We present an optimization model for assigning orders to couriers developed for an Italian meal delivery firm focusing on Rome. The firm focuses on top-end restaurants and customers and pursues high Quality of Service through careful management of delays. Our model reflects that in the firm’s business, the majority of orders are placed in advance. This took us to design a sequential decision process implementing a rolling horizon approach where we do not try to anticipate future demands. We, therefore, iterate the solution of a fully deterministic optimization problem, the Offline Couriers Assignment Problem (ocap), where we assume full knowledge of the orders and aim at minimizing delays and rejections. We solve ocap through integer linear programming and in particular by a “flow-like” formulation on a suitable network whose size is kept as small as possible. We validate both the quality of this formulation and the sequential decision process through some computational tests on real instances collected on the ground. We make these instances available to the scientific community.
{"title":"Courier assignment in meal delivery via integer programming: A case study in Rome","authors":"Matteo Cosmi , Gianpaolo Oriolo , Veronica Piccialli , Paolo Ventura","doi":"10.1016/j.omega.2024.103237","DOIUrl":"10.1016/j.omega.2024.103237","url":null,"abstract":"<div><div>We present an optimization model for assigning orders to couriers developed for an Italian meal delivery firm focusing on Rome. The firm focuses on top-end restaurants and customers and pursues high Quality of Service through careful management of delays. Our model reflects that in the firm’s business, the majority of orders are placed in advance. This took us to design a sequential decision process implementing a rolling horizon approach where we do not try to anticipate future demands. We, therefore, iterate the solution of a fully deterministic optimization problem, the <em>Offline Couriers Assignment Problem</em> (<span>ocap</span>), where we assume full knowledge of the orders and aim at minimizing delays and rejections. We solve <span>ocap</span> through integer linear programming and in particular by a “flow-like” formulation on a suitable network whose size is kept as small as possible. We validate both the quality of this formulation and the sequential decision process through some computational tests on real instances collected on the ground. We make these instances available to the scientific community.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103237"},"PeriodicalIF":6.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/j.omega.2024.103246
Simona Mancini , Marlin W. Ulmer , Margaretha Gansterer
Many larger grocery stores offer home delivery services. However, the delivery cost is usually high and such services are rarely profitable. One way of reducing cost is by outsourcing some orders to in-store customers for a compensation. While initially single orders were dynamically assigned to customers, companies started exploring the assignment of order bundles instead to reduce per-order compensation and exploit consolidation potential. We investigate the value of dynamic assignment of bundles in this work. To this end, we consider a setting where all orders are known and, over time, unknown in-store customers enter the system for a short time and offer transportation of bundles of orders for compensation. The store decides dynamically which bundle to assign to which in-store customer (if any). At the end of the time horizon, the remaining orders are delivered by a dedicated fleet of store employees. The goal of the store is to minimize the compensation prices together with the delivery cost. We propose a threshold-based policy with scenario-based tuning. Popularity and compensation price thresholds are determined a priori by solving a set of perfect information scenarios. In every state, bundles are only assigned if they are popular enough and the compensation is comparably low. The thresholds (i.e., popularity threshold and compensation threshold) are adapted over time to account for the decrease in assignment opportunities. We show the effectiveness of our policy in a comprehensive computational study and highlight the value of bundle assignments compared to assigning individual orders. We further show that our strategy not only reduces the compensation paid to in-store customers but also the final routing cost.
