Problem definition: The Centers for Medicare & Medicaid Services launched the Primary Care First (PCF) initiative in January 2021. The initiative builds upon prior innovative payment models and aims at incentivizing a redesign of primary care delivery, including new modes of delivery, such as remote care. To achieve this goal, the initiative blends capitation and fee-for-service (FFS) payments and includes performance-based adjustments linked to service quality and health outcomes. We analyze a model motivated by this new payment system, and its impact on the different stakeholders, and derive insights on how to design it to reach the best possible outcome. Methodology/results: We propose an analytical model that captures patient heterogeneity in terms of health complexity, provider choice of care-delivery mode (referral to a specialist, in-person visit, or remote care), and quality of service (health outcomes and wait time). We analyze the provider decision on the mode of care delivery under both FFS and PCF and study whether PCF can be designed to yield a socially optimal outcome. We characterize analytically when patients, payer, and providers are better off under PCF and show that, in many cases, PCF can be designed to yield a socially optimal outcome. We numerically calibrate our model for 14 states in the United States. We observe that the average health status in a state is a source of heterogeneity that crucially drives the performance of PCF. We find that the model motivated by the current PCF implementation results in too much adoption of referral care and too little adoption of remote care. In addition, states with poor average health status may use more in-person care than socially optimal under a baseline (low) level of capitation. Moreover, relying on high levels of capitation leads to low adoption of in-person care. Managerial implications: Our results have health policy implications by shedding light on how PCF might impact patients, payer, and providers. Under the current performance-based adjustments, low levels of capitation should be preferred. PCF has the potential to be designed to achieve socially optimal outcomes. However, the fee per visit may need to be tailored to the local population’s health status. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1207 .
{"title":"Primary Care First Initiative: Impact on Care Delivery and Outcomes","authors":"Elodie Adida, Fernanda Bravo","doi":"10.1287/msom.2023.1207","DOIUrl":"https://doi.org/10.1287/msom.2023.1207","url":null,"abstract":"Problem definition: The Centers for Medicare & Medicaid Services launched the Primary Care First (PCF) initiative in January 2021. The initiative builds upon prior innovative payment models and aims at incentivizing a redesign of primary care delivery, including new modes of delivery, such as remote care. To achieve this goal, the initiative blends capitation and fee-for-service (FFS) payments and includes performance-based adjustments linked to service quality and health outcomes. We analyze a model motivated by this new payment system, and its impact on the different stakeholders, and derive insights on how to design it to reach the best possible outcome. Methodology/results: We propose an analytical model that captures patient heterogeneity in terms of health complexity, provider choice of care-delivery mode (referral to a specialist, in-person visit, or remote care), and quality of service (health outcomes and wait time). We analyze the provider decision on the mode of care delivery under both FFS and PCF and study whether PCF can be designed to yield a socially optimal outcome. We characterize analytically when patients, payer, and providers are better off under PCF and show that, in many cases, PCF can be designed to yield a socially optimal outcome. We numerically calibrate our model for 14 states in the United States. We observe that the average health status in a state is a source of heterogeneity that crucially drives the performance of PCF. We find that the model motivated by the current PCF implementation results in too much adoption of referral care and too little adoption of remote care. In addition, states with poor average health status may use more in-person care than socially optimal under a baseline (low) level of capitation. Moreover, relying on high levels of capitation leads to low adoption of in-person care. Managerial implications: Our results have health policy implications by shedding light on how PCF might impact patients, payer, and providers. Under the current performance-based adjustments, low levels of capitation should be preferred. PCF has the potential to be designed to achieve socially optimal outcomes. However, the fee per visit may need to be tailored to the local population’s health status. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1207 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124531989","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 focuses on subscription box services in which a provider selects the assortment of products to include in the box by taking into account the customer’s preferences. Customers interested in purchasing a product choose between engaging in active search (i.e., visit physical stores) or subscribing to a box delivery service. We study the subscription box company’s problem of selecting the optimal contents of the box to maximize expected revenue (by driving demand from customers). Methodology/results: Because a product may be both available at a store and included in the box, the assortment in a box affects the set of stores that a customer would visit under active search and, therefore, the customer’s subscription decision. We model such interaction by applying a cross-nested logit framework that correlates the contents in the box with the products available at the stores. We find that the box should include a collection of popular subsets of the store products for customers that experience a relatively low or relatively high search cost. If a preview of the box is available, we find that, for customers with intermediate values of the search cost, it may be optimal to include a so-called utility loss leader, that is, a product with relatively low valuation, to entice customers to subscribe to the box delivery service and therefore increase the likelihood of a sale. We use rational expectations to model a setting in which a preview of the box is not available. In such cases, it is never optimal to include a utility loss leader in the box. Managerial implications: Our model captures the impact of product overlap across different shopping channels on customer choice and the subscription box company assortment decision. We derive insights on how the subscription service provider should determine the contents of the box in anticipation of the customer’s search behavior. We also examine the decision of offering exclusive products in addition to branded items. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1204 .
