Problem definition: Prior studies have identified the role of downstream retailers’ strategic inventory in mitigating double marginalization within decentralized supply chains. Our work adds to this literature by introducing two relevant features that naturally appear in a dynamic environment: network externality and copycatting. We demonstrate how strategic inventory and network externality can be used to manage competition from within and outside the supply chain. Methodology/results: We develop a game-theoretical model to capture the strategic interaction within a brand-name supply chain, which enjoys positive externalities from early-period sales but faces competition from copycats in later periods. We show that copycats, on the one hand, deter the retailer’s strategic inventory by exerting external competition and on the other hand, can amplify the benefit of the retailer’s strategic inventory in allaying internal double marginalization and enhancing the supply chain’s external competitiveness. We further show that network externality, on the one hand, brings immediate gains to the supply chain’s external battle with copycats and on the other hand, creates internal inefficiency in the form of cross-period double marginalization best exhibited under the supplier’s dynamic contract. When network externality and strategic inventory are optimized jointly, we find that they are always complementary in increasing the supplier’s payoff but can be substitutive to the retailer under a large inventory cost and weak network externality. Managerial implications: Our work provides firms ways of managing decentralized supply chains in the face of copycats. We propose strategic inventory and network externality to combat copycats and provide normative guidance on their operating mechanisms. Funding: C. Jin received financial support from the Singapore Ministry of Education Academic Research Fund [Tier 1 Grant 251RES2101]. Y.-J. Chen received financial support from Hong Kong RGC [Grants GRF 16500821 and HKUST C6020-21GF]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0182 .
问题定义:先前的研究已经确定了下游零售商的战略库存在缓解分散供应链中的双重边缘化中的作用。我们的工作通过引入在动态环境中自然出现的两个相关特征:网络外部性和复制来补充这些文献。我们展示了如何使用战略库存和网络外部性来管理来自供应链内外的竞争。方法/结果:我们开发了一个博弈论模型来捕捉品牌供应链中的战略互动，品牌供应链在早期销售中享有正外部性，但在后期面临来自模仿者的竞争。研究表明，模仿者一方面通过施加外部竞争来威慑零售商的战略库存，另一方面可以放大零售商战略库存在缓解内部双重边缘化和提高供应链外部竞争力方面的效益。我们进一步表明，网络外部性一方面为供应链与模仿者的外部战斗带来了直接收益，另一方面，在供应商动态合同下，以跨期双重边缘化的形式产生了内部效率低下。当网络外部性和战略库存共同优化时，我们发现它们在增加供应商收益方面总是互补的，但在库存成本大、网络外部性弱的情况下，它们可以替代零售商。管理启示:我们的工作为企业提供了在面对模仿者时管理分散供应链的方法。我们提出了战略盘查和网络外部性来打击模仿者，并对其运行机制提供规范指导。资助:C. Jin获得新加坡教育部学术研究基金[Tier 1 Grant 251RES2101]的财政支持。Y.-J。陈获香港研究资助局资助[拨款GRF 16500821及科大C6020-21GF]。补充材料:在线附录可在https://doi.org/10.1287/msom.2021.0182上获得。
{"title":"Managing Competition from Within and Outside: Using Strategic Inventory and Network Externality to Combat Copycats","authors":"Chen Jin, Chenguang (Allen) Wu, Ying‐Ju Chen","doi":"10.1287/msom.2021.0182","DOIUrl":"https://doi.org/10.1287/msom.2021.0182","url":null,"abstract":"Problem definition: Prior studies have identified the role of downstream retailers’ strategic inventory in mitigating double marginalization within decentralized supply chains. Our work adds to this literature by introducing two relevant features that naturally appear in a dynamic environment: network externality and copycatting. We demonstrate how strategic inventory and network externality can be used to manage competition from within and outside the supply chain. Methodology/results: We develop a game-theoretical model to capture the strategic interaction within a brand-name supply chain, which enjoys positive externalities from early-period sales but faces competition from copycats in later periods. We show that copycats, on the one hand, deter the retailer’s strategic inventory by exerting external competition and on the other hand, can amplify the benefit of the retailer’s strategic inventory in allaying internal double marginalization and enhancing the supply chain’s external competitiveness. We further show that network externality, on the one hand, brings immediate gains to the supply chain’s external battle with copycats and on the other hand, creates internal inefficiency in the form of cross-period double marginalization best exhibited under the supplier’s dynamic contract. When network externality and strategic inventory are optimized jointly, we find that they are always complementary in increasing the supplier’s payoff but can be substitutive to the retailer under a large inventory cost and weak network externality. Managerial implications: Our work provides firms ways of managing decentralized supply chains in the face of copycats. We propose strategic inventory and network externality to combat copycats and provide normative guidance on their operating mechanisms. Funding: C. Jin received financial support from the Singapore Ministry of Education Academic Research Fund [Tier 1 Grant 251RES2101]. Y.-J. Chen received financial support from Hong Kong RGC [Grants GRF 16500821 and HKUST C6020-21GF]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0182 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130534295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Ghiassi-Farrokhfal, Rodrigo Belo, Mohammed Reza Hesamzadeh, D. Bunn
Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive. Funding: Rodrigo Belo acknowledges funding by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016), POR Lisboa, and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .
