Problem definition: Artificial intelligence (AI) assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. In this paper, we investigate the impact of the AI smartness (i.e., whether the AI assistant is powered by machine learning intelligence) and the impact of AI transparency (i.e., whether physicians are informed of the AI assistant). Methodology/results: We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, that is, adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that the smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing. Moreover, the impact of AI transparency on the adoption rate is contingent on the smartness level of the AI assistant: the transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the AI assistant is smart. Managerial implications: Our study can guide platforms in designing their AI strategies. Platforms should improve the smartness of AI assistants. If such an improvement is too costly, the platform should transparentize the AI assistant, especially when it is not smart. Funding: This research was supported by a Behavioral Research Assistance Grant from the C. T. Bauer College of Business, University of Houston. H. Zhao acknowledges support from Hong Kong General Research Fund [9043593]. Y. (R.) Tan acknowledges generous support from CEIBS Research [Grant AG24QCS]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0093 .
问题的定义：人工智能（AI）助手--可为个人执行任务或提供服务的软件代理--是最有前途的人工智能应用之一。然而，人们对服务提供者（即医生）在现实医疗环境中采用人工智能助手的情况知之甚少。在本文中，我们研究了人工智能智能性（即人工智能助手是否由机器学习智能驱动）和人工智能透明度（即医生是否了解人工智能助手）的影响。方法/结果：我们与一家领先的医疗保健平台合作开展了一项实地实验，比较了医生在透明和不透明条件下采用智能和自动人工智能助手的行为，即采用率和采用时间。我们发现，智能化可以提高采用率并缩短采用时间，而透明化只能缩短采用时间。此外，人工智能透明度对采用率的影响取决于人工智能助手的智能程度：只有当人工智能助手不具备智能算法时，透明度才会提高采用率；而当人工智能助手具备智能时，透明度则不会提高采用率。管理意义：我们的研究可以指导平台设计其人工智能战略。平台应提高人工智能助手的智能程度。如果这种改进成本过高，平台应将人工智能助手透明化，尤其是当它不智能时。研究经费本研究得到了休斯顿大学 C. T. Bauer 商学院的行为研究资助。H. Zhao 感谢香港一般研究基金 [9043593] 的支持。Y. (R.) Tan 感谢中欧国际工商学院研究基金[Grant AG24QCS]的慷慨资助。补充材料：在线附录见 https://doi.org/10.1287/msom.2023.0093 。
{"title":"Physician Adoption of AI Assistant","authors":"Ting Hou, Meng Li, Y. Tan, Huazhong Zhao","doi":"10.1287/msom.2023.0093","DOIUrl":"https://doi.org/10.1287/msom.2023.0093","url":null,"abstract":"Problem definition: Artificial intelligence (AI) assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. In this paper, we investigate the impact of the AI smartness (i.e., whether the AI assistant is powered by machine learning intelligence) and the impact of AI transparency (i.e., whether physicians are informed of the AI assistant). Methodology/results: We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, that is, adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that the smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing. Moreover, the impact of AI transparency on the adoption rate is contingent on the smartness level of the AI assistant: the transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the AI assistant is smart. Managerial implications: Our study can guide platforms in designing their AI strategies. Platforms should improve the smartness of AI assistants. If such an improvement is too costly, the platform should transparentize the AI assistant, especially when it is not smart. Funding: This research was supported by a Behavioral Research Assistance Grant from the C. T. Bauer College of Business, University of Houston. H. Zhao acknowledges support from Hong Kong General Research Fund [9043593]. Y. (R.) Tan acknowledges generous support from CEIBS Research [Grant AG24QCS]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0093 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829614","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}
Mingming Leng, R. Becerril-Arreola, M. Parlar, Mark Ferguson
Problem definition: We investigate how the characteristics of consumers and a service firm influence the firm’s optimal pricing and promised delivery-time decisions as well as the optimal investment in the quality of delivery reliability information available to consumers. Methodology/results: We use utility, queuing, and choice modeling theories to model consumers’ behavior and to find solutions to the firm’s profit maximization problem. Managerial implications: The optimal strategy is to disclose either error-free delivery reliability information or no information at all. We also delineate conditions for each of the two strategies to dominate. Funding: This research was supported by the General Research Fund (GRF) of the Hong Kong Research Grants Council under Research Project LU13500822. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0223 .
