Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2020 Manufacturing and Service Operations Management Society’s student paper competition. The 2020 prize committee was chaired by Vishal Agrawal (Georgetown), Feryal Erhun (University of Cambridge), and Jun Li (University of Michigan). The judges were Adem Orsdemir, Antoine Desir, Anton Ovchinnikov, Anyan Qi, Arian Aflaki, Arzum Akkas, Ashish Kabra, Bin Hu, Bob Batt, Bora Keskin, Can Zhang, Carri Chan, Chloe Kim Glaeser, Daniel Lin, Eduard Calvo, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Enis Kayış, Ersin Korpeoglu, Fabian Sting, Fang Liu, Fanyin Zheng, Fei Gao, Florin Ciocan, Gah-Yi Ban, Gizem Korpeoglu, Guihua Wang, Guillaume Roels, Guoming Lai, Hessam Bavafa, Hummy Song, Ioannis (Yannis) Stamatopoulos, Ioannis Bellos, Iris Wang, Itir Karaesmen, Jiankun Sun, Jiaru Bai, Jing Wu, Joann de Zegher, Joel Wooten, John Silberholz, Jose Guajardo, Kaitlin Daniels, Karen Zheng, Ken Moon, Kenan Arifoglu, Lennart Baardman, Leon Valdes, Lesley Meng, Linwei Xin, Luyi Gui, Luyi Yang, Mary Parkinson, Mazhar Arikan, Michael Freeman, Ming Hu, Morvarid Rahmani, Mumin Kurtulus, Nan Yang, Necati Tereyagoglu, Nektarios Oraiopoulos, Nikos Trichakis, Nil Karacaoglu, Nitin Bakshi, Niyazi Taneri, Nur Sunar, Olga Perdikaki, Ovunc Yilmaz, Ozan Candogan, Ozge Sahin, Panos Markou, Pascale Crama, Pengyi Shi, Pnina Feldman, Qiuping Yu, Renyu (Philip) Zhang, Robert Bray, Ruslan Momot, Ruxian Wang, Saed Alizamir, Safak Yucel, Samantha Keppler, Santiago Gallino, Serdar Simsek, Seyed Emadi, Shiliang (John) Cui, Shouqiang Wang, Simone Marinesi, So Yeon Chun, Song-Hee Kim, Soo-Haeng Cho, Soroush Saghafian, Stefanus Jasin, Suresh Muthulingam, Suvrat Dhanorkar, Tian Chan, Tim Kraft, Tom Tan, Tugce Martagan, Velibor Misic, Weiming Zhu, Xiaoshan Peng, Xiaoyang Long, Yasemin Limon, Yehua Wei, Yiangos Papanastasiou, Ying-Ju Chen, and Zumbul Atan.

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 would like to express her deepest gratitude to all those who served as reviewers for the journal in 2020. 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 2020 Meritorious Service Award to…

Problem definition: A firm faces random demand for a service it delivers on a given future date. To boost demand, the firm hires a sales agent who exerts unobservable effort continuously over time. The firm is concerned not only with increasing current demand, but also with smoothing demand over time to avoid losing goodwill if realized demand exceeds available inventory. Methodology/results: We study the firm’s incentive design problem using a novel continuous-time principal-agent framework, in which demand drifts over time in response to the agent’s unobserved effort, as well as the price the firm charges. To induce the agent’s sales effort, the firm chooses an incentive scheme that depends on the remaining inventory and the time to the service (e.g., time to departure in the case of airlines). We characterize the firm’s optimal incentive scheme under both static and dynamic pricing policies. Using parameter values calibrated from the airline industry, we numerically show that under dynamic pricing, the use of a static incentive scheme helps the firm reap nearly all the benefits of the corresponding dynamic incentive scheme. In contrast, the use of a fully static strategy results in a significant loss of efficiency. Managerial implications: Comparing partially dynamic strategies, we find that dynamic contracting outperforms dynamic pricing when inventory is abundant. However, under limited inventory, the relative advantage depends nonmonotonically on demand elasticity: dynamic pricing dominates over the empirically relevant range of moderate to high elasticity, whereas dynamic contracting becomes more effective when elasticity is very low or in theoretical limits of extreme elasticity. History: This paper was selected as part of the 1RR initiative between the M&SOM journal and the MSOM Society. This paper was part of the 2024 MSOM Interface of Finance, Operations and Risk Management (iFORM) SIG Conference.

