Problem definition: The narrow definition of supply chain finance (SCF) as a financing scheme for accounts receivables fails to capture the knowledge creation of MSOM research scholars of the last 25 years who labored in the area under this label. A redefined definition of the research-and-application field, under the acronym integrated SCF (iSCF), better reflects the interplay of operational, financial, and risk management decisions our scholars and practitioners care about. The foundational knowledge of this field is ready to come into the classroom and elevate SCF courses from the typical “accounting and finance for supply chain managers” content to a supply-chain-centric viewpoint of important decisions in working capital management, effective cash-flow hedging, and integrated risk management of global supply chain risks. We outline the main research themes behind the iSCF research, and teaching, field; highlight concepts ready for the classroom; and pose some open research questions pointing to the future promise of the field. Methodology/results: Forum paper. MSOM Fellow opinion on topic of supply chain finance and risk management based on his published research. Managerial implications: A supply-chain-centric view on state-of-the-art practices for improved working capital, hedging, and risk management in global supply chains (integrated supply chain finance).
{"title":"Supply Chain Finance Redefined: A Supply Chain-Centric Viewpoint of Working Capital, Hedging, and Risk Management","authors":"Panos Kouvelis","doi":"10.1287/msom.2022.0606","DOIUrl":"https://doi.org/10.1287/msom.2022.0606","url":null,"abstract":"Problem definition: The narrow definition of supply chain finance (SCF) as a financing scheme for accounts receivables fails to capture the knowledge creation of MSOM research scholars of the last 25 years who labored in the area under this label. A redefined definition of the research-and-application field, under the acronym integrated SCF (iSCF), better reflects the interplay of operational, financial, and risk management decisions our scholars and practitioners care about. The foundational knowledge of this field is ready to come into the classroom and elevate SCF courses from the typical “accounting and finance for supply chain managers” content to a supply-chain-centric viewpoint of important decisions in working capital management, effective cash-flow hedging, and integrated risk management of global supply chain risks. We outline the main research themes behind the iSCF research, and teaching, field; highlight concepts ready for the classroom; and pose some open research questions pointing to the future promise of the field. Methodology/results: Forum paper. MSOM Fellow opinion on topic of supply chain finance and risk management based on his published research. Managerial implications: A supply-chain-centric view on state-of-the-art practices for improved working capital, hedging, and risk management in global supply chains (integrated supply chain finance).","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679856","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: A critical decision made by firms is whether to adopt a responsive supply chain (prioritizing speed) or an efficient supply chain (prioritizing cost). We consider the environmental implications of this choice, distinguishing between responsiveness achieved via three pathways: responsive offshore supply chains increase speed by using expedited production and distribution methods; responsive nearshore supply chains increase speed by reducing the physical distance between source and destination for all production; and hybrid nearshore supply chains produce in multiple locations simultaneously, increasing speed by reducing distance on some portion of production. Methodology/results: Using a model wherein responsiveness increases fixed and marginal costs, decreases leadtimes, and changes the per-unit environmental impact of production and distribution, we identify several results. First, all types of responsiveness can decrease environmental impact relative to an efficient supply chain, showing any form of responsiveness has potential to improve sustainability. Second, despite this, all types of responsiveness can also increase environmental impact relative to an efficient supply chain, particularly if demand variability is high. This is precisely when responsiveness is most profitable to the firm, indicating a tension between firm and environmental preferences. Third, a win-win outcome in which responsiveness both maximizes firm profit and minimizes environmental impact is most likely to occur when demand variability is high and unsatisfied customers substitute with a product that generates high environmental impact. Fourth, the firm may have incentive to choose a supply chain that does not minimize (and may maximize) environmental impact, especially at low-to-moderate demand variability. Managerial implications: While responsive supply chains can improve sustainability, they also generate the potential for misalignment of profit and environmental performance. We discuss the implications of this for firms and for policymakers seeking to encourage firms to use supply chains that generate the least environmental impact. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0152 .