{"title":"Disclosing Delivery Performance Information When Consumers Are Sensitive to Promised Delivery Time, Delivery Reliability, and Price","authors":"Mingming Leng, R. Becerril-Arreola, M. Parlar, Mark Ferguson","doi":"10.1287/msom.2023.0223","DOIUrl":"https://doi.org/10.1287/msom.2023.0223","url":null,"abstract":"Problem definition: We investigate how the characteristics of consumers and a service firm influence the firm’s optimal pricing and promised delivery-time decisions as well as the optimal investment in the quality of delivery reliability information available to consumers. Methodology/results: We use utility, queuing, and choice modeling theories to model consumers’ behavior and to find solutions to the firm’s profit maximization problem. Managerial implications: The optimal strategy is to disclose either error-free delivery reliability information or no information at all. We also delineate conditions for each of the two strategies to dominate. Funding: This research was supported by the General Research Fund (GRF) of the Hong Kong Research Grants Council under Research Project LU13500822. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0223 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"52 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644529","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: Loyalty programs have greatly expanded in scale and scope, and loyalty points issued by firms serve as a new form of currency alongside the traditional currency of money. In this paper, we study how consumers decide to pay with points or money for a purchase and how these decisions are affected by consumers’ points earning characteristics. Methodology/results: We develop a model of consumers’ payment choices and estimate it on proprietary loyalty program data from a major U.S. airline company using a hierarchical Bayesian framework. Our results demonstrate that mental accounting, the subjective perceived value of points, and the reference exchange rate play important roles in consumers’ payment choices. Moreover, the primary points earning source and the total earning level are jointly associated with consumers’ attitudes toward points and money: Consumers who earn many points and mostly with the focal firm tend to value points more than money, while those who earn few points or mostly through a cobranded credit card tend to value money more than points. To better understand heterogeneity in consumers’ attitudes toward points, we propose a probabilistic segmentation of consumers and identify four behavioral segments with distinctive characteristics. Through counterfactual analysis, we demonstrate how a firm can implement money and point pricing policies to optimally target and influence consumers’ payment choices. Managerial implications: It is important for firms to understand how consumers think about points and decide to pay with points or money as it affects firms’ cash flows, profitability, and consumer loyalty and engagement. We demonstrate that firms can design optimal price targeting policies by utilizing the significant level of heterogeneity in consumers’ attitudes toward points. Furthermore, our results indicate that firms that are expanding their partnership networks and offering more points earning sources should consider the impact that this would have on consumers’ mental accounting of money and points, and consequently their payment choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0287 .
{"title":"Loyalty Currency and Mental Accounting: Do Consumers Treat Points Like Money?","authors":"Freddy Lim, So Yeon Chun, Ville Satopää","doi":"10.1287/msom.2022.0287","DOIUrl":"https://doi.org/10.1287/msom.2022.0287","url":null,"abstract":"Problem definition: Loyalty programs have greatly expanded in scale and scope, and loyalty points issued by firms serve as a new form of currency alongside the traditional currency of money. In this paper, we study how consumers decide to pay with points or money for a purchase and how these decisions are affected by consumers’ points earning characteristics. Methodology/results: We develop a model of consumers’ payment choices and estimate it on proprietary loyalty program data from a major U.S. airline company using a hierarchical Bayesian framework. Our results demonstrate that mental accounting, the subjective perceived value of points, and the reference exchange rate play important roles in consumers’ payment choices. Moreover, the primary points earning source and the total earning level are jointly associated with consumers’ attitudes toward points and money: Consumers who earn many points and mostly with the focal firm tend to value points more than money, while those who earn few points or mostly through a cobranded credit card tend to value money more than points. To better understand heterogeneity in consumers’ attitudes toward points, we propose a probabilistic segmentation of consumers and identify four behavioral segments with distinctive characteristics. Through counterfactual analysis, we demonstrate how a firm can implement money and point pricing policies to optimally target and influence consumers’ payment choices. Managerial implications: It is important for firms to understand how consumers think about points and decide to pay with points or money as it affects firms’ cash flows, profitability, and consumer loyalty and engagement. We demonstrate that firms can design optimal price targeting policies by utilizing the significant level of heterogeneity in consumers’ attitudes toward points. Furthermore, our results indicate that firms that are expanding their partnership networks and offering more points earning sources should consider the impact that this would have on consumers’ mental accounting of money and points, and consequently their payment choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0287 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"48 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644766","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}