In the context of rapid economic development, the status of quality in enterprises is gradually rising, and high-quality development has become a goal that enterprises continue to pursue. In this era of quality wins, the importance of quality cannot be ignored. This has also led many manufacturing companies to set up quality improvement teams to enhance the quality of products or services and thus enhance their competitiveness. Innate immunity is the basis of immunity and the foundation of maintaining the health of the team. Therefore, the quality improvement team of a manufacturing company first needs to enhance its own immunity and strive to maintain the health of the team's own innate immune system in order to maintain the normal operation of the team. Therefore, this article aims at related issues, uses the principle of bionics, and takes innate immunity as the starting point to study the operating mechanism of quality improvement team innate immune system health in manufacturing enterprises. The antecedent variables are selected as information sharing and employee incentives; the mediating variables are the constructive elements of the innate immunity of organizational quality are quality resources and quality culture; the moderating variable is team conflict; the result variable is the health of the immune system. Through the combination and configuration of all parties, jointly construct its theoretical model, study the operating mechanism, and ultimately improve the quality of the manufacturing enterprise. The healthy operation of the team's innate immune system provides a theoretical basis.

Problem definition: Autonomous sensors connected through the internet of things (IoT) are deployed by different firms in the same environment. The sensors measure an important operating-condition state variable, but their measurements are noisy, so estimates are imperfect. Sensors can improve their own estimates by soliciting estimates from other sensors. The choice of which sensors to communicate with (target) is challenging because sensors (1) are constrained in the number of sensors they can target and (2) only have partial knowledge of how other sensors operate—that is, they do not know others’ underlying inference algorithms/models. We study the targeting problem, examine the evolution of interfirm sensor communication patterns, and explore what drives the patterns. Academic/practical relevance: Many industries are increasingly using sensors to drive improvements in key performance metrics (e.g., asset uptime) through better information on operating conditions. Sensors will communicate among themselves to improve estimation. This IoT vision will have a major impact on operations management (OM), and OM scholars need to develop and examine models and frameworks to better understand sensor interactions. Methodology: Analytic modeling combining decision-making, estimation, optimization, and learning is used. Results: We show that when selecting its target(s), each sensor needs to consider both the measurement quality of the other sensors and its level of familiarity with their inference models. We establish that the state of the environment plays a key role in mediating quality and familiarity. When sensor qualities are public, we show that each sensor eventually settles on a constant target set, but this long-run target set is sample-path dependent (i.e., dependent on past states) and varies by sensor. The long-run network, however, can be fully defined at time zero as a random directed graph, and hence, one can probabilistically predict it. This prediction can be made perfect (i.e., the network can be identified in a deterministic way) after observing the state values for a limited number of periods. When sensor qualities are private, our results reveal that sensors may not settle on a constant target set but the subset among which it cycles can still be stochastically predicted. Managerial implications: Our work allows managers to predict (and influence) the set of other firms with which their sensors will form information links. Analogous to a manufacturer mapping its supplier base to help manage supply continuity, our work enables a firm to map its sensor-based-information suppliers to help manage information continuity.

Problem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm’s profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers’ limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design.

Problem definition: Among the most vexing issues in the U.S. healthcare ecosystem is inappropriate use of percutaneous coronary intervention (PCI) procedures, also known as overstenting. A key driver of overstenting is physician subjectivity in eyeballing a coronary angiogram. Advanced tests such as fractional flow reserve (FFR) provide more precise and objective measures of PCI appropriateness, yet the decision to perform these tests is endogenous and not immune to clinical ambiguity associated with eyeballing. Additionally, conflicts of interest, arising from revenue-generating incentives, play a role in overstenting. Academic/practical relevance: Conventional wisdom suggests more precise diagnostic testing will help reduce overtreatment. However, the literature rarely recognizes that the testing decision is itself endogenous. Our research highlights the role of endogeneity surrounding interventional cardiology decision making. Methodology: This study uses stochastic modeling and simulation. Results: Under a low conflict-of-interest level, the physician performs the advanced test for intermediate lesions. Under a high conflict-of-interest level, however, the physician would perform the advanced test only for high-grade lesions, because of a financial disincentive: Performing the advanced test may lower PCI revenue if the test results argue against the procedure. Surprisingly, despite this disincentive, a more revenue-driven physician can be more inclined to perform the advanced test. Managerial implications: Our model leads to implications for various efforts aimed at tackling overstenting: (1) Attention should be paid not only to the sheer quantity of FFR procedures but to which patients receive FFR procedures; (2) reducing the risk of the advanced test has a behavior-inducing effect, yet a modest risk reduction may lower patient welfare; and (3) offering a bonus to the physician for performing FFR procedures equal to a third of its reimbursement rate will cause only a 5% increase in average physician payment while inducing a 26% decline in overstenting. In addition, we show implementing a bundled payment scheme may discourage the use of FFR procedures and lead to more salient overstenting.

