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 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.

[1]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[2]  Judith Rousseau,et al.  Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food‐Borne Diseases , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[3]  David Corney.  Designing Food with Bayesian Belief Networks , 2000 .

[4]  Ann E. Nicholson,et al.  Using Mutual Information to Determine Relevance in Bayesian Networks , 1998, PRICAI.

[5]  FOOD TRACEABILITY SYSTEMS AND INFORMATION SHARING IN FOOD SUPPLY CHAIN Ionuţ ANICA-POPA , 2012 .

[6]  Chenggang Bai,et al.  Cost-benefit factor analysis in e-services using bayesian networks , 2009, Expert Syst. Appl..

[7]  Anna Nagurney,et al.  Competitive Food Supply Chain Networks with Application to Fresh Produce , 2012 .

[8]  Han-Lin Li,et al.  Incorporating Competence Sets of Decision Makers by Deduction Graphs , 1999, Oper. Res..

[9]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[10]  James M. Tien,et al.  Big Data: Unleashing information , 2013, 2013 10th International Conference on Service Systems and Service Management.

[11]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[12]  YU P.L..  Optimal Competence Set Expansion Using Deduction Graphs , 2005 .

[13]  M.A.J.S. van Boekel,et al.  Bayesian solutions for food-science problems? , 2004 .

[14]  Alfred Stein,et al.  Bayesian networks and food security - An introduction , 2004 .

[15]  P. I. Bidyuk,et al.  Construction and Methods of Learning of Bayesian Networks , 2005 .

[16]  A. Fearne,et al.  Towards a framework for improvement in the management of demand in agri‐food supply chains , 2006 .

[17]  Ronald D. Anderson,et al.  A Bayesian Network Estimation of the Service-Profit Chain for Transport Service Satisfaction , 2004, Decis. Sci..

[18]  J. Tien.  The next industrial revolution: Integrated services and goods , 2012, Journal of Systems Science and Systems Engineering.

[19]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[20]  Erhan Çene,et al.  Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey , 2015 .

[21]  John N. Hooker,et al.  Solving Fixed-Charge Network Flow Problems with a Hybrid Optimization and Constraint Programming Approach , 2002, Ann. Oper. Res..

[22]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[23]  Michael P. Wellman,et al.  Real-world applications of Bayesian networks , 1995, CACM.

[24]  Pascal J. Goldschmidt-Clermont,et al.  Healthcare: A complex service system , 2009 .