It is well‐established in the literature that a horizontal merger in a supply chain is profitable (or beneficial to firms at another tier) if the merger synergy exceeds some threshold referred to as the profitable (or beneficial) threshold. Our paper goes one step further by finding that the profitable threshold is always lower than the beneficial threshold, which implies that a firm may have an incentive to prevent a horizontal merger between its suppliers or customers, but never wants to precipitate one. Moreover, we propose a strategy to achieve the preventive goal—a firm can develop an instrumental merger option with another rival firm, which can help prevent the target merger in one of two ways. First, if the synergy of the instrumental merger is high relative to the synergy of the target merger, the firm can carry out the instrumental merger—even an unprofitable one—preemptively to make the target merger unprofitable. Second, if the synergy of the target merger is not too high and the synergy of the instrumental merger is moderate, that is, the instrumental merger is profitable and will inflict severe harm on the firms in the target merger, the firm can reserve the instrumental merger option as a deterrent and the profitable target merger is deterred. An interesting implication of the deterrent role of the instrumental merger option is that when the firm has two candidate merger partners, it may choose the partner with a lower synergy for the deterrence purpose.
{"title":"Creating a merger option to strategically prevent a merger among suppliers or customers","authors":"Lezhen Wu, Dingwei Gu, Zhiyong Yao, Wen Zhou","doi":"10.1002/nav.22148","DOIUrl":"https://doi.org/10.1002/nav.22148","url":null,"abstract":"It is well‐established in the literature that a horizontal merger in a supply chain is profitable (or beneficial to firms at another tier) if the merger synergy exceeds some threshold referred to as the profitable (or beneficial) threshold. Our paper goes one step further by finding that the profitable threshold is always lower than the beneficial threshold, which implies that a firm may have an incentive to prevent a horizontal merger between its suppliers or customers, but never wants to precipitate one. Moreover, we propose a strategy to achieve the preventive goal—a firm can develop an instrumental merger option with another rival firm, which can help prevent the target merger in one of two ways. First, if the synergy of the instrumental merger is high relative to the synergy of the target merger, the firm can carry out the instrumental merger—even an unprofitable one—preemptively to make the target merger unprofitable. Second, if the synergy of the target merger is not too high and the synergy of the instrumental merger is moderate, that is, the instrumental merger is profitable and will inflict severe harm on the firms in the target merger, the firm can reserve the instrumental merger option as a deterrent and the profitable target merger is deterred. An interesting implication of the deterrent role of the instrumental merger option is that when the firm has two candidate merger partners, it may choose the partner with a lower synergy for the deterrence purpose.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89633586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julius Barth, SumsChi-Kwong Li, Hrayer Aprahamian, D. Gupta
Motivated by the COVID‐19 pandemic, we study how a public health authority may allocate vaccines from a limited stockpile to different jurisdictions over time. We propose an epidemiological model with time‐varying contact rates determined by a stylized behavioral feedback mechanism to reflect multi‐wave transmission dynamics. We evaluate the performance of various information‐sensitive allocation policies (e.g., allocation proportional to local incidence) as alternatives to the widely used pro‐rata policy. We also obtain optimized allocation strategies under the proposed epidemiological model with fairness and implementable freeze‐period constraints. For the case of a multi‐wave epidemic as represented by our compartmental model with behavioral feedback, we find that none of the alternative policies offers consistently more efficient allocations than a simple pro‐rata policy across a broad range of behavioral parameter settings. In fact, in some cases the alternative policies may actually result in less efficient allocations than the pro‐rata policy. Thus our results support the conclusion that the widely used pro‐rata policy can be well justified because it is simple to explain/implement and does not cause unexpected adverse effects. However, if policy makers are willing to invest in more tailored strategies based on numerical optimization, then the identified optimized strategies are a more favorable option as they allow for a more efficient allocation of vaccines.