问题定义:随着可再生能源的兴起和化石燃料的衰落，电力市场正在转向以低成本发电机(lcg)为主导的容量组合。在这一转变过程中，政策制定者一直在考虑当前的市场设计是否仍然从根本上符合目的。本研究分析了一个关键方面:实时不平衡定价机制的设计。目前，市场主要采用单一定价或双重定价作为不平衡定价机制。单一定价机制对买卖采用相同的价格，而双重定价机制使用不同的价格。欧洲最近的统一倡议将单一定价作为默认值，将双重定价作为例外。这就留下了双重定价何时有利的问题。我们比较了当前实践中两种双重定价机制与单一定价设计的经济效率，并确定了双重定价可能有益的条件。我们还证明了最优定价机制的存在性。方法/结果:我们首先分析比较了单一定价机制和双重定价机制的经济效率。此外，我们制定了一个最优定价机制，可以阻止潜在的lcg行使市场力量。我们的分析结果描述了双重定价优于单一定价的条件。我们进一步通过模拟比较了这些机制与我们提出的最优机制的经济效率。我们证明了与其他定价机制相比，所提出的定价机制将是最有效的，并讨论了其实用性。管理意义:我们的分析比较揭示了每种定价机制更适合的市场条件，以及是否需要重新设计。特别是，我们的研究结果表明，现有的定价机制适用于低/中等LCGs市场份额，但不适用于决策者目前在向脱碳过渡中设想的高市场份额，此时最优定价机制将变得更具吸引力。资金:Rodrigo Belo感谢funda para a Ciência e a tecologia (UIDB/00124/2020, UIDP/00124/2020)和社会科学数据库(PINFRA/22209/2016)、里斯本和北港(POR社会科学数据库，PINFRA/22209/2016)的资助。补充材料:电子伴侣可在https://doi.org/10.1287/msom.2021.0555上获得。
{"title":"Optimal Electricity Imbalance Pricing for the Emerging Penetration of Renewable and Low-Cost Generators","authors":"Y. Ghiassi-Farrokhfal, Rodrigo Belo, Mohammed Reza Hesamzadeh, D. Bunn","doi":"10.1287/msom.2021.0555","DOIUrl":"https://doi.org/10.1287/msom.2021.0555","url":null,"abstract":"Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive. Funding: Rodrigo Belo acknowledges funding by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016), POR Lisboa, and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128319651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: The job of any marketplace is to facilitate the matching of supply with demand in real time. Success is often measured using various metrics. The challenge is to design matching algorithms to balance the tradeoffs among multiple objectives in a stochastic environment, to arrive at a “compromise” solution, which minimizes say the [Formula: see text]-norm–based distance function between the attained performance metrics and the target performances. Methodology/results: We observe that the sample average approximation formulation of this multiobjective stochastic optimization problem can be solved by an online algorithm that uses only gradient information from “historical” (i.e., past) sample information and not on the current state of the system. The online algorithm relies on a set of weight functions, which are updated adaptively over time, based on real-time tracking of the gaps in attained performance and the performance target. This allows us to recast the online algorithm as a randomized algorithm to solve the original stochastic problem. When the predetermined performance targets are attainable, our randomized policy achieves the targets with a near-optimal performance guarantee (measured by regret, or deviation away from the optimal performance). When the targets are not attainable, our policy generates a compromise solution to the multiobjective stochastic optimization problem, even when the efficient frontier for this stochastic optimization problem cannot be explicitly characterized a priori. We implement our model to address a challenge faced by a ride-sourcing platform that matches passengers and drivers in real time. Four performance metrics—platform revenue, driver service score, pick-up distance, and number of matched pairs—are simultaneously considered in the design of ride-matching algorithm, without prespecifying the weight on each performance metric. This mechanism has been extensively tested using synthetic and real data. Managerial implications: We show that, under appropriate conditions, all parties in the ride-sourcing ecosystem, from drivers, passengers, to the platform, can be better off under our compromise matching policy compared with other popular policies currently in use. In particular, the platform can obtain higher revenue and ensure better drivers (with higher service scores) are assigned more orders, and passengers are more likely to be matched to better drivers (albeit with a slight increase in the waiting time) compared with existing policies that focus on pick-up distance minimization. The ability to balance the conflicting goals in multiple objectives in a stochastic operating environment has the potential to contribute to the long-term sustainable growth of ride-sourcing platforms. Funding: This work was supported by the Singapore Ministry of Education AcRF Tier 3 [Grant MOE-2019-T3-1-010], the Hong Kong University of Science and Technology [Grant R9827], the Singapore Management Universit
问题定义:任何市场的工作都是促进供给与需求的实时匹配。成功通常是用不同的指标来衡量的。我们面临的挑战是设计匹配算法来平衡随机环境中多个目标之间的权衡，以达到一个“折衷”的解决方案，最小化达到的性能指标和目标性能之间基于规范的距离函数。方法/结果:我们观察到这个多目标随机优化问题的样本平均近似公式可以通过一个在线算法来解决，该算法只使用来自“历史”(即过去)样本信息的梯度信息，而不使用系统的当前状态。在线算法依赖于一组权函数，这些权函数随时间自适应更新，基于实时跟踪已达到的性能和性能目标之间的差距。这允许我们将在线算法重新定义为随机算法来解决原始的随机问题。当预定的性能目标可以实现时，我们的随机策略以接近最优的性能保证实现目标(通过后悔或偏离最优性能来衡量)。当目标无法实现时，我们的策略生成多目标随机优化问题的折衷解，即使该随机优化问题的有效边界不能明确地先验表征。我们实施我们的模型是为了解决乘车外包平台面临的挑战，该平台需要实时匹配乘客和司机。在设计乘车匹配算法时，同时考虑了平台收入、司机服务评分、接送距离和配对配对数量这四个性能指标，而没有预先指定每个性能指标的权重。这一机制已经使用合成数据和实际数据进行了广泛的测试。管理启示:我们表明，在适当的条件下，与目前使用的其他流行政策相比，在我们的妥协匹配政策下，拼车生态系统中的所有各方，从司机、乘客到平台，都可以获得更好的收益。特别是，与现有的以接送距离最小化为重点的政策相比，平台可以获得更高的收入，并确保更好的司机(服务分数更高)获得更多的订单，乘客更有可能匹配到更好的司机(尽管等待时间略有增加)。在随机操作环境中平衡多个目标中相互冲突的目标的能力，有可能有助于网约车平台的长期可持续增长。资助:本研究由新加坡教育部AcRF第3级(Grant MOE-2019-T3-1-010)、香港科技大学(Grant R9827)、新加坡管理大学(Lee Kong Chian Fellowship)和新加坡教育部AcRF第2级(Grant T2EP20121-0035)资助。补充材料:在线附录可在https://doi.org/10.1287/msom.2020.0247上获得。
