{"title":"Introduction to Special Section on Data-Driven Research Challenge","authors":"Gad Allon","doi":"10.1287/msom.2023.1232","DOIUrl":"https://doi.org/10.1287/msom.2023.1232","url":null,"abstract":"","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"19 4","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140520814","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: For grocery retailers, managing perishable food that is nearing expiry is a major challenge. Donating food to food banks is socially responsible, as it improves local communities and reduces waste generation. It also diverts food to a secondary, quality-differentiated market. Academic/practical relevance: In this paper, we quantify the economic impacts of this secondary market for food by examining donation and pricing behaviors for competing retailers. Methodology: We use a structural model of price-discriminating oligopoly retailers to study the effect of food donations on store and category-level demand and equilibrium prices. Empirically, we estimate the food donation effect using a unique data set of food donations and sales for several categories of perishable foods across two major retail chains that compete in the same market. Results: The competitive effects of food donations follow from the price-discrimination logic. First, food donations create a direct demand effect. Donations raise the average quality of products on display, shifting demand curves inward and rotating them clockwise (e.g., more inelastic). Second, food donations create a market enhancement effect, softening price competition and raising equilibrium prices among competing retailers. Managerial implications: Food donations increase food prices and store profits, tying the socially responsible option to an economic benefit. This study contributes a new type of reuse operation to the literature on closed-loop supply chains. Funding: Funding from the Agriculture and Food Research Initiative (National Institute for Food and Agriculture, U.S. Department of Agriculture) [Grant 2020-67021-31377] is acknowledged. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.1185 .
{"title":"Food Donations, Retail Operations, and Retail Pricing","authors":"John Lowrey, T. Richards, S. Hamilton","doi":"10.1287/msom.2022.1185","DOIUrl":"https://doi.org/10.1287/msom.2022.1185","url":null,"abstract":"Problem definition: For grocery retailers, managing perishable food that is nearing expiry is a major challenge. Donating food to food banks is socially responsible, as it improves local communities and reduces waste generation. It also diverts food to a secondary, quality-differentiated market. Academic/practical relevance: In this paper, we quantify the economic impacts of this secondary market for food by examining donation and pricing behaviors for competing retailers. Methodology: We use a structural model of price-discriminating oligopoly retailers to study the effect of food donations on store and category-level demand and equilibrium prices. Empirically, we estimate the food donation effect using a unique data set of food donations and sales for several categories of perishable foods across two major retail chains that compete in the same market. Results: The competitive effects of food donations follow from the price-discrimination logic. First, food donations create a direct demand effect. Donations raise the average quality of products on display, shifting demand curves inward and rotating them clockwise (e.g., more inelastic). Second, food donations create a market enhancement effect, softening price competition and raising equilibrium prices among competing retailers. Managerial implications: Food donations increase food prices and store profits, tying the socially responsible option to an economic benefit. This study contributes a new type of reuse operation to the literature on closed-loop supply chains. Funding: Funding from the Agriculture and Food Research Initiative (National Institute for Food and Agriculture, U.S. Department of Agriculture) [Grant 2020-67021-31377] is acknowledged. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.1185 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"20 1","pages":"792-810"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75066563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers to balance the tradeoff between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. Academic/practical relevance: Modern buying channels, including e-commerce and public procurement, often consist of a platform that mediates transactions. Frequently, these platforms implement simple and transparent mechanisms to induce suppliers’ direct participation, which typically results in pay-as-bid (or first-price) mechanisms where suppliers set their prices. Methodology: We introduce a novel class of assortment mechanisms that we call k-soft reserves (k-SRs): If at least k suppliers choose a price below the soft-reserve price, then only those suppliers are added to the assortment; otherwise, all the suppliers are added. Results: We show the optimality of k-SRs for a class of stylized symmetric models to derive the intuition behind these mechanisms. Then, through extensive numerical simulations, we provide evidence of the robustness of k-SRs in more general and realistic settings. Managerial implications: Our results give intuitive and simple-to-use prescriptions on how to optimize pay-as-bid assortment mechanisms in practice, with an emphasis on public procurement settings. Funding: J. Choi thanks the Samsung Scholarship and Stanford Graduate School of Business for financial support. G. Weintraub thanks Joseph and Laurie Lacob for the support during the 2018–2019 academic year as a Joseph and Laurie Lacob Faculty Scholar at Stanford Graduate School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1180 .