{"title":"Dynamic assignment of delivery order bundles to in-store customers","authors":"Simona Mancini , Marlin W. Ulmer , Margaretha Gansterer","doi":"10.1016/j.omega.2024.103246","DOIUrl":"10.1016/j.omega.2024.103246","url":null,"abstract":"<div><div>Many larger grocery stores offer home delivery services. However, the delivery cost is usually high and such services are rarely profitable. One way of reducing cost is by outsourcing some orders to in-store customers for a compensation. While initially single orders were dynamically assigned to customers, companies started exploring the assignment of order bundles instead to reduce per-order compensation and exploit consolidation potential. We investigate the value of dynamic assignment of bundles in this work. To this end, we consider a setting where all orders are known and, over time, unknown in-store customers enter the system for a short time and offer transportation of bundles of orders for compensation. The store decides dynamically which bundle to assign to which in-store customer (if any). At the end of the time horizon, the remaining orders are delivered by a dedicated fleet of store employees. The goal of the store is to minimize the compensation prices together with the delivery cost. We propose a threshold-based policy with scenario-based tuning. Popularity and compensation price thresholds are determined a priori by solving a set of perfect information scenarios. In every state, bundles are only assigned if they are popular enough and the compensation is comparably low. The thresholds (i.e., popularity threshold and compensation threshold) are adapted over time to account for the decrease in assignment opportunities. We show the effectiveness of our policy in a comprehensive computational study and highlight the value of bundle assignments compared to assigning individual orders. We further show that our strategy not only reduces the compensation paid to in-store customers but also the final routing cost.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103246"},"PeriodicalIF":6.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1016/j.omega.2024.103248
Huanhuan Jin , Nanyue Jiang , Weihua Su , Streimikiene Dalia
Environmental problems arising from global industrialization highlight the urgent need to green supply chains. However, the intensification of digitalization and global supply chain risks underscore the leading role of state-owned suppliers. From the supply chain perspective, this study analyzes the impact of the digitalization of downstream customer enterprises on the green total factor productivity (GTFP) of state-owned upstream suppliers using data on Chinese listed companies from 2007 to 2022. The benchmark regression shows that the digitalization of downstream customer enterprises significantly improves the GTFP of state-owned upstream suppliers. Mechanism testing shows that the digitalization of downstream customer enterprises promotes the GTFP of state-owned suppliers by reducing implicit costs and increasing the proportion of highly educated human resources. In improving the GTFP of state-owned suppliers, the external contractual environment, internal environmental responsibility, and market competitiveness act as “accelerators,” whereas the intensity of environmental regulation acts as a “speed bump” in the short term. In contrast to existing research, which mainly focuses on the impact of enterprise digitalization on the production efficiency of internal or upstream suppliers, this study facilitates the understanding of the pillar role of state-owned suppliers in the national economy and their stabilizing function in the supply chain.
{"title":"How does customer enterprise digitalization improve the green total factor productivity of state-owned suppliers: From the supply chain perspective","authors":"Huanhuan Jin , Nanyue Jiang , Weihua Su , Streimikiene Dalia","doi":"10.1016/j.omega.2024.103248","DOIUrl":"10.1016/j.omega.2024.103248","url":null,"abstract":"<div><div>Environmental problems arising from global industrialization highlight the urgent need to green supply chains. However, the intensification of digitalization and global supply chain risks underscore the leading role of state-owned suppliers. From the supply chain perspective, this study analyzes the impact of the digitalization of downstream customer enterprises on the green total factor productivity (GTFP) of state-owned upstream suppliers using data on Chinese listed companies from 2007 to 2022. The benchmark regression shows that the digitalization of downstream customer enterprises significantly improves the GTFP of state-owned upstream suppliers. Mechanism testing shows that the digitalization of downstream customer enterprises promotes the GTFP of state-owned suppliers by reducing implicit costs and increasing the proportion of highly educated human resources. In improving the GTFP of state-owned suppliers, the external contractual environment, internal environmental responsibility, and market competitiveness act as “accelerators,” whereas the intensity of environmental regulation acts as a “speed bump” in the short term. In contrast to existing research, which mainly focuses on the impact of enterprise digitalization on the production efficiency of internal or upstream suppliers, this study facilitates the understanding of the pillar role of state-owned suppliers in the national economy and their stabilizing function in the supply chain.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103248"},"PeriodicalIF":6.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1016/j.omega.2024.103233
Juan Carlos Gonçalves-Dosantos , Ricardo Martínez , Joaquín Sánchez-Soriano
The streaming industry has experienced exponential growth over the past decade. Streaming platforms provide subscribers with unlimited access to a diverse range of services, including movies, TV shows, and music, in exchange for a subscription fee. We take an axiomatic approach to the problem of how to share the overall revenue obtained from subscription sales among services or content producers. In doing so, we provide normative justifications for several distribution rules. We formulate several axioms that convey ethical and operational principles. In the first group, we consider properties that guarantee equal and impartial treatment of services and subscribers. In the second group, we introduce requirements designed to safeguard allocation schemes from inconvenient alterations, namely, changes in the units of measurement of inputs, subscription sharing, or group decomposition. Our analysis reveals that different combinations of these axioms define two classes of rules that strike a balance between three focal schemes, each representing distinct perspectives on the egalitarian and proportional principles. To illustrate the practical implications of our theoretical model, we explore its potential application by assessing how various types of content impact the revenues of some of the most well-known Twitch streamers.
