A Decision Making Framework for Evaluating Suppliers of Automotive Parts Industry Based on Cognitive Map

Document Type: Research Paper

Authors

1 Faculty of Industrial Engineering, Urmia University of Technology, Iran

2 Department of Industrial Management, Allameh Tabataba'i University, Iran

Abstract

Evaluating the suppliers and selecting an appropriate set of them are one of the fundamental strategies to enhancing the product/service quality, and the reputation of the organization. Hence, identifying criteria for suppliers’ evaluation, determining how important they are, and providing a framework for using them in evaluation process, play an important role in the success of an organization. As the evaluation criteria in the real world influence on each other, the actual weights of criteria in this study is achieved by considering both the relations among these criteria and expert’s opinion. Thus, cognitive maps method is used to determine the weight of evaluation criteria with causal relationships between them in the automotive industry. Then, a framework for the evaluation and gradation of suppliers based on the weighted criteria is presented. This framework was implemented in one of the active company in automotive spare parts industry, according to the role of the automotive industry in GDP.

Keywords

Main Subjects


1-       Kopczak, L. R. (1997). “Logistics partnerships and supply chain restructuring: Survey results from the US computer industry”, Production and Operations Management, Vol. 6, No. 3, PP. 226- 247.

2-       Aissaoui, N., Haouari, M. & Hassini, E. (2007). “Supplier selection and order lot sizing modeling: A review”, Computers & operations research, Vol. 34, No.12, PP. 3516- 3540.

3-       Ghobadian, A., Stainer, A. & Kiss, T. (1993). “A computerized vendor rating system”, In Proceedings of the first international symposium on logistics, The University of Nottingham, United Kingdom, PP. 321- 328.

4-       Chen, Y. J. (2011). “Structured methodology for supplier selection and evaluation in a supply chain”, Information Sciences, Vol. 181, No. 9, PP. 1651- 1670.

5-       Lewis, H. (1940). Industrial Purchasing; Principles and Practice, Business Publications.

6-       Amin, S. H. & Razmi, J. (2009). “An integrated fuzzy model for supplier management: A case study of ISP selection and evaluation”, Expert systems with applications, Vol. 36, No. 4, PP. 8639- 8648.

7-       Dickson, G. W. (1966). “An analysis of vendor selection systems and decisions”, Journal of purchasing, Vol. 2, No. 1, PP. 5- 17.

8-       Ellram, L. M. (1990). “The supplier selection decision in strategic partnerships”, Journal of Purchasing and materials Management, Vol. 26, No. 4, PP. 8- 14.

9-       Weber, C. A., Current, J. R. & Benton, W. C. (1991). “Vendor selection criteria and methods”, European journal of operational research, Vol. 50, No. 1, PP. 2- 18.

10-   Shipley, D. D. (1985). “Resellers' supplier selection criteria for different consumer products”, European Journal of Marketing, Vol. 19, No. 7, PP. 26- 36.

11-   Lin, R. H. (2009). “An integrated FANP–MOLP for supplier evaluation and order allocation”, Applied Mathematical Modelling, Vol. 33, No. 6, PP. 2730- 2736.

12-   Bai, C. & Sarkis, J. (2010). “Green supplier development: Analytical evaluation using rough set theory”, Journal of Cleaner Production, Vol. 18, No. 12, PP. 1200- 1210.

13-   Wu, D. D. (2010). “A systematic stochastic efficiency analysis model and application to international supplier performance evaluation”, Expert Systems with Applications, Vol. 37, No. 9, PP. 6257- 6264.

14-   Baskaran, V., Nachiappan, S. & Rahman, S. (2012). “Indian textile suppliers' sustainability evaluation using the grey approach”, International Journal of Production Economics, Vol. 135, No. 2, PP. 647- 658.

15-   Kumar, D., Singh, J. & Singh, O. P. (2013). “A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices”, Mathematical and Computer Modelling, Vol. 57, No. 11, PP. 2945- 2960.

16-   Wang, M. & Li, Y. (2014). “Supplier evaluation based on Nash bargaining game model”, Expert Systems with Applications, Vol. 41, No. 9, PP. 4181- 4185.

17-   Beikkhakhian, Y., Javanmardi, M., Karbasian, M. & Khayambashi, B. (2015). “The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods”, Expert Systems with Applications, Vol. 42, No. 15, PP. 6224- 6236.

18-   Azadi, M., Jafarian, M., Saen, R. F. & Mirhedayatian, S. M. (2015). “A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context”, Computers & Operations Research, Vol. 54, No. 1, PP. 274- 285.