{"title":"The Design of Optimal Pay-as-Bid Procurement Mechanisms","authors":"Je-ok Choi, D. Sabán, G. Weintraub","doi":"10.2139/ssrn.3785023","DOIUrl":"https://doi.org/10.2139/ssrn.3785023","url":null,"abstract":"Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers to balance the tradeoff between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. Academic/practical relevance: Modern buying channels, including e-commerce and public procurement, often consist of a platform that mediates transactions. Frequently, these platforms implement simple and transparent mechanisms to induce suppliers’ direct participation, which typically results in pay-as-bid (or first-price) mechanisms where suppliers set their prices. Methodology: We introduce a novel class of assortment mechanisms that we call k-soft reserves (k-SRs): If at least k suppliers choose a price below the soft-reserve price, then only those suppliers are added to the assortment; otherwise, all the suppliers are added. Results: We show the optimality of k-SRs for a class of stylized symmetric models to derive the intuition behind these mechanisms. Then, through extensive numerical simulations, we provide evidence of the robustness of k-SRs in more general and realistic settings. Managerial implications: Our results give intuitive and simple-to-use prescriptions on how to optimize pay-as-bid assortment mechanisms in practice, with an emphasis on public procurement settings. Funding: J. Choi thanks the Samsung Scholarship and Stanford Graduate School of Business for financial support. G. Weintraub thanks Joseph and Laurie Lacob for the support during the 2018–2019 academic year as a Joseph and Laurie Lacob Faculty Scholar at Stanford Graduate School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1180 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"26 1","pages":"613-630"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74637305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider a customer-to-customer (C2C) platform that provides an inspection service. Uncertain about product authenticity, a seller sells a product through the platform. Before purchasing, a buyer obtains a signal of the product authenticity from the product’s price set by the seller. The platform’s inspection service can detect a counterfeit with a probability. If the product passes the inspection, the platform sends it to the buyer and charges the seller a commission fee. Otherwise, the platform returns it to the seller and charges the seller a penalty fee. Methodology/results: We develop a two-stage game-theoretical model. In the first stage, the platform designs a contract specifying the commission and penalty fees. In the second stage, the seller signals the product authenticity by setting a price and the buyer decides whether to purchase it. This results in a contract design problem that governs a signaling game. We find that the effect of inspection is beyond merely detecting counterfeits. The inspection, even an imperfect one, changes the signaling game’s structure and incentivizes the seller whose product is likely authentic to sell through the platform. This can only be achieved by carefully choosing the commission and penalty fees. Moreover, a larger platform’s expected profit does not imply a larger commission fee or price in equilibrium. Under some mild conditions, the optimal commission increases but the optimal penalty decreases as the platform’s inspection capability improves. Managerial implications: The inspection service is not widely available among leading C2C platforms as it is considered imperfect and costly. Our study suggests that its benefit may be underestimated in practice. Moreover, the inspection can eliminate the seller’s information rent and generate more revenue for the platform. This paper provides guidance on how to set commission and penalty fees when the inspection service is provided. Funding: L. Li is supported by the National Natural Science Foundation of China [Grant 72071198] and the Hong Kong Polytechnic University Distinguished Postdoctoral Fellowship Scheme [Grant 1-YWC7]. X. Fang and Y. F. Lim are grateful for the support from the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship and Retail Centre of Excellence Research Grant]. Y. F. Lim is supported by the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], and the Key Program of National Natural Science Foundation of China [Grant 71931009]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1186 .
