Production and Operations Management最新文献

{"title":"Manufacturer Encroachment in the Presence of Production Economies of Scale.","authors":"Wei Li, Jing Chen, Hubert Pun","doi":"10.1177/10591478251378836","DOIUrl":"https://doi.org/10.1177/10591478251378836","url":null,"abstract":"<p><p>It is well established that a manufacturer generally benefits from encroachment with a profitable direct channel and may also benefit from using encroachment as a threat (i.e., without sales in the direct channel); the retailer may also benefit from both encroachment strategies. Our study provides new insights into the manufacturer encroachment literature by considering the upstream manufacturer's production economies of scale. Contrary to conventional wisdom, we show that an increasing level of economies of scale may reduce the manufacturer's profit if the manufacturer encroaches. Furthermore, we find that under strong economies of scale, refraining from encroachment may be the optimal strategy, even if encroachment could increase the manufacturer's wholesale profit. This finding suggests that a manufacturer may choose not to encroach solely due to profit losses in direct selling. Interestingly, we also find that the manufacturer can benefit from encroachment by maintaining an unprofitable direct channel with sales, provided that the level of economies of scale is below a threshold. Moreover, our findings reveal that the retailer can benefit from manufacturer encroachment only when the level of economies of scale remains below this threshold, and that an increasing level of economies of scale reduces the likelihood that the retailer can benefit.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"35 4","pages":"1412-1429"},"PeriodicalIF":5.1,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13038154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147594048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advance Multi-Priority, Multi-Appointment Patient Scheduling With Dependent Demand and Lead Times.","authors":"Shahryar Moradi, Antoine Sauré, Jonathan Patrick","doi":"10.1177/10591478251356474","DOIUrl":"https://doi.org/10.1177/10591478251356474","url":null,"abstract":"<p><p>This study examines a patient scheduling problem with multiple appointment types and priority levels, where certain appointments must precede others and lead times play a crucial role. Although both factors significantly influence the quality of care-particularly when specialist assessments depend on timely diagnostic tests-they have been largely overlooked in existing healthcare scheduling models. To address this gap, we propose a dynamic scheduling model that explicitly incorporates appointment dependencies, lead times, and patient heterogeneity across multiple priority levels. The model reflects the real-world complexities of coordinating diagnostic and consult appointments in time-sensitive clinical settings. Using Approximate Dynamic Programming techniques, we develop an Approximate Optimal Policy (AOP) that efficiently allocates clinical resources, minimizes patient wait times, and ensures the availability of test results prior to consult appointments. We further derive a closed-form solution for the optimal approximation parameters, supported by a mathematical proof, which offers significant computational advantages. We evaluate the performance of the proposed AOP through simulation and compare it against a set of benchmark policies, including heuristics adapted from existing scheduling logic and current clinical practice. The solution is applied to a case study created based on data from a Stroke Prevention Clinic (SPC), where the complexity of care protocols and high demand present substantial scheduling challenges. The results demonstrate that the AOP consistently outperforms all benchmarks in terms of reducing wait times, ensuring timely diagnostic completion before consults, and meeting wait-time targets. We also introduce a practical, easy-to-implement heuristic called (MSP), which is derived from the AOP and designed for operational use. While simpler in structure, MSP performs comparably well and is well-suited for adoption in real healthcare settings due to its interpretability and minimal computational requirements. Finally, although the proposed approach is demonstrated in the context of an SPC, it has broader applicability to other areas such as cancer care, kidney transplant scheduling, and other complex care pathways involving interdependent appointments and prioritization.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"35 3","pages":"855-875"},"PeriodicalIF":5.