Yanjie Liang, Weihua Liu, Kevin W. Li, Chuanwen Dong, Ming K. Lim
Large‐scale platforms (LSPs) with valuation and awareness advantages have enabled competing small‐scale platforms (SSPs) to be embedded in their platforms. This compatibility strategy creates a new channel, that is, the compatible channel, through which customers can purchase services from SSPs via the LSPs. Additionally, numerous platforms have been introducing add‐on services to enhance their profitability. In this study, we develop stylized game models to characterize the interaction between an LSP and an SSP and explore their strategic and operational decisions on platform compatibility under add‐on services. Our major research findings are as follows: First, compatibility has opposite impacts on service pricing. That is, at a low proportion of demand through the compatible channel, the two platforms engage in a price war; otherwise, they both raise prices. Second, we identify the conditions for platform compatibility: compatibility becomes an equilibrium strategy if the proportion of demand through the compatible channel falls within an intermediate range. Third, we find that homogeneous add‐on services stimulate rather than inhibit compatibility due to different profit foci of two platforms. Finally, we conduct extensions to further verify the robustness of the conclusions. Our results provide important implications to the burgeoning debate on when platforms should implement compatibility to achieve a win‐win scenario under a variety of settings.This article is protected by copyright. All rights reserved
{"title":"A Co‐Opetitive game analysis of platform compatibility strategies under add‐on services","authors":"Yanjie Liang, Weihua Liu, Kevin W. Li, Chuanwen Dong, Ming K. Lim","doi":"10.1111/poms.14049","DOIUrl":"https://doi.org/10.1111/poms.14049","url":null,"abstract":"Large‐scale platforms (LSPs) with valuation and awareness advantages have enabled competing small‐scale platforms (SSPs) to be embedded in their platforms. This compatibility strategy creates a new channel, that is, the compatible channel, through which customers can purchase services from SSPs via the LSPs. Additionally, numerous platforms have been introducing add‐on services to enhance their profitability. In this study, we develop stylized game models to characterize the interaction between an LSP and an SSP and explore their strategic and operational decisions on platform compatibility under add‐on services. Our major research findings are as follows: First, compatibility has opposite impacts on service pricing. That is, at a low proportion of demand through the compatible channel, the two platforms engage in a price war; otherwise, they both raise prices. Second, we identify the conditions for platform compatibility: compatibility becomes an equilibrium strategy if the proportion of demand through the compatible channel falls within an intermediate range. Third, we find that homogeneous add‐on services stimulate rather than inhibit compatibility due to different profit foci of two platforms. Finally, we conduct extensions to further verify the robustness of the conclusions. Our results provide important implications to the burgeoning debate on when platforms should implement compatibility to achieve a win‐win scenario under a variety of settings.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47539602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study capacity sizing of park‐and‐ride lots that offer services to commuters sensitive to congestion and parking availability information. The goal is to determine parking lot capacities that maximize the total social welfare for commuters whose parking lot choices are predicted using the multinomial logit model. We formulate the problem as a non‐convex nonlinear program that involves a lower and an upper bound on each lot's capacity, and a fixed‐point constraint reflecting the effects of parking information and congestion on commuters' lot choices. We show that except for at most one lot, the optimal capacity of each lot takes one of three possible values. Based on analytical results, we develop a one‐variable search algorithm to solve the model. We learn from numerical results that the optimal capacity of a lot with a high intrinsic utility tends to be equal to the upper bound. By contrast, a lot with a low or moderate‐sized intrinsic utility tends to attain an optimal capacity on its effective lower bound. We evaluate the performance of the optimal solution under different choice scenarios of commuters who are shared with real‐time parking information. We learn that commuters are better off in an average choice scenario when both the effects of parking information and congestion are considered in the model than when either effect is ignored from the model.This article is protected by copyright. All rights reserved