{"title":"Asymmetric Information of Product Authenticity on C2C E-Commerce Platforms: How Can Inspection Services Help?","authors":"Linqiu Li, X. Fang, Yun Fong Lim","doi":"10.1287/msom.2023.1186","DOIUrl":"https://doi.org/10.1287/msom.2023.1186","url":null,"abstract":"Problem definition: We consider a customer-to-customer (C2C) platform that provides an inspection service. Uncertain about product authenticity, a seller sells a product through the platform. Before purchasing, a buyer obtains a signal of the product authenticity from the product’s price set by the seller. The platform’s inspection service can detect a counterfeit with a probability. If the product passes the inspection, the platform sends it to the buyer and charges the seller a commission fee. Otherwise, the platform returns it to the seller and charges the seller a penalty fee. Methodology/results: We develop a two-stage game-theoretical model. In the first stage, the platform designs a contract specifying the commission and penalty fees. In the second stage, the seller signals the product authenticity by setting a price and the buyer decides whether to purchase it. This results in a contract design problem that governs a signaling game. We find that the effect of inspection is beyond merely detecting counterfeits. The inspection, even an imperfect one, changes the signaling game’s structure and incentivizes the seller whose product is likely authentic to sell through the platform. This can only be achieved by carefully choosing the commission and penalty fees. Moreover, a larger platform’s expected profit does not imply a larger commission fee or price in equilibrium. Under some mild conditions, the optimal commission increases but the optimal penalty decreases as the platform’s inspection capability improves. Managerial implications: The inspection service is not widely available among leading C2C platforms as it is considered imperfect and costly. Our study suggests that its benefit may be underestimated in practice. Moreover, the inspection can eliminate the seller’s information rent and generate more revenue for the platform. This paper provides guidance on how to set commission and penalty fees when the inspection service is provided. Funding: L. Li is supported by the National Natural Science Foundation of China [Grant 72071198] and the Hong Kong Polytechnic University Distinguished Postdoctoral Fellowship Scheme [Grant 1-YWC7]. X. Fang and Y. F. Lim are grateful for the support from the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship and Retail Centre of Excellence Research Grant]. Y. F. Lim is supported by the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], and the Key Program of National Natural Science Foundation of China [Grant 71931009]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1186 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"2 1","pages":"631-647"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83634661","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 the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .
{"title":"Believing in Analytics: Managers' Adherence to Price Recommendations from a DSS","authors":"Felipe Caro, Anna Sáez de Tejada Cuenca","doi":"10.1287/msom.2022.1166","DOIUrl":"https://doi.org/10.1287/msom.2022.1166","url":null,"abstract":"Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"11 1","pages":"524-542"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76664814","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: Initial product design decisions are critically important for mobile apps, which show a relatively short time from launch to peak usage, thus providing a narrow window for success and no time for course correction based on market reaction. Mobile apps are designed using a highly modular architecture based on software development kits (SDKs), with SDK choices being sequentially determined along three dimensions—multiplicity (total number of SDKs), compatibility (SDK co-occurrence frequency), and novelty (SDK degree of newness to the developer). We evaluate the consequence of these decisions on initial market success in the context of mobile gaming app design. Academic/practical relevance: The resulting conceptual framework aids developers in determining the modularity of digital product development. Methodology: We formulate an instrumental variables least absolute shrinkage and selection operator regression model to estimate relationships of interest using a proprietary data set extracted from the application programming interface server of a leading mobile apps intelligence firm. Results: We find a negative impact of SDK multiplicity on initial success. High SDK compatibility can mitigate this negative effect, whereas high SDK novelty can exacerbate the negative effect of multiplicity. Post hoc analysis shows that business-to-consumer (B2C) communication features can also mitigate this negative impact. Managerial implications: Prior product modularity research has predominantly focused on physical products or relied on single-dimensional modularity measures. Our study conceptualizes modularity as multidimensional and investigates how these multidimensional SDK-based modularity choices impact the performance of a key category of digital products—mobile apps. We demonstrate that increasing multiplicity, essential in certain markets, negatively affects initial success. However, firms can enhance SDK compatibility, reduce SDK novelty, and use B2C communication channels to mitigate this negative impact. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1181 .
