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This research aims to aid original equipment manufacturers (OEMs) to model, analyze, evaluate, and benchmark potential design and manufacturing suppliers based on respective product engineering teams’ efficiencies. The product engineering efficiency in this study is modeled in terms of product engineering-related attributes such as commercial lead time, number of parts, number of green features, number of end products developed, and so forth. Essentially, these parameters capture more complex interactions than simple traditional supplier selection criteria such as cost, quality, delivery, and flexibility. Due to the presence of information uncertainty in terms of bounds related to the suppliers’ related parameters, a number of data envelopment analysis (DEA) efficiency measurement models have been deployed. The proposed decision support system is novel because it models both the self-assessment type and cross-efficiency type using DEA such that maximum discrimination can be achieved amongst suppliers in the presence of interval data. The study is demonstrated for ten different sheet-metal cabin suppliers. Comparison with some well-known, relevant methods is also carried out to illustrate the validity of the proposed method. The research can specifically help supply chain managers to align the evaluation of potential suppliers with their firm's commercial considerations in the presence of information uncertainty.

international journal of production research

Assessing the impact of supplier benchmarking in manufacturing value chains: an Intelligent decision support system for original equipment manufacturers