{"title":"Managing Customer Search: Assortment Planning for a Subscription Box Service","authors":"Fernando Bernstein, Yuan Guo","doi":"10.1287/msom.2023.1204","DOIUrl":"https://doi.org/10.1287/msom.2023.1204","url":null,"abstract":"Problem definition: This paper focuses on subscription box services in which a provider selects the assortment of products to include in the box by taking into account the customer’s preferences. Customers interested in purchasing a product choose between engaging in active search (i.e., visit physical stores) or subscribing to a box delivery service. We study the subscription box company’s problem of selecting the optimal contents of the box to maximize expected revenue (by driving demand from customers). Methodology/results: Because a product may be both available at a store and included in the box, the assortment in a box affects the set of stores that a customer would visit under active search and, therefore, the customer’s subscription decision. We model such interaction by applying a cross-nested logit framework that correlates the contents in the box with the products available at the stores. We find that the box should include a collection of popular subsets of the store products for customers that experience a relatively low or relatively high search cost. If a preview of the box is available, we find that, for customers with intermediate values of the search cost, it may be optimal to include a so-called utility loss leader, that is, a product with relatively low valuation, to entice customers to subscribe to the box delivery service and therefore increase the likelihood of a sale. We use rational expectations to model a setting in which a preview of the box is not available. In such cases, it is never optimal to include a utility loss leader in the box. Managerial implications: Our model captures the impact of product overlap across different shopping channels on customer choice and the subscription box company assortment decision. We derive insights on how the subscription service provider should determine the contents of the box in anticipation of the customer’s search behavior. We also examine the decision of offering exclusive products in addition to branded items. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1204 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121194077","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: Consumers often try a few varieties of an experience product before establishing a shopping routine. In retailing, a sample box typically refers to a package of multiple trial-sized varieties within a product category. Sample boxes potentially create value by helping consumers resolve their uncertainties regarding these varieties earlier and at a lower cost. In this paper, we study how firms and consumers share this added value under different market scenarios. We also derive the optimal pricing of sample boxes in product categories for which consumers make ongoing purchases over time. Academic/practical relevance: We thus extend the literature by proposing a framework that integrates sequential search and seller-induced learning. Methodology: We analyze a firm’s pricing decisions when consumers either purchase full-sized options sequentially or bypass that process via a sample box. We use dynamic programming to analyze consumers’ search problem (in the absence of a sample box) and nonlinear optimization to analyze the firm’s problem. Results: As anticipated, the informational value of a sample box yields an optimal price premium relative to the prices of individual products. Despite this price premium, we show that the firm’s expected profit may decrease because a sample box accelerates consumer learning, and thus, it may help consumers settle upon an outside option earlier. We establish that a firm can reverse the potential adverse profit impact of selling sample boxes by introducing an optimally specified future credit. Managerial implications: Offering sample boxes is a common practice in retailing. Contrasting the resulting expected profits with and without the sample box option, our results highlight that managers may be ill-advised to offer a sample box in the absence of the future credit mechanism. This study is the first to address the pricing of sample boxes and show the optimality of offering credit toward a subsequent purchase. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1206 .