{"title":"Sustainability Implications of Supply Chain Responsiveness","authors":"A. Tuna, R. Swinney","doi":"10.1287/msom.2022.0152","DOIUrl":"https://doi.org/10.1287/msom.2022.0152","url":null,"abstract":"Problem definition: A critical decision made by firms is whether to adopt a responsive supply chain (prioritizing speed) or an efficient supply chain (prioritizing cost). We consider the environmental implications of this choice, distinguishing between responsiveness achieved via three pathways: responsive offshore supply chains increase speed by using expedited production and distribution methods; responsive nearshore supply chains increase speed by reducing the physical distance between source and destination for all production; and hybrid nearshore supply chains produce in multiple locations simultaneously, increasing speed by reducing distance on some portion of production. Methodology/results: Using a model wherein responsiveness increases fixed and marginal costs, decreases leadtimes, and changes the per-unit environmental impact of production and distribution, we identify several results. First, all types of responsiveness can decrease environmental impact relative to an efficient supply chain, showing any form of responsiveness has potential to improve sustainability. Second, despite this, all types of responsiveness can also increase environmental impact relative to an efficient supply chain, particularly if demand variability is high. This is precisely when responsiveness is most profitable to the firm, indicating a tension between firm and environmental preferences. Third, a win-win outcome in which responsiveness both maximizes firm profit and minimizes environmental impact is most likely to occur when demand variability is high and unsatisfied customers substitute with a product that generates high environmental impact. Fourth, the firm may have incentive to choose a supply chain that does not minimize (and may maximize) environmental impact, especially at low-to-moderate demand variability. Managerial implications: While responsive supply chains can improve sustainability, they also generate the potential for misalignment of profit and environmental performance. We discuss the implications of this for firms and for policymakers seeking to encourage firms to use supply chains that generate the least environmental impact. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0152 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117241785","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 consider network revenue management problems with flexible products. We have a network of resources with limited capacities. To each customer arriving into the system, we offer an assortment of products. The customer chooses a product within the offered assortment or decides to leave without a purchase. The products are flexible in the sense that there are multiple possible combinations of resources that we can use to serve a customer with a purchase for a particular product. We refer to each such combination of resources as a route. The service provider chooses the route to serve a customer with a purchase for a particular product. Such flexible products occur, for example, when customers book at-home cleaning services but leave the timing of service to the company that provides the service. Our goal is to find a policy to decide which assortment of products to offer to each customer to maximize the total expected revenue, making sure that there are always feasible route assignments for the customers with purchased products. Methodology/results: We start by considering the case in which we make the route assignments at the end of the selling horizon. The dynamic programming formulation of the problem is significantly different from its analogue without flexible products as the state variable keeps track of the number of purchases for each product rather than the remaining capacity of each resource. Letting L be the maximum number of resources in a route, we give a policy that obtains at least [Formula: see text] fraction of the optimal total expected revenue. We extend our policy to the case in which we make the route assignments periodically over the selling horizon. Managerial implications: To our knowledge, the policy that we develop is the first with a performance guarantee under flexible products. Thus, our work constructs policies that can be implemented in practice under flexible products, also providing performance guarantees. Funding: The work of H. Topaloglu was partly funded by the National Science Foundation [Grant CMMI-1825406]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0583 .
{"title":"Performance Guarantees for Network Revenue Management with Flexible Products","authors":"Wenchang Zhu, Huseyin Topaloglu","doi":"10.1287/msom.2022.0583","DOIUrl":"https://doi.org/10.1287/msom.2022.0583","url":null,"abstract":"Problem definition: We consider network revenue management problems with flexible products. We have a network of resources with limited capacities. To each customer arriving into the system, we offer an assortment of products. The customer chooses a product within the offered assortment or decides to leave without a purchase. The products are flexible in the sense that there are multiple possible combinations of resources that we can use to serve a customer with a purchase for a particular product. We refer to each such combination of resources as a route. The service provider chooses the route to serve a customer with a purchase for a particular product. Such flexible products occur, for example, when customers book at-home cleaning services but leave the timing of service to the company that provides the service. Our goal is to find a policy to decide which assortment of products to offer to each customer to maximize the total expected revenue, making sure that there are always feasible route assignments for the customers with purchased products. Methodology/results: We start by considering the case in which we make the route assignments at the end of the selling horizon. The dynamic programming formulation of the problem is significantly different from its analogue without flexible products as the state variable keeps track of the number of purchases for each product rather than the remaining capacity of each resource. Letting L be the maximum number of resources in a route, we give a policy that obtains at least [Formula: see text] fraction of the optimal total expected revenue. We extend our policy to the case in which we make the route assignments periodically over the selling horizon. Managerial implications: To our knowledge, the policy that we develop is the first with a performance guarantee under flexible products. Thus, our work constructs policies that can be implemented in practice under flexible products, also providing performance guarantees. Funding: The work of H. Topaloglu was partly funded by the National Science Foundation [Grant CMMI-1825406]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0583 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132814626","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: Energy storage has become an indispensable part of power distribution systems, necessitating prudent investment decisions. We analyze an energy storage facility location problem and compare the benefits of centralized storage (adjacent to a central energy generation site) versus distributed storage (localized at demand sites). This problem encompasses optimizing storage capacities across all locations, with the objective of minimizing the total storage investment and energy generation costs. Methodology/results: We employ a stylized model that captures essential features of an energy distribution system, including convex costs, stochastic demand, storage efficiency, and line losses. Using dynamic programming, we optimize storage operations and derive value function properties that are key to analyzing the storage investment decisions. We discern fundamental differences between centralization/localization decisions at the capacity investment stage and the centralization/localization decisions at the storage operations level. Operationally, centrally stored energy offers more flexibility, which is consistent with the conventional understanding of inventory pooling. However, we find that localized storage often emerges as the preferred option at the investment stage under various circumstances. Managerial implications: Storage investment should first be made at the demand locations with positive minimum demand regardless of the level of demand variability. Subsequent storage investment should consider the tradeoffs between centralized versus localized investment. Operationally, the relative magnitudes of storage and line losses drive different optimal storage policies. Despite the differences, these policies are guided by common principles such as pooling inventory and balancing local storage levels. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0652 .
