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

Ji, Guojun; Hu, Limei; Tan, Kim Hua

Abstract

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.

Citation

Ji, G., Hu, L., & Tan, K. H. (2017). A study on decision-making of food supply chain based on big data. Journal of Systems Science and Systems Engineering, 26(2), 183-198. https://doi.org/10.1007/s11518-016-5320-6

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2016
• Online Publication Date: Jan 24, 2017
• Publication Date: Apr 7, 2017
• Deposit Date: Feb 9, 2017
• Publicly Available Date: Feb 9, 2017
• Journal: Journal of Systems Science and Systems Engineering
• Print ISSN: 1004-3756
• Electronic ISSN: 1861-9576
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 2
• Pages: 183-198
• DOI: https://doi.org/10.1007/s11518-016-5320-6
• Keywords: Big data, Bayesian network, deduction graph model, food supply chain
• Public URL: https://nottingham-repository.worktribe.com/output/855049
• Publisher URL: http://link.springer.com/article/10.1007%2Fs11518-016-5320-6
• Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s11518-016-5320-6