{"title":"Retail Sample Boxes: Counteracting the Adverse Effect of Accelerated Learning via Future Credit","authors":"Alireza Yazdani, E. Çil, Michael S. Pangburn","doi":"10.1287/msom.2023.1206","DOIUrl":"https://doi.org/10.1287/msom.2023.1206","url":null,"abstract":"Problem definition: Consumers often try a few varieties of an experience product before establishing a shopping routine. In retailing, a sample box typically refers to a package of multiple trial-sized varieties within a product category. Sample boxes potentially create value by helping consumers resolve their uncertainties regarding these varieties earlier and at a lower cost. In this paper, we study how firms and consumers share this added value under different market scenarios. We also derive the optimal pricing of sample boxes in product categories for which consumers make ongoing purchases over time. Academic/practical relevance: We thus extend the literature by proposing a framework that integrates sequential search and seller-induced learning. Methodology: We analyze a firm’s pricing decisions when consumers either purchase full-sized options sequentially or bypass that process via a sample box. We use dynamic programming to analyze consumers’ search problem (in the absence of a sample box) and nonlinear optimization to analyze the firm’s problem. Results: As anticipated, the informational value of a sample box yields an optimal price premium relative to the prices of individual products. Despite this price premium, we show that the firm’s expected profit may decrease because a sample box accelerates consumer learning, and thus, it may help consumers settle upon an outside option earlier. We establish that a firm can reverse the potential adverse profit impact of selling sample boxes by introducing an optimally specified future credit. Managerial implications: Offering sample boxes is a common practice in retailing. Contrasting the resulting expected profits with and without the sample box option, our results highlight that managers may be ill-advised to offer a sample box in the absence of the future credit mechanism. This study is the first to address the pricing of sample boxes and show the optimality of offering credit toward a subsequent purchase. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1206 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133183494","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: Employers across many sectors of the economy have been fast to adopt variable work scheduling policies. The cost of this flexibility for employers is usually borne by employees, for whom unstable work schedules create several disruptions. In the context of home healthcare, we examine how employer-driven volatility in nurses’ schedules impacts their decision to voluntarily leave their job. Methodology/results: Using an instrumental variables approach, we causally identify the effect of schedule volatility on nurses’ voluntary turnover. We begin by constructing an operational measure of schedule volatility using time-stamped work log data from one of the largest home health agencies in the United States. Because this measure may be endogenous to the worker’s decision to quit, we instrument for schedule volatility using paid days off taken by other nurses in the same branch. We find that higher levels of schedule volatility substantially increase a worker’s likelihood of quitting. Specifically, a one-standard-deviation increase in schedule volatility increases the average worker’s propensity to quit on a given day by more than threefold. Translated into yearly terms, 30 days of high schedule volatility over the course of the year increases the average worker’s probability of quitting that year by 20%. Our policy simulations of counterfactual scheduling policies suggest that excess schedule volatility can explain a significant portion of voluntary turnover, and some interventions have the potential to substantially reduce workers’ daily propensity to quit. Managerial implications: This work contributes to the understanding of the extent to which employees value control over their own work schedules and are averse to volatile work schedules that are dictated by employers. Especially in the current environment where there is a growing emphasis on work-life balance and employee-driven flexibility, finding a way to support stable schedules could be important for employers to attract and retain workers. Funding: This work was supported by the National Research Service Award Postdoctoral Fellowship, the Wharton Dean's Research Fund, the Agency for Healthcare Research and Quality [T32 Grant 5T32HS26116], and the Claude Marion Endowed Faculty Scholar Award. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1205 .