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13046263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who Benefits From Government Tax-Subsidies for Corporate Charitable Food Donations?","authors":"Armağan Özbilge, Saif Benjaafar, Elkafi Hassini, Mahmut Parlar","doi":"10.1177/10591478251350098","DOIUrl":"https://doi.org/10.1177/10591478251350098","url":null,"abstract":"<p><p>Leveraging government tax incentives to prompt corporate charitable giving has gained considerable popularity over the past decade. This paper sheds light on the broader consequences of the U.S. government's tax-subsidy policy for charitable food donations, which is determined based on the fair market value (FMV) of the donated products. We incorporate the tax-subsidy into a monopolist food retailer's after-tax profit function. Market demand is both price- and quality-dependent, and the shelf-life of the goods is determined by their initial quality and deterioration rate. The retailer makes joint quantity and pricing decisions over two periods, procuring goods at the start of the selling season and (possibly) donating at the end of period 1. We characterize the retailer's optimal policy and specify conditions under which she donates some, all, or none of her leftover inventory. We explore the impact of government tax-subsidies on the retailer's actions, consumer surplus, quantity of donations, and total welfare. We show that tax-subsidies may motivate retailers to intentionally create supply scarcity (by donating more) to increase FMV (determined by the second-period price), thereby enhancing tax deductions. While tax-subsidies encourage donations, they can also unintentionally harm consumers by reducing supply and raising prices. Interestingly, we show that a higher subsidy does not necessarily lead to more donations; the retailer may choose to donate fewer units to achieve the same tax deduction while increasing sales revenue. We investigate conditions under which tax-subsidies can simultaneously increase donations, consumer surplus, and retail profit. We show that this outcome is possible only when retailers donate low-quality goods in modest quantities. Our findings reveal how FMV-dependent tax-subsidies can backfire, reducing both consumer surplus and total welfare while benefiting the retailer. Governments must carefully weigh the benefits of donations against potential harm caused to consumers.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"35 2","pages":"649-664"},"PeriodicalIF":5.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13044475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Political Uncertainty and the Timing of Mass Layoffs.","authors":"Varouj Aivazian, Tzu-Ting Chiu, Miguel Minutti-Meza, Dushyantkumar Vyas","doi":"10.1177/10591478251331149","DOIUrl":"https://doi.org/10.1177/10591478251331149","url":null,"abstract":"<p><p>This study examines the relation between political uncertainty arising from state-level election cycles and the timing of employee dismissal and plant closure notices filed by US firms under the Worker Adjustment and Retraining Notification (WARN) Act of 1988 (hereafter, WARN notices). We appeal to a real options framework to predict that firms delay layoff decisions and the issuance of WARN notices until the resolution of political uncertainty. Using establishment-level data on layoffs disclosed in WARN notices and state elections occurring between 1994 and 2022, we document that the likelihood of issuing WARN notices declines during the election quarter but increases in the subsequent quarter. Cross-sectional findings show that political uncertainty plays a significant role in the timing of WARN notices during election periods while other factors, including partisanship, economic conditions, union strength, and firm visibility, may also play a role. Further, firms that delay WARN notices do not experience a significant deterioration in their medium-term financial performance. Overall, our findings provide evidence that firms delay labor adjustment decisions and the announcements of such decisions in response to political uncertainty.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"34 10","pages":"3193-3213"},"PeriodicalIF":5.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13046240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sellers' Peer Comparison Under Uncertainty in Online Marketplace.","authors":"Yun Zhou, Zhoupeng Jack Zhang, Ming Hu, Haitao Tony Cui","doi":"10.1177/10591478251325541","DOIUrl":"10.1177/10591478251325541","url":null,"abstract":"<p><p>How will peer pressure among sellers affect their operations in an online marketplace? Motivated by online platforms' marketplace designs that prompt sellers to compare their performances, in this paper, we develop and study a price competition model in which sellers account for both profits and peer comparison outcomes. In our model, two sellers offer substitutable products, and each of them sets a price ex ante to maximize their expected total utility, which is the sum of one's profit and the payoff from peer comparison. In particular, peer comparison takes place ex post based on sellers' realized sales. It results in a penalty for one's underperformance (i.e., sellers are behind-averse) or a reward for outperformance (i.e., sellers are ahead-seeking) relative to the other seller. Contrary to what extant research on social comparison would predict, we find that peer comparison is not always pro-competitive. Indeed, while the behind-aversion aspect of peer comparison fosters