{"title":"On the Distributed Energy Storage Investment and Operations","authors":"Owen Q. Wu, R. Kapuscinski, S. Suresh","doi":"10.1287/msom.2020.0652","DOIUrl":"https://doi.org/10.1287/msom.2020.0652","url":null,"abstract":"Problem definition: Energy storage has become an indispensable part of power distribution systems, necessitating prudent investment decisions. We analyze an energy storage facility location problem and compare the benefits of centralized storage (adjacent to a central energy generation site) versus distributed storage (localized at demand sites). This problem encompasses optimizing storage capacities across all locations, with the objective of minimizing the total storage investment and energy generation costs. Methodology/results: We employ a stylized model that captures essential features of an energy distribution system, including convex costs, stochastic demand, storage efficiency, and line losses. Using dynamic programming, we optimize storage operations and derive value function properties that are key to analyzing the storage investment decisions. We discern fundamental differences between centralization/localization decisions at the capacity investment stage and the centralization/localization decisions at the storage operations level. Operationally, centrally stored energy offers more flexibility, which is consistent with the conventional understanding of inventory pooling. However, we find that localized storage often emerges as the preferred option at the investment stage under various circumstances. Managerial implications: Storage investment should first be made at the demand locations with positive minimum demand regardless of the level of demand variability. Subsequent storage investment should consider the tradeoffs between centralized versus localized investment. Operationally, the relative magnitudes of storage and line losses drive different optimal storage policies. Despite the differences, these policies are guided by common principles such as pooling inventory and balancing local storage levels. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0652 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115517462","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: Infectious disease screening can be expensive and capacity constrained. We develop cost- and capacity-efficient testing designs for multidisease screening, considering (1) multiplexing (disease bundling), where one assay detects multiple diseases using the same specimen (e.g., nasal swabs, blood), and (2) pooling (specimen bundling), where one assay is used on specimens from multiple subjects bundled in a testing pool. A testing design specifies an assay portfolio (mix of single-disease/multiplex assays) and a testing method (pooling/individual testing per assay). Methodology/results: We develop novel models for the nonlinear, combinatorial multidisease testing design problem: a deterministic model and a distribution-free, robust variation, which both generate Pareto frontiers for cost- and capacity-efficient designs. We characterize structural properties of optimal designs, formulate the deterministic counterpart of the robust model, and conduct a case study of respiratory diseases (including coronavirus disease 2019) with overlapping clinical presentation. Managerial implications: Key drivers of optimal designs include the assay cost function, the tester’s preference toward cost versus capacity efficiency, prevalence/coinfection rates, and for the robust model, prevalence uncertainty. When an optimal design uses multiple assays, it does so in conjunction with pooling, and it uses individual testing for at most one assay. Although prevalence uncertainty can be a design hurdle, especially for emerging or seasonal diseases, the integration of multiplexing and pooling, and the ordered partition property of optimal designs (under certain coinfection structures) serve to make the design more structurally robust to uncertainty. The robust model further increases robustness, and it is also practical as it needs only an uncertainty set around each disease prevalence. Our Pareto designs demonstrate the cost versus capacity trade-off and show that multiplexing-only or pooling-only designs need not be on the Pareto frontier. Our case study illustrates the benefits of optimally integrated designs over current practices and indicates a low price of robustness. Funding: This work was supported by the National Science Foundation [Grant 1761842]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0296 .
