Ranking Knowledge Management Factors in Supply Chain of National Iranian Copper Industries Company Using FAHP Method

Document Type: Original Research

Authors

1 Department of Management, Behshahr Branch, Islamic Azad University, Behshahr, Iran

2 Department of Management, Sari Branch, Islamic Azad University, Sari, Iran

3 Department of Management, Farabi Campus, University of Tehran, Qom, Iran

4 Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract

Knowledge management is considered as one of the most significant competitive resources for any organization such that most believe that the faster the companies acquire knowledge taking into application, the more they are successful in a competitive market. On the other hand, the competition among companies is no more important; instead, the competition among supply chains is highly focused in order to provide the most value to the customer. Research statistical population included 30 individuals of all practitioners and middle management experts in National copper industries company headquarter. The present research used Fuzzy Analytic Hierarchy Process (FAHP) to rank knowledge management effective factors in supply chain of national Iranian copper industries company. Research results showed that management factors, knowledge creation, acquisition and production process, knowledge assessment and feedback process, knowledge transformation, sharing and distribution process, organizational culture, knowledge use, application and utilization process as well as employees’ characteristics are in order the most to the least important knowledge management criteria in supply chain of national copper industries company through using FAHP technique.

Keywords


Aliahmadi, A. R., Eskandari, M. J., Sadeghi, I, and Nouzari H. (2011). Studying the role of knowledge management means in staff empowerment by using FAHP. “Future management” journal, 10 (27). Summer.
Ahmadvand, A.M., and Bagheri alaaei, M. (2014). Selecting knowledge management strategy through using AHP and TOPSIS. M.Sc. thesis, Technical and engineering faculty. 
Habibi, A., Izadyar, S., and Sarafrazi, A. (2014). Fuzzy multi-criteria decision-making. Katibe Gil publication.
Shafiei nikabadi, M. (2013). A framework for knowledge management processes in supply chain. Research quarterly, Iran Science and Information Technology Research Center. 3(28). Pp. 611-642.
Shafiei nikabadi, M. (2011). A model of knowledge management for improving supply chain performance in automobile industry. PhD. Thesis, Alame Tabatabei University, Faculty of Accounting and Management.
Taheri, F., Joybari, M. A.R., and Rostami, L. M. (2015). Identifying and ranking knowledge management factors in supply chain through using AHP and TOPSIS techniques (case study: Neka industrial estate). M.A. thesis, Islamic Azad University, Sari.
Barth, S. (1999). Managing knowledge across borders. Available WWW: Http: // www.destinationkm.com/articles/default.asp?ArticleID=832& Key Words = %22 supply+chains%22. (Accessed 3 May 2003).
Chang, D. Y (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, Vol. 95, 1996, pp. 649- 655
Chang, C.W., Wu, C.R. and H.L. Lin (2009). Applying fuzzy hierarchy multiple attributes to construct an expert decision making process. Expert Systems with Applications, Volume 36, Issue 4, 2009, Pages 7363- 7368.
Edwardson, I.R., Durst, S. (2013). The Benefits of Knowledge Management in Small and Medium-Sized Enterprises. Social and Behavioral Sciences, 81, 351-354.
He, Q., Ghobadin, A., & Gallear, D. (2013). Knowledge acquisition in supply chain partnerships: The role of power. International Journal Production Economics, 141(2), 605–618.
Hult G. Ketchen D.J. Stanley S.F. (2004). Information processing knowledge development and strategic supply chain performance Academy of Management Journal 47(2) pp: 241-253.
Kant. R. and M. D. Singh, J. (2011). Knowledge Management Adoption in Supply Chain: Sectorial Evidence from Indian Manufacturing Organizations. Journal of Information & Knowledge Management, Volume 10, Issue 01, March. DOI: 10.1142/S0219649211002833
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.
Li, X., & Hu, J. (2012). Business impact analysis based on supply chain’s knowledge sharing ability. Proceida Environmental sciences, 12(B), 1302–1307.
Natti, S., & Ojasalo, J. (2008). Loose coupling as an inhibitor of internal customer knowledge transfer: Findings from an empirical study in B-to-B professional services. Journal of Business and Industrial Marketing, 23(3), 213–223.
National Library for Health: Knowledge Management specialist library. A B C of knowledge management, NHS National Library for Health: Knowledge Management specialist library; 2005. [Cited 12 Feb. 2009]. Available from: http://www.library.nhs.uk/knowledgemanagement/
Marra, M., Ho, W., & Edwards, J. S. (2011). Supply chain knowledge management: A literature review. Expert Systems with Applications, 39(5), 6103–6110.
Mahmoodi, M. & Safavi Jahromi, G. (2014). A New Fuzzy DEMATEL-TODIM Method for evaluation criteria of Knowledge management in supply chain. International Journal of Managing Value and Supply Chains (IJMVSC) Vol.5, No. 2, June.
Mohaghar; A., Rajabani, N.; Karimi Zarchi, M. and Fathi, M. R. (2014). Identifying the Best Method for Using Knowledge Management in Supply Chain Using Fuzzy Logic. International Journal of Business Management and Economics, 1(1), 33-39.
Raisinghani, M. S., & Meade, L. L. (2005). Strategic decisions in supply-chain intelligence using knowledge management: An analytic-network-process framework. Supply Chain Management: An International Journal, 10(2), 151–170.
Patil, S. k. & kant, R. (2014).A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert Systems with Applications.41, 679–693.
Sachin, K. Patil, ravi kant (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Journal expert system with applications, 41, 679-693
Samuel, E. Karine; Goury, L M. Gunasekaran, A & Spalanzani, A. (2011). Knowledge management in supply chain: An empirical study from France. Journal of Strategic Information Systems (20). 283–306.
Schoenherr; T, Griffith, D. A. & Chandra, A. (2014).Knowledge Management in Supply Chains: The Role of Explicit and Tacit Knowledge. Journal of Business Logistics - Vol. 35. 2, p. 121-135.
Zayed Almuiet, M. & Salim, J. (2013). Knowledge Flow in Supply Chain Manufacturing: Case Study in Food Manufacturing Firm. Procedia Technology 11, 463 – 470.
Zhao, J., Pablo, P., & Qi, Z. (2012). Enterprise knowledge management model based on China’s practice and case study. Computers in Human Behavior, 28(2), 324–330.
Zhengyi, Y., & Ronghua, J. (2005). Artificial neural network and its application in the performance evaluation enterprise knowledge management research. Guangxi Social Sciences, 126, 58–61.
Yu, C. S. (2002). A GP-AHP method for solving group decision-making fuzzy AHP problems. Computers and Operations Research, 29, 1969–2001.