Identifying the Best Method for Using Knowledge Management in Supply Chain Using Fuzzy Logic

Ali Mohaghar, Neda Rajabani, Mohammad Karimi Zarchi, Mohammad Reza Fathi


In today’s competitive business environment, supply chains must respond rapidly to changing customer demands. Knowledge is one of the most decisive factors capable of offering competitive advantages for supply chain partners. The purpose of this paper is applying a new integrated method to selecting the best solution of knowledge management adoption in supply chain. Proposed approach is based on Fuzzy AHP and TOPSIS methods. Fuzzy AHP method is used in determining the weights of the criteria by decision makers and then selecting solution of knowledge management are determined by TOPSIS method. A real case demonstrates the application of the proposed method.

Full Text:



Alavi, M. and Leidner, D.E. (2001), “Knowledge management and knowledge management systems: conceptual foundations and research issues”, MIS Quarterly Review, Vol. 25 No. 1, pp. 107-36.

Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247.

Barson, R.J., Foster, G., Struck, T., Ratchev, S., Pawar, K., Weber, F. and Wunram, M. (2000), “Inter- and intraorganisational barriers to sharing knowledge in the extended supply chain”, e2000 Conference Proceedings, Bremen, Germany, pp. 235-9.

Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655

Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2), 343–350.

Christopher, M. (1994), Logistics and Supply Chain Management, Pitman Publishing, New York, NY.

Dag ˘deviren, M., Yüksel, _ I., & Kurt, M. (2008). A fuzzy analytic network process (ANP) model to identify faulty behavior risk (FBR) in work system, Safety Science, 46( 5), 771–783.

Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21(3), 215–231.

Duhon, B. (1998), “It’s all in our heads”, Inform, Vol. 12 No. 8, pp. 8-13.

Dyer, J.H. and Singh, H. (1998), “The relational view: cooperative strategy and sources of inter organizational competitive advantage”, Academy of Management Review, Vol. 23 No. 4, pp. 660-79.

Feller, J., Farhankangas, A. and Smeds, R. (2006), “Process learning in alliances developing radical versus incremental innovations: evidence from the telecommunications industry”, Knowledge and Process Management, Vol. 13 No. 3, pp. 175-91.

Grant, R.M. (1996), “Toward a knowledge-based theory of the firm”, Strategic Management Journal, Vol. 17, pp. 232-49.

Hult, G.T.M., Ketchen, D.J. Jr and Slater, S.F. (2004), “Information processing, knowledge development, and strategic supply chain performance”, Academy of Management Journal, Vol. 47 No. 2, pp. 241-53.

Hung, H.-F., Kao, H.-P. and Chu, Y.-Y. (2008), “An empirical study on knowledge integration, technology innovation and experimental practice”, Expert Systems with Applications, Vol. 35, pp. 177-86.

Hwang .C.L and K. Yoon,.(1981). Multiple Attributes Decision Making Methods and Applications, spring, New York.

Ireland, R.D., Hitt, M.A. and Vaidyanath, D. (2002), ‘‘Alliance management as a source of competitive advantage’’, Journal of Management, Vol. 28 No. 3, pp. 413-46.

Lambert, D. and Cooper, M.C. (2000), “Issues in supply chain management”, Industrial Marketing Management, Vol. 29 No. 1, pp. 65-83.

Leung, L. C., & Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, 124(1), 102–113.

Lee, A. H. I., Chen, W.-C., & Chang, C.-J. (2008). A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Systems with Applications, 34(1), 96–107.

Liang, G. S., & Wang, M. J. (1994). Personnel selection using fuzzy MCDM algorithm, European Journal of Operational Research, 78, 22–33.

Mikhailov, L. (2004). A fuzzy approach to deriving priorities from interval pairwise comparison judgments. European Journal of Operational Research, 159(3), 687–704.

Nunes M.B., Annansingh F., Eaglestone B., Wakefield R., Knowledge management issues in knowledge-intensive SMEs, Journal of Documentation, 2006; 62: 101-119.

Ogulin, R. (2003), “Emerging requirements for networked supply chains”, in Gattorna, J.L., Ogulin, R. and Reynolds, M.W. (Eds), Gower Handbook of Supply Chain Management, Gower Publishing, Burlington, VT, pp. 486-500.

Opricovic. S and Tzeng. G.H. (2003). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research 156 (2),pp. 445–455.

Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1–3), 229–241.

Wu, C. (2008), “Knowledge creation in a supply chain”, Supply Chain Management: An International Journal, Vol. 13 No. 3, pp. 241-50.

Walters, D. and Lancester, G. (2000), “Implementing value strategy through the value chain”, Management Decision, Vol. 38 No. 3, pp. 160-78.

Yu, C. S. (2002). A GP-AHP method for solving group decision-making fuzzy AHP problems. Computers and Operations Research, 29, 1969–2001.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Copyright ©2013 Academic Journals Center

To make sure that you can receive messages from us, please add the '' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.