Shibshankar Dey, Ali Kaan Kurbanzade, Esma S. Gel, Joseph Mihaljevic, Sanjay Mehrotra
During various stages of the COVID‐19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio‐economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID‐19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inventory management, allocation strategies, distribution logistics, and route optimization encountered during the COVID‐19 crisis. A unified framework is employed to describe the models, emphasizing their integration with epidemiological models to facilitate a holistic understanding. This article also summarizes evolving nature of literature, relevant research gaps, and authors' perspectives for model selection. Finally, future research scopes are detailed both in the context of modeling and solutions approaches.
{"title":"Optimization modeling for pandemic vaccine supply chain management: A review and future research opportunities","authors":"Shibshankar Dey, Ali Kaan Kurbanzade, Esma S. Gel, Joseph Mihaljevic, Sanjay Mehrotra","doi":"10.1002/nav.22181","DOIUrl":"https://doi.org/10.1002/nav.22181","url":null,"abstract":"During various stages of the COVID‐19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio‐economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID‐19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inventory management, allocation strategies, distribution logistics, and route optimization encountered during the COVID‐19 crisis. A unified framework is employed to describe the models, emphasizing their integration with epidemiological models to facilitate a holistic understanding. This article also summarizes evolving nature of literature, relevant research gaps, and authors' perspectives for model selection. Finally, future research scopes are detailed both in the context of modeling and solutions approaches.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140631167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Promotional effort is a common strategy to induce sales and broaden the market scope by enhancing the products' utility to customers. In this article, we incorporate promotional effort into the customer choice model and study the joint promotional effort and assortment optimization problems, where the customer's choice behavior follows the widely used multinomial‐logit (MNL) model. Motivated by various marketing scenarios, we introduce two distinct models that address the allocation of promotional effort: (1) differentiated promotional effort—the retailer can arbitrarily allocate promotional resources to each offered product; (2) uniform promotional effort—the retailer can determine a promotional level, and the promotional effort is equally distributed to each offered product. In the first model, the revenue‐ordered assortment strategy is optimal, and we can efficiently determine the optimal promotional effort level. In the second model, the revenue‐ordered assortment is no longer optimal. We develop a polynomial time algorithm to solve the joint optimization problem under this model. Using the algorithmic results, we conduct comparative analyses between the assortment optimization problem under the proposed models and the classic MNL model, which does not exert any promotional effort. We show that the assortment size shrinks when the retailer makes the promotional effort in the decision, which indicates that product variety and promotional effort are strategic substitutes. Moreover, the retailer and customers can be better off in the presence of promotional efforts, irrespective of the format. Additionally, we conduct extensive numerical experiments to demonstrate our analytical results and gain more managerial insights.
{"title":"Joint promotional effort and assortment optimization under the multinomial logit model","authors":"Hua Xiao, Min Gong, Zhaotong Lian, Kameng Nip","doi":"10.1002/nav.22187","DOIUrl":"https://doi.org/10.1002/nav.22187","url":null,"abstract":"Promotional effort is a common strategy to induce sales and broaden the market scope by enhancing the products' utility to customers. In this article, we incorporate promotional effort into the customer choice model and study the joint promotional effort and assortment optimization problems, where the customer's choice behavior follows the widely used multinomial‐logit (MNL) model. Motivated by various marketing scenarios, we introduce two distinct models that address the allocation of promotional effort: (1) <jats:italic>differentiated promotional effort</jats:italic>—the retailer can arbitrarily allocate promotional resources to each offered product; (2) <jats:italic>uniform promotional effort</jats:italic>—the retailer can determine a promotional level, and the promotional effort is equally distributed to each offered product. In the first model, the revenue‐ordered assortment strategy is optimal, and we can efficiently determine the optimal promotional effort level. In the second model, the revenue‐ordered assortment is no longer optimal. We develop a polynomial time algorithm to solve the joint optimization problem under this model. Using the algorithmic results, we conduct comparative analyses between the assortment optimization problem under the proposed models and the classic MNL model, which does not exert any promotional effort. We show that the assortment size shrinks when the retailer makes the promotional effort in the decision, which indicates that product variety and promotional effort are strategic substitutes. Moreover, the retailer and customers can be better off in the presence of promotional efforts, irrespective of the format. Additionally, we conduct extensive numerical experiments to demonstrate our analytical results and gain more managerial insights.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies the interactions of capital structure and product market competition under two supply chain structures: a distribution structure where two competing retailers source from a common supplier and a parallel structure where each retailer sources from a dedicated supplier. The analyses demonstrate that if the retailers have access to external credits, they will borrow more (less), buy more (less), and incur a higher (lower) default probability under a parallel structure than under a distribution structure if the retailers sell substitutes (complements). The retailers' leverage improves the profit of their upstream suppliers and of the channel but improves their own profits only if they sell complements. If they sell substitutes, borrowing makes them worse off, leading to the prisoner's dilemma phenomenon. If only one retailer has access to external credit, then it will enjoy a leadership premium (loss) if the retailers sell substitutes (complements), and this effect trickles up to the upstream supplier of the leveraged retailer under the parallel structure. Thus, the dedicated suppliers will abet the leveraged retailers to compete more aggressively than unleveraged ones. However, the common supplier is independent of their downstream customer's capital structure. These results reveal that the impacts of external credit on the supply chain profitability depend on the stages the supply chain members are in, the degree of product differentiation, the supply chain structure, the uncertainty of demand, and whether the competitor also has access to external credits.
