Problem definition: We study the coordination of an E-commerce supply chain between online sellers and third party shippers to meet random demand surges, induced by, for instance, online shopping holidays. Academic/practical relevance: Motivated by the challenge of meeting the unpredictable demand surges in E-commerce, we study shipping contracts and supply chain coordination between online sellers and third party shippers in a novel model taking into account the unique features of the shipping industry. Methodology: We compare two shipping contracts: the risk penalty (proposed by UPS) and the flat rate (used by FedEx), and analyze their impact on the seller, the shipper, and the supply chain. Results: Under information symmetry, the sophisticated risk penalty contract is no better than the simple flat rate contract for the shipper, against common belief. Although both the risk penalty and the flat rate can coordinate the supply chain, the risk penalty does so only if the shipper makes zero profit, but the flat rate can provide a positive profit for both. These results represent a new form of double marginalization and risk-sharing, in sharp contrast to the well-known literature on the classic supplier-retailer supply chain, where risk-sharing contracts (similar to the risk penalty) can bring benefits to all parties, but the single wholesale price contract (similar to the flat rate) can achieve supply chain coordination only when the supplier makes zero profit. We also find that only the online seller, but not the shipper, has the motivation to vertically integrate the seller-shipper supply chain. Under information asymmetry, however, the risk penalty brings more benefit to the shipper than the flat rate, but hurts the seller and the supply chain. Managerial implications: Our results imply that information plays an important role in the shipper’s choices of shipping contracts. Under information symmetry, the risk penalty is unnecessarily complex because the simple flat rate is as good as the risk penalty for the shipper; moreover, it is better for the seller-shipper coordination. However, under information asymmetry, the shipper faces additional shipping risk that can be offset by the extra flexibility of the risk penalty. Our study also explains and supports the recent practice of online sellers (e.g., Amazon.com and JD.com), but not shippers, to vertically integrate the supply chain by consistently expanding their shipping capabilities.
{"title":"Supply Chain Coordination for E-Commerce: Risk Penalty vs. Flat Rate","authors":"J. Song, Yao Zhao","doi":"10.1287/msom.2021.1008","DOIUrl":"https://doi.org/10.1287/msom.2021.1008","url":null,"abstract":"Problem definition: We study the coordination of an E-commerce supply chain between online sellers and third party shippers to meet random demand surges, induced by, for instance, online shopping holidays. Academic/practical relevance: Motivated by the challenge of meeting the unpredictable demand surges in E-commerce, we study shipping contracts and supply chain coordination between online sellers and third party shippers in a novel model taking into account the unique features of the shipping industry. Methodology: We compare two shipping contracts: the risk penalty (proposed by UPS) and the flat rate (used by FedEx), and analyze their impact on the seller, the shipper, and the supply chain. Results: Under information symmetry, the sophisticated risk penalty contract is no better than the simple flat rate contract for the shipper, against common belief. Although both the risk penalty and the flat rate can coordinate the supply chain, the risk penalty does so only if the shipper makes zero profit, but the flat rate can provide a positive profit for both. These results represent a new form of double marginalization and risk-sharing, in sharp contrast to the well-known literature on the classic supplier-retailer supply chain, where risk-sharing contracts (similar to the risk penalty) can bring benefits to all parties, but the single wholesale price contract (similar to the flat rate) can achieve supply chain coordination only when the supplier makes zero profit. We also find that only the online seller, but not the shipper, has the motivation to vertically integrate the seller-shipper supply chain. Under information asymmetry, however, the risk penalty brings more benefit to the shipper than the flat rate, but hurts the seller and the supply chain. Managerial implications: Our results imply that information plays an important role in the shipper’s choices of shipping contracts. Under information symmetry, the risk penalty is unnecessarily complex because the simple flat rate is as good as the risk penalty for the shipper; moreover, it is better for the seller-shipper coordination. However, under information asymmetry, the shipper faces additional shipping risk that can be offset by the extra flexibility of the risk penalty. Our study also explains and supports the recent practice of online sellers (e.g., Amazon.com and JD.com), but not shippers, to vertically integrate the supply chain by consistently expanding their shipping capabilities.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"608 1","pages":"1110-1127"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77018768","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 eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.