Guokai Li, Ningyuan Chen, Guillermo Gallego, P. Gao, Steven Kou
Problem definition: We consider two business models for a two-sided economy under uncertainty: dealership and marketplace with fulfillment services. Although both business models can bridge the gap between demand and supply, it is not clear which model is better for the firm or for the consumers. Methodology/results: We show that, whereas the two models differ substantially in pricing power, inventory risk, fee structure, and fulfillment time, both models share several important features with the revenues earned by the firm from the two models converging when the markets are thick. We also show that, for thick markets, there is a one-to-one mapping between their corresponding optimal policies. Managerial implications: Our results provide guidelines for firms entering two-sided markets: when the market is thick, the two business models are similar; when the market is thin, they should carefully inspect a number of market conditions before making the choice. Funding: The research of G. Li is partially supported by the National Natural Science Foundation of China [Grants 72150002, 72394361] and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. The research of N. Chen is partially supported by the Natural Sciences and Engineering Research Council of Canada Discovery [Grant RGPIN-2020-04038]. The research of G. Gallego is partially supported by Collaborative Research Funding Hong Kong [Grant C6032-21G]. The research of P. Gao is supported by the National Natural Science Foundation of China [Grants 72201234, 72192805], Collaborative Research Funding Hong Kong [Grant C6032-21G], and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0253 .
{"title":"Dealership or Marketplace with Fulfillment Services: A Dynamic Comparison","authors":"Guokai Li, Ningyuan Chen, Guillermo Gallego, P. Gao, Steven Kou","doi":"10.1287/msom.2022.0253","DOIUrl":"https://doi.org/10.1287/msom.2022.0253","url":null,"abstract":"Problem definition: We consider two business models for a two-sided economy under uncertainty: dealership and marketplace with fulfillment services. Although both business models can bridge the gap between demand and supply, it is not clear which model is better for the firm or for the consumers. Methodology/results: We show that, whereas the two models differ substantially in pricing power, inventory risk, fee structure, and fulfillment time, both models share several important features with the revenues earned by the firm from the two models converging when the markets are thick. We also show that, for thick markets, there is a one-to-one mapping between their corresponding optimal policies. Managerial implications: Our results provide guidelines for firms entering two-sided markets: when the market is thick, the two business models are similar; when the market is thin, they should carefully inspect a number of market conditions before making the choice. Funding: The research of G. Li is partially supported by the National Natural Science Foundation of China [Grants 72150002, 72394361] and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. The research of N. Chen is partially supported by the Natural Sciences and Engineering Research Council of Canada Discovery [Grant RGPIN-2020-04038]. The research of G. Gallego is partially supported by Collaborative Research Funding Hong Kong [Grant C6032-21G]. The research of P. Gao is supported by the National Natural Science Foundation of China [Grants 72201234, 72192805], Collaborative Research Funding Hong Kong [Grant C6032-21G], and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0253 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676824","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}
Pub Date : 2024-07-01DOI: 10.1287/msom.2024.intro.v26.n4
G. Perakis
{"title":"Introduction: Frontiers in Operations","authors":"G. Perakis","doi":"10.1287/msom.2024.intro.v26.n4","DOIUrl":"https://doi.org/10.1287/msom.2024.intro.v26.n4","url":null,"abstract":"","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"17 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698446","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: Through the laws of inheritance, knowing an individual’s genetic status informs disease risk for family members, but current protocols for deciding whom to genetically test only consider one person at a time rather than design an optimal testing plan for the entire family. Methodology/results: We develop a Markov decision process framework for maximizing the net benefits of genetic testing that integrates a Bayesian network of genetic statuses, with a functional representation of cost-effectiveness. Our model provides a contingent sequence of family members to test one at a time, that is, a plan that dynamically incorporates new test results, revealed sequentially at random, to decide who next to test. In the general case, we show that optimal stopping follows a structure with two-sided thresholds, previously known only for individual testing. Although the optimal testing sequence, in general, is contingent on the family test results, in the special case of sibling-only tests we can identify this sequence a priori. Our numerical case study, which was conducted in a realistic BRCA1/2 testing setting, demonstrates that an optimal policy significantly improves cost-effectiveness over existing policies. Thus, our framework offers a promising and powerful new approach to genetic testing. Managerial implications: In an optimal policy, prioritizing testing family members who might otherwise not have been tested can lead to an overall improvement in familial health value, surpassing even the most cost-effective existing protocols. From a management perspective, healthcare organizations and insurance companies can potentially save costs by implementing this approach for such families. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: D. Adelman is grateful for financial support from Booth School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0057 .