{"title":"Multiobjective Stochastic Optimization: A Case of Real-Time Matching in Ride-Sourcing Markets","authors":"Guodong Lyu, Wang Chi Cheung, C. Teo, Hai Wang","doi":"10.1287/msom.2020.0247","DOIUrl":"https://doi.org/10.1287/msom.2020.0247","url":null,"abstract":"Problem definition: The job of any marketplace is to facilitate the matching of supply with demand in real time. Success is often measured using various metrics. The challenge is to design matching algorithms to balance the tradeoffs among multiple objectives in a stochastic environment, to arrive at a “compromise” solution, which minimizes say the [Formula: see text]-norm–based distance function between the attained performance metrics and the target performances. Methodology/results: We observe that the sample average approximation formulation of this multiobjective stochastic optimization problem can be solved by an online algorithm that uses only gradient information from “historical” (i.e., past) sample information and not on the current state of the system. The online algorithm relies on a set of weight functions, which are updated adaptively over time, based on real-time tracking of the gaps in attained performance and the performance target. This allows us to recast the online algorithm as a randomized algorithm to solve the original stochastic problem. When the predetermined performance targets are attainable, our randomized policy achieves the targets with a near-optimal performance guarantee (measured by regret, or deviation away from the optimal performance). When the targets are not attainable, our policy generates a compromise solution to the multiobjective stochastic optimization problem, even when the efficient frontier for this stochastic optimization problem cannot be explicitly characterized a priori. We implement our model to address a challenge faced by a ride-sourcing platform that matches passengers and drivers in real time. Four performance metrics—platform revenue, driver service score, pick-up distance, and number of matched pairs—are simultaneously considered in the design of ride-matching algorithm, without prespecifying the weight on each performance metric. This mechanism has been extensively tested using synthetic and real data. Managerial implications: We show that, under appropriate conditions, all parties in the ride-sourcing ecosystem, from drivers, passengers, to the platform, can be better off under our compromise matching policy compared with other popular policies currently in use. In particular, the platform can obtain higher revenue and ensure better drivers (with higher service scores) are assigned more orders, and passengers are more likely to be matched to better drivers (albeit with a slight increase in the waiting time) compared with existing policies that focus on pick-up distance minimization. The ability to balance the conflicting goals in multiple objectives in a stochastic operating environment has the potential to contribute to the long-term sustainable growth of ride-sourcing platforms. Funding: This work was supported by the Singapore Ministry of Education AcRF Tier 3 [Grant MOE-2019-T3-1-010], the Hong Kong University of Science and Technology [Grant R9827], the Singapore Management Universit","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Khorasani, Lakshminarayana Nittala, V. Krishnan
Problem definition: Firms seek to use the contest format to source solutions from a broader network of outside solvers. We study the application of the contest approach in multistage settings and show how and when screening of contestants between stages can produce improved contest outcomes. Methodology/results: We present an application-driven game-theoretic model to capture imperfections in screening using the true-positive rate (sensitivity) and the true-negative rate (specificity). Specifically, we consider a two-stage contest with a screening decision by the firm between the stages. Solvers face uncertainty about their probability of fit, and the final quality of the solution is dependent on the performance across both stages. We identify two mechanisms through which screening induces greater effort, namely the encouragement effect and the competitive contest effect, and characterize how screening should be tuned to the problem setting. We find that filtering out true negatives in contests with exogenous solvers’ probability of fit is optimal for solution-seeking firms. Our results indicate that in case of problems with endogenous probability of fit and less up-front complexity, coarse (imperfect) screening is beneficial in order to manage competition and stimulate greater effort, but it behooves the firm to resort to more accurate screening otherwise. We also derive nuanced results for the case when a seeker faces screening constraints and must balance screening sensitivity and specificity. Managerial implications: Our work provides firms an additional degree of freedom in terms of specific and sensitive screening to design and run contests and to better engage outside solvers. We derive actionable results and translate them into a managerial framework to help fine-tune the screening mechanism for improved contest performance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0378 .