{"title":"The Effects of Patent Extension and Take-Back Regulation on Green Pharmacy","authors":"Tianqin Shi, N. Petruzzi, Dilip Chhajed","doi":"10.1287/msom.2021.1009","DOIUrl":"https://doi.org/10.1287/msom.2021.1009","url":null,"abstract":"Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"93 10 1","pages":"810-826"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91079561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Lei, Stefanus Jasin, J. Uichanco, A. Vakhutinsky
Problem definition: We study a joint product framing and order fulfillment problem with both inventory and cardinality constraints faced by an e-commerce retailer. There is a finite selling horizon and no replenishment opportunity. In each period, the retailer needs to decide how to “frame” (i.e., display, rank, price) each product on his or her website as well as how to fulfill a new demand. Academic/practical relevance: E-commerce retail is known to suffer from thin profit margins. Using the data from a major U.S. retailer, we show that jointly planning product framing and order fulfillment can have a significant impact on online retailers’ profitability. This is a technically challenging problem as it involves both inventory and cardinality constraints. In this paper, we make progress toward resolving this challenge. Methodology: We use techniques such as randomized algorithms and graph-based algorithms to provide a tractable solution heuristic that we analyze through asymptotic analysis. Results: Our proposed randomized heuristic policy is based on the solution of a deterministic approximation to the stochastic control problem. The key challenge is in constructing a randomization scheme that is easy to implement and that guarantees the resulting policy is asymptotically optimal. We propose a novel two-step randomization scheme based on the idea of matrix decomposition and a rescaling argument. Managerial implications: Our numerical tests show that the proposed policy is very close to optimal, can be applied to large-scale problems in practice, and highlights the value of jointly optimizing product framing and order fulfillment decisions. When inventory across the network is imbalanced, the widespread practice of planning product framing without considering its impact on fulfillment can result in high shipping costs, regardless of the fulfillment policy used. Our proposed policy significantly reduces shipping costs by using product framing to manage demand so that it occurs close to the location of the inventory.
{"title":"Joint Product Framing (Display, Ranking, Pricing) and Order Fulfillment Under the Multinomial Logit Model for E-Commerce Retailers","authors":"Y. Lei, Stefanus Jasin, J. Uichanco, A. Vakhutinsky","doi":"10.1287/msom.2021.1012","DOIUrl":"https://doi.org/10.1287/msom.2021.1012","url":null,"abstract":"Problem definition: We study a joint product framing and order fulfillment problem with both inventory and cardinality constraints faced by an e-commerce retailer. There is a finite selling horizon and no replenishment opportunity. In each period, the retailer needs to decide how to “frame” (i.e., display, rank, price) each product on his or her website as well as how to fulfill a new demand. Academic/practical relevance: E-commerce retail is known to suffer from thin profit margins. Using the data from a major U.S. retailer, we show that jointly planning product framing and order fulfillment can have a significant impact on online retailers’ profitability. This is a technically challenging problem as it involves both inventory and cardinality constraints. In this paper, we make progress toward resolving this challenge. Methodology: We use techniques such as randomized algorithms and graph-based algorithms to provide a tractable solution heuristic that we analyze through asymptotic analysis. Results: Our proposed randomized heuristic policy is based on the solution of a deterministic approximation to the stochastic control problem. The key challenge is in constructing a randomization scheme that is easy to implement and that guarantees the resulting policy is asymptotically optimal. We propose a novel two-step randomization scheme based on the idea of matrix decomposition and a rescaling argument. Managerial implications: Our numerical tests show that the proposed policy is very close to optimal, can be applied to large-scale problems in practice, and highlights the value of jointly optimizing product framing and order fulfillment decisions. When inventory across the network is imbalanced, the widespread practice of planning product framing without considering its impact on fulfillment can result in high shipping costs, regardless of the fulfillment policy used. Our proposed policy significantly reduces shipping costs by using product framing to manage demand so that it occurs close to the location of the inventory.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"24 1","pages":"1529-1546"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84599500","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: On-demand delivery has become increasingly popular around the world. Motivated by a large grocery chain store who offers fast on-demand delivery services, we model and solve a stochastic dynamic driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The system operator needs to dispatch a set of drivers and specify their delivery routes facing random demand that arrives over a fixed number of periods. The resulting stochastic dynamic program is challenging to solve because of the curse of dimensionality. Methodology/results: We propose a novel structured approximation framework to approximate the value function via a parametrized dispatching and routing policy. We analyze the structural properties of the approximation framework and establish its performance guarantee under large-demand scenarios. We then develop efficient exact algorithms for the approximation problem based on Benders decomposition and column generation, which deliver verifiably optimal solutions within minutes. Managerial implications: The evaluation results on a real-world data set show that our framework outperforms the current policy of the company by 36.53% on average in terms of delivery time. We also perform several policy experiments to understand the value of dynamic dispatching and routing with varying fleet sizes and dispatch frequencies. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72222011 and 72171112], China Association for Science and Technology [Grant 2019QNRC001], and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1171 .