问题定义:经济中许多部门的雇主都在迅速采用可变的工作安排政策。对雇主来说，这种灵活性的成本通常由雇员承担，对他们来说，不稳定的工作时间表会造成一些干扰。在家庭医疗保健的背景下，我们研究了雇主驱动的护士时间表波动如何影响他们自愿离职的决定。方法/结果:使用工具变量方法，我们确定了时间表波动对护士自愿离职的影响。我们首先使用来自美国最大的家庭健康机构之一的带时间戳的工作日志数据构建一个时间表波动性的操作度量。由于这一措施可能是工人决定辞职的内生因素，我们使用同一分支中其他护士的带薪休假来测量时间表的波动性。我们发现，较高水平的时间表波动性大大增加了员工辞职的可能性。具体来说，工作日程的波动性每增加一个标准偏差，员工在某一天的辞职倾向就会增加三倍以上。按年计算，如果一年中有30天的工作日程高度不稳定，那么普通员工当年辞职的可能性会增加20%。我们对反事实调度政策的政策模拟表明，过度的调度波动可以解释自愿离职的很大一部分，一些干预措施有可能大大降低工人的日常辞职倾向。管理意义:这项工作有助于理解员工在多大程度上重视控制自己的工作时间表，并反对雇主规定的不稳定的工作时间表。尤其是在当前的环境中，人们越来越强调工作与生活的平衡和员工驱动的灵活性，找到一种方法来支持稳定的时间表对雇主吸引和留住员工可能很重要。资助:本研究得到了国家研究服务奖博士后奖学金、沃顿商学院院长研究基金、美国医疗保健研究与质量局[T32 Grant 5T32HS26116]和克劳德·马里恩捐赠教师学者奖的支持。补充材料:电子伴侣可在https://doi.org/10.1287/msom.2023.1205上获得。
{"title":"“I Quit”: Schedule Volatility as a Driver of Voluntary Employee Turnover","authors":"A. Bergman, G. David, Hummy Song","doi":"10.1287/msom.2023.1205","DOIUrl":"https://doi.org/10.1287/msom.2023.1205","url":null,"abstract":"Problem definition: Employers across many sectors of the economy have been fast to adopt variable work scheduling policies. The cost of this flexibility for employers is usually borne by employees, for whom unstable work schedules create several disruptions. In the context of home healthcare, we examine how employer-driven volatility in nurses’ schedules impacts their decision to voluntarily leave their job. Methodology/results: Using an instrumental variables approach, we causally identify the effect of schedule volatility on nurses’ voluntary turnover. We begin by constructing an operational measure of schedule volatility using time-stamped work log data from one of the largest home health agencies in the United States. Because this measure may be endogenous to the worker’s decision to quit, we instrument for schedule volatility using paid days off taken by other nurses in the same branch. We find that higher levels of schedule volatility substantially increase a worker’s likelihood of quitting. Specifically, a one-standard-deviation increase in schedule volatility increases the average worker’s propensity to quit on a given day by more than threefold. Translated into yearly terms, 30 days of high schedule volatility over the course of the year increases the average worker’s probability of quitting that year by 20%. Our policy simulations of counterfactual scheduling policies suggest that excess schedule volatility can explain a significant portion of voluntary turnover, and some interventions have the potential to substantially reduce workers’ daily propensity to quit. Managerial implications: This work contributes to the understanding of the extent to which employees value control over their own work schedules and are averse to volatile work schedules that are dictated by employers. Especially in the current environment where there is a growing emphasis on work-life balance and employee-driven flexibility, finding a way to support stable schedules could be important for employers to attract and retain workers. Funding: This work was supported by the National Research Service Award Postdoctoral Fellowship, the Wharton Dean's Research Fund, the Agency for Healthcare Research and Quality [T32 Grant 5T32HS26116], and the Claude Marion Endowed Faculty Scholar Award. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1205 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123445790","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: Most humanitarian organizations (HOs) allow donors to earmark their donations (i.e., designate their contributions to a specific purpose). Allowing earmarking may increase donations; however, it creates operational inefficiencies that undermine the impact of those donations. Extant literature has mainly studied earmarking and its operational consequences in the absence of funding competition. We examine how competition for funding impacts earmarking decisions, fundraising costs, and HO performance in short-term disaster response. In addition to the competition model, we analyze two collaborative fundraising models: (i) full collaboration, where HOs contact donors as a unit and donors cannot donate to specific HOs on the fundraiser, and (ii) partial collaboration, where HOs contact donors as a unit and donors choose among the contacting HOs. Methodology: We use game theory to model the interactions between multiple HOs and a market of donors and build a multinomial logit model for the donor choice problem. Results: We find that competition for funding contributes to the prevalence of earmarked donations, increases fundraising costs, and hurts HO performance and utility. We show that two collaborative fundraising models can mitigate these issues depending on the availability of funding resources. When funding is abundant, full collaboration improves HO utility and reduces earmarking and fundraising costs. When funding is scarce, partial collaboration reduces fundraising costs and improves performance and HO utility. When funding is intermediate, these two forms of collaboration do not necessarily benefit HOs. Managerial implications: We illustrate how funding availability drives earmarking and fundraising decisions and key performance metrics of different funding models during short-term disaster response. Using data from the 2010 Haiti earthquake, our numerical study indicates that partial collaboration benefits response to disasters with funding shortage, whereas full collaboration suits disaster response with sufficient funding. HOs competing for funds can use our insights to improve their response effectiveness. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1202 .