{"title":"Optimal capacity sizing of park‐and‐ride lots with information‐aware commuters","authors":"Xinchang Wang, Qie He","doi":"10.1111/poms.14053","DOIUrl":"https://doi.org/10.1111/poms.14053","url":null,"abstract":"We study capacity sizing of park‐and‐ride lots that offer services to commuters sensitive to congestion and parking availability information. The goal is to determine parking lot capacities that maximize the total social welfare for commuters whose parking lot choices are predicted using the multinomial logit model. We formulate the problem as a non‐convex nonlinear program that involves a lower and an upper bound on each lot's capacity, and a fixed‐point constraint reflecting the effects of parking information and congestion on commuters' lot choices. We show that except for at most one lot, the optimal capacity of each lot takes one of three possible values. Based on analytical results, we develop a one‐variable search algorithm to solve the model. We learn from numerical results that the optimal capacity of a lot with a high intrinsic utility tends to be equal to the upper bound. By contrast, a lot with a low or moderate‐sized intrinsic utility tends to attain an optimal capacity on its effective lower bound. We evaluate the performance of the optimal solution under different choice scenarios of commuters who are shared with real‐time parking information. We learn that commuters are better off in an average choice scenario when both the effects of parking information and congestion are considered in the model than when either effect is ignored from the model.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45398236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider a firm using presales with two payments – including an upfront deposit and a postponed arrear – to sell a product to time‐inconsistent consumers under various return policies. With the passage of time, consumers decide whether to preorder the product by paying the deposit, whether to settle the balance by paying the arrear by the due date, and, when returns are allowed, whether to return a fully paid product for a refund after its fitness is revealed. Canceling a preorder before settling the balance or failing to settle the balance on time causes a deposit payer to lose deposit. Consumers use quasi‐hyperbolic discounting to weigh intertemporal utilities and exhibit time‐inconsistency when making purchase decisions. Our results indicate that, as consumers' time‐inconsistency weakens, the firm manages the deposit and the arrear to induce no, some, and all deposit payers, in that sequence, to settle the balance. It has mixed effects on the profit of the firm and consumer surplus, but always causes social welfare to improve since actual sales increase. Regarding the choice of return policy, the firm prefers full returns while consumers prefer no returns when consumers are time‐inconsistent; they share similar preferences when consumers are time‐consistent. Moreover, we find that consumers' imbalance (caused by time‐inconsistent preference) is less likely to occur as consumers are more sophisticated, as the presales period shortens, as the product value increases, or as the product is more likely to be a good fit. In addition, allowing a quick refund for fully paid but not yet delivered products can completely deter imbalance, while this option harms the firm and may or may not benefit consumers. All this yields novel insights into managing presales with two payments and returns when consumers are time‐inconsistent.This article is protected by copyright. All rights reserved
{"title":"Managing presales with two payments and return policy when consumers are time‐inconsistent","authors":"Yunjuan Kuang, Li Jiang","doi":"10.1111/poms.14052","DOIUrl":"https://doi.org/10.1111/poms.14052","url":null,"abstract":"We consider a firm using presales with two payments – including an upfront deposit and a postponed arrear – to sell a product to time‐inconsistent consumers under various return policies. With the passage of time, consumers decide whether to preorder the product by paying the deposit, whether to settle the balance by paying the arrear by the due date, and, when returns are allowed, whether to return a fully paid product for a refund after its fitness is revealed. Canceling a preorder before settling the balance or failing to settle the balance on time causes a deposit payer to lose deposit. Consumers use quasi‐hyperbolic discounting to weigh intertemporal utilities and exhibit time‐inconsistency when making purchase decisions. Our results indicate that, as consumers' time‐inconsistency weakens, the firm manages the deposit and the arrear to induce no, some, and all deposit payers, in that sequence, to settle the balance. It has mixed effects on the profit of the firm and consumer surplus, but always causes social welfare to improve since actual sales increase. Regarding the choice of return policy, the firm prefers full returns while consumers prefer no returns when consumers are time‐inconsistent; they share similar preferences when consumers are time‐consistent. Moreover, we find that consumers' imbalance (caused by time‐inconsistent preference) is less likely to occur as consumers are more sophisticated, as the presales period shortens, as the product value increases, or as the product is more likely to be a good fit. In addition, allowing a quick refund for fully paid but not yet delivered products can completely deter imbalance, while this option harms the firm and may or may not benefit consumers. All this yields novel insights into managing presales with two payments and returns when consumers are time‐inconsistent.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44133476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chandrasekhar Manchiraju, Milind G. Sohoni, V. Deshpande