{"title":"Screening in Multistage Contests","authors":"S. Khorasani, Lakshminarayana Nittala, V. Krishnan","doi":"10.1287/msom.2021.0378","DOIUrl":"https://doi.org/10.1287/msom.2021.0378","url":null,"abstract":"Problem definition: Firms seek to use the contest format to source solutions from a broader network of outside solvers. We study the application of the contest approach in multistage settings and show how and when screening of contestants between stages can produce improved contest outcomes. Methodology/results: We present an application-driven game-theoretic model to capture imperfections in screening using the true-positive rate (sensitivity) and the true-negative rate (specificity). Specifically, we consider a two-stage contest with a screening decision by the firm between the stages. Solvers face uncertainty about their probability of fit, and the final quality of the solution is dependent on the performance across both stages. We identify two mechanisms through which screening induces greater effort, namely the encouragement effect and the competitive contest effect, and characterize how screening should be tuned to the problem setting. We find that filtering out true negatives in contests with exogenous solvers’ probability of fit is optimal for solution-seeking firms. Our results indicate that in case of problems with endogenous probability of fit and less up-front complexity, coarse (imperfect) screening is beneficial in order to manage competition and stimulate greater effort, but it behooves the firm to resort to more accurate screening otherwise. We also derive nuanced results for the case when a seeker faces screening constraints and must balance screening sensitivity and specificity. Managerial implications: Our work provides firms an additional degree of freedom in terms of specific and sensitive screening to design and run contests and to better engage outside solvers. We derive actionable results and translate them into a managerial framework to help fine-tune the screening mechanism for improved contest performance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0378 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124343289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study an optimal contract design problem for a national brand (NB) manufacturer, which sells her product via a retailer. The retailer may introduce his store brand (SB) with private cost information. The manufacturer estimates that the retailer’s SB cost may be high or low with certain probabilities and offers a menu of two-part tariff contracts to screen the retailer’s cost information. Methodology/results: Following the mechanism design theory, we formulate the problem as a two-stage screening game to analyze the strategic interaction between the two players under asymmetric information. Despite the complexity resulting from type-dependent reservation profit of the retailer, we derive the NB manufacturer’s optimal contracts analytically. We prove that there exists a unique threshold such that when the NB cost is below the threshold, the manufacturer offers both types of retailers incentive-compatible contracts; when the NB cost is above the threshold, the manufacturer offers a menu of contracts to shut down the low-type retailer and engage the high-type retailer only. Managerial implications: We find that when the NB product becomes more competitive (i.e., a higher quality or a lower cost), both the NB manufacturer and the retailer are better off. This result implies that under asymmetric information, the retailer has incentive to enhance the NB product quality or reduce its cost. Additionally, the private information is valuable to both members only when a contract without shutdown is offered. Moreover, such information is more valuable to both players when the NB product becomes more competitive. However, when SB quality improves or when SB cost decreases, the value of information may increase or decrease to both supply chain members. Finally, we derive a surprising result that under asymmetric information, the expected consumer surplus may increase because of a lower SB quality or a higher low-type SB cost. Funding: G. Xiao acknowledges financial support from the Research Grants Council of Hong Kong [General Research Fund Grant PolyU 15505621]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0187 .
