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Development of Multi-Stage Supplier Performance Evaluation using DEA and Econometrics

Author Affiliations

  • 1Dept. of Management Studies, M.S. Ramaiah School of Adv. Studies, Bengaluru, Karnataka, INDIA
  • 2 Dept. of Automotive and Aeronautical Engg, M.S. Ramaiah School of Adv. Studies, Bengaluru, Karnataka, INDIA
  • 3 Department of Engineering Management, Coventry University, Coventry, UNITED KINGDOM

Res. J. Management Sci., Volume 1, Issue (2), Pages 8-14, September,6 (2012)

Abstract

Many supply chain research schools believe “competition is no longer between companies; it is between supply chains”. In order to create agile supply chains, dynamic (time-dependent) performance evaluation system for assessing trading partners has become necessary. This has led to multi-stage development of mathematical models using inter-disciplinary approaches. In this study, Gear suppliers of a tiller and tractor manufacturing company have been considered. The proposed performance evaluation framework has been synthesized under five stages of model development using data envelopment analysis (DEA) and econometrics. Further, the multi-stage evaluation incorporates non-controllable and categorical formulation, which makes the model realistic from buyers’ perspective. Nonetheless, in the last stage of model development, an attempt to convert static to dynamic DEA model has been made considering inter-temporal effects between input-outputs. This effect has been captured as the lagged effect using Vector Auto Regression (VAR) model. Lastly, the proposed framework has been validated using system efficiency DEA model and Wilcoxon-Mann-Whitney rank sum test. Results have revealed that static evaluation overestimates dynamic evaluation by 4 to 5%. In addition, proposed dynamic evaluation system yielded better DEA results in terms of efficient Decision Making Units (DMUs) in numbers, average efficiency (~23%) and standard deviation (~38%). Therefore, multi-stage supplier performance evaluation methodology considering lagged effect has been considered as the original contribution in this study. In summary, combining DEA and econometric models, offer wide scope for the buyer to carry out performance evaluation under Multi Criteria Decision Making (MCDM) environment.

References

  1. Amit Sachan and Subhash Datta, Review of Supply Chain Management and Logistics Research, International J. of Physical Distribution and Logistics Management, 35(9), 664-705 (2005)
  2. Nimawat Dheeraj and Namdev Vishal, An Overview of Green Supply Chain Management in India, Research J. of Recent Sci., 1(6), 77-82 (2012)
  3. Raorane A.A., Kulkarni R.V. and Jitkar B.D., Association Rule – Extracting Knowledge Using Market Basket Analysis, Research J. of Recent Sci., 1(2), 19-27 (2012)
  4. Charles A. Weber, A Data Envelopment Analysis Approach to Measuring Vendor Performance, Supply Chain Management: An International J., 1(1), 28–39 (1996)
  5. Wai Peng Wong and Kuan Yew Wong, A Review on Benchmarking of Supply Chain Performance Measures, Benchmarking: An International J., 15(1), 25-51 (2008)
  6. Laura B. Forker and David Mendez, An analytical Method for Benchmarking Best Peer Suppliers, International J. of Operations and Production Management, 21(1/2), 195-209 (2001)
  7. John Seydel, Supporting the Paradigm Shift in Vendor Selection: Multicriteria Methods for Sole-Sourcing, Managerial Finance, 31(3), 49-66 (2005)
  8. Sharath Kumar K.M. and Narahari H.K., Estimating Net Dependence Risk from Suppliers’ Perspective using Supply Chain Analytics for DEA Evaluation, International Review of Applied Engineering and Research, 1(2), 155–174 (2011)
  9. Marcello Braglia and Alberto Petroni, A Quality Assurance-Oriented Methodology for Handling Trade-offs in Supplier Selection, International J. of Physical Distribution and Logistics Management, 30(2), 96-111 (2000)
  10. 0.Charles A. Weber, John Current and Anand Desai, An Optimization Approach to Determining the Number of Vendors to Employ, Supply Chain Management: An International J., 5(2), 90-98 (2000)
  11. Jongkyung Park, Kitae Shin, Tai-Woo Chang and Jinwoo Park, An Integrative Framework for Supplier Relationship Management, Industrial Management and Data Systems, 110(4), 495-515 (2010)
  12. 2.Reza Mohammady Garfamy, A Data Envelopment Analysis Approach Based on Total Cost of Ownership for Supplier Selection, J. of Enterprise Information Management, 19(6), 662-678 (2006)
  13. Ramakrishnan Ramanathan, Supplier Selection Problem: Integrating DEA with the Approaches of Total Cost of Ownership and AHP, Supply Chain Management: An International J., 12(4), 258–261 (2007)
  14. Pralay Pal and Bimal Kumar, 16T toward a Dynamic Vendor Evaluation Model in Integrated SCM Processes, Supply Chain Management: An International J., 13(6), 391–397 (2008)
  15. Liang-Chuan Wu, Supplier Selection under Uncertainty: a Switching Options Perspective, Industrial Management and Data Systems, 109(2), 191-205 (2009)
  16. Chien-Ming Chen, Evaluation and Design of Supply Chain Operations using DEA, Ph.D. Thesis, Erasmus University Rotterdam (2009)
  17. Damodar N. Gujarati and Sangeetha, Basic Econometrics, 4th edition, McGraw-Hill (2007)