A two-stage classification approach for AI technical service supplier selection based on multi-stakeholder concern

Abstract

Artificial intelligence (AI) technology, which can help supply chains improve operational efficiency and reduce production costs, has gradually been adopted by many enterprises in recent years. In general, stakeholders in the supply chain have their own requirements for AI technical services. However, most traditional service supplier selection methods rarely explicitly consider the interest demands of multi-stakeholders and coordinate them, which may lead to unsatisfactory decision results. Therefore, to support the successful implementation of AI technology in supply chain management, this paper proposes a two-stage AI technical service supplier classification method that considers the requirements of multi-stakeholders. In the first stage, we identify the opinions of stakeholders via cluster analysis and determine group coordination needs while fully considering fairness concerns. Moreover, in the second stage, we evaluate and classify AI technical service suppliers based on the group opinions of stakeholders and decision makers' risk preferences. As AI technical service suppliers are mostly emerging internet enterprises, we construct a criteria system to help assess these suppliers in advance. To verify the practicability and effectiveness of our method, we conduct a case study on the automobile supply chain. Furthermore, this work can provide guidance for AI technical service suppliers to improve their enterprise construction.