{"title":"Spatiotemporal vaccine allocation policies for epidemics with behavioral feedback dynamics","authors":"Julius Barth, SumsChi-Kwong Li, Hrayer Aprahamian, D. Gupta","doi":"10.1002/nav.22142","DOIUrl":"https://doi.org/10.1002/nav.22142","url":null,"abstract":"Motivated by the COVID‐19 pandemic, we study how a public health authority may allocate vaccines from a limited stockpile to different jurisdictions over time. We propose an epidemiological model with time‐varying contact rates determined by a stylized behavioral feedback mechanism to reflect multi‐wave transmission dynamics. We evaluate the performance of various information‐sensitive allocation policies (e.g., allocation proportional to local incidence) as alternatives to the widely used pro‐rata policy. We also obtain optimized allocation strategies under the proposed epidemiological model with fairness and implementable freeze‐period constraints. For the case of a multi‐wave epidemic as represented by our compartmental model with behavioral feedback, we find that none of the alternative policies offers consistently more efficient allocations than a simple pro‐rata policy across a broad range of behavioral parameter settings. In fact, in some cases the alternative policies may actually result in less efficient allocations than the pro‐rata policy. Thus our results support the conclusion that the widely used pro‐rata policy can be well justified because it is simple to explain/implement and does not cause unexpected adverse effects. However, if policy makers are willing to invest in more tailored strategies based on numerical optimization, then the identified optimized strategies are a more favorable option as they allow for a more efficient allocation of vaccines.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89387575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For many developing countries, COVID‐19 vaccination roll‐out programs are not only slow but vaccination centers are also exposed to the risk of natural disaster, like flooding, which may slow down vaccination progress even further. Policy‐makers in developing countries therefore seek to implement strategies that hedge against distribution risk in order for vaccination campaigns to run smoothly and without delays. We propose a stochastic‐dynamic facility location model that allows policy‐makers to choose vaccination facilities while accounting for possible facility failure. The model is a multi‐stage stochastic variant of the classic facility location problem where disruption risk is modelled as a binary multivariate random process–a problem class that has not yet been studied in the extant literature. To solve the problem, we propose a novel approximate dynamic programming algorithm which trains the shadow price of opening a flood‐prone facility on historical data, thereby alleviating the need to fit a stochastic model. We trained the model using rainfall data provided by the local government of several major cities in the Philippines which are exposed to multiple flooding events per year. Numerical results demonstrate that the solution approach yields approximately 30%–40% lower cost than a baseline approach that does not consider the risk of flooding. Recommendations based on this model were implemented following a collaboration with two large cities in the Philippines which are exposed to multiple flooding events per year.
{"title":"Optimizing vaccine distribution in developing countries under natural disaster risk","authors":"Bonn Kleiford Seranilla, N. Löhndorf","doi":"10.1002/nav.22143","DOIUrl":"https://doi.org/10.1002/nav.22143","url":null,"abstract":"For many developing countries, COVID‐19 vaccination roll‐out programs are not only slow but vaccination centers are also exposed to the risk of natural disaster, like flooding, which may slow down vaccination progress even further. Policy‐makers in developing countries therefore seek to implement strategies that hedge against distribution risk in order for vaccination campaigns to run smoothly and without delays. We propose a stochastic‐dynamic facility location model that allows policy‐makers to choose vaccination facilities while accounting for possible facility failure. The model is a multi‐stage stochastic variant of the classic facility location problem where disruption risk is modelled as a binary multivariate random process–a problem class that has not yet been studied in the extant literature. To solve the problem, we propose a novel approximate dynamic programming algorithm which trains the shadow price of opening a flood‐prone facility on historical data, thereby alleviating the need to fit a stochastic model. We trained the model using rainfall data provided by the local government of several major cities in the Philippines which are exposed to multiple flooding events per year. Numerical results demonstrate that the solution approach yields approximately 30%–40% lower cost than a baseline approach that does not consider the risk of flooding. Recommendations based on this model were implemented following a collaboration with two large cities in the Philippines which are exposed to multiple flooding events per year.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79169660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Balasubramanian, Arulanantha Prabu Ponnachiyur Maruthasalam
Retailers often sell their own store brands that compete with the national brands. Store brand competition induces the supplier to quote lower wholesale prices for national brands. In multi‐period selling environments, retailers may complement this wholesale price reduction by carrying strategic inventories. Hence, store brand carrying retailers face the following dilemma. On the one hand, strong store brand competition might be sufficient to lower the national brand's wholesale price substantially. In this case, the retailer need not carry any strategic inventory. On the other hand, the wholesale price reduction induced solely by store brand competition might not be substantial. In this case, the retailer might want to carry either national brand or store brand strategic inventory to complement the wholesale price reduction. We address the above dilemma by investigating the effect of store brand competition on the strategic inventory decision using a two‐period game‐theoretic framework. We enhance the current understanding of the retailer's strategic inventory by incorporating the impact of store brand competition. Our analysis reveals that it is never optimal for the retailer to carry store brand as strategic inventory even when the store brand and the national brand are close substitutes. We also find that a store brand selling retailer may become worse off when being endowed with the option of carrying strategic inventory. However, when the holding cost is sufficiently low, we show that the strategic inventory and store brand competition complement each other and benefit the retailer.
