppliers with limited capacity under uncertain demand for the final product. Whereas the buyer takes the lead in forming the sourcing network and designing the contract mechanism, due to their substantial bargaining power, the suppliers take the lead in determining the terms of the contract. Academic/practical relevance: We identify contracting mechanisms that will ensure the stability of the sourcing network in the long term, where a stable sourcing network requires an effective profit-allocation scheme that motivates all members to join and stay in the network. Methodology: We apply methods from game theory to model the network and analyze the Nash equilibrium of a noncooperative game under a proposed contracting mechanism. We then use a cooperative game model to study the stability of the resulting equilibrium. Results: We show that the optimal network profit, as a set function of the set of suppliers, is submodular, which allows us to demonstrate that the core of the cooperative game is not empty. We also establish a set of conditions that are equivalent to, but much simpler than, the original conditions for the core. We use these results to demonstrate that the proposed fixed-fee contracting mechanism can implement a stable network in the competitive setting by achieving a profit allocation that is in the core of the cooperative game. We also demonstrate that the grand coalition is stable in a farsighted sense under the Shapley value allocation. Managerial implications: Under the fixed-fee mechanism, the buyer’s decisions maximize the network profit, and each supplier earns a profit equal to its marginal contribution. When the aggregate capacity of the supplier network is high relative to demand, or demand is more likely to be small, the fixed-fee mechanism is likely to outperform the Shapley value allocation from the perspective of the buyer.

Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance, to personalize treatment or pricing. We investigate how to estimate price sensitivity from transaction-level data. In causal inference terms, we estimate heterogeneous treatment effects when (a) the response to treatment (here, whether a customer buys a product) is binary, and (b) treatment assignments are partially observed (here, full information is only available for purchased items). Methodology/Results: We propose a recursive partitioning procedure to estimate heterogeneous odds ratio, a widely used measure of treatment effect in medicine and social sciences. We integrate an adversarial imputation step to allow for robust estimation even in presence of partially observed treatment assignments. We validate our methodology on synthetic data and apply it to three case studies from political science, medicine, and revenue management. Managerial implications: Our robust heterogeneous odds ratio estimation method is a simple and intuitive tool to quantify heterogeneity in patients or customers and personalize interventions, while lifting a central limitation in many revenue management data. History: This paper has been accepted as part of the 2020 MSOM Data Driven Research Challenge.

Problem definition: This paper investigates the impact of COVID-19 on e-commerce sales and the underlying operational driver. Academic/practical relevance: As COVID-19 continues to disrupt offline retail, anecdotal evidence suggests a rapid growth of e-commerce. However, the pandemic may also significantly decrease offline logistics capacity, which in turn decreases e-commerce sales. Then, how does e-commerce respond to COVID-19, and what are the corresponding opportunities and challenges? Methodology: We leverage e-commerce sales data from Alibaba and construct a city-day panel across three years, representing sales for all buyers and sellers on the platform across 339 cities in mainland China. We develop three identification strategies to estimate the overall impact of COVID-19 (based on a year-on-year comparison), the impact of COVID-19 intensity (based on the different number of cases across cities), and the impact of corresponding containment measures (leveraging policy changes of checkpoint, partial shutdown, and complete shutdown measures across cities). Results: We provide two key findings. First, across different identification strategies, we observe a common drop and recovery pattern, which illustrates the digital resilience of e-commerce during the pandemic. For example, we estimate an overall decrease of 22% in e-commerce sales during the period of the Wuhan shutdown (January 23–April 7, 2020). However, it recovers in most cities within five weeks. Second, we identify a key operational driver—logistics capacity—that significantly explains the decline and recovery of e-commerce sales during and after the outbreak. Managerial implications: We provide important evidence on how e-commerce responds to and recovers from COVID-19, contrary to the common perception. The evidence in the recovery phase can also inform platforms and policymakers to design digital strategies and invest in logistics infrastructure.