问题的定义：通过遗传规律，了解个人的基因状况可告知家庭成员的疾病风险，但目前决定对谁进行基因检测的方案每次只考虑一个人，而不是为整个家庭设计最佳检测计划。方法/结果：我们开发了一个马尔可夫决策过程框架，用于实现基因检测净效益的最大化，该框架将基因状态的贝叶斯网络与成本效益的功能表示相结合。我们的模型提供了一个每次检测一个家庭成员的或然序列，即一个动态纳入随机顺序揭示的新检测结果的计划，以决定下一个检测对象。在一般情况下，我们证明了最优停止遵循一个具有双面阈值的结构，而这种结构以前只为个人测试所知。虽然一般情况下，最佳测试顺序取决于家族测试结果，但在只有兄弟姐妹测试的特殊情况下，我们可以先验地确定这一顺序。我们在现实的 BRCA1/2 检测环境中进行了数值案例研究，结果表明，与现有政策相比，最优政策能显著提高成本效益。因此，我们的框架为基因检测提供了一种前景广阔、功能强大的新方法。管理意义：在最优政策中，优先对那些可能不会接受检测的家庭成员进行检测，可全面提高家族健康价值，甚至超过最具成本效益的现有方案。从管理的角度来看，医疗机构和保险公司通过对这些家庭实施这种方法，有可能节约成本。历史：本文已被《制造与服务运营管理》（Manufacturing & Service Operations Management）的 "运营前沿"（Frontiers in Operations Initiative）杂志录用。资助：D. Adelman 感谢布斯商学院的资助。补充材料：在线附录见 https://doi.org/10.1287/msom.2023.0057 。
{"title":"Frontiers in Operations: Optimal Genetic Testing of Families","authors":"Daniel Adelman, Kanix Wang","doi":"10.1287/msom.2023.0057","DOIUrl":"https://doi.org/10.1287/msom.2023.0057","url":null,"abstract":"Problem definition: Through the laws of inheritance, knowing an individual’s genetic status informs disease risk for family members, but current protocols for deciding whom to genetically test only consider one person at a time rather than design an optimal testing plan for the entire family. Methodology/results: We develop a Markov decision process framework for maximizing the net benefits of genetic testing that integrates a Bayesian network of genetic statuses, with a functional representation of cost-effectiveness. Our model provides a contingent sequence of family members to test one at a time, that is, a plan that dynamically incorporates new test results, revealed sequentially at random, to decide who next to test. In the general case, we show that optimal stopping follows a structure with two-sided thresholds, previously known only for individual testing. Although the optimal testing sequence, in general, is contingent on the family test results, in the special case of sibling-only tests we can identify this sequence a priori. Our numerical case study, which was conducted in a realistic BRCA1/2 testing setting, demonstrates that an optimal policy significantly improves cost-effectiveness over existing policies. Thus, our framework offers a promising and powerful new approach to genetic testing. Managerial implications: In an optimal policy, prioritizing testing family members who might otherwise not have been tested can lead to an overall improvement in familial health value, surpassing even the most cost-effective existing protocols. From a management perspective, healthcare organizations and insurance companies can potentially save costs by implementing this approach for such families. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: D. Adelman is grateful for financial support from Booth School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0057 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"53 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339577","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 customer service chat (CSC) systems, in which agents can serve multiple customers simultaneously, with generally distributed service and patience times. The multitasking capability of agents introduces idiosyncratic challenges when making routing and staffing decisions. Methodology/results: To determine the dynamic matching of arriving customers with available agents, we first formulate a routing linear program (LP) based on system primitives. Inspired by the optimal solution of the routing LP, we design a parsimonious dynamic routing policy that is independent of arrival rate and service capacity information. We also use the optimal solution to develop closed-form approximations for crucial performance metrics and show that a similar LP can be utilized to make staffing decisions. Through extensive simulation experiments, we showcase the efficacy of our approximations and staffing decisions. Furthermore, under our proposed policy, the CSC system exhibits a unique stationary fluid model in which the steady-state performance measures align with our approximations. Managerial implications: The extant literature primarily focuses on Markovian systems with exponential distributions. In this paper, customers’ service and patience times are allowed to be generally distributed to agree with practical settings. Our findings indicate that the distributions have a significant impact on routing policies, staffing decisions, and system performance. Funding: Z. Long was supported by the National Natural Science Foundation of China [Grants 72101112, 72132005, and 72271119] and Jiangsu Province, China [Grant BK20210171]. J. Zhang was supported by the Hong Kong Research Grants Council, General Research Fund [Grants 16208120 and 16214121]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0114 .
