Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2019 Manufacturing and Service Operations Management Society’s student paper competition. The 2019 prize committee was chaired by Feryal Erhun (University of Cambridge), Antonio Moreno (Harvard University), and Yi Xu (University of Maryland). The other committee members were Elodie Adida, Vishal Agrawal, Arzum Akkaş, Mazhar Arıkan, Jiaru Bai, Gah-Yi Ban, Hamsa Bastani, Bob Batt, Elena Belavina, Ioannis Bellos, Kostas Bimpikis, Fernanda Bravo, Robert Bray, Eduard Calvo, Ozan Candogan, Tian Chan, Ying-Ju Chen, Soo-Haeng Cho, So Yeon Chun, Florin Ciocan, Pascale Crama, Ruomeng Cui, Kaitlin Daniels, Kris Ferreira, Santiago Gallino, Esma Gel, Chloe Kim Glaeser, Xiting Gong, Jose Guajardo, Ming Hu, Dan Iancu, Stefanus Jasin, Houyuan Jiang, Ashish Kabra, Itir Karaesmen Aydin, Enis Kayış, Diwas KC, Bora Keskin, Song-Hee Kim, Tim Kraft, Mirko Kremer, Mümin Kurtuluş, Guoming Lai, Daniel Lin, Fang Liu, Velibor Mišić, Suresh Muthulingam, Aris Oraiopoulos, Adem Orsdemir, Yiangos Papanastasiou, Chris Parker, Olga Perdikaki, Anyan Qi, Morvarid Rahmani, Guillaume Roels, Soroush Saghafian, Ozge Sahin, Burhan Sandıkçı, Juan Serpa, Pengyi Shi, Hummy Song, Brad Staats, Yannis Stamatopoulos, Sandra Sulz, Nur Sunar, Nicos Trichakis, John Turner, Jingqi Wang, Ruxian Wang, Shouqiang Wang, Yehua Wei, Wenqiang Xiao, Linwei Xin, Nan Yang, Renyue Zhang, Karen Zheng, and Weiming Zhu.

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 Christopher Tang would like to express his deepest gratitude to all those who served as reviewers for the journal in 2019. 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 2019 Meritorious Service Award to …

Problem definition: Tasks sequentially arrive, and their values to the workers who are going to perform them are independent random variables. The common way to allocate tasks to workers is according to the first-in, first-out order. But this method both is inefficient and seems unfair to those who receive a low-valued task after a long wait. We are looking for a better allocation method. Academic/practical relevance: Finding a fair and efficient task allocation method is an aspiration of manpower firms that employ a pool of workers, such as salespersons, technicians, emergency medical stuff, nurses, or taxi drivers. We present many more implementations, such as turn taking and load management. Methodology: We propose a self-selected task allocation method and discuss its importance and implementations. The proposed method is defined as a cyclic queueing game with a fixed number of players. Every unit of time a prize with a random value is offered to the players according to their order in the queue, and a player who accepts a prize moves to the end of the queue. The process of choosing which prizes to accept in each position is presented as a noncooperative multiplayer game. We analyze strategies and symmetric equilibria for three variations. Results: We provide closed-form solutions and suggest a novel intuitive interpretation to find equilibria via calculating maximum-profit strategies. We complement the theoretical results by conducting a numerical study. Managerial implications: The proposed method is natural and easy to implement, its outcome is better than the common allocation by seniority, and the ratio of the expected value obtained under the two methods is unbounded.

Policy modeling refers to the application of operations research and allied quantitative methods to model policy problems. The idea is to help make better policy choices when data are uncertain or ...

Problem definition: People often make service-quality judgments based on information about the quality of each server even though they care primarily about the quality each customer experiences. When and how do server-level quality metrics differ from customer-experienced ones? Can people properly account for these differences, or do they drive human judgment and decision biases? Academic/practical relevance: Biased judgments about service quality can cause governments to fund programs suboptimally, organizations to promote the wrong employees, and customers to make disappointing purchases. We further our understanding of the role that cognitive biases play in services and how to manage quality information in light of them. Methodology: We use a mathematical model to define the gap between server-level and customer-experienced quality metrics. We use secondary data in the context of the higher-education industry to quantify the customer–server quality gap in practice. We construct a behavioral model to derive hypotheses about how environmental factors impact the direction and magnitude of judgment biases. Controlled laboratory experiments test the hypothesized biases and mitigation techniques. Results: Our empirical study reveals that the two measures differ enough to drive significant differences in the rank order of school majors, teachers, and airports. Our experiments support our main conjecture that judgments and decisions about customer-experienced metrics are biased toward server-level metrics. Consequently, (1) judgments about customer-experienced quality are biased high/low when quality and server load are negatively/positively correlated, (2) judgments about a server’s absolute impact on customer experience are biased high/low when a server has a smaller/larger load than average, and (3) providing customer-experienced quality metrics mitigate these biases. Managerial implications: Our results help identify when and why service-quality metrics are likely to mislead judgments and bias decisions as well as who is likely to benefit from such biases. The results also guide system designers on how to report metrics when seeking to help support effective decision making.

