Partner selection for supply chain collaboration: New data envelopment analysis models

Abstract

Partner selection is crucial for ensuring successful supply chain collaboration. This study focuses on selecting the best partner for a predefined two-stage supply chain using data envelopment analysis to assess the performance of collaborative systems. We distinguish between two levels of supply chain collaboration: chain-to-chain and stage-to-stage collaboration. The former involves partner selection within the same supply chain across two stages, while the latter allows for selected partners from different supply chains across two stages. We incorporate the technology learning effect and introduce three degrees of collaboration (minor, major, and medium) for both chain and stage collaboration levels. Solutions are provided for each collaboration level and degree, with comparative analysis indicating that major collaboration in stage-to-stage level yields superior performance. A numerical example and a real-world case study are presented to illustrate our models and findings, demonstrating that our approach offers superior benefits and more flexible options compared to existing methods. Thus, the proposed approach not only contributes to advancing theoretical understanding but also provides practical implications for optimizing collaborative relationships within complex multi-stage supply chain environments.