{"title":"Impact of store brand competition on retailer's strategic inventory in decentralized supply chains","authors":"G. Balasubramanian, Arulanantha Prabu Ponnachiyur Maruthasalam","doi":"10.1002/nav.22144","DOIUrl":"https://doi.org/10.1002/nav.22144","url":null,"abstract":"Retailers often sell their own store brands that compete with the national brands. Store brand competition induces the supplier to quote lower wholesale prices for national brands. In multi‐period selling environments, retailers may complement this wholesale price reduction by carrying strategic inventories. Hence, store brand carrying retailers face the following dilemma. On the one hand, strong store brand competition might be sufficient to lower the national brand's wholesale price substantially. In this case, the retailer need not carry any strategic inventory. On the other hand, the wholesale price reduction induced solely by store brand competition might not be substantial. In this case, the retailer might want to carry either national brand or store brand strategic inventory to complement the wholesale price reduction. We address the above dilemma by investigating the effect of store brand competition on the strategic inventory decision using a two‐period game‐theoretic framework. We enhance the current understanding of the retailer's strategic inventory by incorporating the impact of store brand competition. Our analysis reveals that it is never optimal for the retailer to carry store brand as strategic inventory even when the store brand and the national brand are close substitutes. We also find that a store brand selling retailer may become worse off when being endowed with the option of carrying strategic inventory. However, when the holding cost is sufficiently low, we show that the strategic inventory and store brand competition complement each other and benefit the retailer.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83544850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Driessen, Joost de Kruijff, J. Arts, G. van Houtum
A line replaceable unit (LRU) is a collection of connected parts in a system that is replaced when any part of the LRU fails. Companies use LRUs as a mechanism to reduce downtime of systems following a failure. The design of LRUs determines how fast a replacement is performed, so a smart design reduces replacement and downtime cost. A firm must purchase/repair a LRU upon failure, and large LRUs are more expensive to purchase/repair. Hence, a firm seeks to design LRUs such that the average costs per time unit are minimized. We formalize this problem in a new model that captures how parts in a system are connected, and how they are disassembled from the system. Our model optimizes the design of LRUs such that the replacement (and downtime) costs and LRU purchase/repair costs are minimized. We present a set partitioning formulation for which we prove a rare result: the optimal solution is integer, despite a nonintegral feasible polyhedron. Second, we formulate our problem as a binary linear program (BLP). The article concludes by numerically comparing the computation times of both formulations and illustrates the effects of various parameters on the model's outcome.