The journal is pleased to publish the abstracts of the six finalists of the 2021 Manufacturing and Service Operations Management Society’s student paper competition. The 2021 prize committee was chaired by Vishal Agrawal (Georgetown University), Florin Ciocan (INSEAD), and Yanchong Zheng (Massachusetts Institute of Technology). The judges were Adam Elmachtoub, Adem Orsdemir, Amrita Kundu, Antoine Desir, Anyan Qi, Arian Aflaki, Arzum Akkas, Ashish Kabra, Bin Hu, Bora Keskin, Brent Moritz, Can Zhang, Chloe Kim Glaeser, Dan Iancu, Daniel Freund, Daniel Lin, Daniela Saban, David F. Drake, Dawson Kaaua, Divya Singhvi, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Enis Kayis, Ersin Korpeoglu, Evgeny Kagan, Fabian Sting, Fanyin Zheng, Fei Gao, Fernanda Bravo, Francisco Castro, Georgina Hall, Gonzalo Romero, Guangwen Kong, Guoming Lai, Hamsa Bastani, Hessam Bavafa, Hummy Song, Ioannis (Yannis) Stamatopoulos, Ioannis Bellos, Iris Wang, Jake Feldman, Jason Acimovic, Jiankun Sun, Jiaru Bai, John Silberholz, Joline Uichanco, Jonas Oddur Jonasson, Jose Guajardo, Kaitlin Daniels, Kenan Arifoglu, Lennart Baardman, Leon Valdes, Lesley Meng, Luyi Gui, Luyi Yang, Mary Parkinson, Mazhar Arikan, Mehmet Ayvaci, Miao Bai, Michael Freeman, Ming Hu, Morvarid Rahmani, Mumin Kurtulus, Nan Yang, Nektarios Oraiopoulos, Nikhil Garg, Nil Karacaoglu, Nitin Bakshi, Nur Sunar, Olga Perdikaki, Ovunc Yilmaz, Ozan Candogan, Ozge Sahin, Panos Markou, Pascale Crama, Pengyi Shi, Pnina Feldman, Qiuping Yu, Renyu Zhang, Ruslan Momot, Ruth Beer, Ruxian Wang, Saed Alizamir, Safak Yucel, Samantha Keppler, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Sebastien Martin, Serdar Simsek, Seyed Emadi, Shima Nassiri, Shouqiang Wang, Siddharth Singh, Simone Marinesi, So Yeon Chun, Somya Singhvi, Song-Hee Kim, Soo-Haeng Cho, Soroush Saghafian, Sriram Dasu, Stefanus Jasin, Stephen Leider, Suresh Muthulingam, Suvrat Dhanorkar, Tian Chan, Tim Kraft, Tom TAN, Tugce Martagan, Velibor Misic, Vishal Gupta, Weiming Zhu, Xiajun Amy Pan, Xiaoshan Peng, Xiaoyang Long, Yangfang (Helen) Zhou, Yehua Wei, Yiangos Papanastasiou, Ying-Ju Chen, Yinghao Zhang, Yoni Gur, Yuqian Xu, Zhaohui (Zoey) Jiang, Zumbul Atan.