{"title":"Revenue distribution in streaming","authors":"Juan Carlos Gonçalves-Dosantos , Ricardo Martínez , Joaquín Sánchez-Soriano","doi":"10.1016/j.omega.2024.103233","DOIUrl":"10.1016/j.omega.2024.103233","url":null,"abstract":"<div><div>The streaming industry has experienced exponential growth over the past decade. Streaming platforms provide subscribers with unlimited access to a diverse range of services, including movies, TV shows, and music, in exchange for a subscription fee. We take an axiomatic approach to the problem of how to share the overall revenue obtained from subscription sales among services or content producers. In doing so, we provide normative justifications for several distribution rules. We formulate several axioms that convey ethical and operational principles. In the first group, we consider properties that guarantee equal and impartial treatment of services and subscribers. In the second group, we introduce requirements designed to safeguard allocation schemes from inconvenient alterations, namely, changes in the units of measurement of inputs, subscription sharing, or group decomposition. Our analysis reveals that different combinations of these axioms define two classes of rules that strike a balance between three focal schemes, each representing distinct perspectives on the egalitarian and proportional principles. To illustrate the practical implications of our theoretical model, we explore its potential application by assessing how various types of content impact the revenues of some of the most well-known Twitch streamers.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"132 ","pages":"Article 103233"},"PeriodicalIF":6.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1016/j.omega.2024.103249
Shuai Wang , Qian Wang , Helen Lu , Dongxue Zhang , Qianyi Xing , Jianzhou Wang
High-quality risk management is the key to ensuring the safe, efficient, and stable operation of the financial system. The current Basel Accord requires financial institutions to regularly calculate and disclose Value at Risk (VaR) and Expected Shortfall (ES) measures. However, the inaccuracy and instability of traditional risk models have reduced users' confidence. Therefore, we propose two new probabilistic deep learning frameworks for estimating VaR and ES. The trained first framework can output expectiles that are more sensitive to tail risks to map VaR and ES measures. In the second framework, we propose to approximate VaR and ES measures with spline quantile function and estimate the parameters by designing various deep learning architectures. To ensure the effectiveness of the proposed architectures, we derived the training loss and constraints for them. In addition, we solve the problem that existing machine learning risk models are difficult to estimate ES. In this way, combining various individual risk models has great potential for risk management. Therefore, we propose a regularization-based combination framework that adaptively selects and shrinks individual risk models. The developed individual methods and combinations outperform existing methods in backtesting, assisting financial institutions to allocate capital more effectively according to the Basel Capital Accord.
