This paper determines two groups of contracts -- downside-protection and upside-protection contracts -- that facilitate vertical information sharing in a supply chain while precluding horizontal information leakage among competing newsvendors. In particular, we consider a supply chain in which retailers replenish inventory from a common supplier to satisfy uncertain demand and are engaged in newsvendor competition. Each retailer has imperfect demand information. Yet, one of the retailers (the incumbent) has a more accurate demand forecast than the other (the entrant). Substantial anecdotal evidence and academic research have shown that information leakage among such competing retailers precludes vertical information sharing and is a reason for retailers to abandon collaborative forecast-sharing initiatives, leading to sub-optimized supply chains. We show that whether a contract can prevent information leakage is due only to how the supply chain's expected supply-demand mismatch costs are distributed among the supplier and retailers, but does not depend on each party's expected revenue. In addition, we show that wholesale-price contracts and two-part tariff contracts, which are extensively used in practice, cannot prevent information leakage. By using the wholesale-price contract as a benchmark contract, we determine that downside-protection contracts and upside-protection contracts can prevent information leakage. We define a downside-protection contract as one that effectively reduces retailers' cost of excess inventory by shifting some of their overage cost to the supplier. Examples of such contracts include buy-back and revenue-sharing contracts. We define an upside-protection contract as one that effectively increases retailers' cost of inventory shortage by shifting some of the supplier's underage cost to retailers. Examples of such contracts include penalty and rebate contracts. We show that these two groups of contracts can prevent information leakage. We identify necessary and sufficient nonleakage conditions and mechanisms for each group to prevent information leakage in the supply chain.
{"title":"Supply Chain Contracts that Prevent Information Leakage","authors":"Yiwei Chen, Ö. Özer","doi":"10.2139/ssrn.2728017","DOIUrl":"https://doi.org/10.2139/ssrn.2728017","url":null,"abstract":"This paper determines two groups of contracts -- downside-protection and upside-protection contracts -- that facilitate vertical information sharing in a supply chain while precluding horizontal information leakage among competing newsvendors. In particular, we consider a supply chain in which retailers replenish inventory from a common supplier to satisfy uncertain demand and are engaged in newsvendor competition. Each retailer has imperfect demand information. Yet, one of the retailers (the incumbent) has a more accurate demand forecast than the other (the entrant). Substantial anecdotal evidence and academic research have shown that information leakage among such competing retailers precludes vertical information sharing and is a reason for retailers to abandon collaborative forecast-sharing initiatives, leading to sub-optimized supply chains. We show that whether a contract can prevent information leakage is due only to how the supply chain's expected supply-demand mismatch costs are distributed among the supplier and retailers, but does not depend on each party's expected revenue. In addition, we show that wholesale-price contracts and two-part tariff contracts, which are extensively used in practice, cannot prevent information leakage. By using the wholesale-price contract as a benchmark contract, we determine that downside-protection contracts and upside-protection contracts can prevent information leakage. We define a downside-protection contract as one that effectively reduces retailers' cost of excess inventory by shifting some of their overage cost to the supplier. Examples of such contracts include buy-back and revenue-sharing contracts. We define an upside-protection contract as one that effectively increases retailers' cost of inventory shortage by shifting some of the supplier's underage cost to retailers. Examples of such contracts include penalty and rebate contracts. We show that these two groups of contracts can prevent information leakage. We identify necessary and sufficient nonleakage conditions and mechanisms for each group to prevent information leakage in the supply chain.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77240340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our research focuses on the storage decision in a semi-automated storage system. In a semi-automated storage system, the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. The storage pods are mobile in that a pod can be lifted and transported by a robotic drive. These storage pods are stored within a storage zone that has stationary stations for picking and stowing on its boundary. The robotic drives transport the pods to these stations at which operators conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time, and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. With this model, we can characterize the possible improvement from applying a velocity-based storage policy in comparison to the random storage policy that returns the pod to a randomly-chosen storage location. Within the category of velocity-based storage, we show that class-based storage with two or three classes can achieve most of the benefits from full-velocity storage. We show that the benefits from velocity-based storage increase with greater variation in the pod velocities. To validate the fluid model we build a discrete-time simulator with real industry data. We observe an 8% to 11% reduction in the travel distance with 2-class or 3-class storage system, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization levels and space utilization levels.