{"title":"Competition and Collaboration on Fundraising for Short-Term Disaster Response: The Impact on Earmarking and Performance","authors":"A. Aflaki, Alfonso J. Pedraza-Martinez","doi":"10.1287/msom.2023.1202","DOIUrl":"https://doi.org/10.1287/msom.2023.1202","url":null,"abstract":"Problem definition: Most humanitarian organizations (HOs) allow donors to earmark their donations (i.e., designate their contributions to a specific purpose). Allowing earmarking may increase donations; however, it creates operational inefficiencies that undermine the impact of those donations. Extant literature has mainly studied earmarking and its operational consequences in the absence of funding competition. We examine how competition for funding impacts earmarking decisions, fundraising costs, and HO performance in short-term disaster response. In addition to the competition model, we analyze two collaborative fundraising models: (i) full collaboration, where HOs contact donors as a unit and donors cannot donate to specific HOs on the fundraiser, and (ii) partial collaboration, where HOs contact donors as a unit and donors choose among the contacting HOs. Methodology: We use game theory to model the interactions between multiple HOs and a market of donors and build a multinomial logit model for the donor choice problem. Results: We find that competition for funding contributes to the prevalence of earmarked donations, increases fundraising costs, and hurts HO performance and utility. We show that two collaborative fundraising models can mitigate these issues depending on the availability of funding resources. When funding is abundant, full collaboration improves HO utility and reduces earmarking and fundraising costs. When funding is scarce, partial collaboration reduces fundraising costs and improves performance and HO utility. When funding is intermediate, these two forms of collaboration do not necessarily benefit HOs. Managerial implications: We illustrate how funding availability drives earmarking and fundraising decisions and key performance metrics of different funding models during short-term disaster response. Using data from the 2010 Haiti earthquake, our numerical study indicates that partial collaboration benefits response to disasters with funding shortage, whereas full collaboration suits disaster response with sufficient funding. HOs competing for funds can use our insights to improve their response effectiveness. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1202 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609857","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 significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer’s orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer’s shopping journey. Using a consumer’s sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer’s order features. We determine the model’s coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for “enterprise” customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers’ demographic information has a significant effect on the threshold. Specifically, the threshold is higher for “plus” consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted for the Manufacturing & Service Operations Management Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201 .
{"title":"Managing the Personalized Order-Holding Problem in Online Retailing","authors":"Shouchang Chen, Zhenzhen Yan, Yun Fong Lim","doi":"10.1287/msom.2023.1201","DOIUrl":"https://doi.org/10.1287/msom.2023.1201","url":null,"abstract":"Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer’s orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer’s shopping journey. Using a consumer’s sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer’s order features. We determine the model’s coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for “enterprise” customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers’ demographic information has a significant effect on the threshold. Specifically, the threshold is higher for “plus” consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted for the Manufacturing & Service Operations Management Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133230555","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}
Ziliang Jin, Yulan Wang, Yun Fong Lim, Kai Pan, Z. Shen
Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL. Funding: This work was supported by the Research Grants Council of Hong Kong [Grants 15501319 and 15505318] and the National Natural Science Foundation of China [Grant 71931009]. Z. Jin was supported by the Hong Kong PhD Fellowship Scheme. Y. F. Lim was supported by the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1199 .