{"title":"Learning about tail risk: Machine learning and combination with regularization in market risk management","authors":"Shuai Wang , Qian Wang , Helen Lu , Dongxue Zhang , Qianyi Xing , Jianzhou Wang","doi":"10.1016/j.omega.2024.103249","DOIUrl":"10.1016/j.omega.2024.103249","url":null,"abstract":"<div><div>High-quality risk management is the key to ensuring the safe, efficient, and stable operation of the financial system. The current Basel Accord requires financial institutions to regularly calculate and disclose Value at Risk (VaR) and Expected Shortfall (ES) measures. However, the inaccuracy and instability of traditional risk models have reduced users' confidence. Therefore, we propose two new probabilistic deep learning frameworks for estimating VaR and ES. The trained first framework can output expectiles that are more sensitive to tail risks to map VaR and ES measures. In the second framework, we propose to approximate VaR and ES measures with spline quantile function and estimate the parameters by designing various deep learning architectures. To ensure the effectiveness of the proposed architectures, we derived the training loss and constraints for them. In addition, we solve the problem that existing machine learning risk models are difficult to estimate ES. In this way, combining various individual risk models has great potential for risk management. Therefore, we propose a regularization-based combination framework that adaptively selects and shrinks individual risk models. The developed individual methods and combinations outperform existing methods in backtesting, assisting financial institutions to allocate capital more effectively according to the Basel Capital Accord.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103249"},"PeriodicalIF":6.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1016/j.omega.2024.103247
Wen-Min Lu , Chien-Heng Chou , Irene Wei Kiong Ting , Shang-Ming Liu
This study develops an innovative value creation process for the electric vehicle (EV) industry. First, this study conducts data envelopment analysis to measure the innovation, operation, and market efficiency performance of the EV industry. Second, this study conducts bootstrapped truncated regression to explore the impact of environmental, social, and governance (ESG) factors on the performance of the EV industry. Third, this study uses the classification & regression tree (CART), random forest, and eXtreme gradient boosting (XGBoost) algorithms to assist managers in identifying the key predictive variables for further classification and prediction. Results reveal significant differences in innovation performance across five industry sectors, among which the charging pile system sector exhibits the highest average value, and the battery system sector exhibits the lowest average value. The truncated regression analysis shows that innovation performance in Taiwan's EV industry is significantly influenced by energy management, data security, employee information statistics, and control over equity and board seats. Corporate governance transparency positively impacts operational performance, while energy and water management enhance market performance, with product quality and safety having a negative effect on market performance. This study identifies the relative importance of the classification attribute variables based on the classification rules of the target attributes by conducting further analysis with the CART decision model and constructs an optimal prediction model.
{"title":"A New Integrated Approach for Evaluating Sustainable Development in the Electric Vehicle Sector","authors":"Wen-Min Lu , Chien-Heng Chou , Irene Wei Kiong Ting , Shang-Ming Liu","doi":"10.1016/j.omega.2024.103247","DOIUrl":"10.1016/j.omega.2024.103247","url":null,"abstract":"<div><div>This study develops an innovative value creation process for the electric vehicle (EV) industry. First, this study conducts data envelopment analysis to measure the innovation, operation, and market efficiency performance of the EV industry. Second, this study conducts bootstrapped truncated regression to explore the impact of environmental, social, and governance (ESG) factors on the performance of the EV industry. Third, this study uses the classification & regression tree (CART), random forest, and eXtreme gradient boosting (XGBoost) algorithms to assist managers in identifying the key predictive variables for further classification and prediction. Results reveal significant differences in innovation performance across five industry sectors, among which the charging pile system sector exhibits the highest average value, and the battery system sector exhibits the lowest average value. The truncated regression analysis shows that innovation performance in Taiwan's EV industry is significantly influenced by energy management, data security, employee information statistics, and control over equity and board seats. Corporate governance transparency positively impacts operational performance, while energy and water management enhance market performance, with product quality and safety having a negative effect on market performance. This study identifies the relative importance of the classification attribute variables based on the classification rules of the target attributes by conducting further analysis with the CART decision model and constructs an optimal prediction model.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"133 ","pages":"Article 103247"},"PeriodicalIF":6.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.omega.2024.103245
Lili Liu , Sheng Ang , Feng Yang , Xiaoqi Zhang
Partner selection is crucial for ensuring successful supply chain collaboration. This study focuses on selecting the best partner for a predefined two-stage supply chain using data envelopment analysis to assess the performance of collaborative systems. We distinguish between two levels of supply chain collaboration: chain-to-chain and stage-to-stage collaboration. The former involves partner selection within the same supply chain across two stages, while the latter allows for selected partners from different supply chains across two stages. We incorporate the technology learning effect and introduce three degrees of collaboration (minor, major, and medium) for both chain and stage collaboration levels. Solutions are provided for each collaboration level and degree, with comparative analysis indicating that major collaboration in stage-to-stage level yields superior performance. A numerical example and a real-world case study are presented to illustrate our models and findings, demonstrating that our approach offers superior benefits and more flexible options compared to existing methods. Thus, the proposed approach not only contributes to advancing theoretical understanding but also provides practical implications for optimizing collaborative relationships within complex multi-stage supply chain environments.