{"title":"Supply chain structure, debt financing and product market competition","authors":"Qiaohai (Joice) Hu","doi":"10.1002/nav.22188","DOIUrl":"https://doi.org/10.1002/nav.22188","url":null,"abstract":"This paper studies the interactions of capital structure and product market competition under two supply chain structures: a distribution structure where two competing retailers source from a common supplier and a parallel structure where each retailer sources from a dedicated supplier. The analyses demonstrate that if the retailers have access to external credits, they will borrow more (less), buy more (less), and incur a higher (lower) default probability under a parallel structure than under a distribution structure if the retailers sell substitutes (complements). The retailers' leverage improves the profit of their upstream suppliers and of the channel but improves their own profits only if they sell complements. If they sell substitutes, borrowing makes them worse off, leading to the prisoner's dilemma phenomenon. If only one retailer has access to external credit, then it will enjoy a leadership premium (loss) if the retailers sell substitutes (complements), and this effect trickles up to the upstream supplier of the leveraged retailer under the parallel structure. Thus, the dedicated suppliers will abet the leveraged retailers to compete more aggressively than unleveraged ones. However, the common supplier is independent of their downstream customer's capital structure. These results reveal that the impacts of external credit on the supply chain profitability depend on the stages the supply chain members are in, the degree of product differentiation, the supply chain structure, the uncertainty of demand, and whether the competitor also has access to external credits.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article focuses on air defense in maritime environment, which involves protecting friendly naval assets from aerial threats. Specifically, we define and address the Naval Air Defense Planning (NADP) problem, which consists of maneuvering decisions of the ships and scheduling weapons and sensors to the threats in order to maximize the total expected survival probability of friendly units. The NADP problem is more realistic and applicable than previous studies, as it considers features such as sensor assignment requirements, weapon and sensor blind sectors, sequence‐dependent setup times, and ship's infrared/radar signature. In this study, a mixed‐integer nonlinear programming model of the NADP problem is presented and heuristic solution approaches are developed. Computational results demonstrate that these heuristic approaches are both fast and efficient in solving the NADP problem.
{"title":"Naval Air Defense Planning problem: A novel formulation and heuristics","authors":"Caner Arslan, Orhan Karasakal, Ömer Kırca","doi":"10.1002/nav.22186","DOIUrl":"https://doi.org/10.1002/nav.22186","url":null,"abstract":"This article focuses on air defense in maritime environment, which involves protecting friendly naval assets from aerial threats. Specifically, we define and address the Naval Air Defense Planning (NADP) problem, which consists of maneuvering decisions of the ships and scheduling weapons and sensors to the threats in order to maximize the total expected survival probability of friendly units. The NADP problem is more realistic and applicable than previous studies, as it considers features such as sensor assignment requirements, weapon and sensor blind sectors, sequence‐dependent setup times, and ship's infrared/radar signature. In this study, a mixed‐integer nonlinear programming model of the NADP problem is presented and heuristic solution approaches are developed. Computational results demonstrate that these heuristic approaches are both fast and efficient in solving the NADP problem.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices.