{"title":"Disease Bundling or Specimen Bundling? Cost- and Capacity-Efficient Strategies for Multidisease Testing with Genetic Assays","authors":"D. R. Bish, E. Bish, Hussein El Hajj","doi":"10.1287/msom.2022.0296","DOIUrl":"https://doi.org/10.1287/msom.2022.0296","url":null,"abstract":"Problem definition: Infectious disease screening can be expensive and capacity constrained. We develop cost- and capacity-efficient testing designs for multidisease screening, considering (1) multiplexing (disease bundling), where one assay detects multiple diseases using the same specimen (e.g., nasal swabs, blood), and (2) pooling (specimen bundling), where one assay is used on specimens from multiple subjects bundled in a testing pool. A testing design specifies an assay portfolio (mix of single-disease/multiplex assays) and a testing method (pooling/individual testing per assay). Methodology/results: We develop novel models for the nonlinear, combinatorial multidisease testing design problem: a deterministic model and a distribution-free, robust variation, which both generate Pareto frontiers for cost- and capacity-efficient designs. We characterize structural properties of optimal designs, formulate the deterministic counterpart of the robust model, and conduct a case study of respiratory diseases (including coronavirus disease 2019) with overlapping clinical presentation. Managerial implications: Key drivers of optimal designs include the assay cost function, the tester’s preference toward cost versus capacity efficiency, prevalence/coinfection rates, and for the robust model, prevalence uncertainty. When an optimal design uses multiple assays, it does so in conjunction with pooling, and it uses individual testing for at most one assay. Although prevalence uncertainty can be a design hurdle, especially for emerging or seasonal diseases, the integration of multiplexing and pooling, and the ordered partition property of optimal designs (under certain coinfection structures) serve to make the design more structurally robust to uncertainty. The robust model further increases robustness, and it is also practical as it needs only an uncertainty set around each disease prevalence. Our Pareto designs demonstrate the cost versus capacity trade-off and show that multiplexing-only or pooling-only designs need not be on the Pareto frontier. Our case study illustrates the benefits of optimally integrated designs over current practices and indicates a low price of robustness. Funding: This work was supported by the National Science Foundation [Grant 1761842]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0296 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133634631","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: Social media has become an indispensable platform for disseminating quality information to consumers across various service sectors. Recently, it has extended its influence to healthcare services, which traditionally relied on government report cards to disclose standardized quality information to the public. This article explores the impact of social media on consumer demand for healthcare services and compares its effectiveness with government report cards. Methodology/results: We analyze quality ratings of U.S. nursing homes collected from two information channels: (1) consumer ratings on Yelp and (2) government ratings on Nursing Home Compare, both of which adopt a five-star quality rating scale and are accessible on the Internet. We employ the method of difference-in-differences with continuous treatment intensity and instrumental variables to analyze the data. Using nursing home resident admissions as a proxy for consumer demand, we find that higher Yelp ratings led to higher consumer demand, particularly among Medicare-covered consumers. Furthermore, the effect of Yelp ratings was primarily driven by extreme ratings (one-star or five-star), as opposed to neutral ratings. We also find that Yelp ratings exerted a stronger effect on consumer demand than government ratings. This dominance of Yelp ratings over government ratings was observed primarily in markets with high Yelp penetration or markets with low and medium consumer education levels. Although higher Yelp ratings were associated with increased net incomes, we find little evidence that nursing homes made quality improvement in response to their Yelp ratings. Managerial implications: We recommend that the Centers for Medicare and Medicaid Services recognize social media platforms as valuable sources of information and collaborate with reputable platforms, such as Yelp, to promote public awareness of government report cards like Nursing Home Compare. Moreover, we advise nursing home operators to proactively manage their reputation on social media by promptly addressing consumer complaints and implementing quality improvement measures. Funding: Y. Li was supported by the Shanghai Sailing Program [Grant 22YF1451000] and the Fundamental Research Funds for the Central Universities. S. F. Lu was supported by the Gerald Lyles rising star fund. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0303 .