{"title":"Partner selection for supply chain collaboration: New data envelopment analysis models","authors":"Lili Liu , Sheng Ang , Feng Yang , Xiaoqi Zhang","doi":"10.1016/j.omega.2024.103245","DOIUrl":"10.1016/j.omega.2024.103245","url":null,"abstract":"<div><div>Partner selection is crucial for ensuring successful supply chain collaboration. This study focuses on selecting the best partner for a predefined two-stage supply chain using data envelopment analysis to assess the performance of collaborative systems. We distinguish between two levels of supply chain collaboration: chain-to-chain and stage-to-stage collaboration. The former involves partner selection within the same supply chain across two stages, while the latter allows for selected partners from different supply chains across two stages. We incorporate the technology learning effect and introduce three degrees of collaboration (minor, major, and medium) for both chain and stage collaboration levels. Solutions are provided for each collaboration level and degree, with comparative analysis indicating that major collaboration in stage-to-stage level yields superior performance. A numerical example and a real-world case study are presented to illustrate our models and findings, demonstrating that our approach offers superior benefits and more flexible options compared to existing methods. Thus, the proposed approach not only contributes to advancing theoretical understanding but also provides practical implications for optimizing collaborative relationships within complex multi-stage supply chain environments.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"132 ","pages":"Article 103245"},"PeriodicalIF":6.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Implementing job rotation (JR) can yield positive or negative impacts depending on the firm characteristics and industry type. As a result, a generic framework for its implementation is difficult to develop and presently does not exist in literature. Existing models/frameworks have limited application as they are either too specific or generic, but imprecise. Consequently, managers often rely on their intuition to develop JR schedules which sometimes leads to suboptimal/unwanted outcomes. In this article, we propose a generic JR framework, which can be used by managers to develop optimal JR schedules in firms across many industries, such as IT, manufacturing, banking, medical, construction, chemical, aviation, etc. The proposed framework contributes to theory by addressing important questions, such as when to implement JR, and how to quantify some of its most important impacts. We also demonstrate the utility of the proposed framework through an illustrative industrial example.
{"title":"A novel framework for optimizing job rotation schedules across industries","authors":"Priyank Sinha , Sameer Kumar , Chandra Prakash Garg , Charu Chandra","doi":"10.1016/j.omega.2024.103235","DOIUrl":"10.1016/j.omega.2024.103235","url":null,"abstract":"<div><div>Implementing job rotation (JR) can yield positive or negative impacts depending on the firm characteristics and industry type. As a result, a generic framework for its implementation is difficult to develop and presently does not exist in literature. Existing models/frameworks have limited application as they are either too specific or generic, but imprecise. Consequently, managers often rely on their intuition to develop JR schedules which sometimes leads to suboptimal/unwanted outcomes. In this article, we propose a generic JR framework, which can be used by managers to develop optimal JR schedules in firms across many industries, such as IT, manufacturing, banking, medical, construction, chemical, aviation, etc. The proposed framework contributes to theory by addressing important questions, such as when to implement JR, and how to quantify some of its most important impacts. We also demonstrate the utility of the proposed framework through an illustrative industrial example.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"132 ","pages":"Article 103235"},"PeriodicalIF":6.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.omega.2024.103227
Lu Wang , Tianhu Deng , Qiaofeng Li
The automotive industry has been significantly impacted by the global semiconductor shortage since 2020. Traditional strategies, such as maintaining safety stock and sourcing from backup suppliers, have been proven insufficient in mitigating supply shortages. To address the global chip shortage and build supply chain viability, leading automotive manufacturers worldwide have adopted an innovative adaptation strategy known as the feature removal strategy. This strategy involves temporarily removing non-vital features and retrofitting them once supply shortages are alleviated, thereby mitigating disruptions. Given the increasing frequency, severity, and unpredictability of global chip shortages, it is crucial to investigate the potential benefits of the feature removal strategy for automotive manufacturers. This study aims to address this gap analytically. We develop a stochastic dynamic programming model to optimize pricing and production decisions under the feature removal strategy. We reformulate the model into an equivalent convex problem and propose structural properties to manage the complexity arising from the high dimensionality of state and action spaces. Comparative analyses with benchmark strategies underscore the efficacy of the feature removal strategy in enhancing profitability and sustaining supply chain viability, especially in prolonged supply shortage scenarios.