{"title":"Routing and Staffing in Customer Service Chat Systems with Generally Distributed Service and Patience Times","authors":"Zhenghua Long, Tolga Tezcan, Jiheng Zhang","doi":"10.1287/msom.2022.0114","DOIUrl":"https://doi.org/10.1287/msom.2022.0114","url":null,"abstract":"Problem definition: We study customer service chat (CSC) systems, in which agents can serve multiple customers simultaneously, with generally distributed service and patience times. The multitasking capability of agents introduces idiosyncratic challenges when making routing and staffing decisions. Methodology/results: To determine the dynamic matching of arriving customers with available agents, we first formulate a routing linear program (LP) based on system primitives. Inspired by the optimal solution of the routing LP, we design a parsimonious dynamic routing policy that is independent of arrival rate and service capacity information. We also use the optimal solution to develop closed-form approximations for crucial performance metrics and show that a similar LP can be utilized to make staffing decisions. Through extensive simulation experiments, we showcase the efficacy of our approximations and staffing decisions. Furthermore, under our proposed policy, the CSC system exhibits a unique stationary fluid model in which the steady-state performance measures align with our approximations. Managerial implications: The extant literature primarily focuses on Markovian systems with exponential distributions. In this paper, customers’ service and patience times are allowed to be generally distributed to agree with practical settings. Our findings indicate that the distributions have a significant impact on routing policies, staffing decisions, and system performance. Funding: Z. Long was supported by the National Natural Science Foundation of China [Grants 72101112, 72132005, and 72271119] and Jiangsu Province, China [Grant BK20210171]. J. Zhang was supported by the Hong Kong Research Grants Council, General Research Fund [Grants 16208120 and 16214121]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0114 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"31 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340481","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 paper considers a tandem queueing system in which stage 1 has one station serving multiple classes of arriving customers with different service requirements and related delay costs, and stage 2 has multiple parallel stations, with each station providing one type of service. Each station has many statistically identical servers. The objective is to design a joint capacity allocation between stages/stations and scheduling rule of different classes of customers to minimize the system’s long-run average cost. Methodology/results: Using fluid approximation, we convert the stochastic problem into a fluid optimization problem and develop a solution procedure. Based on the solution to the fluid optimization problem, we propose a simple and easy-to-implement capacity allocation and scheduling policy and establish its asymptotic optimality for the stochastic system. The policy has an explicit index-based scheduling rule that is independent of the arrival rates, and resource allocation is determined by the priority orders established between the classes and stations. We conduct numerical experiments to validate the accuracy of the fluid approximation and demonstrate the effectiveness of our proposed policy. Managerial implications: Tandem queueing systems are ubiquitous. Our results provide useful guidelines for the allocation of limited resources and the scheduling of customer service in those systems. Our proposed policy can improve the system’s operational efficiency and customers’ service quality. Funding: Z. Zhong’s research is partially supported by the Fundamental Research Funds for the Central Universities [Grant 2023ZYGXZR074] and the Hunan Provincial Natural Science Foundation of China [Grant 2022JJ40109]. P. Cao’s research is partially supported by the National Natural Science Foundation of China [Grant 72122019]. J. Huang’s research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14501621] and the National Natural Science Foundation of China [Grant 72222023]. S. X. Zhou’s research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14500921], the National Natural Science Foundation of China [Grant 72394395], and the Asian Institute of Supply Chains and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0266 .