线路可替换单元(line replaceable unit, LRU)是系统中连接部件的集合，当LRU中的任何一个部件出现故障时，该部件都可以被替换。公司使用lru作为一种机制来减少系统故障后的停机时间。lru的设计决定了更换的执行速度，因此智能设计可以减少更换和停机成本。当LRU出现故障时，企业必须购买/修理，而大型LRU的购买/修理成本更高。因此，公司寻求设计lru，使每时间单位的平均成本最小化。我们用一个新模型形式化了这个问题，该模型捕获了系统中的部件是如何连接的，以及它们是如何从系统中拆卸出来的。我们的模型优化了LRU的设计，使更换(和停机)成本和LRU购买/维修成本最小化。给出了一个集划分公式，并证明了一个罕见的结果:尽管存在非整可行多面体，但最优解是整数。其次，我们将问题表述为二元线性规划(BLP)。最后通过数值比较两种公式的计算时间，并说明了各种参数对模型结果的影响。
{"title":"Optimal design of line replaceable units","authors":"J. Driessen, Joost de Kruijff, J. Arts, G. van Houtum","doi":"10.1002/nav.22146","DOIUrl":"https://doi.org/10.1002/nav.22146","url":null,"abstract":"A line replaceable unit (LRU) is a collection of connected parts in a system that is replaced when any part of the LRU fails. Companies use LRUs as a mechanism to reduce downtime of systems following a failure. The design of LRUs determines how fast a replacement is performed, so a smart design reduces replacement and downtime cost. A firm must purchase/repair a LRU upon failure, and large LRUs are more expensive to purchase/repair. Hence, a firm seeks to design LRUs such that the average costs per time unit are minimized. We formalize this problem in a new model that captures how parts in a system are connected, and how they are disassembled from the system. Our model optimizes the design of LRUs such that the replacement (and downtime) costs and LRU purchase/repair costs are minimized. We present a set partitioning formulation for which we prove a rare result: the optimal solution is integer, despite a nonintegral feasible polyhedron. Second, we formulate our problem as a binary linear program (BLP). The article concludes by numerically comparing the computation times of both formulations and illustrates the effects of various parameters on the model's outcome.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87951099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the problem of designing optimal targeted mass screening of non‐uniform populations. Mass screening is an essential tool that is widely utilized in a variety of settings, for example, preventing infertility through screening programs for sexually transmitted diseases, ensuring a safe blood supply for transfusion, and mitigating the transmission of infectious diseases. The objective of mass screening is to maximize the overall classification accuracy under limited budget. In this paper, we address this problem by proposing a proactive optimization‐based framework that factors in population heterogeneity, limited budget, different testing schemes, the availability of multiple assays, and imperfect assays. By analyzing the resulting optimization problem, we take advantage of the structure of the problem as a multi‐dimensional fractional knapsack problem and identify an efficient globally convergent threshold‐style solution scheme that fully characterizes an optimal solution across the entire budget spectrum. Using real‐world data, we conduct a geographic‐based nationwide case study on targeted COVID‐19 screening in the United States. Our results reveal that the identified screening strategies substantially outperform conventional practices by significantly lowering misclassifications while utilizing the same amount of budget. Moreover, our results provide valuable managerial insights with regard to the distribution of testing schemes, assays, and budget across different geographic regions.
{"title":"Optimal targeted mass screening in non‐uniform populations with multiple tests and schemes","authors":"Jiayi Lin, Hrayer Aprahamian, G. Golovko","doi":"10.1002/nav.22141","DOIUrl":"https://doi.org/10.1002/nav.22141","url":null,"abstract":"We study the problem of designing optimal targeted mass screening of non‐uniform populations. Mass screening is an essential tool that is widely utilized in a variety of settings, for example, preventing infertility through screening programs for sexually transmitted diseases, ensuring a safe blood supply for transfusion, and mitigating the transmission of infectious diseases. The objective of mass screening is to maximize the overall classification accuracy under limited budget. In this paper, we address this problem by proposing a proactive optimization‐based framework that factors in population heterogeneity, limited budget, different testing schemes, the availability of multiple assays, and imperfect assays. By analyzing the resulting optimization problem, we take advantage of the structure of the problem as a multi‐dimensional fractional knapsack problem and identify an efficient globally convergent threshold‐style solution scheme that fully characterizes an optimal solution across the entire budget spectrum. Using real‐world data, we conduct a geographic‐based nationwide case study on targeted COVID‐19 screening in the United States. Our results reveal that the identified screening strategies substantially outperform conventional practices by significantly lowering misclassifications while utilizing the same amount of budget. Moreover, our results provide valuable managerial insights with regard to the distribution of testing schemes, assays, and budget across different geographic regions.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78931270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seru production systems are an effective way to respond to ever‐changing market demand. This article focuses on maximizing the throughput of rotating serus with nonpreemptive stations, where a worker's operations cannot be disrupted. We analyze the effects of unbalanced worker velocities on non‐value‐added idle times. Through the use of dynamical system theories, we explicate the mechanism and dynamics of rotating serus, and identify the rules used to coordinate workers and distribute work content among stations to achieve the highest throughput. These findings provide practical guidelines for managers in floor shops for optimizing rotating seru design and maximizing throughput. Additionally, we explore the chaotic characteristics of rotating serus and simulate the effect of various factors on throughput. Finally, our comparative analysis demonstrates that the rotating seru offers a viable alternative to existing production systems to adapt to fluctuating demand.