{"title":"Impact of Modularity Design on Mobile App Launch Success","authors":"Moonwon Chung, Luv Sharma, M. Malhotra","doi":"10.1287/msom.2022.1181","DOIUrl":"https://doi.org/10.1287/msom.2022.1181","url":null,"abstract":"Problem definition: Initial product design decisions are critically important for mobile apps, which show a relatively short time from launch to peak usage, thus providing a narrow window for success and no time for course correction based on market reaction. Mobile apps are designed using a highly modular architecture based on software development kits (SDKs), with SDK choices being sequentially determined along three dimensions—multiplicity (total number of SDKs), compatibility (SDK co-occurrence frequency), and novelty (SDK degree of newness to the developer). We evaluate the consequence of these decisions on initial market success in the context of mobile gaming app design. Academic/practical relevance: The resulting conceptual framework aids developers in determining the modularity of digital product development. Methodology: We formulate an instrumental variables least absolute shrinkage and selection operator regression model to estimate relationships of interest using a proprietary data set extracted from the application programming interface server of a leading mobile apps intelligence firm. Results: We find a negative impact of SDK multiplicity on initial success. High SDK compatibility can mitigate this negative effect, whereas high SDK novelty can exacerbate the negative effect of multiplicity. Post hoc analysis shows that business-to-consumer (B2C) communication features can also mitigate this negative impact. Managerial implications: Prior product modularity research has predominantly focused on physical products or relied on single-dimensional modularity measures. Our study conceptualizes modularity as multidimensional and investigates how these multidimensional SDK-based modularity choices impact the performance of a key category of digital products—mobile apps. We demonstrate that increasing multiplicity, essential in certain markets, negatively affects initial success. However, firms can enhance SDK compatibility, reduce SDK novelty, and use B2C communication channels to mitigate this negative impact. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1181 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"29 1","pages":"756-774"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81003601","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}
Ximin Huang, A. Atasu, Necati Tereyağoğlu, L. B. Toktay
Problem definition: We study the economic rationale for certified pre-owned (CPO) programs and characterize the mechanism through which they influence secondary markets to create value for durable goods firms and consumers. Academic/practical relevance: Our study on understanding the market impact of CPO programs is highly relevant for industry given their prevalence in practice. Our work also contributes to the adverse selection literature in the context of the “lemons problem” by uncovering the mechanism underpinning CPO programs and how they improve market efficiency. Methodology: We use a game-theoretic durable goods model that captures CPO program features to identify how CPO programs improve market efficiency. We further investigate how CPO program size, as determined by program qualification criteria, plays a role in improving market efficiency. Results: We show that the mechanism underpinning CPO programs consists of two mutually reinforcing effects: a more effective segmentation of used product buyers through high- and low-quality product differentiation and a higher trade-in discount to used product owners. The former effect generates a higher revenue, which, in turn, allows for a higher trade-in discount. This translates to more high-quality products being traded in, more new products being purchased, and more used products being sold in the secondary market (i.e., increased market efficiency). We also show that these two effects are strengthened as the CPO program size increases. Finally, we test hypotheses deriving from our theoretical results in the context of the U.S. automotive industry. Our empirical findings are consistent with the conclusions of our theoretical work. Managerial implications: Durable goods firms can proactively manage their CPO programs by appropriately setting trade-in discounts and managing price discrimination between CPO and non-CPO products. CPO program qualification criteria can be used to alter CPO program size and, hence, the level of market efficiency improvement induced by these choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1176 .