{"title":"Capacity Allocation and Scheduling in Two-Stage Service Systems with Multiclass Customers","authors":"Z. Zhong, Ping Cao, Junfei Huang, Sean X. Zhou","doi":"10.1287/msom.2023.0266","DOIUrl":"https://doi.org/10.1287/msom.2023.0266","url":null,"abstract":"Problem definition: This paper considers a tandem queueing system in which stage 1 has one station serving multiple classes of arriving customers with different service requirements and related delay costs, and stage 2 has multiple parallel stations, with each station providing one type of service. Each station has many statistically identical servers. The objective is to design a joint capacity allocation between stages/stations and scheduling rule of different classes of customers to minimize the system’s long-run average cost. Methodology/results: Using fluid approximation, we convert the stochastic problem into a fluid optimization problem and develop a solution procedure. Based on the solution to the fluid optimization problem, we propose a simple and easy-to-implement capacity allocation and scheduling policy and establish its asymptotic optimality for the stochastic system. The policy has an explicit index-based scheduling rule that is independent of the arrival rates, and resource allocation is determined by the priority orders established between the classes and stations. We conduct numerical experiments to validate the accuracy of the fluid approximation and demonstrate the effectiveness of our proposed policy. Managerial implications: Tandem queueing systems are ubiquitous. Our results provide useful guidelines for the allocation of limited resources and the scheduling of customer service in those systems. Our proposed policy can improve the system’s operational efficiency and customers’ service quality. Funding: Z. Zhong’s research is partially supported by the Fundamental Research Funds for the Central Universities [Grant 2023ZYGXZR074] and the Hunan Provincial Natural Science Foundation of China [Grant 2022JJ40109]. P. Cao’s research is partially supported by the National Natural Science Foundation of China [Grant 72122019]. J. Huang’s research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14501621] and the National Natural Science Foundation of China [Grant 72222023]. S. X. Zhou’s research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14500921], the National Natural Science Foundation of China [Grant 72394395], and the Asian Institute of Supply Chains and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0266 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"5 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349627","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: Leading specialty coffee roasters rely on direct trade to source premium coffee beans. We examine a roaster who sells two basic types of roasts: (1) a single-origin roast sourced from a specific locale and (2) a blend roast that uses a mix of beans from sources that vary over the course of a year. The prices of blend roasts are lower than those of single-origin roasts and appeal to a larger market. We study how characteristics of the operating and market environment affect the optimal sourcing strategy for single-origin beans. Methodology/results: We develop a two-stage stochastic program with recourse that reflects these characteristics. A roaster has the option to allocate some of the single-origin beans for sale under a blend label, known as downward substitution. We identify three distinct optimal sourcing strategies—specialized (no downward substitution), diversified (consistent downward substitution), and mixed (between these extremes)—and show that they are robust under different definitions of yield and demand. Managerial implications: We identify four main insights: (1) Two factors determine which strategy is optimal: the mean price of the inferior product (blend label) and the marginal cost of the superior product (single-origin label). (2) When compared with the newsvendor model, we find distinct structural differences across strategies. For example, whereas the effects of increasing uncertainty on optimal quantity align with the newsvendor model under a mixed strategy, the effects are distinctly different under specialized and diversified strategies (e.g., monotonic decreasing behavior for specialized, no change in quantity under diversified). (3) The weighted average price of an agricultural product is decreasing in negative yield–price correlation. We coin this as the “farmer’s curse,” which carries lessons for direct trade sourcing (e.g., advocating against paying the grower at postharvest market prices). (4) We find evidence of a virtuous feedback loop wherein the grower–roaster relationship becomes stronger over time. Our findings also point to a simple signal that policymakers may use to identify coffee growing locales where targeted interventions can improve grower welfare. Funding: B. Kazaz is thankful for summer support provided by the Whitman School of Management, Syracuse University. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0586 .