{"title":"Can feature removal benefit the automotive manufacturers amid supply shortages? An analytical investigation","authors":"Lu Wang , Tianhu Deng , Qiaofeng Li","doi":"10.1016/j.omega.2024.103227","DOIUrl":"10.1016/j.omega.2024.103227","url":null,"abstract":"<div><div>The automotive industry has been significantly impacted by the global semiconductor shortage since 2020. Traditional strategies, such as maintaining safety stock and sourcing from backup suppliers, have been proven insufficient in mitigating supply shortages. To address the global chip shortage and build supply chain viability, leading automotive manufacturers worldwide have adopted an innovative adaptation strategy known as the feature removal strategy. This strategy involves temporarily removing non-vital features and retrofitting them once supply shortages are alleviated, thereby mitigating disruptions. Given the increasing frequency, severity, and unpredictability of global chip shortages, it is crucial to investigate the potential benefits of the feature removal strategy for automotive manufacturers. This study aims to address this gap analytically. We develop a stochastic dynamic programming model to optimize pricing and production decisions under the feature removal strategy. We reformulate the model into an equivalent convex problem and propose structural properties to manage the complexity arising from the high dimensionality of state and action spaces. Comparative analyses with benchmark strategies underscore the efficacy of the feature removal strategy in enhancing profitability and sustaining supply chain viability, especially in prolonged supply shortage scenarios.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"132 ","pages":"Article 103227"},"PeriodicalIF":6.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The implementation of direct channels allows manufacturers to distribute new and remanufactured products through separate channels. This prompts manufacturers to carefully consider which products to channel directly. In the remanufacturing process, manufacturers often delegate the collection process to retailers. However, only retailers know their own collection efficiency information, whereas manufacturers can only know its probability distribution. To explore the choice of encroachment channel strategy under information asymmetry, we constructed a dual-channel closed-loop supply chain model, in which the manufacturer can design non-linear contracts to incentivize the retailer to choose contracts that align with its capabilities, ultimately maximizing its profit. We discuss the case of manufacturer dual product encroachment and the impact of consumer channel preferences in the extended model. The findings reveal that, contrary to previous studies, the profits of low-type manufacturers are not always reduced, and the effect of information asymmetry may be opposite. Moreover, information asymmetry can be detrimental to high-type retailers. The optimal channel choice is affected by factors such as remanufacturing cost, consumer channel preference, information asymmetry, and reserved profit differences. Manufacturers and retailers can achieve a win-win situation through new product encroachment, which can also counteract the negative effects of information asymmetry and enhance consumer surplus.
{"title":"Managing manufacturer encroachment and product conflicts in a closed-loop supply chain: The case of information asymmetry","authors":"Senlin Zhao , Mengxiang Wang , Qin Zhou , Xiqiang Xia","doi":"10.1016/j.omega.2024.103236","DOIUrl":"10.1016/j.omega.2024.103236","url":null,"abstract":"<div><div>The implementation of direct channels allows manufacturers to distribute new and remanufactured products through separate channels. This prompts manufacturers to carefully consider which products to channel directly. In the remanufacturing process, manufacturers often delegate the collection process to retailers. However, only retailers know their own collection efficiency information, whereas manufacturers can only know its probability distribution. To explore the choice of encroachment channel strategy under information asymmetry, we constructed a dual-channel closed-loop supply chain model, in which the manufacturer can design non-linear contracts to incentivize the retailer to choose contracts that align with its capabilities, ultimately maximizing its profit. We discuss the case of manufacturer dual product encroachment and the impact of consumer channel preferences in the extended model. The findings reveal that, contrary to previous studies, the profits of low-type manufacturers are not always reduced, and the effect of information asymmetry may be opposite. Moreover, information asymmetry can be detrimental to high-type retailers. The optimal channel choice is affected by factors such as remanufacturing cost, consumer channel preference, information asymmetry, and reserved profit differences. Manufacturers and retailers can achieve a win-win situation through new product encroachment, which can also counteract the negative effects of information asymmetry and enhance consumer surplus.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"132 ","pages":"Article 103236"},"PeriodicalIF":6.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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