{"title":"Lemons, Trade-Ins, and Certified Pre-Owned Programs","authors":"Ximin Huang, A. Atasu, Necati Tereyağoğlu, L. B. Toktay","doi":"10.1287/msom.2022.1176","DOIUrl":"https://doi.org/10.1287/msom.2022.1176","url":null,"abstract":"Problem definition: We study the economic rationale for certified pre-owned (CPO) programs and characterize the mechanism through which they influence secondary markets to create value for durable goods firms and consumers. Academic/practical relevance: Our study on understanding the market impact of CPO programs is highly relevant for industry given their prevalence in practice. Our work also contributes to the adverse selection literature in the context of the “lemons problem” by uncovering the mechanism underpinning CPO programs and how they improve market efficiency. Methodology: We use a game-theoretic durable goods model that captures CPO program features to identify how CPO programs improve market efficiency. We further investigate how CPO program size, as determined by program qualification criteria, plays a role in improving market efficiency. Results: We show that the mechanism underpinning CPO programs consists of two mutually reinforcing effects: a more effective segmentation of used product buyers through high- and low-quality product differentiation and a higher trade-in discount to used product owners. The former effect generates a higher revenue, which, in turn, allows for a higher trade-in discount. This translates to more high-quality products being traded in, more new products being purchased, and more used products being sold in the secondary market (i.e., increased market efficiency). We also show that these two effects are strengthened as the CPO program size increases. Finally, we test hypotheses deriving from our theoretical results in the context of the U.S. automotive industry. Our empirical findings are consistent with the conclusions of our theoretical work. Managerial implications: Durable goods firms can proactively manage their CPO programs by appropriately setting trade-in discounts and managing price discrimination between CPO and non-CPO products. CPO program qualification criteria can be used to alter CPO program size and, hence, the level of market efficiency improvement induced by these choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1176 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"14 1","pages":"737-755"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90836094","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 the robust formulation of an inventory model with positive fixed ordering costs, where the unfulfilled demand is either backlogged or lost, the lead time is allowed to be positive, the demand is potentially intertemporally correlated, and the information about the demand distribution is limited. Methodology/results: We propose a robust cycle-based policy that manages inventory by dividing the planning horizon into nonoverlapping inventory cycles, where an order is placed at the beginning of each cycle. Our policy selects the lengths and order quantities for all inventory cycles to minimize the worst-case total cost incurred over the planning horizon. When the uncertain demand belongs to a general polyhedral uncertainty set, the decisions in our policy can be computed by solving linear programs (LPs) for the backlogging model with any lead time and the lost-sales model with zero lead time; however, the number of LPs that need to be solved grows exponentially in the length of the planning horizon. In the special case where the uncertain demand belongs to a box uncertainty set, the decisions in our policy can be computed using a dynamic programming (DP) recursion whose complexity grows polynomially in the length of the planning horizon. We also propose a one-cycle look-ahead heuristic to handle large problem instances with a general polyhedral uncertainty set. This heuristic can be applied for both the backlogging and lost-sales models with any lead time, and it only requires solving LPs whose number grows quadratically in the length of the planning horizon. Results from extensive computational experiments clearly show that both a rolling-cycle implementation of our policy and the one-cycle look-ahead heuristic have very strong empirical performance. Managerial implications: Our robust cycle-based policy and the one-cycle look-ahead heuristic are conceptually simple and can accommodate multiple realistic features in inventory management problems. They provide a very effective approach to robust inventory management, especially in the lost-sales setting. Funding: Y. Chen was supported by a start-up grant from Nanyang Technological University. C. Wang was supported by the National Natural Science Foundation of China [Grant 71802115] and the Tsinghua University Initiative Scientific Research Program. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1168 .
{"title":"Robust Inventory Management: A Cycle-Based Approach","authors":"Yupeng Chen, G. Iyengar, Chun Wang","doi":"10.1287/msom.2022.1168","DOIUrl":"https://doi.org/10.1287/msom.2022.1168","url":null,"abstract":"Problem definition: We study the robust formulation of an inventory model with positive fixed ordering costs, where the unfulfilled demand is either backlogged or lost, the lead time is allowed to be positive, the demand is potentially intertemporally correlated, and the information about the demand distribution is limited. Methodology/results: We propose a robust cycle-based policy that manages inventory by dividing the planning horizon into nonoverlapping inventory cycles, where an order is placed at the beginning of each cycle. Our policy selects the lengths and order quantities for all inventory cycles to minimize the worst-case total cost incurred over the planning horizon. When the uncertain demand belongs to a general polyhedral uncertainty set, the decisions in our policy can be computed by solving linear programs (LPs) for the backlogging model with any lead time and the lost-sales model with zero lead time; however, the number of LPs that need to be solved grows exponentially in