{"title":"Direct Trade Sourcing Strategies for Specialty Coffee","authors":"Scott Webster, Burak Kazaz, Shahryar Gheibi","doi":"10.1287/msom.2021.0586","DOIUrl":"https://doi.org/10.1287/msom.2021.0586","url":null,"abstract":"Problem definition: Leading specialty coffee roasters rely on direct trade to source premium coffee beans. We examine a roaster who sells two basic types of roasts: (1) a single-origin roast sourced from a specific locale and (2) a blend roast that uses a mix of beans from sources that vary over the course of a year. The prices of blend roasts are lower than those of single-origin roasts and appeal to a larger market. We study how characteristics of the operating and market environment affect the optimal sourcing strategy for single-origin beans. Methodology/results: We develop a two-stage stochastic program with recourse that reflects these characteristics. A roaster has the option to allocate some of the single-origin beans for sale under a blend label, known as downward substitution. We identify three distinct optimal sourcing strategies—specialized (no downward substitution), diversified (consistent downward substitution), and mixed (between these extremes)—and show that they are robust under different definitions of yield and demand. Managerial implications: We identify four main insights: (1) Two factors determine which strategy is optimal: the mean price of the inferior product (blend label) and the marginal cost of the superior product (single-origin label). (2) When compared with the newsvendor model, we find distinct structural differences across strategies. For example, whereas the effects of increasing uncertainty on optimal quantity align with the newsvendor model under a mixed strategy, the effects are distinctly different under specialized and diversified strategies (e.g., monotonic decreasing behavior for specialized, no change in quantity under diversified). (3) The weighted average price of an agricultural product is decreasing in negative yield–price correlation. We coin this as the “farmer’s curse,” which carries lessons for direct trade sourcing (e.g., advocating against paying the grower at postharvest market prices). (4) We find evidence of a virtuous feedback loop wherein the grower–roaster relationship becomes stronger over time. Our findings also point to a simple signal that policymakers may use to identify coffee growing locales where targeted interventions can improve grower welfare. Funding: B. Kazaz is thankful for summer support provided by the Whitman School of Management, Syracuse University. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0586 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"96 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352578","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: Gift card promotions offer a tiered incentive (a gift card) to customers for purchasing regularly-priced products, with higher expenditure receiving a larger incentive. We empirically investigate the impact of gift card promotions on customer purchase behavior by collaborating with a major U.S.-based department store that targets gift card promotion emails on its online channel. Methodology/results: We use a collection of discontinuities in the retailer’s targeting policies and corresponding fuzzy regression discontinuity designs to estimate localized causal effects of the gift card promotion. We find that gift card promotion provided to customers whose most recent purchase was 4 and 13 months ago, respectively, increased their purchase probability by 11.24% and 26.64%, and their average expenditure by 9.53% and 1.9%, respectively. Overall, the gift card promotion generated, on average, $100K in incremental sales per promotion from these customers alone. Furthermore, the promotion induced customers to seek out products from niche categories and increased spending on high-end brands. Gift card promotion emails also had a dominant advertisement effect, that is, customers who received the gift card promotion email increased expenditure even if they did not participate in the promotion. Managerial implications: (i) Gift card promotions are a viable strategy to boost short-term sales and to alter the distribution of the types (brand, category) of purchased products. (ii) Retailers should optimize gift card promotion tiers based on the tradeoff between increased customer expenditure and promotion expenses and consider the advertisement effect when targeting the promotion emails. (iii) Contrary to popular belief, retailers benefit from customers’ redeeming gift cards because they spend more due to the gift card, validating the dual value of the delayed incentive. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0218 .
{"title":"Dual Value of Delayed Incentives: An Empirical Investigation of Gift Card Promotions","authors":"Bharadwaj Kadiyala, Özalp Özer, A. S. Şimşek","doi":"10.1287/msom.2022.0218","DOIUrl":"https://doi.org/10.1287/msom.2022.0218","url":null,"abstract":"Problem definition: Gift card promotions offer a tiered incentive (a gift card) to customers for purchasing regularly-priced products, with higher expenditure receiving a larger incentive. We empirically investigate the impact of gift card promotions on customer purchase behavior by collaborating with a major U.S.-based department store that targets gift card promotion emails on its online channel. Methodology/results: We use a collection of discontinuities in the retailer’s targeting policies and corresponding fuzzy regression discontinuity designs to estimate localized causal effects of the gift card promotion. We find that gift card promotion provided to customers whose most recent purchase was 4 and 13 months ago, respectively, increased their purchase probability by 11.24% and 26.64%, and their average expenditure by 9.53% and 1.9%, respectively. Overall, the gift card promotion generated, on average, $100K in incremental sales per promotion from these customers alone. Furthermore, the promotion induced customers to seek out products from niche categories and increased spending on high-end brands. Gift card promotion emails also had a dominant advertisement effect, that is, customers who received the gift card promotion email increased expenditure even if they did not participate in the promotion. Managerial implications: (i) Gift card promotions are a viable strategy to boost short-term sales and to alter the distribution of the types (brand, category) of purchased products. (ii) Retailers should optimize gift card promotion tiers based on the tradeoff between increased customer expenditure and promotion expenses and consider the advertisement effect when targeting the promotion emails. (iii) Contrary to popular belief, retailers benefit from customers’ redeeming gift cards because they spend more due to the gift card, validating the dual value of the delayed incentive. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0218 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":" 70","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141365376","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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