问题定义:社交媒体已经成为向各个服务部门的消费者传播高质量信息的不可或缺的平台。最近，它已将影响力扩大到医疗保健服务领域，传统上，医疗保健服务依赖政府成绩单向公众披露标准化的质量信息。本文探讨了社交媒体对消费者医疗保健服务需求的影响，并将其有效性与政府报告卡进行了比较。方法/结果:我们分析了从两个信息渠道收集的美国养老院的质量评级:(1)Yelp上的消费者评级和(2)nursing Home Compare上的政府评级，这两个信息渠道都采用了五星级的质量评级量表，并且可以在互联网上获得。我们采用连续治疗强度和工具变量的差中差法来分析数据。使用养老院居民入院率作为消费者需求的代理，我们发现更高的Yelp评级导致更高的消费者需求，特别是在医疗保险覆盖的消费者中。此外，Yelp评级的影响主要是由极端评级(一星或五星)驱动的，而不是中性评级。我们还发现Yelp评级对消费者需求的影响强于政府评级。这种Yelp评级高于政府评级的主导地位主要出现在Yelp渗透率较高的市场或消费者教育水平较低和中等的市场。虽然较高的Yelp评级与净收入增加有关，但我们发现很少有证据表明养老院在回应其Yelp评级时做出了质量改进。管理意义:我们建议医疗保险和医疗补助服务中心将社交媒体平台视为有价值的信息来源，并与Yelp等知名平台合作，以提高公众对养老院比较等政府成绩单的认识。此外，我们建议养老院经营者积极管理其在社交媒体上的声誉，及时处理消费者投诉并实施质量改进措施。基金资助:李毅获上海帆船计划[基金号:22YF1451000]和中央高校基本科研业务费专项资助。吕绍夫得到了杰拉尔德莱尔斯新星基金的支持。补充材料:在线附录可在https://doi.org/10.1287/msom.2021.0303上获得。
{"title":"Does Social Media Dominate Government Report Cards in Influencing Nursing Home Demand?","authors":"Yuanchen Li, Lauren Xiaoyuan Lu, S. F. Lu","doi":"10.1287/msom.2021.0303","DOIUrl":"https://doi.org/10.1287/msom.2021.0303","url":null,"abstract":"Problem definition: Social media has become an indispensable platform for disseminating quality information to consumers across various service sectors. Recently, it has extended its influence to healthcare services, which traditionally relied on government report cards to disclose standardized quality information to the public. This article explores the impact of social media on consumer demand for healthcare services and compares its effectiveness with government report cards. Methodology/results: We analyze quality ratings of U.S. nursing homes collected from two information channels: (1) consumer ratings on Yelp and (2) government ratings on Nursing Home Compare, both of which adopt a five-star quality rating scale and are accessible on the Internet. We employ the method of difference-in-differences with continuous treatment intensity and instrumental variables to analyze the data. Using nursing home resident admissions as a proxy for consumer demand, we find that higher Yelp ratings led to higher consumer demand, particularly among Medicare-covered consumers. Furthermore, the effect of Yelp ratings was primarily driven by extreme ratings (one-star or five-star), as opposed to neutral ratings. We also find that Yelp ratings exerted a stronger effect on consumer demand than government ratings. This dominance of Yelp ratings over government ratings was observed primarily in markets with high Yelp penetration or markets with low and medium consumer education levels. Although higher Yelp ratings were associated with increased net incomes, we find little evidence that nursing homes made quality improvement in response to their Yelp ratings. Managerial implications: We recommend that the Centers for Medicare and Medicaid Services recognize social media platforms as valuable sources of information and collaborate with reputable platforms, such as Yelp, to promote public awareness of government report cards like Nursing Home Compare. Moreover, we advise nursing home operators to proactively manage their reputation on social media by promptly addressing consumer complaints and implementing quality improvement measures. Funding: Y. Li was supported by the Shanghai Sailing Program [Grant 22YF1451000] and the Fundamental Research Funds for the Central Universities. S. F. Lu was supported by the Gerald Lyles rising star fund. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0303 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"4 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659138","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: Strategic inventory refers to the inventory held by firms purely out of strategic considerations other than operational reasons (e.g., economies of scale). In this paper, we investigate the roles of strategic inventory in a system with two parallel supply chains under both full bargaining and partial bargaining, which differ in whether inventory is included in the bargaining terms. Methodology/results: (i) Under full bargaining, horizontal competition can induce an asymmetric equilibrium, whereby only one of the chains carries strategic inventory and benefits from it when the holding cost is small. The whole system, however, is worse off. (ii) Under partial bargaining, regardless of whether there is horizontal competition, the retailer in a supply chain always carries inventory when his bargaining power is small and the inventory holding cost is low. Furthermore, with horizontal competition, inventory hurts (improves) the system performance when the inventory holding cost is small (above a threshold and not too big). (iii) Full bargaining can be inferior to partial bargaining when there is horizontal competition. Managerial implications: The conventional wisdom about strategic inventory should be taken with caution. Specifically, the traditional role of strategic inventory empowering the retailer in a supply chain is completely dominated by the full bargaining framework, yet it is still present if inventory is not bargained. The inventory driven by horizontal competition plays a different strategic role of signaling to the competitor to avoid an otherwise adverse quantity competition if both retailers carried high inventory. Furthermore, despite the full cooperation nature of the full bargaining framework, it is not always in the retailer’s interest to give up the decision power on inventory (partial bargaining) and include it in the negotiation process (full bargaining). Funding: Q. Tang was supported by Nanyang Technological University [Start-Up Grant 020022-00001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0223 .
