A study on decision-making of food supply chain based on big data

详细信息    查看全文

• 作者：Guojun Ji ; Limei Hu ; Kim Hua Tan
• 关键词：Big data ; Bayesian network ; deduction graph model ; food supply chain
• 刊名：Journal of Systems Science and Systems Engineering
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：26
• 期：2
• 页码：183-198
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Complexity; Economic Theory/Quantitative Economics/Mathematical Methods; Operation Research/Decision Theory;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-9576
• 卷排序：26

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 from big data.