Airlines showcase their on‐time performance (OTP), a globally accepted operational performance metric, to demonstrate punctuality, service reliability, and attract air travelers. Airlines can adopt passive strategies, such as “schedule padding”, and active strategies, such as making “operational changes/improvements”, to improve their OTP. While there is a high degree of variation in airlines' OTP from year to year, it is unclear if and the extent to which airlines' active or passive actions impact their OTP because of factors, like weather, outside an airline's control. We develop a framework in this paper to study the impact of these active and passive actions on the OTP of airlines. Additionally, we study the effect of these strategies on OTP rankings, routinely used to compare airlines. Our methodology builds on the structural estimation model developed in prior literature and replicates the typical schedule planning process observed in the airline industry. We use an eleven‐year panel data of flights operated by US domestic carriers from 2005 to 2015 to measure OTP changes, schedule padding, and operational changes. Broadly, active actions include (i) changes to the airline's network structure (for example, flight routes and schedules), and (ii) other operational changes to improve flight performance (operational improvement). We demonstrate through our analysis that operational improvements have the highest association with both the change in OTP and OTP rankings of airlines, followed by schedule padding; the impact of network changes on OTP and OTP rankings is the lowest. Our framework also accounts for the impact of competition on an airline's OTP ranking. We show that while an airline's own actions can improve its OTP ranking, a competitor's action may negatively affect the ranking. In fact, a competitor's passive strategy of schedule padding may have a higher impact than an airline's own active strategy of changes in network structure. Our results also indicate that the potential impact of operations improvements is the highest for full‐service airlines and the lowest for leisure airlines. Furthermore, we show that the impact of operational improvements and buffer adjustments decreases with an increase in the variability of the travel time of a route.This article is protected by copyright. All rights reserved
{"title":"It's not simply luck: the impact of network strategy, schedule padding, and operational improvements on domestic on‐time performance in the US airline industry","authors":"Chandrasekhar Manchiraju, Milind G. Sohoni, V. Deshpande","doi":"10.1111/poms.14050","DOIUrl":"https://doi.org/10.1111/poms.14050","url":null,"abstract":"Airlines showcase their on‐time performance (OTP), a globally accepted operational performance metric, to demonstrate punctuality, service reliability, and attract air travelers. Airlines can adopt passive strategies, such as “schedule padding”, and active strategies, such as making “operational changes/improvements”, to improve their OTP. While there is a high degree of variation in airlines' OTP from year to year, it is unclear if and the extent to which airlines' active or passive actions impact their OTP because of factors, like weather, outside an airline's control. We develop a framework in this paper to study the impact of these active and passive actions on the OTP of airlines. Additionally, we study the effect of these strategies on OTP rankings, routinely used to compare airlines. Our methodology builds on the structural estimation model developed in prior literature and replicates the typical schedule planning process observed in the airline industry. We use an eleven‐year panel data of flights operated by US domestic carriers from 2005 to 2015 to measure OTP changes, schedule padding, and operational changes. Broadly, active actions include (i) changes to the airline's network structure (for example, flight routes and schedules), and (ii) other operational changes to improve flight performance (operational improvement). We demonstrate through our analysis that operational improvements have the highest association with both the change in OTP and OTP rankings of airlines, followed by schedule padding; the impact of network changes on OTP and OTP rankings is the lowest. Our framework also accounts for the impact of competition on an airline's OTP ranking. We show that while an airline's own actions can improve its OTP ranking, a competitor's action may negatively affect the ranking. In fact, a competitor's passive strategy of schedule padding may have a higher impact than an airline's own active strategy of changes in network structure. Our results also indicate that the potential impact of operations improvements is the highest for full‐service airlines and the lowest for leisure airlines. Furthermore, we show that the impact of operational improvements and buffer adjustments decreases with an increase in the variability of the travel time of a route.