{"title":"On-Demand Delivery from Stores: Dynamic Dispatching and Routing with Random Demand","authors":"Sheng Liu, Zhixing Luo","doi":"10.1287/msom.2022.1171","DOIUrl":"https://doi.org/10.1287/msom.2022.1171","url":null,"abstract":"Problem definition: On-demand delivery has become increasingly popular around the world. Motivated by a large grocery chain store who offers fast on-demand delivery services, we model and solve a stochastic dynamic driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The system operator needs to dispatch a set of drivers and specify their delivery routes facing random demand that arrives over a fixed number of periods. The resulting stochastic dynamic program is challenging to solve because of the curse of dimensionality. Methodology/results: We propose a novel structured approximation framework to approximate the value function via a parametrized dispatching and routing policy. We analyze the structural properties of the approximation framework and establish its performance guarantee under large-demand scenarios. We then develop efficient exact algorithms for the approximation problem based on Benders decomposition and column generation, which deliver verifiably optimal solutions within minutes. Managerial implications: The evaluation results on a real-world data set show that our framework outperforms the current policy of the company by 36.53% on average in terms of delivery time. We also perform several policy experiments to understand the value of dynamic dispatching and routing with varying fleet sizes and dispatch frequencies. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72222011 and 72171112], China Association for Science and Technology [Grant 2019QNRC001], and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1171 .","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"16 1","pages":"595-612"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75335953","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 problem of managing uncertain capacities for revenue optimization over a network of resources. The uncertainty could be due to (i) the need to reallocate initial capacities among resources or (ii) the random availability of physical capacities by the time of service execution. Academic/practical relevance: The analyzed control policy is aligned with the current industry practice, with a virtual capacity and a bid price associated with each network resource. The seller collects revenues from an arriving stream of customers. Admitted requests that cannot be accommodated within the final, effective capacities incur a penalty cost. The objective is to maximize the total cumulative net revenue (sales revenue minus penalty cost). The problem arises in practice, for instance, when airlines are subject to last-minute change of aircrafts and in cargo revenue management where the capacity left by the passengers’ load is used for freight. Methodology: We present a stochastic dynamic programming formulation for this problem and propose a stochastic gradient algorithm to approximately solve it. All limit points of our algorithm are stationary points of the approximate expected net revenue function. Results: Through an exhaustive numerical study, we show that our controls are computed efficiently and deliver revenues that are almost consistently higher than the ones obtained from benchmarks based on the widely adopted deterministic linear programming model. Managerial implications: We obtain managerial insights about the impact of the timing of the capacity uncertainty clearance, the capacity heterogeneity, the network congestion, and the penalty for not being able to accommodate the previously accepted demand. Our approach tends to offer the best performance across different parameterizations of the problem.