{"title":"Velocity-Based Storage Assignment in Semi-Automated Storage Systems","authors":"Rong Yuan, S. Graves, Tolga Çezik","doi":"10.2139/ssrn.2889354","DOIUrl":"https://doi.org/10.2139/ssrn.2889354","url":null,"abstract":"Our research focuses on the storage decision in a semi-automated storage system. In a semi-automated storage system, the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. The storage pods are mobile in that a pod can be lifted and transported by a robotic drive. These storage pods are stored within a storage zone that has stationary stations for picking and stowing on its boundary. The robotic drives transport the pods to these stations at which operators conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time, and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. With this model, we can characterize the possible improvement from applying a velocity-based storage policy in comparison to the random storage policy that returns the pod to a randomly-chosen storage location. Within the category of velocity-based storage, we show that class-based storage with two or three classes can achieve most of the benefits from full-velocity storage. We show that the benefits from velocity-based storage increase with greater variation in the pod velocities. To validate the fluid model we build a discrete-time simulator with real industry data. We observe an 8% to 11% reduction in the travel distance with 2-class or 3-class storage system, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization levels and space utilization levels.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"21 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73965843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Freight Forwarding and Logistics industry plays a vital role in the overall economy of Pakistan. The purpose of this study is to determine the factors that influence for outsourcing the logistics and at the same time performance of outsourcing has been evaluated with respect to Karachi, Pakistan. Three factors identified as Low Human Assets, Low Physical Assets and Partnership, all have a positive impact on logistics outsourcing and the firm performance especially in terms of Financial Benefit. Online survey was conducted and the data was obtained from a sample size of 165 from the seniority level of the manufacturing industry in Karachi. We measure the inner model by Smart PLS software to indicate the reliability of all factors that significant impact on logistics outsourcing practices. Reliability test was also performed which showed that our study is significant.
{"title":"Factors Influencing Logistics Outsourcing in Pakistan","authors":"Kanwar Uzair, D. Siddiqui","doi":"10.18034/ABR.V8I1.3","DOIUrl":"https://doi.org/10.18034/ABR.V8I1.3","url":null,"abstract":"The Freight Forwarding and Logistics industry plays a vital role in the overall economy of Pakistan. The purpose of this study is to determine the factors that influence for outsourcing the logistics and at the same time performance of outsourcing has been evaluated with respect to Karachi, Pakistan. Three factors identified as Low Human Assets, Low Physical Assets and Partnership, all have a positive impact on logistics outsourcing and the firm performance especially in terms of Financial Benefit. Online survey was conducted and the data was obtained from a sample size of 165 from the seniority level of the manufacturing industry in Karachi. We measure the inner model by Smart PLS software to indicate the reliability of all factors that significant impact on logistics outsourcing practices. Reliability test was also performed which showed that our study is significant.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"2 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76206690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manufacturers using product platforms target economies of scale and scope. However, they face the challenge of a reduced ability to react to a dynamic environment, because a platform provides the technological basis for all assigned products. When manufacturers launch new platforms, the timing is crucial. Technological innovations that have been successfully developed prior to the platform launch can be incorporated into the platform. A higher level of innovation increases the revenues. After the platform launch, the individual product development can be initiated. A shorter development time increases the development costs. As a consequence, manufacturers have to trade-off increased revenues due to a higher level of innovation against increased development costs due to expedited product development. Our paper introduces a stylized optimization model for this dynamic platform planning problem based on a stochastic process for capturing the uncertainty of innovations. We find that the optimal policy for the platform launch is a time-dependent threshold of the observed innovation level. Based on a case from the automotive industry, we find that manufacturers can suffer a loss of flexibility that significantly reduces platform benefits. It even outweighs the economies of scale. Hence, manufacturers have to weigh properly the trade-offs to ensure that the combined effect of scale and scope economies does lead to a positive impact of platforms. In particular, during the product-to-platform assignment, manufacturers need to consider the loss of flexibility, which depends on the product introduction schedule and the product heterogeneity of the products assigned to a platform.