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45516323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper introduces the general philosophy of the Operational Data Analytics (ODA) framework for data‐based decision modeling. The fundamental development of this framework lies in establishing the direct mapping from data to decision by identifying the appropriate class of operational statistics. The efficient decision making relies on a careful balance between data integration and decision validation . Through a canonical decision making problem under uncertainty, we show that the existing approaches (including statistical estimation and then optimization, retrospective optimization, sample average approximation, regularization, robust optimization, and robust satisficing) can all be unified through the lens of the ODA formulation. To make the key concepts accessible, we demonstrate, using a simple running example, how some of the existing approaches may become equivalent under the ODA framework, and how the ODA solution can improve the decision efficiency, especially in the small sample regime.
{"title":"The framework of parametric and nonparametric operational data analytics","authors":"Qi Feng, J. George Shanthikumar","doi":"10.1111/poms.14038","DOIUrl":"https://doi.org/10.1111/poms.14038","url":null,"abstract":"Abstract This paper introduces the general philosophy of the Operational Data Analytics (ODA) framework for data‐based decision modeling. The fundamental development of this framework lies in establishing the direct mapping from data to decision by identifying the appropriate class of operational statistics. The efficient decision making relies on a careful balance between data integration and decision validation . Through a canonical decision making problem under uncertainty, we show that the existing approaches (including statistical estimation and then optimization, retrospective optimization, sample average approximation, regularization, robust optimization, and robust satisficing) can all be unified through the lens of the ODA formulation. To make the key concepts accessible, we demonstrate, using a simple running example, how some of the existing approaches may become equivalent under the ODA framework, and how the ODA solution can improve the decision efficiency, especially in the small sample regime.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135971636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When scheduling the distribution of ordered items from a warehouse to customers, the transportation planning is generally done first and serves as input for planning warehouse operations. Such a sequential approach can lead to substantial inefficiencies when the customer deliveries are restricted by time windows and the warehouse has limited resources available (both order pickers and space in the staging area). This paper studies the trade‐offs between warehouse operations and transportation planning. The goal is to understand the impact of three specific managerial interventions: adopting an integrated planning approach, expanding the available staging space, and expanding the delivery time windows. To this end, we propose a mathematical model for a general vehicle routing problem that incorporates order batching, order picker scheduling, staging, and vehicle loading. We introduce a novel idea to express the picking time of an order batch as a function of the batch size and develop a metaheuristic to solve this integrated problem. Furthermore, we develop exact algorithms to provide optimal solutions for the individual warehouse and transportation problems in a sequential planning approach. Managerial insights are distilled from case studies in two warehouses, one for ambient products and the other for refrigerated products, of a leading grocery retailer in the Netherlands. Our results show that integrated planning outperforms the other managerial interventions and generates cost savings between 9 and 11%. Savings are generally realized by executing larger order batch sizes to be picked in the warehouses at the expense of additional routing cost (around 2 to 3%). The second intervention in the form of time window expansions of only 15 minutes for customer deliveries can lead to cost savings between 4 to 6%, which results from a reduction in both transportation and warehousing cost. Expanding the capacity of the staging area is only meaningful when the staging space is highly utilized, and only results in cost savings for the warehouse operations.This article is protected by copyright. All rights reserved
