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Journal of Intelligent Manufacturing

, Volume 26, Issue 5, pp 975–988 | Cite as

A multi-agent based approach for change management in manufacturing enterprises

  • Mustafa Batuhan Ayhan
  • Mehmet Emin Aydin
  • Ercan Öztemel
Article

Abstract

Change management becomes an unavoidable necessity for manufacturing enterprises. Since change in business processes carries significant impact on the performance of manufacturing companies, a change management model is definitely required to remain competitive. Moreover, utilizing agent based systems will provide computational provision and integrity to manage and measure the capabilities to follow the change in a progressive approach by employing the cooperation and collaboration properties of various agents helping for retrieval of the required information in a rapid way. Therefore, in this paper, a multi-agent based change management model is proposed to handle the changes in manufacturing enterprises. The model is validated through a case study done to measure the performance of change management capabilities in a manufacturing company. A sensitivity analysis on the results of this case study is also conducted to reveal the system reactivity to various parameters.

Keywords

Change management Agent based systems Manufacturing Sensitivity analysis 

References

  1. Albino, V., Carbonara, N., & Giannoccaro, I. (2006). Innovation in industrial districts: An agent based simulation model. International Journal of Production Economics, 104(1), 30–45.CrossRefGoogle Scholar
  2. Aydin, M. E. (2012). Coordinating metaheuristic agents with team intelligence. Journal of Intelligent Manufacturing, 23(4), 991–999.CrossRefGoogle Scholar
  3. Aydin, M. E., Safdar, G.A., & Aslam, N. (2013). A novel learning-based spectrum sensing technique for cognitive radio networks. In 27th international conference on advanced information networking and applications (AINA 2013). Barcelona, Spain, pp. 505–510.Google Scholar
  4. Ayhan, M. B. (2010). Development of a change management model for manufacturing systems, Ph.D. dissertation. Goztepe, Istanbul, Turkey: Marmara University.Google Scholar
  5. Ayhan, M. B., Aydin, M. E., & Oztemel, E. (2012). Collective intelligence for monitoring innovation and change in manufacturingindustry. In International conference of manufacturing engineering and engineering management. London, UK.Google Scholar
  6. Ayhan, M. B., Oztemel, E., Aydin, M. E., & Yue, Y. (2013). A quantitative approach for measuring process innovation: a case study in a manufacturing company. International Journal of Production Research, Published Online doi: 10.1080/00207543.2013.774495.
  7. Ayhan, M. B., & Oztemel, E. (2011). A methodology for measuring product innovation: A case study for a manufacturing system. International Journal of Manufacturing Technology and Management, 24(1/2/3/4), 139–152.CrossRefGoogle Scholar
  8. Chan, K. Y., Kwong, C. K., Dillon, T. S., & Fung, K. Y. (2011b). An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness. Journal of Engineering Design, 22(8), 523–542.CrossRefGoogle Scholar
  9. Chan, K. Y., Kwong, C. K., & Hu, B. Q. (2012). Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods. Applied Soft Computing, 12(4), 1371–1378.CrossRefGoogle Scholar
  10. Chan, K. Y., Kwong, C. K., & Tsim, Y. C. (2010). A genetic programming based fuzzy regression approach to modelling manufacturing processes. International Journal of Production Research, 48(7), 1967–1982.CrossRefGoogle Scholar
  11. Chan, K. Y., Kwong, C. K., & Wong, T. C. (2011a). Modelling customer satisfaction for product development using genetic programming. Journal of Engineering Design, 22(1), 55–68.CrossRefGoogle Scholar
  12. Coppin, B. (2004). Artificial intelligence illuminated (1st ed.). USA: Johns and Bartlett Publishers.Google Scholar