{"title":"Vehicle Rebalancing in a Shared Micromobility System with Rider Crowdsourcing","authors":"Ziliang Jin, Yulan Wang, Yun Fong Lim, Kai Pan, Z. Shen","doi":"10.1287/msom.2023.1199","DOIUrl":"https://doi.org/10.1287/msom.2023.1199","url":null,"abstract":"Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL. Funding: This work was supported by the Research Grants Council of Hong Kong [Grants 15501319 and 15505318] and the National Natural Science Foundation of China [Grant 71931009]. Z. Jin was supported by the Hong Kong PhD Fellowship Scheme. Y. F. Lim was supported by the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1199 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115886356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: The undesirable but inevitable consequence of running promotions is that consumers can be trained to time their purchases strategically. In this paper, we study randomized promotions, where the firm randomly offers discounts over time, as an alternative strategy of intertemporal price discrimination. Methodology/results: We consider a base model where a monopolist sells a single product to a market with a constant stream of two market segments. The segments are heterogeneous in both their product valuations and patience levels. The firm precommits to a price distribution, and in each period, a price is randomly drawn from the committed distribution. We characterize the optimal price distribution as a randomized promotion policy and show that it serves as an intertemporal price discrimination mechanism such that high-valuation customers would make a purchase immediately at a regular price upon arrival, and low-valuation customers would wait for a random promotion. Compared against the optimal cyclic pricing policy, which is optimal within the strategy space of all deterministic pricing policies, the optimal randomized pricing policy beats it if low-valuation customers are sufficiently patient and the absolute discrepancy between high and low customer valuations is large enough. We extend the model in three directions. First, we consider the case where a portion of customers are myopic and would never wait. We show that the existence of myopic customers is detrimental to the firm’s profitability, and the expected profit from an optimal randomized pricing policy decreases as the proportion of myopic customers in the population increases. Second, we consider Markovian pricing policies where prices are allowed to be intertemporally correlated in a Markovian fashion. This additional maneuver allows the firm to reap an even higher profit when low-valuation customers are sufficiently patient by avoiding consecutive promotions but, on average, running the promotion more frequently with a smaller discount size. Lastly, we consider a model with multiple customer segments and show that a two-point price distribution remains optimal, and our conclusion from the two-segment base model still holds under certain conditions that are adopted in the literature. Managerial implications: Our results imply that the firm may want to deliberately randomize promotions in the presence of forward-looking customers. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201124], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295], and the Youth Project of the Humanities and Social Science Foundation of the Ministry of Education of China [Grant 22YJC630006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1194 .