{"title":"Dynamics between Warehouse Operations and Vehicle Routing","authors":"Arpan Rijal, Marco Bijvank, R. D. de Koster","doi":"10.1111/poms.14051","DOIUrl":"https://doi.org/10.1111/poms.14051","url":null,"abstract":"When scheduling the distribution of ordered items from a warehouse to customers, the transportation planning is generally done first and serves as input for planning warehouse operations. Such a sequential approach can lead to substantial inefficiencies when the customer deliveries are restricted by time windows and the warehouse has limited resources available (both order pickers and space in the staging area). This paper studies the trade‐offs between warehouse operations and transportation planning. The goal is to understand the impact of three specific managerial interventions: adopting an integrated planning approach, expanding the available staging space, and expanding the delivery time windows. To this end, we propose a mathematical model for a general vehicle routing problem that incorporates order batching, order picker scheduling, staging, and vehicle loading. We introduce a novel idea to express the picking time of an order batch as a function of the batch size and develop a metaheuristic to solve this integrated problem. Furthermore, we develop exact algorithms to provide optimal solutions for the individual warehouse and transportation problems in a sequential planning approach. Managerial insights are distilled from case studies in two warehouses, one for ambient products and the other for refrigerated products, of a leading grocery retailer in the Netherlands. Our results show that integrated planning outperforms the other managerial interventions and generates cost savings between 9 and 11%. Savings are generally realized by executing larger order batch sizes to be picked in the warehouses at the expense of additional routing cost (around 2 to 3%). The second intervention in the form of time window expansions of only 15 minutes for customer deliveries can lead to cost savings between 4 to 6%, which results from a reduction in both transportation and warehousing cost. Expanding the capacity of the staging area is only meaningful when the staging space is highly utilized, and only results in cost savings for the warehouse operations.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44344272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tariff rate quota (TRQ) is a widely utilized market access instrument in global agricultural trade that allows a predetermined quantity of a product to be imported at a lower tariff rate than the usual rate. This study examines the design and administration of TRQ systems from an operations management perspective and analyzes their impact on market access, fill‐rates, and revenue for policy makers. We investigate the two most common TRQ administration methods, namely, licensing and first‐come, first‐served (FCFS) systems. We characterize the Nash equilibria of importers' strategies and observe how information delays and lead times can result in under‐utilization (i.e., imports being less than the quota limit) in licensing, and over‐utilization (i.e., imports exceeding the quota limit) in FCFS TRQ systems. We introduce a dual TRQ system and demonstrate its superiority over licensing and FCFS systems. We study the effects of stock‐keeping options through customs‐bonded warehouses and the choice of logistics channels on arrival patterns and fill‐rates. We conduct a case study of the UK and the EU imported beef market using customs data. Our numerical study provides an explanation for the sub‐optimality of the current TRQ systems and proposes modifications to transform the existing systems. Our findings offer practical directions for agricultural traders to reassess their supply chain strategies by considering the logistical implications of TRQ systems and understanding their competition. This study also urges policy makers to adopt an integrative approach in (re)designing TRQ systems, recognizing the pivotal role of supply chains in global agricultural trade.This article is protected by copyright. All rights reserved
{"title":"Global agricultural supply chains under tariff‐rate quotas R.4","authors":"Behzad Hezarkhani, Sobhan Arisian, A. Mansouri","doi":"10.1111/poms.14054","DOIUrl":"https://doi.org/10.1111/poms.14054","url":null,"abstract":"The tariff rate quota (TRQ) is a widely utilized market access instrument in global agricultural trade that allows a predetermined quantity of a product to be imported at a lower tariff rate than the usual rate. This study examines the design and administration of TRQ systems from an operations management perspective and analyzes their impact on market access, fill‐rates, and revenue for policy makers. We investigate the two most common TRQ administration methods, namely, licensing and first‐come, first‐served (FCFS) systems. We characterize the Nash equilibria of importers' strategies and observe how information delays and lead times can result in under‐utilization (i.e., imports being less than the quota limit) in licensing, and over‐utilization (i.e., imports exceeding the quota limit) in FCFS TRQ systems. We introduce