Vendor Performance Measurement Using Fuzzy Logic Controller

Volume 2, Issue 2, pp 311--318 http://dx.doi.org/10.22436/jmcs.002.02.11

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Authors

Hadi Shirouyehzad - Faculty Member of IE Department Islamic Azad University, Najafabad Branch Hamidreza Panjehfouladgaran - M.S.c. of Industrial and system engineering Reza Dabestani - Management department, Isfahan University Mostafa Badakhshian - Department, Deputy of Transportation and Traffic, Municipality of Isfahan

Abstract

The era of globalization has begun and organizations endeavor to increase their market share in the competitive environment. To achieve the mentioned goal, organizations should increase their effectiveness as a major strategy in order to improve their performance. Performance measurement as a managerial key can be used for monitoring activities in organizations. Vendors’ selection is one of the issues which influence the efficiency of organizations. Therefore, performance measurement of vendors plays a vital role in firms. Many conceptual and analytical models have been developed for addressing vendor selection problems. Hence, a suitable approach is needed to consider all the factors in order to select the most efficient vendor. In this paper, fuzzy logic controller as a robust and easy understanding approach is applied to transform the quantitative variable to linguistic terms in order to measure the vendors’ performance. Four criteria which can influence vendors’ performance are considered. The criteria are service quality, price, lateness deliveries and rate of rejected parts.

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Keywords

• Vendor Selection
• Performance Measurement
• Fuzzy Logic Controller (FLC).

MSC

•  03B52
•  93C42

References

• [1] M. Hugos, Essentials of Supply Chain Management, John Wiley & Sons, New Jersey (2003)
  • View Article
  • Google Scholar


• [2] S. Chopra, P. Meindl, Supply chain management: strategy, planning and operations, Prentice Hall, Upper Saddle Rive (2001)
  • Google Scholar


• [3] J. K. Mentzer, Fundamentals of supply chain management: twelve drivers of competitive advantage, Sage Publication, London (2004)
  • View Article
  • Google Scholar


• [4] M. Kumar, P. Vrat, R. Shankar, A fuzzy goal programming approach for vendor selection problem in a supply chain, Computers and Industrial Engineering, 46 (2004), 69--85
  • View Article
  • Google Scholar


• [5] S. Talluri, R. Narasimhan, A. Nair, Vendor performance with supply risk: a chance-constrained DEA approach, Int. J. Prod. Econ., 100 (2006), 212--222
  • View Article
  • Google Scholar


• [6] C. A. Weber, J. R. Current, W. C. Benton, Vendor selection criteria and methods, Eur. J. Oper. Res., 50 (1991), 2--18
  • View Article
  • Google Scholar


• [7] G. W. Dickson, An analysis of vendor selection systems and decisions, Journal of Purchasing, 2 (1966), 5--17
  • View Article
  • Google Scholar


• [8] Y. Wind, P. J. Robinson, The determinants of vendor selection: the evaluation function approach, Journal of Purchasing and Materials Management, 4 (1968), 29--41
  • View Article
  • Google Scholar


• [9] S. N. Chapman, Just-in-time supplier inventory: An empirical implementation model, Int. J. Prod. Res., 27 (1989), 1993--2007
  • View Article
  • Google Scholar


• [10] S. N. Chapman, P. L. Carter, Supplier/customer inventory relationships under just-in-time, Decision Sciences, 21 (1990), 35--51
  • View Article
  • Google Scholar


• [11] C. A. Weber, J. Current, A. Desai, An optimization approach to determining the number of vendors to employ, Supply Chain Management: An International Journal, 5 (2000), 90--98
  • View Article
  • Google Scholar


• [12] C. K. Kwong, W. H. Ip, J. W. K. Chan, Combining scoring method and fuzzy expert systems approach to supplier assessment: a case study, Integrated Manufacturing Systems, 13 (2002), 512--519
  • View Article
  • Google Scholar


• [13] H. C. W. Lau, W. Kai Pang, C. W. Wong, Methodology for monitoring supply chain performance: a fuzzy logic approach, Logistics Information Management, 15 (2002), 271--280
  • View Article
  • Google Scholar


• [14] F. T. S. Chan, H. J. Qi, An innovative performance measurement method for supply chain management, Supply Chain Management: An International Journal, 8 (2003), 209--223
  • View Article
  • Google Scholar


• [15] A. C. F. Guimarães, C. M. F. Lapa, Fuzzy Inference to risk Assessment on Nuclear Engineering Systems, Applied Soft Computing, 7 (2007), 17--28
  • View Article
  • Google Scholar


• [16] A. Parasuraman, V. A. Zeithaml, L. L. Berry, A conceptual model of service quality and its implications for future research, Journal of Marketing, 49 (1985), 41--50
  • View Article
  • Google Scholar


• [17] D. A. Garvin, Managing Quality: The Strategic and Competitive Edge (1st Edition), The Free Press, New York (1988)
  • View Article
  • Google Scholar


• [18] C. Gronroos, Quality comes to service, in: The Service Quality Handbook, 1993 (1993), 17--24
  • Google Scholar


• [19] G. D. Kang, J. James, K. Alexandris, Measurement of internal service quality: application of the SERVQUAL battery to internal service quality, Managing service quality, 12 (2002), 278--291
  • View Article
  • Google Scholar


• [20] V. Jain, M. K. Tiwari, F. T. S. Chan, Evaluation of the supplier performance using an evolutionary fuzzy-based approach, Journal of Manufacturing Technology Management, 15 (2004), 735--744
  • View Article
  • Google Scholar


• [21] R. Ohdar, P. K. Ray, Performance measurement and evaluation of suppliers in supply chain: an evolutionary approach, Journal of Manufacturing Technology Management, 15 (2004), 723--734
  • View Article
  • Google Scholar


• [22] C. Unahabhokha, K. Platts, K. Hua Tan, Predictive performance measurement system: A fuzzy expert system approach, Benchmarking: An International Journal, 14 (2007), 77--89
  • View Article
  • Google Scholar