Problem definition : We study how to optimally control a multistage supply chain in which each location can initiate multiple flows of product, including the reverse flow of orders. We also quantify the resulting value generated by reverse logistics and identify the drivers of that value. Academic/practical relevance : Reverse logistics has been gaining recognition in practice and theory for helping companies better match supply with demand, and thus reduce costs in their supply chains. Nevertheless, there remains a lack of clarity in practice and the research literature regarding precisely what in reverse logistics is so important, exactly how reverse logistics creates value, and what the drivers of that value are. Methodology : We first formulate a multistage inventory model to jointly optimize ordering decisions pertaining to regular, reverse, and expedited flows of product in a logistics supply chain, where the physical transformation of the product is completed at the most upstream location. With multiple product flows, the feasible region for the problem acquires multidimensional boundaries that lead to the curse of dimensionality. Next, we extend our analysis to product-transforming supply chains, in which product transformation is allowed to occur at each location. In such a system, it becomes necessary to keep track of both the location and stage of completion of each unit of inventory; thus, the number of state and decision variables increases with the square of the number of locations. Results : To solve the reverse logistics problem in logistics supply chains, we develop a different solution method that allows us to reduce the dimensionality of the feasible region and identify the structure of the optimal policy. We refer to this policy as a nested echelon base stock policy, as decisions for different product flows are sequentially nested within each other. We show that this policy renders the model analytically and numerically tractable. Our results provide actionable policies for firms to jointly manage the three different product flows in their supply chains and allow us to arrive at insights regarding the main drivers of the value of reverse logistics. One of our key findings is that, when it comes to the value generated by reverse logistics, demand variability (i.e., demand uncertainty across periods) matters more than demand volatility (i.e., demand uncertainty within each period). To analyze product-transforming supply chains, we first identify a policy that provides a lower bound on the total cost. Then, we establish a special decomposition of the objective cost function that allows us to propose a novel heuristic policy. We find that the performance gap of our heuristic policy relative to the lower-bounding policy averages less than 5% across a range of parameters and supply chain lengths. Managerial implications : Researchers can build on our methodology to study more complex reverse logistics settings, as well as tackle other inventory problems with multidimensional boundaries of the feasible region. Our insights can help companies involved in reverse logistics to better manage their orders for products, and better understand the value created by this capability and when (not) to invest in reverse logistics.

Problem definition: U.S. nursing homes allocate federal licensed beds among three categories: Medicare-dedicated beds, Medicaid-dedicated beds, and flexible beds for both populations. This article studies how nonprofit and for-profit nursing homes make bed allocation decisions and how this affects access to care for the economically disadvantaged Medicaid population. Methodology/results: Analyzing U.S. nursing home data during 2012–2017, we observe that nonprofit nursing homes on average had a higher Medicare-dedicated bed share than for-profit ones. Furthermore, nursing homes reduced their Medicare-dedicated bed share after being converted from nonprofit to for profit. Motivated by these empirical observations, we construct a queueing network model to study the bed allocation decision of a nursing home, whose objective function is formulated as the sum of its profit and its resident welfare, including the costs of blocking and waiting, weighted by the degree of altruism. The higher the altruism is, the more a nursing home acts like a nonprofit organization that cares about not only profit but also, resident welfare. We show that when prospective Medicaid demand is sufficiently high, more altruistic nursing homes choose a higher Medicare-dedicated bed share, thereby lowering Medicaid access; when prospective Medicare demand is sufficiently low, they choose a lower Medicare-dedicated bed share, thereby increasing Medicaid access. We further calibrate the model to predict the directional change in bed allocation following ownership conversions. Managerial implications: Contrary to common concerns, our empirical analysis shows that the expansion of the for-profit nursing home sector does not necessarily reduce access to care for the Medicaid population. Our theoretical model demonstrates that the impact of ownership type on bed allocation is shaped by the local demand mix from prospective Medicare and Medicaid residents. These findings offer actionable guidance for regulators evaluating ownership conversion requests, with our data-calibrated framework helping to inform approval decisions. Funding: The authors acknowledge financial support from the National Natural Science Foundation of China [Grant W2433175]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0937 .

