Developing a Model for Identification of the Effect of Knowledge Levels on Supply Chain Performance

Document Type: Original Research

Authors

1 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, College of management and economics, Tehran Science & Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Industrial Engineering, College of Technical & Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Twenty first century is described by knowledge development and its effect on all organizational dimensions. Today, knowledge is considered as the key and sometimes the only source of competitive advantage for organizations; that is why managers and organizations focus on utilizing some methods for knowledge acquisition, storage, and knowledge application in the present dynamic and competitive environment to provide access and quick transfer of knowledge in system using knowledge management. Therefore, the present research intends to present a model for identifying the effect of knowledge level on supply chain performance using modeling structural equations. Research statistical population included all automotive industries in Iran such as component makers, sale representatives, manufacturing units, etc. 350 were randomly selected as research sample and a questionnaire was distributed, 240 of which were returned. Finally, the positive, significant effect of business attitudes, organizational memory and individuals’ knowledge on supply chain performance in Iran automotive industry was maintained; whereas, the positive, significant effect of customer knowledge, beneficiaries’ relationships, knowledge in processes, and knowledge in manufacturing and services was rejected.

Keywords


Cai, J. Liu, X. Xiao, Zh. Liu, J. (2009). Improving Supply Chain Performance Management: A Systematic Approach to Analyzing Iterative KPI Accomplishment. Decision Support Systems, 46, 512-521.
Chan, F.T.S, (2003), Performance Measurement in a supply Chain, International Journal of Advanced Manufacturing Technology, 21(1), 534-548.
Choi, T. Y. & Eboch, K. (1998). The TQM paradox: Relations among TQM practices, plant performance, and customer satisfaction. Journal of Operations Management, 17, 59-75.
Crone, M. Roper, S. (2001). Local learning from multinational plants: knowledge transfers in the supply chain, Regional Studies 35 (6), 535–548.
Davenport, T.H. & Prusak, L. (1998), Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston, MA
Dixon, J. R. Dixon. (1992). measuring manufacturing flexibility: An empirical investigation, European Journal of Operational Research 60 (2), 131-143.
Eskandari, K., Aghazade, H. (2016). Success of CRM and the role of knowledge management (case study: Municipality, Osku city). Value chain management, 1(3). 
Jafarnezhad, A., Morovati sharifabadi, A., and Asadiyan ardakani, F. (2013). Selected issues of supply chain management. 1st edition, Mehrban Nashr publication; 324.
Lee, M. R., & Lan, Y. C. (2011). Toward a unified knowledge management model for SMEs. Expert Systems with Applications, 38(1), 729-735.
Leibowitz, J., and Y. Chen. (2001). Developing knowledge-sharing proficiencies: building a supportive culture for knowledge-sharing. Knowledge Management Review 3 (6): 12-15.
Liu, S., & Deng, Z. (2015). Understanding knowledge management capability in business process outsourcing. Management Decision, 53(1), 124 -138.
Nonaka, I. and Takeuchi,   H. (1995) The knowledge creating company - How Japanese Companies Create the Dynamics of Innovation. Oxford University
Pilevari, N. (2009). Explaining and evaluating Model of Agility in Supply Chain Based Expert Systems ( Doctoral Dissertation). Science and Research University. Tehran.
Ranjbarfard, M., Aghdasi, M., Albadvi, A., and Hassanzade, M. (2013). Identifying knowledge management barriers for 4 business processes. IT management, 5(1); Pp. 61-88.
Shafiee.M, Lotfi.F, Saleh.H, (2014), Supp;y chain Performance Evaluation With Data Envelopment Analysis and Balanced Scorcadr Approach, Applied Mathematical Modeling, Vol38, Issues 21-22, PP 5092-5112.
Taebi. P ; Pilevari. N, (2015), Providing a Model for Identification of Impact of Agility Enablers on Supply Chain Performance in Iran's Automotive Industry, Buletin Teknologi Makanan 2(7), 183-192.
Tseng, S.M. (2009). A study on customer supplier, and competitor knowledge using the knowledge chain model, International Journal of Information Management 29; 488–496.