{"title":"Optimal Contract Design for a National Brand Manufacturer Under Store Brand Private Information","authors":"Xin-yan Cao, X. Fang, Guang Xiao, N. Yang","doi":"10.1287/msom.2021.0187","DOIUrl":"https://doi.org/10.1287/msom.2021.0187","url":null,"abstract":"Problem definition: We study an optimal contract design problem for a national brand (NB) manufacturer, which sells her product via a retailer. The retailer may introduce his store brand (SB) with private cost information. The manufacturer estimates that the retailer’s SB cost may be high or low with certain probabilities and offers a menu of two-part tariff contracts to screen the retailer’s cost information. Methodology/results: Following the mechanism design theory, we formulate the problem as a two-stage screening game to analyze the strategic interaction between the two players under asymmetric information. Despite the complexity resulting from type-dependent reservation profit of the retailer, we derive the NB manufacturer’s optimal contracts analytically. We prove that there exists a unique threshold such that when the NB cost is below the threshold, the manufacturer offers both types of retailers incentive-compatible contracts; when the NB cost is above the threshold, the manufacturer offers a menu of contracts to shut down the low-type retailer and engage the high-type retailer only. Managerial implications: We find that when the NB product becomes more competitive (i.e., a higher quality or a lower cost), both the NB manufacturer and the retailer are better off. This result implies that under asymmetric information, the retailer has incentive to enhance the NB product quality or reduce its cost. Additionally, the private information is valuable to both members only when a contract without shutdown is offered. Moreover, such information is more valuable to both players when the NB product becomes more competitive. However, when SB quality improves or when SB cost decreases, the value of information may increase or decrease to both supply chain members. Finally, we derive a surprising result that under asymmetric information, the expected consumer surplus may increase because of a lower SB quality or a higher low-type SB cost. Funding: G. Xiao acknowledges financial support from the Research Grants Council of Hong Kong [General Research Fund Grant PolyU 15505621]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0187 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114842762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Hospital-acquired conditions (HACs) represent undesirable complications that occur during a hospital stay. HACs can compromise patient safety and care outcomes and result in unnecessary socio-economic costs. Although hospitals are expected to reduce the incidence of HACs, few studies have examined the implications of HACs on other clinical outcomes and measures of hospital performance. This study contributes to the literature by exploring the relationship between exposure to a set of target HACs, length of stay (LOS) performance, and 30-day readmission risk. Methodology/results: To estimate the effects of HACs, we conduct econometric analyses using patient-visit-level data for heart attack, heart failure, and pneumonia patients hospitalized in the U.S. state of Florida during 2010–2014. We define LOS performance as the deviation of LOS from the Geometric Mean LOS (GMLOS), a standard LOS set by the Centers for Medicare and Medicaid Services. First, we find that exposure to HACs leads to a 37% increase in the odds of readmission and a 79% increase in LOS. Second, an increase in LOS is associated with a decrease in readmission risk, and this decrease is stronger for patients exposed to HACs. Third, LOS performance mediates the HACs-readmission risk relationship, such that the increase in the readmission risk of a HAC patient can be fully suppressed by the patient’s LOS. Fourth, we find that for patients exposed to HACs, the benefits of a longer LOS are almost entirely capitalized when the LOS becomes 65% longer than the GMLOS. Managerial implications: We demonstrate that, when addressing the consequences of HACs, clinicians also face indirectly a trade-off between reducing readmissions and controlling costs. We proffer LOS as a potential mechanism under hospitals’ control for mitigating the adverse effects of HACs on readmission risk. Thus, this study offers guidance to clinicians having to decide when to discharge patients with exposure to HACs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0088 .
{"title":"Effects of Hospital-Acquired Conditions on Readmission Risk: The Mediating Role of Length of Stay","authors":"Bogdan C. Bichescu, Haileab Hilafu","doi":"10.1287/msom.2022.0088","DOIUrl":"https://doi.org/10.1287/msom.2022.0088","url":null,"abstract":"Problem definition: Hospital-acquired conditions (HACs) represent undesirable complications that occur during a hospital stay. HACs can compromise patient safety and care outcomes and result in unnecessary socio-economic costs. Although hospitals are expected to reduce the incidence of HACs, few studies have examined the implications of HACs on other clinical outcomes and measures of hospital performance. This study contributes to the literature by exploring the relationship between exposure to a set of target HACs, length of stay (LOS) performance, and 30-day readmission risk. Methodology/results: To estimate the effects of HACs, we conduct econometric analyses using patient-visit-level data for heart attack, heart failure, and pneumonia patients hospitalized in the U.S. state of Florida during 2010–2014. We define LOS performance as the deviation of LOS from the Geometric Mean LOS (GMLOS), a standard LOS set by the Centers for Medicare and Medicaid Services. First, we find that exposure to HACs leads to a 37% increase in the odds of readmission and a 79% increase in LOS. Second, an increase in LOS is associated with a decrease in readmission risk, and this decrease is stronger for patients exposed to HACs. Third, LOS performance mediates the HACs-readmission risk relationship, such that the increase in the readmission risk of a HAC patient can be fully suppressed by the patient’s LOS. Fourth, we find that for patients exposed to HACs, the benefits of a longer LOS are almost entirely capitalized when the LOS becomes 65% longer than the GMLOS. Managerial implications: We demonstrate that, when addressing the consequences of HACs, clinicians also face indirectly a trade-off between reducing readmissions and controlling costs. We proffer LOS as a potential mechanism under hospitals’ control for mitigating the adverse effects of HACs on readmission risk. Thus, this