{"title":"Maximizing the throughput of a rotating Seru with nonpreemptive discrete stations","authors":"Yingkun Gai, Yong Yin, Dongni Li, Yaoxin Zhang, Jiafu Tang","doi":"10.1002/nav.22140","DOIUrl":"https://doi.org/10.1002/nav.22140","url":null,"abstract":"Seru production systems are an effective way to respond to ever‐changing market demand. This article focuses on maximizing the throughput of rotating serus with nonpreemptive stations, where a worker's operations cannot be disrupted. We analyze the effects of unbalanced worker velocities on non‐value‐added idle times. Through the use of dynamical system theories, we explicate the mechanism and dynamics of rotating serus, and identify the rules used to coordinate workers and distribute work content among stations to achieve the highest throughput. These findings provide practical guidelines for managers in floor shops for optimizing rotating seru design and maximizing throughput. Additionally, we explore the chaotic characteristics of rotating serus and simulate the effect of various factors on throughput. Finally, our comparative analysis demonstrates that the rotating seru offers a viable alternative to existing production systems to adapt to fluctuating demand.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75976366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of e‐commerce, both e‐tailers and manufacturers have actively cooperated with third‐party platforms to expand distribution channels and enhance competitiveness. There are three typical choices for firms to cooperate with a third‐party platform: (i) non‐cooperation, (ii) agency selling cooperation mode, and (iii) reselling cooperation mode. We consider a three‐tier supply chain and establish a stylized theoretical model to explore whether and how the e‐tailer or the manufacturer cooperates with a third‐party platform. We investigate the optimal channel cooperation choices from both the e‐tailer's and the manufacturer's perspectives under various channel structures and cooperation modes, revealing their different roles in cooperating with third‐party platforms. Our results indicate that large potential market size of the third‐party platform motivates the e‐tailer to cooperate with the third‐party platform. On this basis, agency selling cooperation mode is preferred by the e‐tailer at mild channel competition and reasonable commission rate, while reselling cooperation mode dominates at high channel competition or when the commission rate exceeds a threshold. Interestingly, we find that the manufacturer has generally similar mode preference with the e‐tailer, and they have opposite preferences under the condition of low commission rate and moderate channel competition intensity. More importantly, our results reveal that direct channel cooperation between the manufacturer and third‐party platform harms the e‐tailer's interests, whereas the channel cooperation between the e‐tailer and third‐party platform may increase profits for all supply chain participants. Our study provides valuable insights for e‐tailers, manufacturers and third‐party platforms to make better channel cooperation decisions and achieve successful partnership in online retailing.
{"title":"Cooperation strategies with third‐party platform: E‐tailer and manufacturer perspectives","authors":"Rui Mao, Hongqiao Chen, Houcai Shen","doi":"10.1002/nav.22136","DOIUrl":"https://doi.org/10.1002/nav.22136","url":null,"abstract":"With the rapid development of e‐commerce, both e‐tailers and manufacturers have actively cooperated with third‐party platforms to expand distribution channels and enhance competitiveness. There are three typical choices for firms to cooperate with a third‐party platform: (i) non‐cooperation, (ii) agency selling cooperation mode, and (iii) reselling cooperation mode. We consider a three‐tier supply chain and establish a stylized theoretical model to explore whether and how the e‐tailer or the manufacturer cooperates with a third‐party platform. We investigate the optimal channel cooperation choices from both the e‐tailer's and the manufacturer's perspectives under various channel structures and cooperation modes, revealing their different roles in cooperating with third‐party platforms. Our results indicate that large potential market size of the third‐party platform motivates the e‐tailer to cooperate with the third‐party platform. On this basis, agency selling cooperation mode is preferred by the e‐tailer at mild channel competition and reasonable commission rate, while reselling cooperation mode dominates at high channel competition or when the commission rate exceeds a threshold. Interestingly, we find that the manufacturer has generally similar mode preference with the e‐tailer, and they have opposite preferences under the condition of low commission rate and moderate channel competition intensity. More importantly, our results reveal that direct channel cooperation between the manufacturer and third‐party platform harms the e‐tailer's interests, whereas the channel cooperation between the e‐tailer and third‐party platform may increase profits for all supply chain participants. Our study provides valuable insights for e‐tailers, manufacturers and third‐party platforms to make better channel cooperation decisions and achieve successful partnership in online retailing.