competition, the ahead-seeking aspect can be anti-competitive when the market uncertainty is sufficiently large. This is because market uncertainty causes a greater variation in sellers' performance disparity ex post (<i>uncertainty effect</i>), which can have a more salient impact on sellers than the expectation of their performance gap (<i>comparison effect</i>); While sellers' behind-aversion further aggravates the uncertainty effect and encourages them to take more aggressive actions, their ahead-seeking counterbalances the tension by absorbing part of it into the comparison effect and moderating the marginal disutility of lagging behind. Overall, we find that peer comparison can intensify sellers' price competition, which lowers the expected profits and utilities for both sellers, benefits the consumers, and reduces the hosting platform's profit. Our main insights are robust in a number of extensions, including general demand specifications, seller asymmetry, sellers' misperceptions of market uncertainties, and consumers' reference-dependent decision-making. They highlight the importance of sellers' behavioral regularities in online platforms' daily operations and shed light on marketplace designs regarding algorithmic transparency, information sharing, and so forth.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"34 9","pages":"2679-2699"},"PeriodicalIF":5.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translating Empirical State-Dependent Service Times Into Queueing Models.","authors":"Likang Ding, Bora Kolfal, Armann Ingolfsson","doi":"10.1177/10591478241309662","DOIUrl":"https://doi.org/10.1177/10591478241309662","url":null,"abstract":"<p><p>Recent empirical studies suggest that human behavior in queues causes workload-dependent service times. We investigate the translation of empirical service times into state-dependent queueing models. To this end, we identify two types of state-dependent models, static and dynamic, and two types of corresponding behavioral mechanisms. For example, we view customer early task initiation as a static mechanism and social speedup pressure as a dynamic mechanism. For each model type, we discuss behavioral mechanisms consistent with the model assumptions and indicate how empirical service times can be translated into model input parameters. We illustrate how translating service times into dynamic models can result in invalid service rates, which provides evidence against dynamic mechanisms. For dynamic models, we find that mean service times are in general not the inverse of service rates, the directional change in service rates is not always the opposite of the directional change in mean service times, and workload measurement timing can drastically impact mean service time patterns. We provide closed-form equations to convert service times into service rates and vice versa, and find conditions under which monotonic mean service times imply monotonic service rates and vice versa. Our results provide guidelines for researchers to select and specify an appropriate state-dependent queueing model from service time data, and expand the scope of previously published analytical results.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"34 7","pages":"2015-2031"},"PeriodicalIF":5.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13044487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Agent Deep Reinforcement Learning for Multi-Echelon Inventory Management.","authors":"Xiaotian Liu, Ming Hu, Yijie Peng, Yaodong Yang","doi":"10.1177/10591478241305863","DOIUrl":"https://doi.org/10.1177/10591478241305863","url":null,"abstract":"<p><p>We apply heterogeneous-agent proximal policy optimization (HAPPO), a multi-agent deep reinforcement learning (MADRL) algorithm, to the decentralized multi-echelon inventory management problems in both a serial supply chain and a supply chain network. We also examine whether the upfront-only information-sharing mechanism used in MADRL helps alleviate the bullwhip effect. Our results show that policies constructed by HAPPO achieve lower overall costs than policies constructed by single-agent deep reinforcement learning and other heuristic policies. Also, the application of HAPPO results in a less significant bullwhip effect than policies constructed by single-agent deep reinforcement learning where information is not shared among actors. Somewhat surprisingly, compared to using the overall costs of the system as a minimization target for each actor, HAPPO achieves lower overall costs when the minimization target for each actor is a combination of its own costs and the overall costs of the system. Our results provide a new perspective on the benefit of information sharing inside the supply chain that helps alleviate the bullwhip effect and improve the overall performance of the system. Upfront information sharing and action coordination in model training among actors is essential, with the former even more essential, for improving a supply chain's overall performance when applying MADRL. Neither actors being fully self-interested nor actors being fully system-focused leads to the best practical performance of policies learned and constructed by MADRL. Our results also verify MADRL's potential in solving various multi-echelon inventory management problems with complex supply chain structures and in non-stationary market environments.