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Supply chain performance appraisement, benchmarking and decision-making: empirical study using grey theory and grey-MOORA

Author

Listed:
  • Santosh Kumar Sahu
  • Saurav Datta
  • Saroj Kumar Patel
  • Siba Sankar Mahapatra

Abstract

Present work aims to develop efficient decision-support systems to facilitate supply chain performance appraisement, benchmarking and related decision-making. Supply chain performance extent depends on multiple criteria/attributes. Most of the criterions/attributes being intangible in nature; supply chain performance appraisement relies on the judgement (evaluation) of the decision-makers. Moreover, quantitative appraisement of supply chain performance appears very difficult due to involvement of ill-defined (vague) performance measures as well as metrics. In order to overcome this, the study explores theory of grey numbers in order to tackle incomplete and inconsistent subjective judgement of the decision-makers. Firstly, a grey-based decision support system has been postulated to evaluate a unique performance index of a supply chain; to identify ill-performing areas and to benchmark performance of candidate enterprises possessing similar supply chain architecture. Finally, grey-MOORA approach has been inculcated to provide a strong mathematic base of the said performance appraisement platform. Empirical data have been analysed and results obtained thereof, have been reported to exhibit application potential of the decision-support systems in appropriate situation.

Suggested Citation

  • Santosh Kumar Sahu & Saurav Datta & Saroj Kumar Patel & Siba Sankar Mahapatra, 2013. "Supply chain performance appraisement, benchmarking and decision-making: empirical study using grey theory and grey-MOORA," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 3(3), pages 233-289.
    • Handle: RePEc:ids:ijpmbe:v:3:y:2013:i:3:p:233-289
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    Cited by:

    1. D. E. Ighravwe & S. A. Oke, 2017. "A manufacturing system energy-efficient optimisation model for maintenance-production workforce size determination using integrated fuzzy logic and quality function deployment approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 683-703, December.

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