{"title":"Selling formats and platform information sharing under manufacturer competition","authors":"Xue Li, Shilu Tong, Xiaoqiang Cai, Jian Chen","doi":"10.1002/nav.22184","DOIUrl":"https://doi.org/10.1002/nav.22184","url":null,"abstract":"Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Assessing the efficacy of algorithms plays a pivotal role in advancing various fields, both in theory and practice. Unlike the predictive models, due to the intricate relationship between decisions and the underlying data‐generating processes, the evaluation of decision algorithms cannot directly rely on real data. Hence, a simulator becomes indispensable for appraising decision algorithm effectiveness. In this paper, we aim to leverage assortment decisions, a widely used application in revenue management, to illustrate the utilization of a machine learning‐based simulation. The process can be summarised as: we utilize the modified Transformer‐based choice model, acting as a simulator, to generate a synthetic dataset that mimics consumer purchasing behavior. After training the MNL, DeepFM, and DeepFM‐a models, all of which can swiftly provide assortment decisions in real‐time, we utilize the simulator to evaluate the revenue generated by each assortment prescribed by different choice models. This approach mitigates the challenge of validating decision models that alter real‐world observed data. To show the benefit of such a simulation approach, we have conducted various numerical studies. These studies aim to examine the impact of outside option attractiveness, data size, the number of features, and cardinality. Admittedly, due to the close alignment between the simulator and complex consumer purchase choice datasets, some numerical observations may be challenging to explain. Nevertheless, by employing the simulator, we are able to contrast the differences between the MNL and DeepFM/DeepFM‐a models, shedding light on their respective model misspecifications.
{"title":"Transformer‐based choice model: A tool for assortment optimization evaluation","authors":"Zhenkang Peng, Ying Rong, Tianning Zhu","doi":"10.1002/nav.22183","DOIUrl":"https://doi.org/10.1002/nav.22183","url":null,"abstract":"Assessing the efficacy of algorithms plays a pivotal role in advancing various fields, both in theory and practice. Unlike the predictive models, due to the intricate relationship between decisions and the underlying data‐generating processes, the evaluation of decision algorithms cannot directly rely on real data. Hence, a simulator becomes indispensable for appraising decision algorithm effectiveness. In this paper, we aim to leverage assortment decisions, a widely used application in revenue management, to illustrate the utilization of a machine learning‐based simulation. The process can be summarised as: we utilize the modified Transformer‐based choice model, acting as a simulator, to generate a synthetic dataset that mimics consumer purchasing behavior. After training the MNL, DeepFM, and DeepFM‐a models, all of which can swiftly provide assortment decisions in real‐time, we utilize the simulator to evaluate the revenue generated by each assortment prescribed by different choice models. This approach mitigates the challenge of validating decision models that alter real‐world observed data. To show the benefit of such a simulation approach, we have conducted various numerical studies. These studies aim to examine the impact of outside option attractiveness, data size, the number of features, and cardinality. Admittedly, due to the close alignment between the simulator and complex consumer purchase choice datasets, some numerical observations may be challenging to explain. Nevertheless, by employing the simulator, we are able to contrast the differences between the MNL and DeepFM/DeepFM‐a models, shedding light on their respective model misspecifications.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In system reliability analysis, researchers are interested in evaluating the characteristics of the lifetime distribution of components in a system based on system lifetime data. In collecting system lifetime data, a censoring scheme is often adopted due to time and budget constraints. In this article, we consider the situation where the system lifetime data from two different coherent systems are subjected to Type‐II right‐censoring, and we are interested in testing the homogeneity of component lifetime distributions based on Type‐II censored system lifetime data with known system structures. Based on the Mann‐Whitney test and empirical likelihood ratio tests developed for testing the homogeneity of component lifetime distributions with complete system lifetime data, we propose different non‐parametric test procedures using the permutation of the unobserved censored system lifetimes. We consider a restricted assumption on the censored lifetimes to reduce the permutations required in the computation. The computational approaches to obtain the critical values of the proposed test procedures are provided using the Monte Carlo method. A practical example is used to illustrate the proposed test procedures. Then, the power performance of the proposed test procedures is evaluated and compared through a Monte Carlo simulation study. The simulation results show that the proposed test procedures provide comparable power performance for Type‐II censored system lifetime data in contrast to the complete sample case under different scenarios. Finally, recommendations based on the simulation results and concluding remarks are provided.