the length of the planning horizon. In the special case where the uncertain demand belongs to a box uncertainty set, the decisions in our policy can be computed using a dynamic programming (DP) recursion whose complexity grows polynomially in the length of the planning horizon. We also propose a one-cycle look-ahead heuristic to handle large problem instances with a general polyhedral uncertainty set. This heuristic can be applied for both the backlogging and lost-sales models with any lead time, and it only requires solving LPs whose number grows quadratically in the length of the planning horizon. Results from extensive computational experiments clearly show that both a rolling-cycle implementation of our policy and the one-cycle look-ahead heuristic have very strong empirical performance. Managerial implications: Our robust cycle-based policy and the one-cycle look-ahead heuristic are conceptually simple and can accommodate multiple realistic features in inventory management problems. They provide a very effective approach to robust inventory management, especially in the lost-sales setting. Funding: Y. Chen was supported by a start-up grant from Nanyang Technological University. C. Wang was supported by the National Natural Science Foundation of China [Grant 71802115] and the Tsinghua University Initiative Scientific Research Program. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1168 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"49 1","pages":"581-594"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75130846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider a single-server queueing system where service quality is either high or low. The server, who knows its exact quality level, can signal this quality information to customers by revealing or concealing its queue length. Based on this queue disclosure action and the observed queue length in the case of a revealed queue, customers decide whether to join the system. Academic/practical relevance: The queue disclosure action is regarded as a signal indicating the service quality. Methodology: We develop a signaling game and adopt the sequential equilibrium concept to solve it. We further apply the perfect sequential equilibrium as an equilibrium-refinement criterion. Results: In our baseline model, where all of the customers are uninformed of service quality, the pure-strategy perfect sequential equilibrium is always a pooling one, except at several discrete values of market size (measured by the potential arrival rate). When the market size is below a certain threshold, both high- and low-quality servers adopt queue concealment; otherwise, both types of servers adopt queue revelation. We also consider a general scenario in which the market is composed of both quality informed and uninformed customers. Under this setting, when the server conceals the queue, we can fully characterize customers’ equilibrium queueing strategies and the corresponding effective arrival rates. The unique sequential equilibrium outcome is still a pooling one when the market size is either below a lower threshold or above an upper threshold. A separating equilibrium can occur only when the market size falls between two thresholds; under that circumstance, the uninformed customers can infer the server’s quality from its queue disclosure behavior. Managerial implications: Under separating sequential equilibria, uninformed customers can fully infer the quality information and thus behave in an informed way. Unlike studies where queue disclosure is not regarded as a quality signal, our study reveals that the signaling effect of queue disclosure increases (decreases) the effective arrival rate of the high-quality (low-quality) server and also increases the customers’ total utility when the server is of low quality. Funding: P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15502820]. The research of M. Haviv was funded by Israel Science Foundation [Grant 1512/19]. Z. Luo acknowledges the financial support from the Internal Start-up Fund of the Hong Kong Polytechnic University [Grant P0039035] and the National Natural Science Foundation of China[Grant 71971184]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505019]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1170 .
{"title":"Signaling Service Quality Through Queue Disclosure","authors":"Pengfei Guo, M. Haviv, Zhenwei Luo, Yulan Wang","doi":"10.1287/msom.2022.1170","DOIUrl":"https://doi.org/10.1287/msom.2022.1170","url":null,"abstract":"Problem definition: We consider a single-server queueing system where service quality is either high or low. The server, who knows its exact quality level, can signal this quality information to customers by revealing or concealing its queue length. Based on this queue disclosure action and the observed queue length in the case of a revealed queue, customers decide whether to join the system. Academic/practical relevance: The queue disclosure action is regarded as a signal indicating the service quality. Methodology: We develop a signaling game and adopt the sequential equilibrium concept to solve it. We further apply the perfect sequential equilibrium as an equilibrium-refinement criterion. Results: In our baseline model, where all of the customers are uninformed of service quality, the pure-strategy perfect sequential equilibrium is always a pooling one, except at several discrete values of market size (measured by the potential arrival rate). When the market size is below a certain threshold, both high- and low-quality servers adopt queue concealment; otherwise, both types of servers adopt queue revelation. We also consider a general scenario in which the market is composed of both quality informed and uninformed customers. Under this setting, when the server conceals the queue, we can fully characterize customers’ equilibrium queueing strategies and the corresponding effective arrival rates. The unique sequential equilibrium outcome is still a pooling one when the market size is either below a lower threshold or above an upper threshold. A separating equilibrium can occur only when the market size