  13. Daft, R. (2008). New era of management (2nd ed.). NY: Thompson South Western.Google Scholar
  14. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 35, 227–230.Google Scholar
  15. Dawid, H. (2006). Chapter 25 agent based models of innovation and technological change. Handbook of Computational Economics, 2, 1235–1272.CrossRefGoogle Scholar
  16. Drucker, P. (2007). The practice of management. Oxford: Elsevier Publisher, Drucker Collection Edition ed.Google Scholar
  17. Fleury, G., Goujon, J.-Y., Gourgand, M., & Lacomme, P. (1999). Multi-agent approach and stochastic optimization: Random events in manufacturing systems. Journal of Intelligent Manufacturing, 10(1), 81–101.CrossRefGoogle Scholar
  18. Garcia, R. (2005). Uses of agent based modeling in innovation / new product development research. The Journal of Product Innovation Management, 22(5), 380–398.CrossRefGoogle Scholar
  19. Gomez-Mejia, L., & Balkin, D. (2011). Management (1st ed.). USA: Prentice Hall.Google Scholar
  20. Goodhue, R., & Thompson, D. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.CrossRefGoogle Scholar
  21. Hayes, J. (2010). The theory and practice of change management (3rd ed.). NY: Palgrave Publishers Ltd.Google Scholar
  22. Hiatt, J. (2006). A model for change in business, government, and our community. Colorado: Prosci Research Publications.Google Scholar
  23. Hitt, M. A., & Porter, L. (2011). Management (3rd ed.). USA: Prentice Hall.Google Scholar
  24. Ito, T., & Abadi, S. M. M. J. (2002). Agent-based material handling and inventory planning in warehouse. Journal of Intelligent Manufacturing, 13(3), 201–210.CrossRefGoogle Scholar
  25. Kilic, H. S., & Durmusoglu, M. B. (2012). Design of kitting system in lean-based assembly lines. Assembly Automation, 32(3), 226–234.CrossRefGoogle Scholar
  26. Kotter, J. P. (1996). Leading change. Boston: Harvard Business School Press.Google Scholar
  27. Kwong, C. K., Chen, Y., Chan, K. Y., & Luo, X. (2010). A generalised fuzzy least-squares regression approach to modelling relationships in QFD. Journal of Engineering Design, 21(5), 601–613.CrossRefGoogle Scholar
  28. Kwong, C. K., Chen, Y., Chan, K. Y., & Wong, H. (2008). The hybrid fuzzy least-squares regression approach to modeling manufacturing processes. IEEE Transactions on Fuzzy Systems, 16(3), 644–651.CrossRefGoogle Scholar
  29. Lee, H., & Ahn, S. (2008). Assessment of process improvement from organizational change. Information & Management, 45(5), 270–280.CrossRefGoogle Scholar
  30. Lopez-Ortega, O., Lopez-Morales, V., & Villar-Medina, I. (2008). Intelligent and collaborative multi-agent system to generate and schedule production orders. Journal of Intelligent Manufacturing, 19(6), 677–687.CrossRefGoogle Scholar
  31. Ma, T., & Nakamori, Y. (2005). Agent based modeling on technological innovation as an evolutionary process. European Journal of Operational Research, 166(3)1, 741–755.Google Scholar
  32. Martino, J. (1993). Technological forecasting: An introduction. The Futurist, 27(4), 13–16.Google Scholar
  33. Meredith, J. (1995). Technological forecasting. Indianapolis: Wiley.Google Scholar
  34. Mesa (2008). SOA in Manufacturing Guidebook. White Paper 27, AMESA International, IBM Corporation and Capgemini co-branded whitepaper.Google Scholar
  35. Ministry of Environment & Forestry (2009). Sanayi kaynakli hava kirliliginin kontrol yonetmeligi. Ministry of Environment and Forestry, Turkey, Resmi Gazete 27277/3, July 2009.Google Scholar
  36. Mohebbi, S., & Shafaei, R. (2012). E-Supply network coordination. The design of intelligent agents for buyer-supplier dynamic negotiations. Journal of Intelligent Manufacturing, 23(3), 375–391.CrossRefGoogle Scholar
  37. Monostori, L., Vancza, J., & Kumara, S. R. T. (2006). Agent based systems for manufacturing. CIRP Annals-Manufacturing Technology, 55(2), 697–720.CrossRefGoogle Scholar
