Multiagent Model to Reduce the Bullwhip Effect

Borja Ponte, David de la Fuente

Abstract

There are several circumstances which, in recent decades, have granted the supply chain management a strategic role in the search for competitive advantage. One of the goals is, undoubtedly, the reduction of Bullwhip Effect, which is generated by the amplification of the variability of orders along the chain, from the customer to the factory. This paper applies multiagent methodology for reducing Bullwhip Effect. To do this, it considers the supply chain as a global multiagent system, formed in turn by four multiagent subsystems. Each one of them represents one of the four levels of the traditional supply chain (Shop Retailer, Retailer, Wholesaler and Factory), and it coordinates various intelligent agents with different objectives. Thus, each level has its own capacity of decision and it seeks to optimize the supply chain management. The problem is analyzed both from a non collaborative approach, where each level seeks the optimal forecasting methodology independently of the rest, and from a collaborative approach, where each level negotiates with the rest looking for the best solution for the whole supply chain.

References

  1. Abraham, B., Ledolter, J. (1983): Statistical Methods for Forecasting. New York Weley.
  2. Box, G. E. P., Jenkins, G. M. (1976): Time Series Analysis: Forecasting and Control. San Francisco: Holden Day.
  3. Chen, L., Lee Hau, L. (2009): Information Sharing and Order Variability Control Under a Generalized Demand Model. Management Science. Vol. 55(5), pp. 781-797.
  4. De la Fuente, D.; Lozano, J. (2007) “Application of distributed intelligence to reduce the bullwhip effect”. International Journal of Production Research. Vol. 44(8), pp. 1815-1833.
  5. Disney, S. M., Towill, D. R. (2002): Transfer function analysis of forecasting induced bullwhip in supply chain. International Journal of Production Economics. Vol. 78, pp. 133-144.
  6. Disney, S. M., Towill, D. R. (2003): The effect of Vendor Managed Inventory (VMI) dynamics on the Bullwhip effect in supply chain. International Journal of Production Economics. Vol. 85, pp. 199-215.
  7. Disney, S. M., Towill, D. R. (2003): On the Bullwhip and inventory variance produced by an ordering policy. Omega. Vol. 31, pp. 157-167.
  8. Forrester, J. W. (1961): Industrial dynamics, MIT Press. Cambridge, MA.
  9. Fox, M. S., Chionglo, J. F., Barbuceanu, M. (1993): The Integrated Supply Chain Management System. Internal Report, Univ. of Toronto.
  10. Holmström, J. (1997): Product range management: a case study of supply chain operations in the European grocery industry. Supply Chain Management. Vol. 2(3), pp. 107-115.
  11. Ji, Y. F., Yang, H. L. (2005): Bullwhip Effect Elimination in Supply Chain with CPFR. Proceedings of the 2005 International Conference on Management Science & Engineering. Vol 1-3, pp. 737-740.
  12. Kimbrough, S. O., Wu, D. J., Zhong, F. (2002): Computer the beer game: can artificial manage supply chains? Decision Support Sytems. Vol. 33, pp. 323-333.
  13. Lee, H. L., Padmanabhan, V., Whang, S. (1997): The bullwhip effect in supply chains. Sloan Management Review. Vol. 38(3), pp. 93-102.
  14. Liang, W. Y., Huang, C. C. (2006): Agent-based demand forecast in multi-echelon supply chain. Decision Support Systems. Vol. 42(1), pp. 390-407.
  15. Machuca, J. A., Barajas, R. (2004): The impact of electronic data interchange on reducing bullwhip effect and supply chain inventory costs. Transportation Research Part E. Vol. 40, pp. 209-228.
  16. Moyaux, T., Chaib-draa, B., D'Amours, S. (2004): An agent simulation model for the Quebec forest supply chain. Lecture Notes in Artificial Intelligence. Vol. 3191, pp.226-241.
  17. Saberi, S., Nookabadi, A. S., Hejazi, S. R. (2012): Applying Agent-Based System and Negotiation Mechanism in Improvement of Inventory Management and Customer Order Fulfilment in Multi Echelon Supply Chain. Arabian Journal for Science and Engineering. Vol. 37(3), pp. 851-861.
  18. Shen, W., Xue, D., Norrie, D. H. (1998): An Agent-Based Manufacturing Enterprise Infrastructure for Distributed Integrated Intelligent Manufacturing Systems. In Proceedings of the Practical Application of Intelligent Agents and Multi-Agent Systems PAAM'98, London, UK.
  19. Sterman, J. D. (1989): Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science. Vol. 35(3), pp. 321-339.
  20. Wilensky, U. (1999): NetLogo. Northwestern University, Evanston, IL: The Center for Connected Learning and Computer - Based Modeling. Retrieved from http://ccl.northwestern.edu/netlogo/
  21. Wu, S. N., Gan, W. H., Wei, F. M. (2011): Analysis on the Bullwhip Effect Based on ABMS. Procedia Engineering. Vol. 15.
  22. Zarandi, M. H. Fazel; Pourakbar, M.; Turksen, I. B. (2008): A fuzzy agent-based model for reduction of bullwhip effect in supply chain systems. Expert systems with applications. Vol. 34(3), pp. 1680-1691.
Download


Paper Citation


in Harvard Style

Ponte B. and de la Fuente D. (2013). Multiagent Model to Reduce the Bullwhip Effect . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8565-38-9, pages 67-76. DOI: 10.5220/0004245600670076


in Bibtex Style

@conference{icaart13,
author={Borja Ponte and David de la Fuente},
title={Multiagent Model to Reduce the Bullwhip Effect},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2013},
pages={67-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004245600670076},
isbn={978-989-8565-38-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Multiagent Model to Reduce the Bullwhip Effect
SN - 978-989-8565-38-9
AU - Ponte B.
AU - de la Fuente D.
PY - 2013
SP - 67
EP - 76
DO - 10.5220/0004245600670076