Problem definition: The U.S. federal government makes significant investments in technology programs to deliver essential services to the public. The execution of a program is monitored against a baseline—an aggregate plan representing the program’s planned budget, schedule and scope. Recent reports suggest that federal technology programs are re-baselined multiple times, resulting in additional spending of taxpayer money. Although a program’s scope has often been considered a driver of baseline changes, we have a limited understanding of the execution factors that may affect this relationship. Academic/Practical Relevance: With increasing bipartisan scrutiny of federal spending in technology programs and continuing debate in the media about their execution, a nuanced understanding of the drivers of baseline changes in federal technology programs is a critical and contemporary line of inquiry relevant to both policymakers and managers. Our study also responds to recent calls in the operations management literature for research on public sector operations. Methodology: The study sample comprises detailed archival data on 240 U.S. federal government technology programs across 24 federal agencies. We estimate a negative binomial regression specification that accounts for agency fixed effects and several program-specific characteristics to test four hypotheses on the interrelationships between a program’s scope, granularity, management competency, execution methodology and baseline changes.. Results: The results indicate that program scope is positively associated with the number of baseline changes. However, increasing levels of program granularity and program management competency attenuate this positive relationship. Additional analysis highlights the significant savings in taxpayer contributions that can occur by reducing baseline changes in programs of greater scope through an increase in the levels of program granularity and program management competency. Managerial Implications: The study results emphasize the need for federal agencies to invest greater efforts in granularizing a program and in identifying managers with high levels of program management competency when program scope is high, as such efforts can translate into a reduction in the number of baseline changes. The results also highlight the role of number of baseline changes as a valuable in-process metric for program managers and federal agencies to monitor the execution of federal technology programs and identify programs with greater potential. for experiencing cost overruns.

Problem definition: We explore the impacts of store-brand (SB) introduction on multilateral contracting in vertical supply relationships that involve two upstream national-brand manufacturers (NBMs) selling through a common retailer. Two different information structures are scrutinized: simultaneous (secret offers) versus sequential contracting (public offers), essentially different timing by which the NBMs contract with the retailer. Academic/practical relevance: SB products are prevalent nowadays; however, the market shares in different categories vary substantially, from negligible sales (e.g., alcoholic beverages) to more than half of the total sales (e.g., milk). As retailers encroach on the NBMs’ product market, their relationships are reshaped accordingly. Thus, investigating whether SB introduction would overturn the conventional wisdom about multilateral contracting is pertinent. Methodology: The methodology is noncooperative game theory. Results: We identify a boundary equilibrium where the sale of the SB is negligible, but its presence enables the retailer to intensify the upstream competition and elicits better wholesale contracts. We show that this equilibrium tends to occur in a wider region under sequential contracting than under simultaneous contracting. In the boundary equilibrium of sequential contracting, the NBM could entail a first-mover advantage, a stark contrast to the second-mover advantage in the nonboundary equilibrium. Further, as opposed to the uniqueness of sequential contracting, we characterize a continuum of boundary equilibria under simultaneous contracting such that symmetric NBMs may even set asymmetric wholesale prices so as to drive the SB out of the market. Managerial implications: We provide a rationale for the observed negligible sales of certain SBs and further shed light on the choice between public and secret offers. Public offers could perform better for the retailer who, in turn, benefits from information leakage. With public offers, the NBMs’ preference for the leadership could also be reversed for SBs with negligible sales. Because of the intricate impact of SBs on contracting sequence, these two instruments should be jointly analyzed.