{"title":"Intertemporal Price Discrimination via Randomized Promotions","authors":"Hongqiao Chen, Ming Hu, Jiahua Wu","doi":"10.1287/msom.2023.1194","DOIUrl":"https://doi.org/10.1287/msom.2023.1194","url":null,"abstract":"Problem definition: The undesirable but inevitable consequence of running promotions is that consumers can be trained to time their purchases strategically. In this paper, we study randomized promotions, where the firm randomly offers discounts over time, as an alternative strategy of intertemporal price discrimination. Methodology/results: We consider a base model where a monopolist sells a single product to a market with a constant stream of two market segments. The segments are heterogeneous in both their product valuations and patience levels. The firm precommits to a price distribution, and in each period, a price is randomly drawn from the committed distribution. We characterize the optimal price distribution as a randomized promotion policy and show that it serves as an intertemporal price discrimination mechanism such that high-valuation customers would make a purchase immediately at a regular price upon arrival, and low-valuation customers would wait for a random promotion. Compared against the optimal cyclic pricing policy, which is optimal within the strategy space of all deterministic pricing policies, the optimal randomized pricing policy beats it if low-valuation customers are sufficiently patient and the absolute discrepancy between high and low customer valuations is large enough. We extend the model in three directions. First, we consider the case where a portion of customers are myopic and would never wait. We show that the existence of myopic customers is detrimental to the firm’s profitability, and the expected profit from an optimal randomized pricing policy decreases as the proportion of myopic customers in the population increases. Second, we consider Markovian pricing policies where prices are allowed to be intertemporally correlated in a Markovian fashion. This additional maneuver allows the firm to reap an even higher profit when low-valuation customers are sufficiently patient by avoiding consecutive promotions but, on average, running the promotion more frequently with a smaller discount size. Lastly, we consider a model with multiple customer segments and show that a two-point price distribution remains optimal, and our conclusion from the two-segment base model still holds under certain conditions that are adopted in the literature. Managerial implications: Our results imply that the firm may want to deliberately randomize promotions in the presence of forward-looking customers. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201124], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295], and the Youth Project of the Humanities and Social Science Foundation of the Ministry of Education of China [Grant 22YJC630006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1194 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122650680","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: How to use social media to predict style color and jeans fit sales for a retailer. Academic/practical relevance: Neither retail practice nor the academic literature provides a method for using social media to predict style color and jeans fit sales for a retailer. We present and validate a systematic approach for doing that. Methodology: Demand forecasting in the fashion industry is challenging due to short product lifetimes, long manufacturing lead times, and constant innovation of fashion products. We investigate the value of social media information for color trends and jeans fit forecasting. We partner with three multinational retailers, two apparel and one footwear, and combine their proprietary data sets with web-crawled publicly available data on Twitter and the Google Search Volume Index. We implement a variety of machine learning models to develop forecasts that can be used in setting the initial shipment quantity for an item, arguably the most important decision for fashion retailers. Results: Our findings show that fine-grained social media information has significant predictive power in forecasting color and fit demands months in advance of the sales season, and therefore greatly helps in making the initial shipment quantity decision. The predictive power of including social media features, measured by the improvement of the out-of-sample mean absolute deviation over current practice ranges from 24% to 57%. Managerial implications: To our knowledge, this study is the first to explore and demonstrate the value of social media information in fashion demand forecasting in a way that is practical and operable for fashion retailers. With consistent results across all three retailers, we demonstrate the robustness of our findings over market and geographic heterogeneity, and different forecast horizons. Moreover, we discuss potential mechanisms that might be driving this significant predictive power. Our results suggest that changes in fashion demand are driven more by “bottom-up” changes in consumer preferences than by “top-down” influence from the fashion industry. Funding: This work was supported by Wharton School Fishman-Davidson Center for Service and Operations Management, the Wharton School Baker Retailing Center, and the Wharton School Risk Management Center Russell Ackoff Doctoral Student Fellowship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1193 .