{"title":"Strategic Inventories in Competitive Supply Chains Under Bargaining","authors":"L. Chen, Weijia Gu, Qinshen Tang","doi":"10.1287/msom.2020.0223","DOIUrl":"https://doi.org/10.1287/msom.2020.0223","url":null,"abstract":"Problem definition: Strategic inventory refers to the inventory held by firms purely out of strategic considerations other than operational reasons (e.g., economies of scale). In this paper, we investigate the roles of strategic inventory in a system with two parallel supply chains under both full bargaining and partial bargaining, which differ in whether inventory is included in the bargaining terms. Methodology/results: (i) Under full bargaining, horizontal competition can induce an asymmetric equilibrium, whereby only one of the chains carries strategic inventory and benefits from it when the holding cost is small. The whole system, however, is worse off. (ii) Under partial bargaining, regardless of whether there is horizontal competition, the retailer in a supply chain always carries inventory when his bargaining power is small and the inventory holding cost is low. Furthermore, with horizontal competition, inventory hurts (improves) the system performance when the inventory holding cost is small (above a threshold and not too big). (iii) Full bargaining can be inferior to partial bargaining when there is horizontal competition. Managerial implications: The conventional wisdom about strategic inventory should be taken with caution. Specifically, the traditional role of strategic inventory empowering the retailer in a supply chain is completely dominated by the full bargaining framework, yet it is still present if inventory is not bargained. The inventory driven by horizontal competition plays a different strategic role of signaling to the competitor to avoid an otherwise adverse quantity competition if both retailers carried high inventory. Furthermore, despite the full cooperation nature of the full bargaining framework, it is not always in the retailer’s interest to give up the decision power on inventory (partial bargaining) and include it in the negotiation process (full bargaining). Funding: Q. Tang was supported by Nanyang Technological University [Start-Up Grant 020022-00001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0223 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129323789","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 consider the parallel machine scheduling (PMS) under job-splitting game defined by a set of manufacturers where each holds uniform parallel machines and each is committed to produce some jobs submitted to her by her clients while bearing the cost of the sum of completion times of her jobs on her machines. An efficient algorithm for this scheduling problem is well known. We consider the corresponding cooperative game, where the manufacturers are players that want to join forces. We show that collaboration is profitable. Yet, the stability of the cooperation depends on the cost allocation scheme; we focus on the core of the game. Methodology/results: We prove that the PMS game is totally balanced and its core is infinitely large, by developing a sophisticated methodology of linear complexity that finds a line segment in its symmetric core. We call this segment the basic core of the game. Managerial implications: This PMS game has the potential for various applications both in traditional industry and in distributed computing systems in the hi-tech industry. The formation of a partnership among entrepreneurs, companies, or manufacturers necessitates not only a plan for joining forces toward the achievement of the ultimate goals, but also an acceptable agreement regarding the cost allocation among the partners. Core allocations guarantee the stability of the partnership as no subset of players can gain by defecting from the grand coalition. Funding: This work was supported by the Henry Crown Israeli Institute for Business Research, the Coller Foundation, and the Israel Science Foundation [Grant 1489/19].
{"title":"The Basic Core of a Parallel Machines Scheduling Game","authors":"Tzvi Alon, Shoshana Anily","doi":"10.1287/msom.2021.0337","DOIUrl":"https://doi.org/10.1287/msom.2021.0337","url":null,"abstract":"Problem definition: We consider the parallel machine scheduling (PMS) under job-splitting game defined by a set of manufacturers where each holds uniform parallel machines and each is committed to produce some jobs submitted to her by her clients while bearing the cost of the sum of completion times of her jobs on her machines. An efficient algorithm for this scheduling problem is well known. We consider the corresponding cooperative game, where the manufacturers are players that want to join forces. We show that collaboration is profitable. Yet, the stability of the cooperation depends on the cost allocation scheme; we focus on the core of the game. Methodology/results: We prove that the PMS game is totally balanced and its core is infinitely large, by developing a sophisticated methodology of linear complexity that finds a line segment in its symmetric core. We call this segment the basic core of the game. Managerial implications: This PMS game has the potential for various applications both in traditional industry and in distributed computing systems in the hi-tech industry. The formation of a partnership among entrepreneurs, companies, or manufacturers necessitates not only a plan for joining forces toward the achievement of the ultimate goals, but also an acceptable agreement regarding the cost allocation among the partners. Core allocations guarantee the stability of the partnership as no subset of players can gain by defecting from the grand coalition. Funding: This work was supported by the Henry Crown Israeli Institute for Business Research, the Coller Foundation, and the Israel Science Foundation [Grant 1489/19].","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128799784","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}
Gian-Gabriel P. Garcia, L. Steimle, Wesley J. Marrero, J. Sussman
Problem definition: Effective hypertension management is critical to reducing the consequences of atherosclerotic cardiovascular disease, a leading cause of death in the United States. Clinical guidelines for hypertension can be enhanced using decision-analytic approaches capable of capturing complexities in treatment planning. However, model-generated recommendations may be uninterpretable/unintuitive, limiting their clinical acceptability. We address this challenge by investigating interpretable treatment plans. Methodology/results: We formulate interpretable treatment plans as Markov decision processes (MDPs) and analyze the problems of optimizing monotone policies, which prohibit decreasing treatment intensity for sicker patients, and class-ordered monotone policies, which generalize monotone policies. We establish that both policies depend on initial state distributions and that optimal monotone policies can be generated tractably for many treatment planning problems. Next, we propose exact formulations for optimizing interpretable policies broadly. Then, we analyze the price of interpretability, proving that the class-ordered monotone policy’s price of interpretability does not exceed the monotone policy’s price of interpretability. Finally, we formulate and evaluate MDPs for hypertension treatment planning using a large nationally representative data set of the U.S. population. We compare the structure and performance of optimal monotone policies and class-ordered monotone policies with optimal MDP-based policies and current clinical guidelines. At the patient level, optimal MDP-based policies may be unintuitive, recommending more aggressive treatment for healthier patients than sicker patients. Conversely, monotone policies and class-ordered monotone policies never deescalate treatment, reflecting clinical intuition. Across 66.5 million patients, optimized monotone policies and class-ordered monotone policies outperform clinical guidelines, saving over 3,246 quality-adjusted life years per 100,000 patients, with both policies paying a low price of interpretability. Sensitivity analysis illustrates that monotone policies and class-ordered monotone policies are robust to various definitions of “interpretability.” Managerial implications: Interpretable policies can be tractably optimized, drastically outperform existing guidelines, and perform near optimally—potentially increasing the acceptability of decision-analytic approaches in practice. Funding: L. N. Steimle and W. J. Marrero received support from the National Science Foundation Graduate Research Fellowship [Grant DGE 1256260]. J. B. Sussman received support from the National Institutes of Health [Grants R01NS102715 and RF1AG068410], the U.S. Department of Veterans Affairs [Grants 1I01-HX003304 and 1I50-HX003251], and the Michigan Department of Health and Human Services. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0373 .