study offers guidance to clinicians having to decide when to discharge patients with exposure to HACs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0088 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114489063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: This study addresses three important questions concerning personalized healthcare: (1) Are outcome differences between hospitals heterogeneous across patients with different features? (2) If they are, how do the best quality hospitals identified using patient-centric information differ from those identified using population-average information? (3) How much will hospitals’ pay-for-performance reimbursements change if their performance is measured based on patient-centric information? Methodology/results: Using patient-level data from 35 hospitals for six cardiovascular surgeries in New York State, we identify patient groups that exhibit significant differences in outcomes with a recently developed instrumental variable tree approach. We find outcome differences between hospitals are heterogeneous not only across procedure types, but also along other dimensions such as patient age and comorbidities. For around 80% of patients, the best quality hospitals indicated by patient-centric information are different from those indicated as best according to population-average information. Managerial implications: We compare potential outcomes when patients are treated at the best quality hospitals based on the two types of information and find complications could be reduced by using patient-centric information instead of population-average information. We also use our model to illustrate how patient-centric information can enhance pay-for-performance programs offered by payers and guide hospitals in targeting quality-improvement efforts. History: This paper was a finalist in the 2017 MSOM Student Paper Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1227 .
{"title":"Personalized Healthcare Outcome Analysis of Cardiovascular Surgical Procedures","authors":"Guihua Wang, Jun Yu Li, W. Hopp","doi":"10.1287/msom.2023.1227","DOIUrl":"https://doi.org/10.1287/msom.2023.1227","url":null,"abstract":"Problem definition: This study addresses three important questions concerning personalized healthcare: (1) Are outcome differences between hospitals heterogeneous across patients with different features? (2) If they are, how do the best quality hospitals identified using patient-centric information differ from those identified using population-average information? (3) How much will hospitals’ pay-for-performance reimbursements change if their performance is measured based on patient-centric information? Methodology/results: Using patient-level data from 35 hospitals for six cardiovascular surgeries in New York State, we identify patient groups that exhibit significant differences in outcomes with a recently developed instrumental variable tree approach. We find outcome differences between hospitals are heterogeneous not only across procedure types, but also along other dimensions such as patient age and comorbidities. For around 80% of patients, the best quality hospitals indicated by patient-centric information are different from those indicated as best according to population-average information. Managerial implications: We compare potential outcomes when patients are treated at the best quality hospitals based on the two types of information and find complications could be reduced by using patient-centric information instead of population-average information. We also use our model to illustrate how patient-centric information can enhance pay-for-performance programs offered by payers and guide hospitals in targeting quality-improvement efforts. History: This paper was a finalist in the 2017 MSOM Student Paper Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1227 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Algorithm-enabled decision support has an increasingly important role in supporting the day-to-day operations of healthcare organizations. Yet, fully realizing the value of algorithmic decision support lies critically in the opportunity to re-engineer the related processes and redefine roles in ways that make organizations more effective. We study how and when algorithm-enabled process innovation (AEPI) creates value in light of dynamic operational environments (i.e., workload) and behavioral responses to algorithmic predictions (i.e., algorithmic accuracy). Our context is an AEPI effort around a rule-based decision-support algorithm for early detection of sepsis—a costly condition that is the leading cause of death for hospitalized patients. We collaborated with a large U.S.-based hospital system and examined whether AEPI developed for sepsis care (sepsis AEPI) impacts patient mortality and when this impact is stronger or weaker. Methodology/results: We utilize a rich set of clinical and nonclinical data in empirically examining the impact of sepsis AEPI on patient mortality. We leverage the staggered implementation of sepsis AEPI across hospital units and conduct our estimation on a carefully matched sample. The matching utilizes data on patient vitals and the logic behind the algorithm to create a robust comparison group consisting of patient visits for which sepsis AEPI would have triggered an alert if it had been in place. Our empirical analysis shows that sepsis AEPI reduces the likelihood of death from sepsis (45% relative reduction in mortality risk due to sepsis). A higher-than-usual workload and an increase in the average number of inaccurate alert experience at a hospital unit (e.g., an oncology unit, which provides care for cancer patients), in general, reduces the effectiveness of AEPI. We also identify diminishing mortality benefits over prolonged periods of adoption; evaluation of the moderators over time helps explain this diminishing impact. Managerial implications: Our findings suggest that streamlining sepsis-care processes through a predictive algorithm (i.e., algorithm-based monitoring of real-time patient data and providing predictions, streamlined communication channels for coordinating care for a patient with sepsis prediction, and a more standardized process for sepsis diagnosis and treatment) can reduce the loss of life from sepsis. For the 3,739 sepsis patients in our study period, AEPI’s benefits would translate to 181 lives saved. We show that such value, however, is sensitive to operational and behavioral factors as the algorithm becomes a routine part of the day-to-day operations of the hospital. Funding: Financial support from University Hospitals is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1226 .
