The interpretive structural modeling of the adaption solutions of the knowledge management in the supply chain of the sanitary ware

Document Type : Technology

Abstract

The aim of this article is to identify the various solutions, which are effective in the adaption of the knowledge management in the supply chain of the Golsar Fars sanitary ware company. To do so, first, by studying the thematic content, the various kinds of solutions, concerning the previous researches are identified, and by using the content validity, those solutions that are related to the sanitary ware companies is extracted. Lastly, by using the interpretive structural modeling, the mutual relations between the solutions are specified, and the effectiveness and impressibility level of them on each other is determined. According to the achieved results, the solution of the use of IT system to present the knowledge, positive management and leadership, and use of supplier development programs have the greatest influence and least impressible according to other solutions. So that driving power of these solutions are 10, 8 and 6 respectively. Also mutual learning for effective knowledge sharing among supply chain solutions has got greatest influence from others. Indeed, dependency of this solution is 10. None of solutions belonged to autonomous variables and categorized in dependent and independent region. Based on ISM graph, it must be demonstrated IT system for well-establishing knowledge management before implementing other solutions.

Keywords

Main Subjects


منابع:
-          Agarwal, A.; Shankar, R. and M.K. Tiwari (2007).”Modeling Agility of Supply Chain”, Industrial Marketing Management, 36(1) , 443-457.
-          Akhavan, Peiman; Zahedi,Mohammadreza ; Najmi  , Ali (2011) . “Key success factors of knowledge management in the automotive industry's supply chains”,Farda Management .tenth year , 26(1), 77-100. (in Persian)
-          Alvandi, N; Mirzaei, R. and Tarokh, M.J. (2014). Investigating the Effective Factor on CSF of KM: Management Consultant Company, Industrial Management Studies, 11(30), 99-127. (in Persian)
-          Azar, Adel ;Tirzo,Ali ;Moghbel Baarz ,abbas;Anvari Rostami , Aliasqar (2010) , “Design and supply chain agility, interpretive structural modeling approach, teaching humanities-research management in Iran” , 4(1) . 1-25. (in Persian)
-          Cerchione, R. and Esposito, E. (2016). A systematic review of supply chain knowledge management research: State of the art and research opportunities, International Journal of Production Economics, 182 (1), 276-292.
-          Cheng, J., Yeh, C., & Tu, C. (2008). Trust and knowledge sharing in green supply chains. Supply Chain Management: An International Journal, 13(4), 283–295.
-          Cheung, C. F., Kwok, S. K., & heung, C. M. (2012). A knowledge-based customization system for supply chain integration. Expert Systems with Applications, 39(4), 3906–3924.
-          Chopra, S., and P.Meindl.( 2007).” Supply chain management” . New Jersey: Prentice-Hall publication.
-          Clemons, R. and Slotnick, S.A. (2016). The effect of supply-chain disruption, quality and knowledge transfer on firm strategy, International Journal of Production Economics, 178(1), 169-186.
-          Collins, J.D. Worthington, W.J. Reyes, P.M. and Romero, M. (2010). “Knowledge Management, Supply Chain Technologies and Firm Performance”, Management Research Review, 33(10), 947-960.
-          Coulson, C.T. (2004). "The knowledge entrepreneurship challenge: Moving on from knowledge sharing to knowledge creation and exploitation", The Learning Organization, 11 (1), 84 – 93.
-          Desai, A. and Rai, S. (2016). Knowledge Management for Downstream Supply Chain Management of Indian Public Sector Oil Companies, Procedia Computer Science, 79(1), 1021-1028.
-          Dorostkar Ahmadi, N. and Shafiei Nikabadi, M. (2015). Presenting an Fuzzy Intelligence Model for Evaluating KM Process in SC: Iran Khodro Corporation, Industrial Management Vision, 18 (5), 153-175. (in Persian)
-          Enriquez, C.A.R., Hernandez, G.A., Miranda, J.M., Cervantes, J.L.S., Mazahua, L.R., Ramirez, C.S. (2016). Supply chain knowledge management supported by a simple knowledge organization system, Electronic Commerce Research and Applications, 19 (1), 1–18
-          Gonzalez, J.C. Saez, P.L. and Lopez, J.E.N. (2015). Absorbing knowledge from supply-chain, industry and science: The distinct moderating role of formal liaison devices on new product development and novelty. Industrial Marketing Management, 47(1), 75-85.
-          Huang, C. C., & Lin, S. (2010). Sharing knowledge in a supply chain using the semantic web. Expert Systems with Applications, 37(4), 3145–3316.