falls between two thresholds; under that circumstance, the uninformed customers can infer the server’s quality from its queue disclosure behavior. Managerial implications: Under separating sequential equilibria, uninformed customers can fully infer the quality information and thus behave in an informed way. Unlike studies where queue disclosure is not regarded as a quality signal, our study reveals that the signaling effect of queue disclosure increases (decreases) the effective arrival rate of the high-quality (low-quality) server and also increases the customers’ total utility when the server is of low quality. Funding: P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15502820]. The research of M. Haviv was funded by Israel Science Foundation [Grant 1512/19]. Z. Luo acknowledges the financial support from the Internal Start-up Fund of the Hong Kong Polytechnic University [Grant P0039035] and the National Natural Science Foundation of China[Grant 71971184]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505019]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1170 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"179 1","pages":"543-562"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76469098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider the rostering decisions—that is, the assignment of workers scheduled for a shift to units—of a long-term care facility. The facility’s objective is to minimize the monthly inconsistency level, a widely promoted quality metric representing the number of different caregivers working in each unit over one month. Methodology/results: We introduce simple rostering heuristics that prioritize either part-time or full-time workers and present a stochastic model of the repeated rostering problem to compare the performance of different strategies analytically. Our analysis shows that in order to minimize the inconsistency level, part-time workers should receive higher priority than full-time workers for assignment to their home units. We also establish an analytical upper bound for a threshold on the time horizon above which a policy giving assignment priority to part-time workers is guaranteed to outperform one giving priority to full-time workers. Using data from more than 15,000 shifts worked by nursing assistants at three nursing homes, we compare the actual rosters to the hindsight optimal consistency-maximizing schedules, demonstrating a significant opportunity for improvement. We then compare the performance of our rostering heuristics via trace-based simulation of the historical schedules. This reinforces the superiority of prioritizing part-time workers, yielding reductions in the inconsistency level between 20% and 30% compared with the historical rosters. Managerial implications: Contrary to popular guidance, our results show that managers should focus on part-time workers and assign them as consistently as possible. Even if some full-time workers are always assigned to their home units (because of preferences or work rules), assignment flexibility among the remaining full-time workers still enables significant improvements in the consistency of care. This flexibility among full-time workers helps because their higher work frequency tends to make a reassignment away from their home unit contribute less to inconsistency, because they are able to work multiple shifts in these nonhome units. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1174 .
{"title":"Flexibility and Consistency in Long-Term Care Rostering","authors":"Vincent W. Slaugh, Alan Scheller-Wolf","doi":"10.1287/msom.2022.1174","DOIUrl":"https://doi.org/10.1287/msom.2022.1174","url":null,"abstract":"Problem definition: We consider the rostering decisions—that is, the assignment of workers scheduled for a shift to units—of a long-term care facility. The facility’s objective is to minimize the monthly inconsistency level, a widely promoted quality metric representing the number of different caregivers working in each unit over one month. Methodology/results: We introduce simple rostering heuristics that prioritize either part-time or full-time workers and present a stochastic model of the repeated rostering problem to compare the performance of different strategies analytically. Our analysis shows that in order to minimize the inconsistency level, part-time workers should receive higher priority than full-time workers for assignment to their home units. We also establish an analytical upper bound for a threshold on the time horizon above which a policy giving assignment priority to part-time workers is guaranteed to outperform one giving priority to full-time workers. Using data from more than 15,000 shifts worked by nursing assistants at three nursing homes, we compare the actual rosters to the hindsight optimal consistency-maximizing schedules, demonstrating a significant opportunity for improvement. We then compare the performance of our rostering heuristics via trace-based simulation of the historical schedules. This reinforces the superiority of prioritizing part-time workers, yielding reductions in the inconsistency level between 20% and 30% compared with the historical rosters. Managerial implications: Contrary to popular guidance, our results show that managers should focus on part-time workers and assign them as consistently as possible. Even if some full-time workers are always assigned to their home units (because of preferences or work rules), assignment flexibility among the remaining full-time workers still enables significant improvements in the consistency of care. This flexibility among full-time workers helps because their higher work frequency tends to make a reassignment away from their home unit contribute less to inconsistency, because they are able to work multiple shifts in these nonhome units. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1174 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"41 1","pages":"719-736"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76364989","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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