19-   Lima-Junior, F. R. & Carpinetti, L. C. R. (2016). “Combining SCOR®model and fuzzy TOPSIS for supplier evaluation and management”, International Journal of Production Economics, Vol. 174, No. 1, PP. 128-141.

20-   Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E. & Omid, M. (2016). “Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry”, Computers & Operations Research, Doi:10.1016/j.cor.2016.02.015.

21-   Yousefi, S., Mahmoudzadeh, H. & Jahangoshai Rezaee, M. (2016). “Using supply chain visibility and cost for supplier selection: a mathematical model”, International Journal of Management Science and Engineering Management, Doi:10.1080/17509653.2016.1218307.

22-   Rezaee, M. J., Yousefi, S. & Hayati, J. (2016) “A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy”, Journal of Industrial Engineering International, Doi:10.1007/s40092-016-0178-2.

23-   Zhou, X., Pedrycz, W., Kuang, Y. & Zhang, Z. (2016). “Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation”, Applied Soft Computing, Vol. 46, 424- 440.

24-   Gandhi, K., Govindan, K. & Jha, P. C. (2016). “Fuzzy bi-criteria decision making approach for supplier selection and distribution network planning in supply chain management”, Journal of Information and Optimization Sciences, Vol. 37, No. 5, PP. 653- 679.

25-   Luzon, B. & El-Sayegh, S. M. (2016). “Evaluating supplier selection criteria for oil and gas projects in the UAE using AHP and Delphi”, International Journal of Construction Management, Vol. 16, No. 2, PP. 175-183.

26-   Kosko, B. (1986). “Fuzzy cognitive maps”, International Journal of man-machine studies, Vol. 24, PP. 65-75.

27-   Papageorgiou, E. I. (2011). “A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques”, Applied Soft Computing, Vol. 11. No. 1, PP. 500- 513.

28-   Büyüközkan, G. & Vardaloğlu, Z. (2012). “Analyzing of CPFR success factors using fuzzy cognitive maps in retail industry”, Expert Systems with Applications, Vol. 39, No. 12, PP. 10438- 10455.

29-   Lee, K. C., Lee, H., Lee, N. & Lim, J. (2013). “An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms”, Industrial Marketing Management, Vol. 42, No. 4, PP. 552- 563.

30-   Olazabal, M. & Pascual, U. (2016). “Use of fuzzy cognitive maps to study urban resilience and transformation”, Environmental Innovation and Societal Transitions, Vol. 18, PP. 18- 40.

31-   Rezaee, M. J., Yousefi, S. & Babaei, M. (2017). “Multi-stage cognitive map for failures assessment of production processes: An extension in structure and algorithm”, Neurocomputing, Vol. 232, PP. 69- 82.

32-   Uzochukwu, B. M., Udoka, S. J. & Balogun, F. (2016). “Development and implementation of product sustainment simulator utilizing fuzzy cognitive map (FCM)”, Benchmarking: An International Journal, Vol. 23, No. 2, PP. 425- 442.

33-   Rezaee, M. J., Yousefi, S. & Hayati, J. (2016). “A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level”, Neural Computing and Applications, Doi:10.1007/s00521-016-2478-2.

34-   Çoban, V. & Onar, S. Ç. (2017). Modelling Solar Energy Usage with Fuzzy Cognitive Maps, In Intelligence Systems in Environmental Management: Theory and Applications, Vol. 113, Springer International Publishing, PP. 159- 187.

35-   Papageorgiou, E. I., Stylios, C. & Groumpos, P. P. (2006). “Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links”, International Journal of Human-Computer Studies, Vol. 64, No. 8, PP. 727- 743.

36-   Schneider, M., Shnaider, E., Kandel, A. & Chew, G. (1998). “Automatic construction of FCMs”, Fuzzy sets and systems, Vol. 93, No. 2, PP. 161- 172.

37-   Meade, L. M. & Sarkis, J. (1999). “Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach”, International Journal of Production Research, Vol. 37, No. 2, PP. 241- 261.

38-   Papageorgiou, E. I. & Kannappan, A. (2012). “Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification”, Applied Soft Computing, Vol. 12, No. 12, PP. 3798- 3809.

39-   Papageorgiou, E. I. & Salmeron, J. L. (2014). Methods and algorithms for fuzzy cognitive map-based modeling. In Fuzzy Cognitive Maps for Applied Sciences and Engineering, Springer Berlin Heidelberg, Vol. 54, PP. 1-28.

40-   Storn, R. & Price, K. (1997). “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces”, Journal of global optimization, Vol. 11, No. 4, PP. 341- 359.