{"title":"Managing Uncertain Capacities for Network Revenue Optimization","authors":"Fabricio Previgliano, Gustavo J. Vulcano","doi":"10.1287/msom.2021.0993","DOIUrl":"https://doi.org/10.1287/msom.2021.0993","url":null,"abstract":"Problem definition: We study the problem of managing uncertain capacities for revenue optimization over a network of resources. The uncertainty could be due to (i) the need to reallocate initial capacities among resources or (ii) the random availability of physical capacities by the time of service execution. Academic/practical relevance: The analyzed control policy is aligned with the current industry practice, with a virtual capacity and a bid price associated with each network resource. The seller collects revenues from an arriving stream of customers. Admitted requests that cannot be accommodated within the final, effective capacities incur a penalty cost. The objective is to maximize the total cumulative net revenue (sales revenue minus penalty cost). The problem arises in practice, for instance, when airlines are subject to last-minute change of aircrafts and in cargo revenue management where the capacity left by the passengers’ load is used for freight. Methodology: We present a stochastic dynamic programming formulation for this problem and propose a stochastic gradient algorithm to approximately solve it. All limit points of our algorithm are stationary points of the approximate expected net revenue function. Results: Through an exhaustive numerical study, we show that our controls are computed efficiently and deliver revenues that are almost consistently higher than the ones obtained from benchmarks based on the widely adopted deterministic linear programming model. Managerial implications: We obtain managerial insights about the impact of the timing of the capacity uncertainty clearance, the capacity heterogeneity, the network congestion, and the penalty for not being able to accommodate the previously accepted demand. Our approach tends to offer the best performance across different parameterizations of the problem.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"106 1","pages":"1202-1219"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75763901","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: Among the most vexing issues in the U.S. healthcare ecosystem is inappropriate use of percutaneous coronary intervention (PCI) procedures, also known as overstenting. A key driver of overstenting is physician subjectivity in eyeballing a coronary angiogram. Advanced tests such as fractional flow reserve (FFR) provide more precise and objective measures of PCI appropriateness, yet the decision to perform these tests is endogenous and not immune to clinical ambiguity associated with eyeballing. Additionally, conflicts of interest, arising from revenue-generating incentives, play a role in overstenting. Academic/practical relevance: Conventional wisdom suggests more precise diagnostic testing will help reduce overtreatment. However, the literature rarely recognizes that the testing decision is itself endogenous. Our research highlights the role of endogeneity surrounding interventional cardiology decision making. Methodology: This study uses stochastic modeling and simulation. Results: Under a low conflict-of-interest level, the physician performs the advanced test for intermediate lesions. Under a high conflict-of-interest level, however, the physician would perform the advanced test only for high-grade lesions, because of a financial disincentive: Performing the advanced test may lower PCI revenue if the test results argue against the procedure. Surprisingly, despite this disincentive, a more revenue-driven physician can be more inclined to perform the advanced test. Managerial implications: Our model leads to implications for various efforts aimed at tackling overstenting: (1) Attention should be paid not only to the sheer quantity of FFR procedures but to which patients receive FFR procedures; (2) reducing the risk of the advanced test has a behavior-inducing effect, yet a modest risk reduction may lower patient welfare; and (3) offering a bonus to the physician for performing FFR procedures equal to a third of its reimbursement rate will cause only a 5% increase in average physician payment while inducing a 26% decline in overstenting. In addition, we show implementing a bundled payment scheme may discourage the use of FFR procedures and lead to more salient overstenting.
{"title":"Clinical Ambiguity and Conflicts of Interest in Interventional Cardiology Decision Making","authors":"Tinglong Dai, Xiaofang Wang, C. Hwang","doi":"10.1287/MSOM.2021.0969","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0969","url":null,"abstract":"Problem definition: Among the most vexing issues in the U.S. healthcare ecosystem is inappropriate use of percutaneous coronary intervention (PCI) procedures, also known as overstenting. A key driver of overstenting is physician subjectivity in eyeballing a coronary angiogram. Advanced tests such as fractional flow reserve (FFR) provide more precise and objective measures of PCI appropriateness, yet the decision to perform these tests is endogenous and not immune to clinical ambiguity associated with eyeballing. Additionally, conflicts of interest, arising from revenue-generating incentives, play a role in overstenting. Academic/practical relevance: Conventional wisdom suggests more precise diagnostic testing will help reduce overtreatment. However, the literature rarely recognizes that the testing decision is itself endogenous. Our research highlights the role of endogeneity surrounding interventional cardiology decision making. Methodology: This study uses stochastic modeling and simulation. Results: Under a low conflict-of-interest level, the physician performs the advanced test for intermediate lesions. Under a high conflict-of-interest level, however, the physician would perform the advanced test only for high-grade lesions, because of a financial disincentive: Performing the advanced test may lower PCI revenue if the test results argue against the procedure. Surprisingly, despite this disincentive, a more revenue-driven physician can be more inclined to perform the advanced test. Managerial implications: Our model leads to implications for various efforts aimed at tackling overstenting: (1) Attention should be paid not only to the sheer quantity of FFR procedures but to which patients receive FFR procedures; (2) reducing the risk of the advanced test has a behavior-inducing effect, yet a modest risk reduction may lower patient welfare; and (3) offering a bonus to the physician for performing FFR procedures equal to a third of its reimbursement rate will cause only a 5% increase in average physician payment while inducing a 26% decline in overstenting. In addition, we show implementing a bundled payment scheme may discourage the use of FFR procedures and lead to more salient overstenting.