{"title":"Value of Algorithm-Enabled Process Innovation: The Case of Sepsis","authors":"Idris Adjerid, M. Ayvaci, Ö. Özer","doi":"10.1287/msom.2023.1226","DOIUrl":"https://doi.org/10.1287/msom.2023.1226","url":null,"abstract":"Problem definition: Algorithm-enabled decision support has an increasingly important role in supporting the day-to-day operations of healthcare organizations. Yet, fully realizing the value of algorithmic decision support lies critically in the opportunity to re-engineer the related processes and redefine roles in ways that make organizations more effective. We study how and when algorithm-enabled process innovation (AEPI) creates value in light of dynamic operational environments (i.e., workload) and behavioral responses to algorithmic predictions (i.e., algorithmic accuracy). Our context is an AEPI effort around a rule-based decision-support algorithm for early detection of sepsis—a costly condition that is the leading cause of death for hospitalized patients. We collaborated with a large U.S.-based hospital system and examined whether AEPI developed for sepsis care (sepsis AEPI) impacts patient mortality and when this impact is stronger or weaker. Methodology/results: We utilize a rich set of clinical and nonclinical data in empirically examining the impact of sepsis AEPI on patient mortality. We leverage the staggered implementation of sepsis AEPI across hospital units and conduct our estimation on a carefully matched sample. The matching utilizes data on patient vitals and the logic behind the algorithm to create a robust comparison group consisting of patient visits for which sepsis AEPI would have triggered an alert if it had been in place. Our empirical analysis shows that sepsis AEPI reduces the likelihood of death from sepsis (45% relative reduction in mortality risk due to sepsis). A higher-than-usual workload and an increase in the average number of inaccurate alert experience at a hospital unit (e.g., an oncology unit, which provides care for cancer patients), in general, reduces the effectiveness of AEPI. We also identify diminishing mortality benefits over prolonged periods of adoption; evaluation of the moderators over time helps explain this diminishing impact. Managerial implications: Our findings suggest that streamlining sepsis-care processes through a predictive algorithm (i.e., algorithm-based monitoring of real-time patient data and providing predictions, streamlined communication channels for coordinating care for a patient with sepsis prediction, and a more standardized process for sepsis diagnosis and treatment) can reduce the loss of life from sepsis. For the 3,739 sepsis patients in our study period, AEPI’s benefits would translate to 181 lives saved. We show that such value, however, is sensitive to operational and behavioral factors as the algorithm becomes a routine part of the day-to-day operations of the hospital. Funding: Financial support from University Hospitals is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1226 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126819475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Motivated by several practical selling scenarios that require previous purchases to unlock future options, we consider a multistage assortment optimization problem, where the seller makes sequential assortment decisions with commitment and the customer makes sequential choices to maximize her expected utility. Methodology/results: We start with the two-stage problem and formulate it as a dynamic combinatorial optimization problem. We show that this problem is polynomial-time solvable when the customer is fully myopic or fully forward-looking. In particular, when the customer is fully forward-looking, the optimal policy entails that the assortment in each stage is revenue-ordered, and a product with higher revenue always leads to a wider range of future options. Moreover, we find that the optimal assortment in the first stage must be smaller than the optimal assortment when there is no second stage and the optimal assortment in the second stage must be larger than the optimal assortment when there is no first stage. When the customer is partially forward-looking, we show that the problem is NP-hard in general. In this case, we establish the polynomial-time solvability under certain conditions. In addition, we propose a 2-approximation algorithm in the general setting. We further extend these results to the multistage problem with an arbitrary number of stages, for which we derive generalized structural properties and efficient algorithms. Managerial implications: Firms can benefit from our study and improve their sequential assortment strategies when their interaction with each customer consists of multiple stages. Funding: This work was supported by the National Science Foundation of China [Grant 72150002] and Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1224 .