-          Kabiri Naeini, M. and Hosseini Nasab, H. (2009). Designing of Knowledge Management Systems for Supply Chain, 5th International Conference of Information Technology. (in Persian)
-          Lin, L. and Kwok, L. (2006).”Challenges to KM at Hewlett Packard China”, Knowledge Management Review, Vol. 9, No.1, pp.20-23.
-          Lynn, M.R.(1988).” Determination and quantification of content validity”, Nurs Res, 35(1), 382-385.
-          Marra, M. Ho, W. and Edwards, J.S. (2012). “Supply chain knowledge management: A literature review”, Expert Systems with Applications, 39(1), 6103-6110.
-          Maqsood, T., & Finegan, D. W. A. (2007). Extending the ‘‘knowledge advantage’’: Creating learning chains. The Learning Organization, 14(2), 123–141.
-          McKenzie, J.F., Wood, M.L. and Kotecki, J.E. (1999) . “Establishing content validity: using qualitative and quantitative steps”, American Journal of Health Behavior, 23(1), 311-318.
-          Mehralizadeh, Y. and Abdi, M.R.(2012). Knowledge Management System: Experience of Official Tax, Chamran University Publication, First Edition. (in Persian)
-          Mentzer, J.T. DeWitt,W. Keebler,J.S. Min,S. Nix,N.W. Smith,C.D. and Zacharia,Z.G. (2001). “Defining Supply Chain Management”, Journal Of Business Logistics, 22(2) , 1-25..
-          Myers, B.M. and Cheung, M.S. (2008).”Sharing Global Supply Chain Knowledge”, MIT Sloan Management Review, 49(4), 67-73.
-          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.
-          Park, J. Y., Im, K. S., & Kim, J. S. (2011). The role of IT human capability in the knowledge transfer process in IT outsourcing context. Information & Management, 48(1), 53–61.
-          Patil, S.K. and 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.
-          Patil, S.K. and Kant, R. (2014). A hybrid approach based on fuzzy DEMATEL and FMCDM to predict success of knowledge management adoption in supply chain, Applied Soft Computing, 18 (1), 126–135.
-          Patil, S.K. and Kant, R. (2016). Evaluating the impact of Knowledge Management adoption on Supply Chain performance by BSC-FANP approach: An empirical case study, TEKHNE (Review of Applied Management Studies), 14 (1), 52-74.
-          Pfohl, H.C.; Gallus, P. and D. Thomas,(2011). “Interpretive Structural Modeling of Supply Chain Risks”, International Journal of Physical Distribution & Logistics Management, 41(9), 839 – 859.
-          Samuel, K.E. Goury. M.L. Gunasekaran, A. and Spalanzani, A. (2011). “Knowledge Management in Supply Chain: An Empirical Study from France”, Journal of Strategic Information Systems, 20(3), 283-306.
-          Shakerian, H., Dehnavi, H., Shateri, F. (2016). A framework for the implementation of knowledge management in supply chain management, Procedia (Social and Behavioral Sciences), 230 (1), 176 – 183.
-          Shih, S. C., Hsu, S., Zhu, Z., & Balasubramanian, S. (2012). Knowledge sharing-A key role in the downstream supply chain. Information & Management, 49(2), 70–80.
-          Shojaei, P. (2016). Modeling the Knowledge Management Barriers in SC by Using ISM and Fuzzy MICMAC, Industrial Management Vision, 21(1), 53-74. (in Persian)
-          Sorensen, L.B. (2005). “How Risk and Uncertainity is Used in Supply Chain Managament: a Literature Study”, International Journal of  Integrated Supply Management, Vol.1, No.4, pp.387-409.
-          Tako, A.A. and S. Robinson (2011). “The application of discrete event simulation and system dynamics in the logistics and supply chain context”: Decision Support System,   PP.1-14.
-          Vaezi , Fereshteh ; Shahraki Alireza (1390) .” Role of knowledge management in the success of the supply chain”, Forogh Tadbir, 18(1), , 33-42. (in Persian)
-          Wang, C., Fergusson, C., & Perry, D. (2008). A conceptual case-based model for knowledge sharing among supply chain members. Business Process Management Journal, 14(2), 147–165.
-          Wallace, L.S., Blake, G.H., Parham,J.S. and Baldridge ,E. (2003).” Development and Content Validation of Family Practice Residency Recruitment Questionnaires, Family Medicine, 35(7), 496-498.
-          Wang, C., Fergusson, C., & Perry, D. (2008). A conceptual case-based model for knowledge sharing among supply chain members. Business Process Management Journal, 14(2), 147–165.
-          Warfield, J.W. (1974)." Developing Interconnected Matrices in Structural Modeling”, IEEE Transcript on Systems Men and Cybernetics, 4(1) , 51-81.
-          Wong, W.P. and Wong, K.Y. (2011).” Supply Chain Management, Knowledge Management Capability, and Their Linkages Towards Firm Performance”, Business Process Management Journal, 17(6), pp.940-964.
-          Wu, D. J. (2001). Software agents for knowledge management: Coordination in multi agent supply chains and auctions. Expert Systems with Applications, 20(1), 51–64.
-          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.