Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Journal of Systems Science and Systems Engineering Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  3. Anica-popa I. (2012). Food traceability systems and information sharing in food supply chain. Management & Marketing, 7(4): 750–759.

    Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  6. Cooper, G. F. & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4): 309–347.

    MATH  Google Scholar 

  7. Corney, D. (2000). Designing food with bayesian belief networks. In Evolutionary Design and Manufacture, pp. 83-94. Springer London.

  8. Heckerman, D., Mamdani, A. & Wellman, M. P. (1995). Real-world applications of Bayesian networks. Communications of the ACM, 38(3): 24–26.

    Article  Google Scholar 

  9. Jensen, F. V. (1996). An introduction to Bayesian networks. London: University College London Press, pp. 33–41.

    Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  11. Li, H. L. (1999). Incorporating competence sets of decision makers by deduction graphs. Operations Research, 47(2): 209–220.

    Article  MathSciNet  MATH  Google Scholar 

  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.

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  16. Papadimitriou, C. H. & Steiglitz, K. (1998). Combinatorial optimization: algorithms and complexity. Courier Dover Publications, pp. 23–35.

    MATH  Google Scholar 

  17. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29(3): 241–288.

    Article  MathSciNet  MATH  Google Scholar 

  18. Stein, A. (2004). Bayesian networks and food security-an introduction. Frontis, 3: 107–116.

    Google Scholar 

  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.

    Article  Google Scholar 

  20. Tien, J. M. (2013). Big data: unleashing information. Journal of Systems Science and Systems Engineering, 22(2): 127–151.

    Article  Google Scholar 

  21. Tien, J. M. (2012). The next industrial revolution: integrated services and goods. Journal of Systems Science and Systems Engineering, 21(3): 257–296.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  23. Van Boekel, M. A. J. S. (2004). Bayesian solutions for food-science problems? Frontis, 3: 17–27.

    Google Scholar 

  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. Yu, M. & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce. European Journal of Operational Research, 224(2): 273–282.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Collaborative Innovation Center for Peaceful Development of Corss-Strait Relations, Xiamen University, Xiamen, Fujian, 361005, China

    Guojun Ji & Limei Hu

  2. School of Management, Xiamen University, Xiamen, Fujian, 361005, China

    Guojun Ji & Limei Hu

  3. Operations Management & Information Systems Division, Nottingham University Business School, Nottingham, UK

    Kim Hua Tan

Authors
  1. Guojun Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Limei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kim Hua Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guojun Ji.

Additional information

Guojun Ji obtained his PhD degree in automation from Southeast University (SEU), Nanjing, China in 1998. He finished postdoctoral work at Washington State University in 1999 and served as a visiting scholar at the University of Washington in 2004. He is currently a professor in the School of Management, Xiamen University, China. His research interests include systematic engineering, supply chain management, information management and logistics engineering, and published papers are over 300. He is also involved in collaborative research in information management, supply chain management and service innovation.

Limei Hu received her master’s degree in management science and engineering from Xiamen University, in 2015; the bachelor’s degree in financial management from Hangzhou Dianzi University of Finance and Economics, in 2012.

Kim Hua Tan is a professor in operations and innovation management. Prior to this, he was a researcher and teaching assistant at Centre for Strategy and Performance, University of Cambridge. Dr. Tan spent many years in industry, before joining academia in 1999. His current research interests are lean management, operations strategy, decision making, and supply chain risk management. Dr. Tan has published a book called ‘Winning Decisions: Translating Business Strategy into Action Plans’ and numerous articles in academic journals such as Decision Sciences, International Journal of Operations and Production Management, International Journal of Production Economics, International Journal of Innovation Management, and others.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, G., Hu, L. & Tan, K.H. A study on decision-making of food supply chain based on big data. J. Syst. Sci. Syst. Eng. 26, 183–198 (2017). https://doi.org/10.1007/s11518-016-5320-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11518-016-5320-6

Keywords

Search

Navigation