{"title":"Tests for homogeneity of distributions of component lifetimes from Type‐II censored system lifetime data with known system structures","authors":"Jingjing Qu, Hon Keung Tony Ng","doi":"10.1002/nav.22182","DOIUrl":"https://doi.org/10.1002/nav.22182","url":null,"abstract":"In system reliability analysis, researchers are interested in evaluating the characteristics of the lifetime distribution of components in a system based on system lifetime data. In collecting system lifetime data, a censoring scheme is often adopted due to time and budget constraints. In this article, we consider the situation where the system lifetime data from two different coherent systems are subjected to Type‐II right‐censoring, and we are interested in testing the homogeneity of component lifetime distributions based on Type‐II censored system lifetime data with known system structures. Based on the Mann‐Whitney test and empirical likelihood ratio tests developed for testing the homogeneity of component lifetime distributions with complete system lifetime data, we propose different non‐parametric test procedures using the permutation of the unobserved censored system lifetimes. We consider a restricted assumption on the censored lifetimes to reduce the permutations required in the computation. The computational approaches to obtain the critical values of the proposed test procedures are provided using the Monte Carlo method. A practical example is used to illustrate the proposed test procedures. Then, the power performance of the proposed test procedures is evaluated and compared through a Monte Carlo simulation study. The simulation results show that the proposed test procedures provide comparable power performance for Type‐II censored system lifetime data in contrast to the complete sample case under different scenarios. Finally, recommendations based on the simulation results and concluding remarks are provided.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140170847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates an original equipment manufacturer's (OEM's) outsourcing choice between a competing manufacturer (CP) and a non‐competing manufacturer (NP). We develop a benchmark self‐produce strategy and two outsourcing strategies to differentiate two manufacturing service providers, and examine the optimal strategy alongside an analysis of the respective incentives (e.g., a lump‐sum payment) from the two service providers. The optimal strategy depends on the difference in production efficiency, degree of product substitution, and joint effect of the transfer payments. The transfer payments contribute to a greater range of Pareto improvements, increasing the possibility of outsourcing cooperation while highlighting the role of competition intensity on the model and outsourcing cooperation partner. Effect analysis shows that in the absence of transfer payment, the optimal strategy is beneficial to social welfare. With transfer payment, the optimal strategy is changed and the firms will be more profitable, but at the expense of customers' surplus, which may result in worse social welfare. An extended analysis of mixed strategies, in which the OEM produces part of the products and outsources the rest to CP/NP, shows that while the mixed‐CP strategy can be an optimal choice, the mixed‐NP strategy will degenerate to either self‐produce or complete outsourcing to NP under certain conditions.
{"title":"Exploring optimal outsourcing strategy with and without transfer payment","authors":"Zheng Luo, Xu Chen, Xiaojun Wang","doi":"10.1002/nav.22178","DOIUrl":"https://doi.org/10.1002/nav.22178","url":null,"abstract":"This study investigates an original equipment manufacturer's (OEM's) outsourcing choice between a competing manufacturer (CP) and a non‐competing manufacturer (NP). We develop a benchmark self‐produce strategy and two outsourcing strategies to differentiate two manufacturing service providers, and examine the optimal strategy alongside an analysis of the respective incentives (e.g., a lump‐sum payment) from the two service providers. The optimal strategy depends on the difference in production efficiency, degree of product substitution, and joint effect of the transfer payments. The transfer payments contribute to a greater range of Pareto improvements, increasing the possibility of outsourcing cooperation while highlighting the role of competition intensity on the model and outsourcing cooperation partner. Effect analysis shows that in the absence of transfer payment, the optimal strategy is beneficial to social welfare. With transfer payment, the optimal strategy is changed and the firms will be more profitable, but at the expense of customers' surplus, which may result in worse social welfare. An extended analysis of mixed strategies, in which the OEM produces part of the products and outsources the rest to CP/NP, shows that while the mixed‐CP strategy can be an optimal choice, the mixed‐NP strategy will degenerate to either self‐produce or complete outsourcing to NP under certain conditions.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amazon, as one of the dominant online retailers (platforms), cooperates with manufacturers under wholesale contract to develop its store brands. Simultaneously, Amazon offers manufacturers with a marketplace and serves manufacturers in the agency selling. In this paper, we build a model to investigate the platform's and the manufacturer's choices on the cooperation modes (i.e., wholesale contract or agency selling) and their quality decisions when they serve consumers with heterogeneous willingness to pay for quality and the platform can dictate the quality under the wholesale contract. We find that the platform and the manufacturer are more likely to align their preferences on selling modes when consumers are homogenous enough. Moreover, when the commission rate is relatively low, both of them may prefer the agency selling. In this case, the manufacturer is willing to offer high quality in the agency selling. In contrast, when the commission rate is sufficiently high, they can only align their preferences on selling modes by choosing the wholesale contract. The product quality under the wholesale contract is higher than that in the agency selling. Finally, we provide three extensions: the platform decides the commission rate, the platform decides the wholesale price and competition between manufacturers.