问题定义:有效的高血压管理对于减少动脉粥样硬化性心血管疾病的后果至关重要，动脉粥样硬化性心血管疾病是美国的主要死亡原因。高血压的临床指南可以通过决策分析方法来加强，这些决策分析方法能够捕捉到治疗计划中的复杂性。然而，模型生成的建议可能无法解释/不直观，限制了它们的临床可接受性。我们通过研究可解释的治疗方案来解决这一挑战。方法/结果:我们将可解释的治疗计划制定为马尔可夫决策过程(mdp)，并分析了单调政策的优化问题，单调政策禁止对病情较重的患者降低治疗强度，类有序单调政策推广单调政策。我们建立了这两种策略都依赖于初始状态分布，并且对于许多治疗计划问题可以生成可跟踪的最优单调策略。接下来，我们提出了广泛优化可解释策略的精确公式。然后，我们分析了可解释性的价格，证明了类序单调策略的可解释性价格不超过单调策略的可解释性价格。最后，我们使用具有全国代表性的美国人口数据集来制定和评估高血压治疗计划的mdp。我们比较了最优单调策略和类有序单调策略的结构和性能，以及基于mdp的最优策略和当前临床指南。在患者层面，基于mdp的最佳政策可能不直观，建议对健康的患者进行比病情较重的患者更积极的治疗。相反，单调政策和分类有序单调政策从不降低治疗的级别，这反映了临床直觉。在6650万名患者中，优化的单调政策和分类有序的单调政策优于临床指南，每10万名患者节省了3246个质量调整生命年，两种政策的可解释性都很低。敏感性分析表明，单调策略和类有序单调策略对各种“可解释性”定义都具有鲁棒性。管理意义:可解释的策略可以被跟踪优化，大大超过现有的指导方针，并执行接近最优-潜在地增加决策分析方法在实践中的可接受性。资助:L. N. Steimle和W. J. Marrero获得了美国国家科学基金会研究生研究奖学金[Grant DGE 1256260]的支持。J. B. Sussman得到了美国国立卫生研究院[赠款R01NS102715和RF1AG068410]、美国退伍军人事务部[赠款1I01-HX003304和1I50-HX003251]和密歇根州卫生与公众服务部的支持。补充材料:电子伴侣可在https://doi.org/10.1287/msom.2021.0373上获得。
{"title":"Interpretable Policies and the Price of Interpretability in Hypertension Treatment Planning","authors":"Gian-Gabriel P. Garcia, L. Steimle, Wesley J. Marrero, J. Sussman","doi":"10.1287/msom.2021.0373","DOIUrl":"https://doi.org/10.1287/msom.2021.0373","url":null,"abstract":"Problem definition: Effective hypertension management is critical to reducing the consequences of atherosclerotic cardiovascular disease, a leading cause of death in the United States. Clinical guidelines for hypertension can be enhanced using decision-analytic approaches capable of capturing complexities in treatment planning. However, model-generated recommendations may be uninterpretable/unintuitive, limiting their clinical acceptability. We address this challenge by investigating interpretable treatment plans. Methodology/results: We formulate interpretable treatment plans as Markov decision processes (MDPs) and analyze the problems of optimizing monotone policies, which prohibit decreasing treatment intensity for sicker patients, and class-ordered monotone policies, which generalize monotone policies. We establish that both policies depend on initial state distributions and that optimal monotone policies can be generated tractably for many treatment planning problems. Next, we propose exact formulations for optimizing interpretable policies broadly. Then, we analyze the price of interpretability, proving that the class-ordered monotone policy’s price of interpretability does not exceed the monotone policy’s price of interpretability. Finally, we formulate and evaluate MDPs for hypertension treatment planning using a large nationally representative data set of the U.S. population. We compare the structure and performance of optimal monotone policies and class-ordered monotone policies with optimal MDP-based policies and current clinical guidelines. At the patient level, optimal MDP-based policies may be unintuitive, recommending more aggressive treatment for healthier patients than sicker patients. Conversely, monotone policies and class-ordered monotone policies never deescalate treatment, reflecting clinical intuition. Across 66.5 million patients, optimized monotone policies and class-ordered monotone policies outperform clinical guidelines, saving over 3,246 quality-adjusted life years per 100,000 patients, with both policies paying a low price of interpretability. Sensitivity analysis illustrates that monotone policies and class-ordered monotone policies are robust to various definitions of “interpretability.” Managerial implications: Interpretable policies can be tractably optimized, drastically outperform existing guidelines, and perform near optimally—potentially increasing the acceptability of decision-analytic approaches in practice. Funding: L. N. Steimle and W. J. Marrero received support from the National Science Foundation Graduate Research Fellowship [Grant DGE 1256260]. J. B. Sussman received support from the National Institutes of Health [Grants R01NS102715 and RF1AG068410], the U.S. Department of Veterans Affairs [Grants 1I01-HX003304 and 1I50-HX003251], and the Michigan Department of Health and Human Services. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0373 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134240764","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 description: In many markets with demand uncertainties, competing retailers may share inventories for common products that they offer consumers. This paper examines how competitors’ product sharing affects their inventory and service-quality decisions. The existing literature has mainly focused on inventory sharing among independent retailers who do not compete with each other. Our research aims to fill the gap in this literature by investigating the tradeoffs of inventory sharing between retailers who directly compete for customers based on service quality. Methodology/results: We develop a