a dual TRQ system and demonstrate its superiority over licensing and FCFS systems. We study the effects of stock‐keeping options through customs‐bonded warehouses and the choice of logistics channels on arrival patterns and fill‐rates. We conduct a case study of the UK and the EU imported beef market using customs data. Our numerical study provides an explanation for the sub‐optimality of the current TRQ systems and proposes modifications to transform the existing systems. Our findings offer practical directions for agricultural traders to reassess their supply chain strategies by considering the logistical implications of TRQ systems and understanding their competition. This study also urges policy makers to adopt an integrative approach in (re)designing TRQ systems, recognizing the pivotal role of supply chains in global agricultural trade.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48806246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider an e‐tailer's upstream supplier who wants to encroach into retailing to earn additional revenue. The supplier needs to decide whether or not to enter the retail market by either selling to consumers on the e‐tailer's platform by paying commission fees (agency encroachment) or opening an independent online/offline retail store (direct encroachment). The e‐tailer has private demand information and decides whether or not to share it with the supplier. Two leadership scenarios—the supplier‐leads (i.e., the supplier selects the channel before the e‐tailer decides whether to share information) and the e‐tailer‐leads (i.e., the supplier selects the channel after the e‐tailer decides whether to share information)—are examined. Our main findings are as follows. First, we show that the e‐tailer has no incentive to share information under no encroachment and direct encroachment. Interestingly, this result holds in both leadership scenarios. Second, a medium commission rate gives rise to an equilibrium of agency encroachment with information sharing by the e‐tailer. This equilibrium is more likely to sustain in the supplier‐leads scenario than in the e‐tailer‐leads scenario. Third, agency encroachment brings the supplier the highest sales volume (at retail in the encroaching channel plus on wholesale to the e‐tailer) when the two parties compete in quantity while direct encroachment does so for a price competition. Fourth, supplier encroachment always improves consumer surplus, but it is not necessarily welfare‐improving. Last, we find that the e‐tailer is more willing to share information to induce the supplier to encroach through his agency channel if he has a significant selling cost advantage over the supplier or can endogenously determine the commission rate.This article is protected by copyright. All rights reserved
{"title":"Games of supplier encroachment channel selection and e‐tailer's information sharing","authors":"Yanli Tang, S. Sethi, Yulan Wang","doi":"10.1111/poms.14055","DOIUrl":"https://doi.org/10.1111/poms.14055","url":null,"abstract":"We consider an e‐tailer's upstream supplier who wants to encroach into retailing to earn additional revenue. The supplier needs to decide whether or not to enter the retail market by either selling to consumers on the e‐tailer's platform by paying commission fees (agency encroachment) or opening an independent online/offline retail store (direct encroachment). The e‐tailer has private demand information and decides whether or not to share it with the supplier. Two leadership scenarios—the supplier‐leads (i.e., the supplier selects the channel before the e‐tailer decides whether to share information) and the e‐tailer‐leads (i.e., the supplier selects the channel after the e‐tailer decides whether to share information)—are examined. Our main findings are as follows. First, we show that the e‐tailer has no incentive to share information under no encroachment and direct encroachment. Interestingly, this result holds in both leadership scenarios. Second, a medium commission rate gives rise to an equilibrium of agency encroachment with information sharing by the e‐tailer. This equilibrium is more likely to sustain in the supplier‐leads scenario than in the e‐tailer‐leads scenario. Third, agency encroachment brings the supplier the highest sales volume (at retail in the encroaching channel plus on wholesale to the e‐tailer) when the two parties compete in quantity while direct encroachment does so for a price competition. Fourth, supplier encroachment always improves consumer surplus, but it is not necessarily welfare‐improving. Last, we find that the e‐tailer is more willing to share information to induce the supplier to encroach through his agency channel if he has a significant selling cost advantage over the supplier or can endogenously determine the commission rate.This article is protected by copyright. All rights reserved","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47816499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yasemin Limon, Christopher S. Tang, Fehmi Tanrısever
