Advertisement

A new multi-agent system framework for tacit knowledge management in manufacturing supply chains

  • Khalid Al-Mutawah
  • Vincent Lee
  • Yen Cheung
Article

Abstract

Participating members in a manufacturing supply chain (MSC) usually make use of individual knowledge for making independent decisions. Recent research, however, indicates that there is a need to handle such distributed knowledge in an integrated manner, especially under uncertain and fast changing environments. A multiagent system (MAS), a branch of distributed artificial intelligence, is a contemporary modelling technique for a distributed system like MSCs in the manufacturing domain. However recent researches indicate that MAS approaches have not adequately addressed the role of sharing tacit knowledge (TK) on MSC performance. This paper, therefore, aims to propose a framework that utilizes MAS techniques with a corresponding TK sharing mechanism dedicated to MSCs. We performed some experiments to simulate the proposed approach. The results showed significant improvements when comparing the proposed approach with another conventional MAS model. The results establish a starting point for researchers interested in enhancing MSC performance using TK management approach, and for managers of MSC to focus on the essentials of sharing TK.

Keywords

Manufacturing supply chain Multiagent system Tacit knowledge Simulation model Dempster–Shafer theory 

References

  1. Anderson E. and Fine C. (1999). Business cycles an productivity in capital equipment chains. In: Tayur, S. (eds) Quantitative models for MSC management. Kluwer Academic Publishers, Boston Google Scholar
  2. Arbib C. and Rossi F. (2000). Optimal resource assignment through negotiation in a multi-agent manufacturing system. IIE Transactions 32(10): 963–974 Google Scholar
  3. Armistead C.G. and Mapes J. (1993). The Impact of MSC Integration on Operating Performance. Logistics Information Management 6: 9–14 CrossRefGoogle Scholar
  4. Arntzen B.C., Brown G.C., Harrison T.P. and Trafton L.L. (1995). Global supply chain management at Digital Equipment Corporation. Interfaces 25(1): 69–93 CrossRefGoogle Scholar
  5. Beamon B.M. (1999). Measuring supply chain performance. International Journal of Operations and Production Management 19(3): 275–292 CrossRefGoogle Scholar
  6. Bhaskaran S. (1998). Simulation analysis of a manufacturing MSC. Decision Sciences 29(3): 633–657 CrossRefGoogle Scholar
  7. Brenner W., Zarnekow R. and Wittig H. (1998). Intelligent software agents: Foundations and applications. Springer, New York Google Scholar
  8. Bessemer process. (2008). In Encyclopedia Britannica. Retrieved June 11, 2008, from Encyclopædia Britannica Online: http://www.britannica.com/eb/article-9078935.
  9. Camm J., Chorman F., Dill J., Evans D., Sweeney G. and Wegryn T. (1997). Blending OR/MS, judgment, and GIS: Restructuring P&G’s MSC. Interfaces 27(1): 128–142 CrossRefGoogle Scholar
  10. Christiaanse E. and Venkatraman N. (2002). Beyond sabre: An empirical test of expertise exploitation in electronic channel. MIS Quarterly 26(1): 15–38 CrossRefGoogle Scholar
  11. Cooper M.C., Ellram L.M., Gardner J.T. and Hanks A.M. (1993). Characteristics of supply chain management and the implications for purchasing and logistics strategy. The International Journal of Logistics Management 4(2): 13–24 CrossRefGoogle Scholar
  12. Cooper M.C. and Ellram L.M. (1997). Meshing multiple alliances. Journal of Business Logistics 18(1): 67–90 Google Scholar
  13. Deal T.E. and Kennedy A.A. (1982). Cultures: A new look through old lenses. Journal of Applied Behavioural Science 19: 487–507 Google Scholar
  14. Ertogral K. and Wu S.D. (2000). Auction-theoretic coordination of production planning in the MSC. IIE Transactions 32(10): 931–940 Google Scholar
  15. Fan M., Stallaert J. and Whinston A.B. (2003). Decentralized mechanism design for MSC organizations using an auction market. Information System Research 14(1): 1–22 CrossRefGoogle Scholar
  16. Fox M.S., Barbuceanu M. and Teigen R. (2000). Agent oriented supply-chain management. The international Journal of Flexible Manufacturing Systems 12: 165–188 CrossRefGoogle Scholar
  17. Fu, Y., Piplani, R., Souza, R., & Wu, J. (2000). Multi-agent enabled modeling and simulation towards collaborative inventory management in MSC. Proceedings of the 32nd conference on Winter simulation, December 10–13, 2000, Orlando, Florida (pp. 1763–1771), Society for Computer Simulation International.Google Scholar
  18. Gardner J. and Cooper M.C. (1988). Elements of strategic partnership. In: McKeon, J.E. (eds) Partnerships: A natural evolution in logistics, pp 15–32. Logistics Resources, Inc., Cleveland, OH Google Scholar