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"34 7","pages":"1836-1856"},"PeriodicalIF":5.1,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13044432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to Balance the Performance and Deterioration of Aging Systems Through Derating.","authors":"Jue Wang","doi":"10.1177/10591478241305339","DOIUrl":"https://doi.org/10.1177/10591478241305339","url":null,"abstract":"<p><p>A common strategy of extending the lifetime of an aging system is to reduce its workload below the normal operating level, a practice known as derating. While derating can slow the deterioration process, it often comes at the expense of reduced performance. Thus, derating involves a trade-off between performance and deterioration. Central to the optimal derating strategy is the relationship between deterioration and workload, also referred to as the pd-relationship. In practice, however, this relationship is rarely known a priori. We consider the workload optimization when the pd-relationship can be adaptively learned through sequential experimentation, or active learning. We show that the workload not only influences the performance and deterioration but also controls the speed of learning. The decision-maker must therefore account for the complex interplay between performance, deterioration, and information in real time. We formulate this problem as a partially observable Markov decision process and characterize the optimal policy. A key structural insight is that the optimal workload is always less than the myopic load. We further propose an efficient algorithm based on the fast Gauss transform to compute the optimal policies. The model is validated with vibration data and the performance of the optimal policy is compared against several heuristic policies.</p>","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"34 7","pages":"1743-1758"},"PeriodicalIF":5.1,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13046252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complementarity analysis of a multi‐item inventory model with leading product pricing","authors":"Sangjo Kim, Youyi Feng, Jianjun Xu","doi":"10.1111/poms.14084","DOIUrl":"https://doi.org/10.1111/poms.14084","url":null,"abstract":"Abstract Our research investigates a joint inventory replenishment and pricing problem, where a seller controls the price of a leading product while restocking for all the products. We demonstrate that the seller's expected value functions present an structure, recommending an optimal order‐up‐to inventory‐level and list‐price policy. Our findings reveal divergent economic relationships between products from the seller's and customer's perspectives, suggesting managers broaden their strategies beyond customer viewpoints. We extend the pricing and inventory analysis to scenarios involving three substitute products under both price and inventory control. In these situations, we further reveal that sellers and customers uniformly view product inventories as substitutes, and the corresponding value functions are submodular and mutually diagonally dominant.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135945127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of COVID‐19 on supply chain credit risk","authors":"Şenay Ağca, John R. Birge, Zi'ang Wang, Jing Wu","doi":"10.1111/poms.14079","DOIUrl":"https://doi.org/10.1111/poms.14079","url":null,"abstract":"Abstract While global supply chains provide firms with a buffer against local shocks, they expose firms to multiregional risks. The COVID‐19 pandemic and its differential impact on different regions in the world offer an opportunity to explore these effects. We investigate the multiregional supply chain risk by focusing on credit risk measured by abnormal credit default swaps (CDS) spreads and US–China supply chain networks. Our evidence shows that local risks propagate through global supply chains to other regions. Using a matched sample, we find that abnormal CDS spreads for firms with Chinese supply chain partners increase by 12% to 13% relative to the average raw CDS spreads due to supply chain disruptions during the economic shutdown in China, and the abnormal CDS spreads decrease by 9% to 13% relative to the average raw CDS spreads when the supply chain activities resume in China. We also find that having a more global customer base can mitigate the effects of local household demand shocks. Lastly, we discover that firm size, supply chain network centrality, cash holdings, inventory, strong credit ratings, capital redeployability, and the number of segments increase resilience to global supply chain shocks, while financial leverage, operational leverage, and market competition weaken supply chain resilience.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}