MATEC Web Conf.
Volume 100, 201713th Global Congress on Manufacturing and Management (GCMM 2016)
|Number of page(s)||10|
|Section||Part 2: Internet +, Big data and Flexible manufacturing|
|Published online||08 March 2017|
A Big Data Decision-making Mechanism for Food Supply Chain
1 Collaborative Innovation Center for Peaceful Development of Corss-Strait Relations; School of Management, Xiamen University, 361005 Fujian China
2 Operations Management & Information Systems Division, Nottingham University Business School
* Corresponding author: firstname.lastname@example.org
Many companies have captured and analyzed huge volumes of data to improve the decision mechanism of supply chain, this paper presents a big data harvest model that uses big data as inputs to make more informed 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 foods 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 decision-making mechanism has vast potential by extracting value from big data.
Key words: Big data / Bayesian network / deduction graph model / food supply chain
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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