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"179 1","pages":"864-882"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88467268","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: Giving out a symbolic “supplier of the year” or “outstanding supplier” award can be beneficial for a buyer as it may incentivize a supplier to exert higher efforts. However, when a good supplier is scarce, the award announces which supplier is particularly good and may increase the cost of building and maintaining the relationship. This paper studies both positive and negative effects of a symbolic award and offers explanations on underlying behavioral mechanisms. Academic/practical relevance: We show that symbolic awards can effectively incentivize suppliers to provide high effort, improving a buyer’s bottom line. This is particularly relevant in cases in which certain aspects of a buyer–supplier relationship are not contractible and suppliers have discretion over the quality provided. The award format significantly influences the award’s effectiveness. Methodology: We develop a game-theoretical model that captures a supplier’s utility for the award in a competitive setting and test the predictions of the model with laboratory experiments. Results: Our experimental results confirm that private symbolic awards have motivating effects and lead to higher buyer profits. When the awards are public, this profit premium diminishes as buyers pay higher prices to get the good suppliers. When the buyer is given the option to make the award public or private, buyers prefer that awards are public over private, anticipating a negative supplier response to their choice of the private award format. Managerial implications: Expressing praise or gratitude for a supplier’s efforts can be highly beneficial for a buyer. However, when there is scarcity of good suppliers, buyers should expect increased competition and accompany the award with efforts to preserve the relationship. Finally, if buyers choose to offer a distinctive award format, private recognitions may be perceived as greedy or self-interested and backfire.
{"title":"Symbolic Awards in Buyer-Supplier Relations","authors":"Ruth Beer, Hyun-Soo Ahn, Stephen Leider","doi":"10.1287/MSOM.2021.0974","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0974","url":null,"abstract":"Problem definition: Giving out a symbolic “supplier of the year” or “outstanding supplier” award can be beneficial for a buyer as it may incentivize a supplier to exert higher efforts. However, when a good supplier is scarce, the award announces which supplier is particularly good and may increase the cost of building and maintaining the relationship. This paper studies both positive and negative effects of a symbolic award and offers explanations on underlying behavioral mechanisms. Academic/practical relevance: We show that symbolic awards can effectively incentivize suppliers to provide high effort, improving a buyer’s bottom line. This is particularly relevant in cases in which certain aspects of a buyer–supplier relationship are not contractible and suppliers have discretion over the quality provided. The award format significantly influences the award’s effectiveness. Methodology: We develop a game-theoretical model that captures a supplier’s utility for the award in a competitive setting and test the predictions of the model with laboratory experiments. Results: Our experimental results confirm that private symbolic awards have motivating effects and lead to higher buyer profits. When the awards are public, this profit premium diminishes as buyers pay higher prices to get the good suppliers. When the buyer is given the option to make the award public or private, buyers prefer that awards are public over private, anticipating a negative supplier response to their choice of the private award format. Managerial implications: Expressing praise or gratitude for a supplier’s efforts can be highly beneficial for a buyer. However, when there is scarcity of good suppliers, buyers should expect increased competition and accompany the award with efforts to preserve the relationship. Finally, if buyers choose to offer a distinctive award format, private recognitions may be perceived as greedy or self-interested and backfire.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"1 1","pages":"1021-1039"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80456059","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 shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.
{"title":"Incentives for Shared Services: Multiserver Queueing Systems with Priorities","authors":"Hanlin Liu, Yimin Yu","doi":"10.2139/ssrn.3887297","DOIUrl":"https://doi.org/10.2139/ssrn.3887297","url":null,"abstract":"Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"6 1","pages":"1751-1759"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83512340","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}
We reviewed research papers related to pandemics/epidemics (disease outbreaks of a global/regional scope) published in major operations management, operations research, and management science journals through the end of 2019. We evaluate and categorize these papers. We study research trends, explore research gaps, and provide directions for more efficient and effective research in the future. In addition, our recommendations include the lessons learned from the ongoing pandemic, COVID-19. We discuss papers in the following categories: (a) warning signals/surveillance, (b) disease propagation leading to pandemic conditions, (c) mitigation, (d) vaccines and therapeutics development, (e) resource management, (f) supply chain configuration, (g) decision support systems for managing pandemics/epidemics, and (h) risk assessment.