  19. Gjerdrum J., Shah N. and Papageorgiou L.G. (2001). A combined optimisation and agent-based approach to MSC modelling and performance assessment. Production Planning and Control 12(1): 81–88 CrossRefGoogle Scholar
  20. Hinkkanen A., Kalakota R., Saengcharoenrat P., Stallaert J. and Whinston A.B. (1997). Distributed decision support systems for real time MSC management using agent technologies. In: Kalakota, R. and Whinston, A.B. (eds) Readings in electronic commerce, pp 275–291. Addison-Wesley, Reading, MA Google Scholar
  21. Hollingsworth D.S. (1988). Building successful global partnerships. Journal of Business Strategy 9(September–October): 12–15 Google Scholar
  22. Kaihara T. (2001). MSC management with market economics. International Journal Production Economics 73(1): 5–14 CrossRefGoogle Scholar
  23. Lee H.L. and Billington C. (1993). Material management in decentralized supply chains. Operations Research 41(5): 835–847 CrossRefGoogle Scholar
  24. Lee, H. L., & Feitzinger, E. (1995). Product configuration and postponement for supply chain efficiency. In Fourth Industrial Engineering Research Conference, Institute of Industrial Engineers.Google Scholar
  25. Lin G., Ettl M., Buckley S., Bagchi S., Yao D.D. and Naccarato B.L. (2000). Extended-enterprise supply-chain management at IBM personal systems group and other divisions. Interfaces 30(1): 7–25 CrossRefGoogle Scholar
  26. Mentzer J.T., DeWitt W., Keebler J.S., Min S., Nix N.W. and Smith C.D. (2001). Defining supply chain management. Journal of Business Logistics 22(21): 1–26 Google Scholar
  27. Miller T. (2001). Hierarchical operations and supply chain planning. Springer, Berlin Google Scholar
  28. Min H. and Zhou G. (2002). MSC modeling: Past, present and future. Computers and Industrial Engineering 43(1–2): 231–249 CrossRefGoogle Scholar
  29. Neely A., Gregory M. and Platts K. (1995). Performance measurement system design. International Journal of Operations & Production Management 15(4): 80–116 CrossRefGoogle Scholar
  30. Nonaka I. and Takeuchi H. (1995). The knowledge creating company. Oxford University Press, USA Google Scholar
  31. Petersen S.A., Divitini M. and Matsken M. (2001). An agent based approach to modelling virtual enterprises. Production Planning and Control 12(3): 224–233 CrossRefGoogle Scholar
  32. Reynolds, R. G., & Peng, B. (2004). Cultural algorithms: Modeling of how cultures learn to solve problems. In 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), Boca Raton, FL, USA: IEEE Computer Society.Google Scholar
  33. Sadeh N.M., Hildum D.W., Kjenstad D. and Tseng A. (2001). MASCOT: An agent-based architecture for coordinated mixed-initiative MSC planning and scheduling. Production Planning and Control 12(3): 212–223 CrossRefGoogle Scholar
  34. Schein E.H. (1992). Organizational culture and leadership: A dynamic view. Jossey-Bass, San Francisco, CA Google Scholar
  35. Shafer G. (1976). A mathematical theory of evidence. Princeton University Press, Princeton, New Jersey Google Scholar
  36. Spear S. and Bowen H.K. (1999). Decoding the DNA of the Toyota Production System. Harvard Business Review 77(5): 96–106 Google Scholar
  37. Star S. (1989). The structure of ill-structured solutions: Heterogeneous problem-solving, boundary Objects and distributed artificial intelligence. In: Gasser, H. (eds) Distributed artificial intelligence Vol. 2, pp 37–54. Morgan Kauffmann, Menlo Park, CA Google Scholar
  38. Swaminathan J.M., Smith S.F. and Sadeh N.M. (1998). Modeling MSC dynamics: A multi-agent approach. Decision Science 29(3): 607–632 CrossRefGoogle Scholar
  39. Tian G.Y. and Yin G. (2002). Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing. Journal of Intelligent Manufacturing 13(5): 323–338 CrossRefGoogle Scholar
  40. VICS Committee on CPFR (2001). Collaborative planning, forecasting and replenishment, White paper. Retrieved November 15, 2007, http://www.vics.org/apps/group_public/download.php/26/CPFR_in_Brazil.pdf.
  41. Voudouris V.T. (1996). Mathematical programming techniques to de-bottleneck the supply chain of fine chemical industries. Computers and Chemical Engineering 20: S1269–S1274 CrossRefGoogle Scholar
  42. Weber Y., Shenkar O. and Raveh A. (1996). National & corporate culture fit in mergers/acquisitions: An exploratory study. Management Sciences 42(8): 1215–1227 CrossRefGoogle Scholar
  43. Whicker M. and Sigelman L. (1991). Computer simulation applications: An introduction. Sage Publications, Newbury Park Google Scholar
  44. Zambonelli F., Jennings N.R. and Wooldridge M. (2003). Developing multiagent systems: The Gaia methodology. ACM Transaction Software Engineering 12(3): 317–370 CrossRefGoogle Scholar
  45. Zuckerman A. (2002). MSC management. Capstone Pub- lishing, Oxford Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Clayton School of Information TechnologyMonash UniversityVictoriaAustralia

Personalised recommendations