{"title":"Developing a store brand or collecting a commission: Amazon's choice and quality decision","authors":"Hui Xiong, Ying‐Ju Chen, Lu Hsiao","doi":"10.1002/nav.22180","DOIUrl":"https://doi.org/10.1002/nav.22180","url":null,"abstract":"Amazon, as one of the dominant online retailers (platforms), cooperates with manufacturers under wholesale contract to develop its store brands. Simultaneously, Amazon offers manufacturers with a marketplace and serves manufacturers in the agency selling. In this paper, we build a model to investigate the platform's and the manufacturer's choices on the cooperation modes (i.e., wholesale contract or agency selling) and their quality decisions when they serve consumers with heterogeneous willingness to pay for quality and the platform can dictate the quality under the wholesale contract. We find that the platform and the manufacturer are more likely to align their preferences on selling modes when consumers are homogenous enough. Moreover, when the commission rate is relatively low, both of them may prefer the agency selling. In this case, the manufacturer is willing to offer high quality in the agency selling. In contrast, when the commission rate is sufficiently high, they can only align their preferences on selling modes by choosing the wholesale contract. The product quality under the wholesale contract is higher than that in the agency selling. Finally, we provide three extensions: the platform decides the commission rate, the platform decides the wholesale price and competition between manufacturers.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considerable human judgment is involved in demand forecasting. When managers judge demands under uncertainty, they inevitably use signals to update their demand information. These signals are seldom perfect; hence, managers hold behavioral bias about the signal fidelity, that is, over‐ or under‐estimating the signal fidelity. This article models managers' behavioral bias about signal fidelity in Bayesian demand forecasting and explores its impact on competitive firms. We find that no matter whether the competitor's manager is unbiased or biased, a firm can benefit from its manager's slight overestimation, but the competitor can benefit from the firm's manager's underestimation. However, when one firm's manager is biased, improving the signal fidelity may not constantly improve firms' profits, revealing the potential risk of behavioral bias on the efficiency of the forecasting systems. We further consider the diversity of biased managers and the information asymmetry regarding the bias. Except that the benefits of behavioral bias exist, we additionally find that managers' heterogeneous behavioral bias can form a hedge effect and bring a win‐win situation. Under asymmetric information, managers' inference bias on the competitor's type may benefit firms by easing the negative impact of managers' behavioral bias about signal fidelity. We finally analyze the social welfare and consumer surplus, check the robustness of the main results and deliver additional findings by considering competing firms, different signal fidelity measures, and the signal‐dependent behavioral bias.
{"title":"Effects of behavioral bias regarding demand forecasting in a competitive market","authors":"Juan Li, Xuan Zhao, Yini Zheng","doi":"10.1002/nav.22179","DOIUrl":"https://doi.org/10.1002/nav.22179","url":null,"abstract":"Considerable human judgment is involved in demand forecasting. When managers judge demands under uncertainty, they inevitably use signals to update their demand information. These signals are seldom perfect; hence, managers hold behavioral bias about the signal fidelity, that is, over‐ or under‐estimating the signal fidelity. This article models managers' behavioral bias about signal fidelity in Bayesian demand forecasting and explores its impact on competitive firms. We find that no matter whether the competitor's manager is unbiased or biased, a firm can benefit from its manager's slight overestimation, but the competitor can benefit from the firm's manager's underestimation. However, when one firm's manager is biased, improving the signal fidelity may not constantly improve firms' profits, revealing the potential risk of behavioral bias on the efficiency of the forecasting systems. We further consider the diversity of biased managers and the information asymmetry regarding the bias. Except that the benefits of behavioral bias exist, we additionally find that managers' heterogeneous behavioral bias can form a hedge effect and bring a win‐win situation. Under asymmetric information, managers' inference bias on the competitor's type may benefit firms by easing the negative impact of managers' behavioral bias about signal fidelity. We finally analyze the social welfare and consumer surplus, check the robustness of the main results and deliver additional findings by considering competing firms, different signal fidelity measures, and the signal‐dependent behavioral bias.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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