  38. Nejad, H. T. N., Sugimura, N., Iwamura, K., & Tanimizu, Y. (2010). Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system. Journal of Intelligent Manufacturing, 21(4), 487–499.CrossRefGoogle Scholar
  39. OECD. (1995). The measurement of specific and technological activities: Proposed guidelines for collecting and interpreting technological innovation data (2nd ed.). Paris: Organization for Economic Co-operation and Development, European Commission and Eurostat.Google Scholar
  40. Oztemel, E., & Ayhan, M. B. (2008). Measuring technology adaptation in manufacturing systems. In E. Oztemel (Ed.), Proceedings of 6th International Symposium on Intelligent and Manufacturing Systems (pp. 636–648). Sakarya: Sakarya University, Turkey.Google Scholar
  41. Oztemel, E, & Ayhan, M. B. (2009). Measuring technological forecasting. In Proceedings of 7th IEEE international conference on industrial informatics. Cardiff, Wales, June 2009, pp. 49–53.Google Scholar
  42. Oztemel, E., & Ayhan, M. B. (2010). Measuring the capability of change in manufacturing processes. In: Oztemel, E. (Ed.) Proceedings of 7th international symposium on intelligent and manufacturing systems. Sarajevo, Bosnia & Herzegovina, September 2010, p. Accepted to be published.Google Scholar
  43. Oztemel, E., Ayhan, M. B., & (2011). Yönetimsel YenilikçilikDerecesinin Ölçülmesi, YAEM 2011, July, 2011. Turkey, Sakarya.Google Scholar
  44. Peppers, M., & Rogers, D. (2004). Managing customer relationships–a strategic Framework. NY: Wiley.Google Scholar
  45. Peters, R., & Waterman, T. (2004). In search of excellence. USA: Harper Business Essentials.Google Scholar
  46. Reddick, C. G. (2011). Customer relationship management (crm), technology and organizational change: Evidence for bureaucratic and e-government paradigms. Government Information Quarterly, 28(3), 346–353.CrossRefGoogle Scholar
  47. Robbins, S., & Coulter, M. (2009). Management (10th ed.). USA: Prentice Hall.Google Scholar
  48. Roy, D., Anciaux, D., & Vernadat, F. (2001). SYROCCO: A novel multi agent shop floor control system. Journal of Intelligent Manufacturing, 12(3), 295–307.Google Scholar
  49. Sabar, M., Montreuil, B., & Frayret, J.-M. (2012). An Agent-Based algorithm for personnel shift-scheduling and re-scheduling in flexible assembly lines. Journal of Intelligent Manufacturing, 23(6), 2623–2634.Google Scholar
  50. Smit, P. J., & Vrba, M. (2007). Management principles, a contemporary edition for Africa (4th ed.). Cape Town: Juta & Co. Publishers. Google Scholar
  51. Stemberger, M. I., & Jaklic, J. (2007). Towards e-government by business process change- a methodology for public sector. International Journal of Information Management, 27(4), 221–232.CrossRefGoogle Scholar
  52. Taylor, F. (2010). The principles of scientific management. NY: Cosimo Publishing.Google Scholar
  53. Tureteken, O., & Schuff, D. (2007). The impact of context aware fish-eye models on understanding business processes: An empirical study of data flow diagrams. Information and Management, 44(1), 40–52.CrossRefGoogle Scholar
  54. Wenrong, L., & Xialong, X. (2011). Study on agent based innovation behavior research technique. Procedia Engineering, 15, 3541–3545.CrossRefGoogle Scholar
  55. Wooldridge, M. (2009). An introduction to multi agent systems (2nd ed.). Glasgow: Wiley.Google Scholar
  56. Wren, D., & Bedeian, A. (2009). The evolution of management thought (6th ed.). New Jersey: Wiley.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mustafa Batuhan Ayhan
    • 1
    • 2
  • Mehmet Emin Aydin
    • 2
  • Ercan Öztemel
    • 1
    • 2
  1. 1.Department of Industrial EngineeringMarmara UniversityKadikoy, IstanbulTurkey
  2. 2.Department of Computer Science and TechnologyUniversity of BedfordshireLuton, BedsUK

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