Problem definition: Buyers can encourage competition among multiple suppliers of goods or services by redistributing their shares of the business based on recent observable performances. This is us...

Problem definition : Given the variety of urgency levels in highly utilized operating rooms, capacity allocation decisions can have a major impact on how wait times are rationed. We examine a longer-term sequential capacity planning problem in which a hospital allocates operating room time to different surgical specialties. We seek to minimize an urgency-weighted wait-time metric. Academic/practical relevance : Our data set on patient selection patterns revealed considerable noise in the queuing discipline. We apply an urn model to generate a probabilistic queuing discipline, which validates well against the selection patterns observed in practice. We believe that this model may prove to be useful for representing noisy queuing disciplines in other settings. Also, our validated simulation model, in combination with our proposed solution approach, demonstrates a substantial reduction in urgency-weighed wait times. Methodology : For representing the noisy queuing discipline, we fit a Wallenius noncentral hypergeometric distribution. We formulate the capacity allocation problem as a Markov decision process. The large state space and detailed system dynamics lead us to simulation-based dynamic programming approaches for finding good capacity allocation decisions. Rather than approximate the expected cost-to-go function, we propose a limited look-ahead policy and embed this in a rolling-horizon framework. Results : Our baseline model-based allocation policy yields a 14.3% reduction in urgency-weighed wait time compared with current practice. It also results in a 21.0% improvement in the number of patients treated within their urgency-based recommended wait-time limits. Managerial implications : In elective surgery settings, it may be important to ration capacity in a way that considers the different urgency levels of patients. We propose a flexible modeling approach for achieving this.

Problem definition : When sourcing through a procurement service provider (PSP), the PSP often collects rebates from unethical manufacturers in developing countries (as referral fees) that are “hidden” from the retailers. Recognizing that a PSP has a strong incentive to solicit quotes from unethical manufacturers, we examine a situation in which the retailer insists on soliciting a quote from a manufacturer designated by the retailer and a separate quote from an unethical manufacturer selected by the PSP. However, when the designated manufacturer is ethical, the PSP has an incentive to inflate the quote from this ethical manufacturer in order to help the unethical manufacturer to win. Facing this situation, is there a mechanism for the retailer to control hidden rebates? Academic/practical relevance : The issue of hidden rebates is a “known secret” in global supply chain practice. Also, hidden rebates increase the customs duty for U.S. importers because of the first sales rule for customs valuation of U.S. imports. Therefore, there is a need to understand the implications of hidden rebates and to control this unethical practice. Methodology : To circumvent the issue of hidden rebates and quote inflations, we develop a deterministic, incentive-compatible mechanism that is based on a simple selection rule (for selecting a manufacturer) and a contingent service fee (as a reward for the service provided by the PSP). Results : Our optimal mechanism creates incentives to (1) deter the PSP from inflating the quote submitted from the ethical manufacturer, (2) reduce the incidence of hidden rebates, and (3) reduce the retailer’s procurement cost and the corresponding import tax significantly. More importantly, relative to the “lowest quote wins” selection rule, the optimal mechanism is Pareto-improving for the retailer and the service provider when the hidden rebate is below a certain threshold. Furthermore, we extend our analysis to the case in which (1) the retailer is not sure whether the designated manufacturer is ethical or not, (2) the retailer does not know the exact value of hidden rebate (but it follows a two-point distribution), and (3) the retailer may verify the quote with its designated manufacturer before a formal contract. We also explore the stochastic incentive-compatible mechanism for the cases in which the penalty is unenforceable or enforceable. Managerial implications : When law enforcement is inconsistent in developing countries, retailers should beware of the existence and implications of hidden rebates. We provide a simple mechanism that a retailer can consider as a practical way to deter the PSP from inflating certain quotes and put hidden rebates under control.