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

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

Authors

Guojun Ji

Limei Hu

Kim Hua Tan



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.

Journal Article Type Article
Publication Date Apr 7, 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
APA6 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
DOI https://doi.org/10.1007/s11518-016-5320-6
Keywords Big data, Bayesian network, deduction graph model, food supply chain
Publisher URL http://link.springer.com/article/10.1007%2Fs11518-016-5320-6
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s11518-016-5320-6

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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