Quantitative models for supply chain performance evaluation: A literature review

- Review of 84 studies of quantitative models to supply chain performance evaluation.
- A conceptual framework is proposed to characterize the studies.
- AHP and DEA are the most used techniques.
- Pairwise comparisons and fuzzy theory are the dominant approaches to uncertainty.
- There are no comparative studies on benefits and drawbacks of different techniques.

This paper presents a review of 84 studies published in the literature from 1995 onwards that propose quantitative models to support supply chain performance evaluation. A conceptual framework is proposed to characterize the studies according to several factors such as the purpose and scope of the model, supply chain strategy, choice of metrics, modeling uncertainty, type of model, techniques, learning capacity, type of application, data source for performance evaluation and validation approach. The reviewed papers were selected from Science Direct, Scopus, Emerald Insight and IEEE Xplore® databases, as well as the Google Scholar search tool. The results show that most of the studies evaluate more than one performance dimension and are based on multicriteria decision making techniques. AHP and DEA are the most used techniques. Pairwise comparisons and the fuzzy set theory are the dominant approaches to deal with uncertainty. Most studies have reported real case applications and do not include a validation procedure. The paper also discusses some research opportunities and suggestions of further studies brought about by reviewing the current body of knowledge on quantitative models for supply chain performance evaluation.

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