{"title":"OM Forum - Pandemics/Epidemics: Challenges and Opportunities for Operations Management Research","authors":"Sushil K. Gupta, M. Starr, R. Farahani, N. Asgari","doi":"10.1287/MSOM.2021.0965","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0965","url":null,"abstract":"We reviewed research papers related to pandemics/epidemics (disease outbreaks of a global/regional scope) published in major operations management, operations research, and management science journals through the end of 2019. We evaluate and categorize these papers. We study research trends, explore research gaps, and provide directions for more efficient and effective research in the future. In addition, our recommendations include the lessons learned from the ongoing pandemic, COVID-19. We discuss papers in the following categories: (a) warning signals/surveillance, (b) disease propagation leading to pandemic conditions, (c) mitigation, (d) vaccines and therapeutics development, (e) resource management, (f) supply chain configuration, (g) decision support systems for managing pandemics/epidemics, and (h) risk assessment.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"65 1","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81095840","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 problem faced by the Philippine Department of Social Welfare (DSWD) in prepositioning relief items before landfall of an oncoming typhoon whose future outcome (trajectory and wind speed) is uncertain. Academic/practical relevance: The importance of prepositioning was a hard lesson learned from Super Typhoon Haiyan that devastated the Philippines in 2013, when many affected by the typhoon did not have immediate access to food and water. In a typhoon-prone country, it is important to build resilience through an effective prepositioning model. Methodology: By engaging with DSWD, we developed a practically relevant stochastic prepositioning model. The probability models of municipality-level demand and of supply damage are both dependent on the typhoon outcome. A linear mixed effects model is used to estimate the dependence of demand on the typhoon outcome using a large data set that includes the municipality-level impact of West Pacific typhoons during 2008–2019. The model has two objectives motivated from the practical realities of the Philippine network: prioritizing regions with high demand and prepositioning in all affected regions proportional to their total demand. Results: We find that the choice of the demand model significantly impacts the distributed relief items in the Philippine setting where it is challenging to adjust region-level supply after a typhoon. By using the historical data on past typhoons, we show that in this setting, our stochastic demand model provides the best distribution to date of any existing demand models. Managerial implications: There currently exists a gap between theory and practice in the management of relief inventories. We contribute toward bridging this gap by engaging with DSWD to develop a practically relevant relief distribution model. Our work is an effective example of collaboration with government and nongovernment agencies in developing a relief distribution model.
{"title":"A Model for Prepositioning Emergency Relief Items Before a Typhoon with an Uncertain Trajectory","authors":"J. Uichanco","doi":"10.1287/MSOM.2021.0980","DOIUrl":"https://doi.org/10.1287/MSOM.2021.0980","url":null,"abstract":"Problem definition: We study the problem faced by the Philippine Department of Social Welfare (DSWD) in prepositioning relief items before landfall of an oncoming typhoon whose future outcome (trajectory and wind speed) is uncertain. Academic/practical relevance: The importance of prepositioning was a hard lesson learned from Super Typhoon Haiyan that devastated the Philippines in 2013, when many affected by the typhoon did not have immediate access to food and water. In a typhoon-prone country, it is important to build resilience through an effective prepositioning model. Methodology: By engaging with DSWD, we developed a practically relevant stochastic prepositioning model. The probability models of municipality-level demand and of supply damage are both dependent on the typhoon outcome. A linear mixed effects model is used to estimate the dependence of demand on the typhoon outcome using a large data set that includes the municipality-level impact of West Pacific typhoons during 2008–2019. The model has two objectives motivated from the practical realities of the Philippine network: prioritizing regions with high demand and prepositioning in all affected regions proportional to their total demand. Results: We find that the choice of the demand model significantly impacts the distributed relief items in the Philippine setting where it is challenging to adjust region-level supply after a typhoon. By using the historical data on past typhoons, we show that in this setting, our stochastic demand model provides the best distribution to date of any existing demand models. Managerial implications: There currently exists a gap between theory and practice in the management of relief inventories. We contribute toward bridging this gap by engaging with DSWD to develop a practically relevant relief distribution model. Our work is an effective example of collaboration with government and nongovernment agencies in developing a relief distribution model.","PeriodicalId":18108,"journal":{"name":"Manuf. Serv. Oper. Manag.","volume":"22 1","pages":"766-790"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74620411","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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