game-theoretical model in which two retailers selling a common product from the same manufacturer compete for customers by offering differentiated services together with the product. Each retailer faces stochastic demand that increases in its service quality and decreases in the competitor’s service quality. When a retailer runs out of stock of the product, it may replenish its inventory directly from the manufacturer and/or request the competitor’s excess inventory if they have an inventory-sharing agreement. We find that inventory sharing may soften or intensify service competition, depending on the transfer price for the shared inventory. Specifically, when retailers agree to share inventory, their service levels decrease in the transfer price if their preseason inventory levels are exogenous, but are nonmonotone in the transfer price if the retailers endogenously choose inventory levels. Moreover, our analysis reveals that the retailers’ equilibrium inventory levels will increase in the transfer price and can be higher or lower than their levels in the case without inventory sharing. We also find that with exogenous inventory, the retailers prefer to share inventory at the highest nonmoot transfer price, whereas with endogenous inventory, the retailers may prefer not to share inventory, even at the optimal transfer price, when the level of competition and the preorder cost are high. Finally, we show that with service competition, inventory sharing cannot achieve full coordination under any transfer price. Managerial implications: When deciding whether to share inventory with competitors, managers should consider not only the benefits of inventory pooling, but also the strategic effect of sharing on the firms’ inventory choices and service levels. Funding: X. Guo has received research support from the Research Grants Council of Hong Kong [RGC Reference No. 15501820]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0584 .
{"title":"Inventory Sharing Under Service Competition","authors":"Xiaomeng Guo, Baojun Jiang","doi":"10.1287/msom.2020.0584","DOIUrl":"https://doi.org/10.1287/msom.2020.0584","url":null,"abstract":"Problem description: In many markets with demand uncertainties, competing retailers may share inventories for common products that they offer consumers. This paper examines how competitors’ product sharing affects their inventory and service-quality decisions. The existing literature has mainly focused on inventory sharing among independent retailers who do not compete with each other. Our research aims to fill the gap in this literature by investigating the tradeoffs of inventory sharing between retailers who directly compete for customers based on service quality. Methodology/results: We develop a game-theoretical model in which two retailers selling a common product from the same manufacturer compete for customers by offering differentiated services together with the product. Each retailer faces stochastic demand that increases in its service quality and decreases in the competitor’s service quality. When a retailer runs out of stock of the product, it may replenish its inventory directly from the manufacturer and/or request the competitor’s excess inventory if they have an inventory-sharing agreement. We find that inventory sharing may soften or intensify service competition, depending on the transfer price for the shared inventory. Specifically, when retailers agree to share inventory, their service levels decrease in the transfer price if their preseason inventory levels are exogenous, but are nonmonotone in the transfer price if the retailers endogenously choose inventory levels. Moreover, our analysis reveals that the retailers’ equilibrium inventory levels will increase in the transfer price and can be higher or lower than their levels in the case without inventory sharing. We also find that with exogenous inventory, the retailers prefer to share inventory at the highest nonmoot transfer price, whereas with endogenous inventory, the retailers may prefer not to share inventory, even at the optimal transfer price, when the level of competition and the preorder cost are high. Finally, we show that with service competition, inventory sharing cannot achieve full coordination under any transfer price. Managerial implications: When deciding whether to share inventory with competitors, managers should consider not only the benefits of inventory pooling, but also the strategic effect of sharing on the firms’ inventory choices and service levels. Funding: X. Guo has received research support from the Research Grants Council of Hong Kong [RGC Reference No. 15501820]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0584 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125577303","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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