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

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 ne...

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Bibliographic Details
Published inJournal of systems science and systems engineering Vol. 26; no. 2; pp. 183 - 198
Main Authors Ji, Guojun, Hu, Limei, Tan, Kim Hua
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017
Springer Nature B.V
School of Management,Xiamen University, Xiamen, Fujian,361005, China%Operations Management & Information Systems Division, Nottingham University Business School
Collaborative Innovation Center for Peaceful Development of Corss-Strait Relations
Subjects
Algorithms
Automation
Bayesian analysis
Big Data
Business models
Complexity
Customers
Data analysis
Decision analysis
Decision making
Economic Theory/Quantitative Economics/Mathematical Methods
Engineering
Food
Food products
Food supply
Marketing
Operations Research/Decision Theory
Privacy
Real time
Suppliers
Supply chains
Big data
food supply chain
deduction graph model
Bayesian network
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Summary: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.
ISSN:1004-3756
1861-9576
DOI:10.1007/s11518-016-5320-6