{"title":"Balancing Benefits and Flexibility Losses in Platform Planning","authors":"P. Jana, S. Graves, M. Grunow","doi":"10.2139/ssrn.3134037","DOIUrl":"https://doi.org/10.2139/ssrn.3134037","url":null,"abstract":"Manufacturers using product platforms target economies of scale and scope. However, they face the challenge of a reduced ability to react to a dynamic environment, because a platform provides the technological basis for all assigned products. When manufacturers launch new platforms, the timing is crucial. Technological innovations that have been successfully developed prior to the platform launch can be incorporated into the platform. A higher level of innovation increases the revenues. After the platform launch, the individual product development can be initiated. A shorter development time increases the development costs. As a consequence, manufacturers have to trade-off increased revenues due to a higher level of innovation against increased development costs due to expedited product development. Our paper introduces a stylized optimization model for this dynamic platform planning problem based on a stochastic process for capturing the uncertainty of innovations. We find that the optimal policy for the platform launch is a time-dependent threshold of the observed innovation level. Based on a case from the automotive industry, we find that manufacturers can suffer a loss of flexibility that significantly reduces platform benefits. It even outweighs the economies of scale. Hence, manufacturers have to weigh properly the trade-offs to ensure that the combined effect of scale and scope economies does lead to a positive impact of platforms. In particular, during the product-to-platform assignment, manufacturers need to consider the loss of flexibility, which depends on the product introduction schedule and the product heterogeneity of the products assigned to a platform.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"159 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2018-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76097435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Disruptions that temporarily interrupt production pose a significant risk for manufacturing firms. To manage this risk, firms can purchase interruption insurance and/or deploy operational measures such as storing inventory or taking preparedness actions that reduce the expected interruption length. In this study, we explore inventory, preparedness, and insurance in a two‐stage production chain that can experience disruptions at either the upstream or downstream stage. We analytically characterize an inventory‐only model and a preparedness‐only model in which the firm uses either inventory or preparedness effort to manage disruption risk, and a joint model in which the firm deploys both operational measures. We identify the relationships between the two operational measures within a stage and across the two stages. We also examine how insurance affects a firm's optimal deployment of and preference between the two operational measures. In addition to providing insights into the interaction of these three risk management measures, our results provide insights into the production chain design. For example, the firm can reduce its disruption risk management cost by allocating more production activity downstream (when possible) and this risk management benefit can, at times, outweigh the possible production cost increase associated with allocating more production downstream.
{"title":"Production Chain Disruptions: Inventory, Preparedness, and Insurance","authors":"Lingxiu Dong, S. Tang, Brian Tomlin","doi":"10.2139/ssrn.2906431","DOIUrl":"https://doi.org/10.2139/ssrn.2906431","url":null,"abstract":"Disruptions that temporarily interrupt production pose a significant risk for manufacturing firms. To manage this risk, firms can purchase interruption insurance and/or deploy operational measures such as storing inventory or taking preparedness actions that reduce the expected interruption length. In this study, we explore inventory, preparedness, and insurance in a two‐stage production chain that can experience disruptions at either the upstream or downstream stage. We analytically characterize an inventory‐only model and a preparedness‐only model in which the firm uses either inventory or preparedness effort to manage disruption risk, and a joint model in which the firm deploys both operational measures. We identify the relationships between the two operational measures within a stage and across the two stages. We also examine how insurance affects a firm's optimal deployment of and preference between the two operational measures. In addition to providing insights into the interaction of these three risk management measures, our results provide insights into the production chain design. For example, the firm can reduce its disruption risk management cost by allocating more production activity downstream (when possible) and this risk management benefit can, at times, outweigh the possible production cost increase associated with allocating more production downstream.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"10 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2018-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85628977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper aims to assess the impact of determinants on service exports in both value added terms and in gross terms for seven Central Eastern European economies in years 1995-2011. The results confirm the importance of increasing labour productivity and highly-skilled and medium-skilled workers for growth in services trade. Exports of services are also supported by linkages between domestic services, especially business services, and the manufacturing sector. The results show the impacts of the determinants are fairly similar when exports are measured in value added terms or in gross terms, however the strength of impact differs in some subgroup of analysed countries.
{"title":"CEE Trade in Services: Value Added versus Gross Terms Approaches","authors":"Aleksandra Kordalska, M. Olczyk","doi":"10.2139/ssrn.3148775","DOIUrl":"https://doi.org/10.2139/ssrn.3148775","url":null,"abstract":"This paper aims to assess the impact of determinants on service exports in both value added terms and in gross terms for seven Central Eastern European economies in years 1995-2011. The results confirm the importance of increasing labour productivity and highly-skilled and medium-skilled workers for growth in services trade. Exports of services are also supported by linkages between domestic services, especially business services, and the manufacturing sector. The results show the impacts of the determinants are fairly similar when exports are measured in value added terms or in gross terms, however the strength of impact differs in some subgroup of analysed countries.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"4 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91088109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the impact of observational learning in large scale congested service systems with servers having heterogenous quality levels and customers that are heterogonously informed about the server quality. Providing congestion information to all customers allows them to avoid congested servers, but, also implies that less informed customers learn about the quality from observing the choices of other customers. Due to an exponentially growing state space in the number of servers, identifying Bayesian equilibria is intractable with a large, discrete number of servers. In this paper, we develop a tractable model with a continuum of servers. We find that the impact of observational learning on the customers' choice behavior may lead to severe "imbalance" of server load in the system, such that a decentralized system significantly under-performs in terms of the social welfare, compared with a centralized system. The decentralized system performs well only when (a) either the congestion costs are high and there are sufficient informed customers, or (b) when the congestion costs are medium or low and the aggregate capacity of high-quality servers matches the aggregate demand of informed customers. We also find situations in which making more customers informed about service quality leads to a decrease in social welfare. Our paper highlights the tension between observational learning and social welfare maximization and thus observational learning in large-scale service systems might require intervention of the platform manager.
