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

Article

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.

Keywords

Big data Bayesian network deduction graph model food supply chain 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Albert, I., Grenier, E., Denis, J. B. & Rousseau, J. (2008). Quantitative risk assessment from farm to fork and beyond: aglobal Bayesian approach concerning food-borne diseases. Risk Analysis, 28(2): 557–571.CrossRefGoogle Scholar
  2. [2]
    Anderson, R. D., Mackoy, R. D., Thompson, V. B. & Harrell, G. (2004). A Bayesian network estimation of the service-profit chain for transport service satisfaction. Decision Sciences, 35(4): 665–689.CrossRefGoogle Scholar
  3. [3]
    Anica-popa I. (2012). Food traceability systems and information sharing in food supply chain. Management & Marketing, 7(4): 750–759.Google Scholar
  4. [4]
    Bidyuk, P. I., Terent’Ev, A. N. & Gasanov, A. S. (2005). Construction and methods of learning of Bayesian networks. Cybernetics and Systems Analysis, 41(4): 587–598.MathSciNetCrossRefMATHGoogle Scholar
  5. [5]
    Cene, E., & Karaman, F. (2015). Analysing organic food buyers’ perceptions with Bayesian networks: a case study in Turkey. Journal of Applied Statistics, 42(7): 1572–1590.MathSciNetCrossRefGoogle Scholar
  6. [6]
    Cooper, G. F. & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4): 309–347.MATHGoogle Scholar
  7. [7]
    Corney, D. (2000). Designing food with bayesian belief networks. In Evolutionary Design and Manufacture, pp. 83-94. Springer London.Google Scholar
  8. [8]
    Heckerman, D., Mamdani, A. & Wellman, M. P. (1995). Real-world applications of Bayesian networks. Communications of the ACM, 38(3): 24–26.CrossRefGoogle Scholar
  9. [9]
    Jensen, F. V. (1996). An introduction to Bayesian networks. London: University College London Press, pp. 33–41.Google Scholar
  10. [10]
    Kim, H. J. & Hooker, J. N. (2002). Solving fixed-charge network flow problems with a hybrid optimization and constraint programming approach. Annals of Operations Research, 115(1): 95–124.MathSciNetCrossRefMATHGoogle Scholar
  11. [11]
    Li, H. L. (1999). Incorporating competence sets of decision makers by deduction graphs. Operations Research, 47(2): 209–220.MathSciNetCrossRefMATHGoogle Scholar
  12. [12]
    Li, J., Tao, F., Cheng, Y. & Zhao, L. (2015). Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 81(1-4): 667-684.Google Scholar
  13. [13]
    Li, H. L. & Yu, P. L. (1994). Optimal competence set expansion using deduction graphs. Journal of Optimization Theory and Applications, 80(1): 75–91.MathSciNetCrossRefMATHGoogle Scholar
  14. [14]
    Lu, J., Bai, C. & Zhang, G. (2009). Cost-benefit factor analysis in e-services using Bayesian networks. Expert Systems with Applications, 36(3): 4617–4625.CrossRefGoogle Scholar
  15. [15]
    Nicholson, A. E. & Jitnah, N. (1998). Using mutual information to determine relevance in Bayesian networks. In PRICAI’98: Topics in Artificial Intelligence, pp. 399–410. Springer Berlin Heidelberg.CrossRefGoogle Scholar
  16. [16]
    Papadimitriou, C. H. & Steiglitz, K. (1998). Combinatorial optimization: algorithms and complexity. Courier Dover Publications, pp. 23–35.MATHGoogle Scholar
  17. [17]
    Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29(3): 241–288.MathSciNetCrossRefMATHGoogle Scholar
  18. [18]
    Stein, A. (2004). Bayesian networks and food security-an introduction. Frontis, 3: 107–116.Google Scholar
  19. [19]
    Taylor, D. H. & Fearne, A. (2006). Towards a framework for improvement in the management of demand in agri-food supply chains. Supply Chain Management: an International Journal, 11(5): 379–384.CrossRefGoogle Scholar
  20. [20]
    Tien, J. M. (2013). Big data: unleashing information. Journal of Systems Science and Systems Engineering, 22(2): 127–151.CrossRefGoogle Scholar
  21. [21]
    Tien, J. M. (2012). The next industrial revolution: integrated services and goods. Journal of Systems Science and Systems Engineering, 21(3): 257–296.CrossRefGoogle Scholar
  22. [22]
    Tien, J. M. & Goldschmidt-Clermont, P. J. (2009). Healthcare: a complex service system. Journal of Systems Science and Systems Engineering, 18(3): 257–282.CrossRefGoogle Scholar
  23. [23]
    Van Boekel, M. A. J. S. (2004). Bayesian solutions for food-science problems? Frontis, 3: 17–27.Google Scholar
  24. [24]
    Wolters, C. J. & Van Gemert, L. J. (1989). Towards an integrated model of sensory attributes, instrumental data and consumer perception of tomatoes. Part I. Relation between consumer perception and sensory attributes. In Workshop on Measuring Consumer Perception of Internal Product Quality, 259: 91–106.Google Scholar
  25. [25]
    Yu, M. & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce. European Journal of Operational Research, 224(2): 273–282.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Collaborative Innovation Center for Peaceful Development of Corss-Strait RelationsXiamen UniversityXiamen, FujianChina
  2. 2.School of ManagementXiamen UniversityXiamen, FujianChina
  3. 3.Operations Management & Information Systems DivisionNottingham University Business SchoolNottinghamUK

Personalised recommendations