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

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Ji, Guojun, Hu, Limei and Tan, Kim Hua (2017) A study on decision-making of food supply chain based on big data. Journal of Systems Science and Systems Engineering, 26 (2). pp. 183-198. ISSN 1861-9576

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Official URL: http://link.springer.com/article/10.1007%2Fs11518-016-5320-6

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.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/855049
Additional Information:	The final publication is available at Springer via http://dx.doi.org/10.1007/s11518-016-5320-6
Keywords:	Big data, Bayesian network, deduction graph model, food supply chain
Schools/Departments:	University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
Identification Number:	10.1007/s11518-016-5320-6
Depositing User:	Howis, Jennifer
Date Deposited:	09 Feb 2017 12:47
Last Modified:	04 May 2020 18:41
URI:	https://eprints.nottingham.ac.uk/id/eprint/40465

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