{"title":"Observational Learning in Large-Scale Congested Service Systems","authors":"Chen Jin, L. Debo, S. Iravani","doi":"10.2139/ssrn.3054728","DOIUrl":"https://doi.org/10.2139/ssrn.3054728","url":null,"abstract":"We study the impact of observational learning in large scale congested service systems with servers having heterogenous quality levels and customers that are heterogonously informed about the server quality. Providing congestion information to all customers allows them to avoid congested servers, but, also implies that less informed customers learn about the quality from observing the choices of other customers. Due to an exponentially growing state space in the number of servers, identifying Bayesian equilibria is intractable with a large, discrete number of servers. In this paper, we develop a tractable model with a continuum of servers. We find that the impact of observational learning on the customers' choice behavior may lead to severe \"imbalance\" of server load in the system, such that a decentralized system significantly under-performs in terms of the social welfare, compared with a centralized system. The decentralized system performs well only when (a) either the congestion costs are high and there are sufficient informed customers, or (b) when the congestion costs are medium or low and the aggregate capacity of high-quality servers matches the aggregate demand of informed customers. We also find situations in which making more customers informed about service quality leads to a decrease in social welfare. Our paper highlights the tension between observational learning and social welfare maximization and thus observational learning in large-scale service systems might require intervention of the platform manager.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"19 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2017-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87856947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract from the main paper: One standard assumption in workforce management is that the firm can dictate to workers when to show up to work. However, that assumption is challenged in modern business environments, such as those arising in the sharing economy, where workers enjoy various degrees of flexibility, including the right to decide when to work. For example, a ride-sharing service cannot impose on its drivers to be on the road at specific times; similarly, a virtual call-center manager cannot direct her agents to be available for select shifts. When self-scheduling is allowed, the number of workers available in any time period is uncertain. In this chapter, we are concerned with the effective management of service systems where capacity, i.e., the number of available agents, is random. We rely on a queueing-theoretic framework, because customers are time-sensitive and delays are ubiquitous in the services industry, and focus on the performance analysis and control of a queueing system with a random number of servers. In particular, we begin by surveying some theoretical results on the control of queueing systems with uncertainty in parameters (here, the number of servers). Then, we illustrate how to apply those theoretical results to study the problems of staffing and controlling queueing systems with self-scheduling servers and impatient, time-sensitive, customers.
{"title":"On Queues with a Random Capacity: Some Theory, and An Application (Technical Appendix)","authors":"Rouba Ibrahim","doi":"10.2139/ssrn.3034807","DOIUrl":"https://doi.org/10.2139/ssrn.3034807","url":null,"abstract":"Abstract from the main paper: One standard assumption in workforce management is that the firm can dictate to workers when to show up to work. However, that assumption is challenged in modern business environments, such as those arising in the sharing economy, where workers enjoy various degrees of flexibility, including the right to decide when to work. For example, a ride-sharing service cannot impose on its drivers to be on the road at specific times; similarly, a virtual call-center manager cannot direct her agents to be available for select shifts. When self-scheduling is allowed, the number of workers available in any time period is uncertain. In this chapter, we are concerned with the effective management of service systems where capacity, i.e., the number of available agents, is random. We rely on a queueing-theoretic framework, because customers are time-sensitive and delays are ubiquitous in the services industry, and focus on the performance analysis and control of a queueing system with a random number of servers. In particular, we begin by surveying some theoretical results on the control of queueing systems with uncertainty in parameters (here, the number of servers). Then, we illustrate how to apply those theoretical results to study the problems of staffing and controlling queueing systems with self-scheduling servers and impatient, time-sensitive, customers.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"172 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2017-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72774521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study a supply chain of durable good product consisted of a retailer with demand forecast information and a manufacturer. The retailer's demand information is about the popularity of the product, which can help the supply chain members to better price the product. We examine how the retailer's policy to share his forecasts with the manufacturer depends on the durability and the production cost of the product. Academic/Practical relevance: In a supply chain, the retailer usually has better demand forecasts than the manufacturer. The demand forecast of the retailer can help the supply chain in many contexts including optimal pricing of a new product. Consequently, in supply chain management, demand information sharing has received substantial attention. This paper studies how the retailer's forecast sharing decision depends on "durability" of the product. Methodology: We employ an analytical microeconomic model for our analysis. Results: We show that when there is medium dispersion in the popularity of the product, the durability of the product has a large impact on the optimal information sharing policy of the retailer. In such cases, the retailer should share his information with the manufacturer when (i) the production cost is large and the product is not very durable, or (ii) when the production cost is small and the product is very durable, or (iii) the production cost is intermediate and the durability of the product is intermediate. Managerial implications: The previous literature analyzed forecasting sharing without durability. We demonstrate the importance of durability on forecasting sharing which impacts the supply chain performance. The insights from the paper helps the retail practitioners' to decide in which context they should share their demand information with their upstream partners, and guides the upstream firms to know when to consider working with the retailers to elicit their forecast information.