{"title":"Assortment Optimization for a Multistage Choice Model","authors":"Yunzong Xu, Zizhuo Wang","doi":"10.1287/msom.2023.1224","DOIUrl":"https://doi.org/10.1287/msom.2023.1224","url":null,"abstract":"Problem definition: Motivated by several practical selling scenarios that require previous purchases to unlock future options, we consider a multistage assortment optimization problem, where the seller makes sequential assortment decisions with commitment and the customer makes sequential choices to maximize her expected utility. Methodology/results: We start with the two-stage problem and formulate it as a dynamic combinatorial optimization problem. We show that this problem is polynomial-time solvable when the customer is fully myopic or fully forward-looking. In particular, when the customer is fully forward-looking, the optimal policy entails that the assortment in each stage is revenue-ordered, and a product with higher revenue always leads to a wider range of future options. Moreover, we find that the optimal assortment in the first stage must be smaller than the optimal assortment when there is no second stage and the optimal assortment in the second stage must be larger than the optimal assortment when there is no first stage. When the customer is partially forward-looking, we show that the problem is NP-hard in general. In this case, we establish the polynomial-time solvability under certain conditions. In addition, we propose a 2-approximation algorithm in the general setting. We further extend these results to the multistage problem with an arbitrary number of stages, for which we derive generalized structural properties and efficient algorithms. Managerial implications: Firms can benefit from our study and improve their sequential assortment strategies when their interaction with each customer consists of multiple stages. Funding: This work was supported by the National Science Foundation of China [Grant 72150002] and Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1224 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116231205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Qualcomm, the largest cellphone chipmaker in the world, had adopted a cross-licensing agreement with its clients, downstream cellphone manufacturers. It requires cellphone manufacturers to allow each other to use their patents for free. This cross-licensing practice has received considerable scrutiny and attention around the world. We study the impacts of cross-licensing in a supply chain in which an upstream supplier requires its downstream competing manufacturers to cross-license, where they are asymmetric in their innovation capabilities. Methodology/results: We build a stylized model of a supply chain consisting of one supplier and two competing manufacturers and conduct game-theoretic analysis. We find that the supplier always prefers adopting cross-licensing ex post after manufacturers’ investments are sunk, but it may prefer committing to no cross-licensing ex ante. Specifically, the supplier should commit to not using cross-licensing if the inferior manufacturer’s cost of innovation is high or the effectiveness of cross-licensing is high. Furthermore, cross-licensing may increase innovation, the superior manufacturer’s profit, and social welfare under certain conditions. Interestingly, when the superior manufacturer’s cost advantage is intermediate, the inferior manufacturer’s innovation level first increases and then decreases in the effectiveness of cross-licensing. In addition, the inferior manufacturer’s profit also first increases and then decreases as the effectiveness level of cross-licensing increases. The cross-licensing policy benefits consumers when its effectiveness level is low and the superior manufacturer’s innovation cost is either high or low. Managerial implications: Our results provide guidance on when a supplier should adopt the cross-licensing strategy. For policy makers, our findings show that cross-licensing can be beneficial for consumers and the society. In particular, to increase social welfare, policy makers may consider encouraging cross-licensing with low effectiveness level when the superior manufacturer’s innovation cost is either low or high. Funding: This research is supported by Fund for Distinguished Young Scholars, Natural Science Foundation of Guangdong Province, China [Grant 2022B1515020027]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2019.0477 .
{"title":"Cross-Licensing in a Supply Chain with Asymmetric Manufacturers","authors":"Jingqi Wang, Tingliang Huang, Junghee Lee","doi":"10.1287/msom.2019.0477","DOIUrl":"https://doi.org/10.1287/msom.2019.0477","url":null,"abstract":"Problem definition: Qualcomm, the largest cellphone chipmaker in the world, had adopted a cross-licensing agreement with its clients, downstream cellphone manufacturers. It requires cellphone manufacturers to allow each other to use their patents for free. This cross-licensing practice has received considerable scrutiny and attention around the world. We study the impacts of cross-licensing in a supply chain in which an upstream supplier requires its downstream competing manufacturers to cross-license, where they are asymmetric in their innovation capabilities. Methodology/results: We build a stylized model of a supply chain consisting of one supplier and two competing manufacturers and conduct game-theoretic analysis. We find that the supplier always prefers adopting cross-licensing ex post after manufacturers’ investments are sunk, but it may prefer committing to no cross-licensing ex ante. Specifically, the supplier should commit to not using cross-licensing if the inferior manufacturer’s cost of innovation is high or the effectiveness of cross-licensing is high. Furthermore, cross-licensing may increase innovation, the superior manufacturer’s profit, and social welfare under certain conditions. Interestingly, when the superior manufacturer’s cost advantage is intermediate, the inferior manufacturer’s innovation level first increases and then decreases in the effectiveness of cross-licensing. In addition, the inferior manufacturer’s profit also first increases and then decreases as the effectiveness level of cross-licensing increases. The cross-licensing policy benefits consumers when its effectiveness level is low and the superior manufacturer’s innovation cost is either high or low. Managerial implications: Our results provide guidance on when a supplier should adopt the cross-licensing strategy. For policy makers, our findings show that cross-licensing can be beneficial for consumers and the society. In particular, to increase social welfare, policy makers may consider encouraging cross-licensing with low effectiveness level when the superior manufacturer’s innovation cost is either low or high. Funding: This research is supported by Fund for Distinguished Young Scholars, Natural Science Foundation of Guangdong Province, China [Grant 2022B1515020027]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2019.0477 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131648775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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