{"title":"The Value of Social Media Data in Fashion Forecasting","authors":"Youran Fu, M. Fisher","doi":"10.1287/msom.2023.1193","DOIUrl":"https://doi.org/10.1287/msom.2023.1193","url":null,"abstract":"Problem definition: How to use social media to predict style color and jeans fit sales for a retailer. Academic/practical relevance: Neither retail practice nor the academic literature provides a method for using social media to predict style color and jeans fit sales for a retailer. We present and validate a systematic approach for doing that. Methodology: Demand forecasting in the fashion industry is challenging due to short product lifetimes, long manufacturing lead times, and constant innovation of fashion products. We investigate the value of social media information for color trends and jeans fit forecasting. We partner with three multinational retailers, two apparel and one footwear, and combine their proprietary data sets with web-crawled publicly available data on Twitter and the Google Search Volume Index. We implement a variety of machine learning models to develop forecasts that can be used in setting the initial shipment quantity for an item, arguably the most important decision for fashion retailers. Results: Our findings show that fine-grained social media information has significant predictive power in forecasting color and fit demands months in advance of the sales season, and therefore greatly helps in making the initial shipment quantity decision. The predictive power of including social media features, measured by the improvement of the out-of-sample mean absolute deviation over current practice ranges from 24% to 57%. Managerial implications: To our knowledge, this study is the first to explore and demonstrate the value of social media information in fashion demand forecasting in a way that is practical and operable for fashion retailers. With consistent results across all three retailers, we demonstrate the robustness of our findings over market and geographic heterogeneity, and different forecast horizons. Moreover, we discuss potential mechanisms that might be driving this significant predictive power. Our results suggest that changes in fashion demand are driven more by “bottom-up” changes in consumer preferences than by “top-down” influence from the fashion industry. Funding: This work was supported by Wharton School Fishman-Davidson Center for Service and Operations Management, the Wharton School Baker Retailing Center, and the Wharton School Risk Management Center Russell Ackoff Doctoral Student Fellowship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1193 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121322723","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 blockchain payment system, such as Bitcoin or Ethereum, validates electronic transactions and stores them in a chain of blocks without a central authority. Miners with computing power compete for the rights to create blocks according to a preset protocol, referred to as hashing or mining, and, in return, earn fees paid by users who submit transactions. Because of security concerns caused by decentralization, a transaction is confirmed after a number of additional blocks are subsequently extended to the block containing it. This confirmation latency introduces an intricate interplay between miners and users. This paper provides approximate system equilibria and studies optimal designs of a blockchain. Methodology/results: The hashing process is essentially a single-server queue with batch services based on a fee-based priority discipline, and confirmation latency adds complexity to the equilibrium behavior and optimal design. We analyze how miners’ participation decisions interact with users’ participation and fee decisions and identify optimal designs when the goal is to maximize the throughput or social welfare. We validate our model and conduct numerical studies using data from Bitcoin. Managerial implications: By incorporating security issues, we uncover the interdependence of the decisions between users and miners and the driver for nonzero entrance fees in practice. We show that miners and users may end up in either a vicious or virtuous cycle, depending on the initial system state. By allowing the entrance fee to be a design parameter, we are able to establish that it is optimal to simply run a blockchain system at its full capacity and a block size as small as possible. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 16200019, 16200617, 16200821, 16208120, and 16214121]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1197 .
{"title":"Blockchain Operations in the Presence of Security Concerns","authors":"Jiahao He, Guangyuan Zhang, Jiheng Zhang, Rachel Q. Zhang","doi":"10.1287/msom.2023.1197","DOIUrl":"https://doi.org/10.1287/msom.2023.1197","url":null,"abstract":"Problem definition: A blockchain payment system, such as Bitcoin or Ethereum, validates electronic transactions and stores them in a chain of blocks without a central authority. Miners with computing power compete for the rights to create blocks according to a preset protocol, referred to as hashing or mining, and, in return, earn fees paid by users who submit transactions. Because of security concerns caused by decentralization, a transaction is confirmed after a number of additional blocks are subsequently extended to the block containing it. This confirmation latency introduces an intricate interplay between miners and users. This paper provides approximate system equilibria and studies optimal designs of a blockchain. Methodology/results: The hashing process is essentially a single-server queue with batch services based on a fee-based priority discipline, and confirmation latency adds complexity to the equilibrium behavior and optimal design. We analyze how miners’ participation decisions interact with users’ participation and fee decisions and identify optimal designs when the goal is to maximize the throughput or social welfare. We validate our model and conduct numerical studies using data from Bitcoin. Managerial implications: By incorporating security issues, we uncover the interdependence of the decisions between users and miners and the driver for nonzero entrance fees in practice. We show that miners and users may end up in either a vicious or virtuous cycle, depending on the initial system state. By allowing the entrance fee to be a design parameter, we are able to establish that it is optimal to simply run a blockchain system at its full capacity and a block size as small as possible. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 16200019, 16200617, 16200821, 16208120, and 16214121]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1197 .","PeriodicalId":119284,"journal":{"name":"Manufacturing & Service Operations Management","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134080253","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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