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89945443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many regulations have been enacted to prevent the multinational firm's (MNF's) tax avoidance and cause the enforcement cost of incoming shifting. This paper investigates the impact of the enforcement cost on a firm's tax‐efficient supply chain allocation strategy, wherein the firm can either create a research and development (R&D) center that innovates the intangible assets or create a distributor that acts as a marketing hub, in a low tax region to explore tax arbitrage. We show that when the firm engages in market competition and the impact of the enforcement cost is low, it prefers to create a distributor in the low tax region to align the benefits of tax saving and internal coordination. While if the impact of the enforcement cost is high, the firm prefers R&D center in the low tax region that can effectively mitigate the enforcement cost and achieve tax saving. When the market competition becomes more intense, the firm becomes more likely to choose R&D center in the low tax region to alleviate market competition. In this scenario, the social welfare is always higher when the firm allocates distributor in the low tax region. When an external supplier exists, the firm is still more likely to choose R&D center in the low tax region to reduce the supplier's wholesale price. What's more, in the presence of external supplier, the social welfare can be higher under either allocation format.
{"title":"Tax‐efficient supply chain allocation in a competitive environment","authors":"Yuan Jiang, Xu Guan, Ying‐ju Chen, Yiwen Bian","doi":"10.1002/nav.22139","DOIUrl":"https://doi.org/10.1002/nav.22139","url":null,"abstract":"Many regulations have been enacted to prevent the multinational firm's (MNF's) tax avoidance and cause the enforcement cost of incoming shifting. This paper investigates the impact of the enforcement cost on a firm's tax‐efficient supply chain allocation strategy, wherein the firm can either create a research and development (R&D) center that innovates the intangible assets or create a distributor that acts as a marketing hub, in a low tax region to explore tax arbitrage. We show that when the firm engages in market competition and the impact of the enforcement cost is low, it prefers to create a distributor in the low tax region to align the benefits of tax saving and internal coordination. While if the impact of the enforcement cost is high, the firm prefers R&D center in the low tax region that can effectively mitigate the enforcement cost and achieve tax saving. When the market competition becomes more intense, the firm becomes more likely to choose R&D center in the low tax region to alleviate market competition. In this scenario, the social welfare is always higher when the firm allocates distributor in the low tax region. When an external supplier exists, the firm is still more likely to choose R&D center in the low tax region to reduce the supplier's wholesale price. What's more, in the presence of external supplier, the social welfare can be higher under either allocation format.","PeriodicalId":19120,"journal":{"name":"Naval Research Logistics (NRL)","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86452036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nils Boysen, D. Briskorn, David Füßler, Konrad Stephan
Due to high real estate costs in urban areas, shop floor space is scarce in most brick‐and‐mortar stores. Maneuvering newly arrived merchandise through narrow aisles during shelf replenishment is time‐consuming for the sales staff and impedes customers. Therefore, many retail chains nowadays aim for store‐friendly shipments (SFS). By mirroring the layout of a store in the buildup of its dedicated shipments, the need for a zigzag movement through the store when replenishing shelves can be avoided. On the negative side, however, additional effort arises in the distribution centers. A suitable warehousing system to assemble SFS without excessive effort is a pocket (or pouch or bag) sorter, where each item is put into its separate bag. These bags, filled with items, are automatically transported while hanging from an overhead conveyor and can be sorted into any sequence before being delivered to the workstations that build SFS. This article investigates the assembly of SFS with a pocket sorter and presents scheduling procedures to enhance the efficiency of this process for a given set of store orders. We demonstrate that, despite its notorious complexity, the problem can be solved by simple decision rules with good performance. In a case study, we show that this approach can dramatically reduce the completion times of store orders, resulting in savings of more than 60% of the total working hours compared to a simple real‐world policy. Another 30% of reduction can be obtained by standardized store layouts.
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