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

  • Guojun Ji
  • Limei Hu
  • Kim Hua Tan


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.


Big data Bayesian network deduction graph model food supply chain 


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Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Collaborative Innovation Center for Peaceful Development of Corss-Strait RelationsXiamen UniversityXiamen, FujianChina
  2. 2.School of ManagementXiamen UniversityXiamen, FujianChina
  3. 3.Operations Management & Information Systems DivisionNottingham University Business SchoolNottinghamUK

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