Performance Evaluation of Auto Parts Suppliers for Collaborative Optimization of Multi-value Chains
The performance of auto parts suppliers is becoming an important factor in multi-value chain collaboration. In order to improve the productivity of all links in the auto parts value chain and the competitiveness of the whole value chain, this paper proposes a performance evaluation method for parts suppliers and for the multi-value chain coordination of automobiles. Firstly, from the supplier business data in the auto parts value chain collaboration platform, the relevant description attributes are extracted, and the initial index system of supplier performance evaluation is established. Then, based on the grey system theory and the neighborhood rough set theory, a screening method for the importance of the performance evaluation indexes of auto parts suppliers is designed. Then, the index weights are calculated by the orness measure. Finally, according to the MEOWA idea, the integrated grayscale attribute values. Corresponding weights are used to calculate the comprehensive performance and guide the performance-based accessory supplier optimization. Data from the experimental results on the actual business shows that the supplier evaluation method can correctly reflect the performance of the parts suppliers and provide a quantitative reference for the business synergy of the parts value chain.
KeywordsMulti-value chain Parts supplier Performance evaluation Grey theory Neighborhood rough set
The author wishes to thank the editor and anonymous referees for their helpful comments and suggested improvements. This paper is supported by The National Key Research and Development Program of China (2017YFB1400902).
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