{"title":"Sequential versus concurrent final phase product development: Approval uncertainty, time‐sensitive consumers, asymmetric competition, and government subsidy","authors":"Yasemin Limon, Christopher S. Tang, Fehmi Tanrısever","doi":"10.1111/poms.14048","DOIUrl":"https://doi.org/10.1111/poms.14048","url":null,"abstract":"","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45054939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we consider an assortment optimization problem in which a platform must choose pairwise disjoint sets of assortments to offer across a series of T stages. Arriving customers begin their search process in the first stage, and progress sequentially through the stages until their patience expires, at which point they make a multinomial logit–based purchasing decision from among all products they have viewed throughout their search process. The goal is to choose the sequential displays of product offerings to maximize expected revenue. Additionally, we impose stage-specific constraints that ensure that as each customer progresses farther and farther through the T stages, there is a minimum level of “desirability” met by the collections of displayed products. We consider two related measures of desirability: purchase likelihood and expected utility derived from the offered assortments. In this way, the offered sequence of assortments must be both high earning and well liked, which breaks from the traditional assortment setting, where customer-centric considerations are generally not explicitly accounted for. We show that our assortment problem of interest is strongly NP-Hard, thus ruling out the existence of a fully polynomial-time approximation scheme (FPTAS). From an algorithmic standpoint, as a warm-up, we develop a simple constant factor approximation scheme in which we carefully stitch together myopically selected assortments for each stage. Our main algorithmic result consists of a polynomial-time approximation scheme (PTAS), which combines a handful of structural results related to the make-up of the optimal assortment sequence within an approximate dynamic programming framework. We also provide an additional approximation scheme, which, under mild assumptions, can handle a cardinality constraint that enforces that an exact number of new products are introduced at each stage. Using an extensive set of numerical experiments, we demonstrate that both algorithms exhibit excellent practical performance, producing sequences of assortments that are, on average, always within 2% of optimal.
{"title":"Display optimization under the multinomial logit choice model: Balancing revenue and customer satisfaction","authors":"Jacob Feldman, Puping Jiang","doi":"10.1111/poms.14040","DOIUrl":"https://doi.org/10.1111/poms.14040","url":null,"abstract":"In this paper, we consider an assortment optimization problem in which a platform must choose pairwise disjoint sets of assortments to offer across a series of <i>T</i> stages. Arriving customers begin their search process in the first stage, and progress sequentially through the stages until their patience expires, at which point they make a multinomial logit–based purchasing decision from among all products they have viewed throughout their search process. The goal is to choose the sequential displays of product offerings to maximize expected revenue. Additionally, we impose stage-specific constraints that ensure that as each customer progresses farther and farther through the <i>T</i> stages, there is a minimum level of “desirability” met by the collections of displayed products. We consider two related measures of desirability: purchase likelihood and expected utility derived from the offered assortments. In this way, the offered sequence of assortments must be both high earning and well liked, which breaks from the traditional assortment setting, where customer-centric considerations are generally not explicitly accounted for. We show that our assortment problem of interest is strongly NP-Hard, thus ruling out the existence of a fully polynomial-time approximation scheme (FPTAS). From an algorithmic standpoint, as a warm-up, we develop a simple constant factor approximation scheme in which we carefully stitch together myopically selected assortments for each stage. Our main algorithmic result consists of a polynomial-time approximation scheme (PTAS), which combines a handful of structural results related to the make-up of the optimal assortment sequence within an approximate dynamic programming framework. We also provide an additional approximation scheme, which, under mild assumptions, can handle a cardinality constraint that enforces that an exact number of new products are introduced at each stage. Using an extensive set of numerical experiments, we demonstrate that both algorithms exhibit excellent practical performance, producing sequences of assortments that are, on average, always within 2% of optimal.","PeriodicalId":20623,"journal":{"name":"Production and Operations Management","volume":"20 10","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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