{"title":"Demand Information Sharing in a Supply Chain of Durable Goods with Pricing Decisions","authors":"N. Khanjari, S. Iravani, Hyoduk Shin","doi":"10.2139/ssrn.3032748","DOIUrl":"https://doi.org/10.2139/ssrn.3032748","url":null,"abstract":"Problem definition: We study a supply chain of durable good product consisted of a retailer with demand forecast information and a manufacturer. The retailer's demand information is about the popularity of the product, which can help the supply chain members to better price the product. We examine how the retailer's policy to share his forecasts with the manufacturer depends on the durability and the production cost of the product. \u0000Academic/Practical relevance: In a supply chain, the retailer usually has better demand forecasts than the manufacturer. The demand forecast of the retailer can help the supply chain in many contexts including optimal pricing of a new product. Consequently, in supply chain management, demand information sharing has received substantial attention. This paper studies how the retailer's forecast sharing decision depends on \"durability\" of the product. \u0000Methodology: We employ an analytical microeconomic model for our analysis. Results: We show that when there is medium dispersion in the popularity of the product, the durability of the product has a large impact on the optimal information sharing policy of the retailer. In such cases, the retailer should share his information with the manufacturer when (i) the production cost is large and the product is not very durable, or (ii) when the production cost is small and the product is very durable, or (iii) the production cost is intermediate and the durability of the product is intermediate. Managerial implications: The previous literature analyzed forecasting sharing without durability. We demonstrate the importance of durability on forecasting sharing which impacts the supply chain performance. The insights from the paper helps the retail practitioners' to decide in which context they should share their demand information with their upstream partners, and guides the upstream firms to know when to consider working with the retailers to elicit their forecast information.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2017-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84847876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a dynamic storable good market where demand changes over time, we investigate the producer's strategic incentives to hold inventories in response to the possibility of buyer stockpiling. The literature on storable goods has demonstrated that buyer stockpiling in anticipation of higher future prices harms the producer's profitability, particularly when the producer cannot commit to future prices. We show that the producer's inventories act as a strategic device to mitigate the loss from the lack of commitment. Our results provide a rationale for the producer's inventory behavior that sheds new light on the well-documented empirical evidence about inventories.
{"title":"Strategic Inventories under Limited Commitment","authors":"Fabio Antoniou, Raffaele Fiocco","doi":"10.2139/ssrn.3014388","DOIUrl":"https://doi.org/10.2139/ssrn.3014388","url":null,"abstract":"In a dynamic storable good market where demand changes over time, we investigate the producer's strategic incentives to hold inventories in response to the possibility of buyer stockpiling. The literature on storable goods has demonstrated that buyer stockpiling in anticipation of higher future prices harms the producer's profitability, particularly when the producer cannot commit to future prices. We show that the producer's inventories act as a strategic device to mitigate the loss from the lack of commitment. Our results provide a rationale for the producer's inventory behavior that sheds new light on the well-documented empirical evidence about inventories.","PeriodicalId":49886,"journal":{"name":"Manufacturing Engineering","volume":"520 ","pages":""},"PeriodicalIF":0.3,"publicationDate":"2017-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2139/ssrn.3014388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72418969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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