As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed pr...As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value t^om big data.展开更多
This paper aim is to examine the optimal pricing and return policies for false failure returns in a dual-channel supply chain. Four prevailing return policies in which a manufacturer both operates an E-shop and sells ...This paper aim is to examine the optimal pricing and return policies for false failure returns in a dual-channel supply chain. Four prevailing return policies in which a manufacturer both operates an E-shop and sells its product through a brick-and-mortar retailer are analyzed, i.e. （I） the manufacturer handlings E-shop＇s returns, while the retailer addresses brick-and-mortar store＇s returns （NR）; （II） the retailer tackles the whole （both E-shop＇s and brick-and-mortar store＇s） returns （ORR）; （III） the manufacturer tackles the whole returns （ORM）; and （IV） the manufacturer and the retailer are jointly responsible for the whole returns （RRM）. Firstly, the optimal pricing and return policies comparing these four scenarios under uniform-pricing strategy are presented. Our conclusions show that the ORR is an optimal return policy. Compared with the NR, consumers will get a lower product pricing under the ORR and a higher product pricing under the ORM. With regard to the RRM, the product pricing is depended on consumer preference, return-rates of the E-shop and the brick-and-mortar store. Then, the optimal pricing and return policies are analyzed under differential-pricing strategy by conducting two-stage sequential games between the manufacturer and the retailer. The findings show that if consumers in the market prefer to purchase via the E-shop, the ORR is an optimal return policy. Otherwise, the NR is the optimal return policy. Compared with the NR, the ORR retailer＇s product pricing will rely on the retailer＇s and the manufacturer＇s return-costs; the RRM retailer＇s product pricing will depend on the return-costs of the retailer and the manufacturer, the return-rates of the E-shop and the brick-and-mortar store and so on. Finally, the influences of the manufacturer and the retailer establishing a Buy-back contract are discussed. Our results illustrated that the Buy-back contract doesn＇t affect optimal pricing and return policies under both the uniform and the differential pricing strategies.展开更多
文摘As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value t^om big data.
文摘This paper aim is to examine the optimal pricing and return policies for false failure returns in a dual-channel supply chain. Four prevailing return policies in which a manufacturer both operates an E-shop and sells its product through a brick-and-mortar retailer are analyzed, i.e. （I） the manufacturer handlings E-shop＇s returns, while the retailer addresses brick-and-mortar store＇s returns （NR）; （II） the retailer tackles the whole （both E-shop＇s and brick-and-mortar store＇s） returns （ORR）; （III） the manufacturer tackles the whole returns （ORM）; and （IV） the manufacturer and the retailer are jointly responsible for the whole returns （RRM）. Firstly, the optimal pricing and return policies comparing these four scenarios under uniform-pricing strategy are presented. Our conclusions show that the ORR is an optimal return policy. Compared with the NR, consumers will get a lower product pricing under the ORR and a higher product pricing under the ORM. With regard to the RRM, the product pricing is depended on consumer preference, return-rates of the E-shop and the brick-and-mortar store. Then, the optimal pricing and return policies are analyzed under differential-pricing strategy by conducting two-stage sequential games between the manufacturer and the retailer. The findings show that if consumers in the market prefer to purchase via the E-shop, the ORR is an optimal return policy. Otherwise, the NR is the optimal return policy. Compared with the NR, the ORR retailer＇s product pricing will rely on the retailer＇s and the manufacturer＇s return-costs; the RRM retailer＇s product pricing will depend on the return-costs of the retailer and the manufacturer, the return-rates of the E-shop and the brick-and-mortar store and so on. Finally, the influences of the manufacturer and the retailer establishing a Buy-back contract are discussed. Our results illustrated that the Buy-back contract doesn＇t affect optimal pricing and return policies under both the uniform and the differential pricing strategies.