Problem definition: We consider the problem of resolving ad hoc unpredictable congestion in environments where customers have private time valuations. We investigate the design of fair, efficient, budget-balanced, and implementable bidding mechanisms for observable queues. Academic/practical relevance: Our primary motivation comes from merging in algorithmic traffic, i.e., a driver wishing to merge in a relatively dense platoon of vehicles in a coordinated and efficient way, using intervehicle communication and micropayments, akin to an arriving customer trading for position in a single-server observable queue. Methodology: We analyze the performance of a mechanism where the queue joiner makes sequential take-it-or-leave-it bids from tail to head (T2H) of a platoon, with the condition that the vehicle can advance to the next position only if it wins the bid. This mechanism is designed so that it is implementable, balances the budget, and imposes no negative externalities. Results: We compared this mechanism with head to tail (H2T) bidding, which favors the merging driver but potentially causes uncompensated externalities. Assuming i.i.d. time valuations, we obtain the optimal bids, value functions, and expected social welfare in closed form in both mechanisms. Moreover, if the time valuation of the merging driver is not high, we show that the expected social welfare of T2H is close to a partial information social optimum and that the expected social welfare of H2T is lower than that of T2H as long as the platoon is not too short. Managerial implications: Our findings suggest that mechanisms based on sequential take-it-or-leave-it bids from T2H of an observable queue have good social welfare performance, even if the corresponding bids are not chosen optimally, as long as the time valuation of the arriving customer is not high. Nevertheless, the tension between individual incentives and social welfare seems hard to resolve, highlighting the role of platforms to enforce the cooperation of involved parties.

Problem definition: Delay announcements have become an essential tool in service system operations: They influence customer behavior and network efficiency. Most current delay announcement methods are designed for relatively simple environments with a single service station or stations in tandem. However, complex service systems, such as healthcare systems, often have fork-join (FJ) structures. Such systems usually suffer from long delays as a result of both resource scarcity and process synchronization, even when queues are fairly short. These systems may thus require more accurate delay estimation techniques than currently available. Methodology/results: We analyze a network comprising a single-server queue followed by a two-station FJ structure using a recursive construction of the Laplace–Stieltjes transform of the joint delay distribution, conditioning on customers’ movements in the network. Delay estimations are made at the time of arrival to the first station. Using data from an emergency department, we examine the accuracy and the robustness of the proposed approach, explore different model structures, and draw insights regarding the conditions under which the FJ structure should be explicitly modeled. We provide evidence that the proposed methodology is better than other commonly used queueing theory estimators such as last-to-enter-service (which is based on snapshot-principle arguments) and queue length, and we replicate previous results showing that the most accurate estimations are obtained when using our model result as a feature in state-of-the-art machine learning estimation methods. Managerial implications: Our results allow management to implement individual, real-time, state-dependent delay announcements in complex FJ networks. We also provide rules of thumb with which one could decide whether to use a model with an explicit FJ structure or to reduce it to a simpler model requiring less computational effort.

Problem definition: Treating many blood-related diseases requires transplantation of genetically compatible hematopoietic stem cells (HSCs) extracted from the bone marrow (BM) of live donors or the umbilical cord blood (CB) of babies. To facilitate the search for HSCs, institutions known as BM registries collect the details of potential donors and CB banks store units of CB. This paper focuses on the problem of joint optimization of the capacity of these two institutions. Academic/practical relevance: With more than 10 million genetic variants, limited inventory relative to this variety, and random replenishment, BM registry and CB bank compositions are random, interdependent, and change nondeterministically over time. Furthermore, BM and CB differ in their supply, costs, genetic matching criteria, and influences on medical outcomes, giving rise to important tradeoffs such that neither is preferred exclusively to the other. Jointly determining the optimal capacity of both sources is therefore both technically challenging and has immediate policy implications. Methodology: We develop a simulation-based approach to estimate the temporal variation in matching probabilities before incorporating the associated regression parameters into a mathematical model closely matching the research context. Results are contrasted against a simplified mathematical model, highlighting the importance of the dynamic setup. Results: Inventories of 17.5 million registered BM donors and 335,000 CB units are estimated as optimal for the U.S. population under reasonable assumptions. Expanding capacity to these levels would satisfy 33% of the currently unmet demand, increasing the transplantation rate to 98.7% and delivering $770 million of extra social surplus annually. Managerial implications: Rigorous policy analyses are imperative for designing evidence-based, cost-effective policies that deliver societal benefits. To this end, we provide quantitative evidence in support of calls for further expansion of the national BM registry and CB banks in the United States. We also propose annual recruitment targets for BM donors and CB units to maintain the two institutions at their suggested levels. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management.