Do Artificial Intelligence-Enabled Digital Strategies Enhance the Circular Supply Chain? An Automotive Case

The adoption of circular economy (CE) practices and artificial intelligence (AI) in the supply chain (SC) has become extremely significant in manufacturing organizations. The CE seeks to facilitate sustainable growth by managing the flow of materials and energy within closed-loop systems. The CE has resulted in the development of sustainable business models. AI capabilities transform work activities, data flows, and organizational processes. Therefore, the present study aims to develop a framework to improve circular supply chain (CSC) adoption in the automobile manufacturing sector by identifying and analyzing CE practices and AI-enabled digital strategies. The proposed framework was analyzed by employing a hybrid approach of Prioritized Weighted Average–Criteria Importance Through Intercriteria Correlation–Preference Ranking Organization Method for Enrichment Evaluations-II (PWA-CRITIC-PROMETHEE-II) under an Interval-Valued Fermatean Fuzzy (IVFF) environment. IVFF-CRITIC was employed to determine the CE practices’ weights, while IVFF-PROMETHEE-II was utilized to establish the relative index of AI-enabled digital strategies to enhance the CSC adoption. The key findings of the current study indicate that “AI-enabled infrastructure configuration for circular economy adoption in the supply chain”, “AI-integrated equipment to facilitate adaptability and mass personalization”, and “Robotics and AI-driven manufacturing and material reclamation” are the most significant AI-based digital strategies that support CE practices to enhance the adoption of a CSC and encourage case example manufacturing organizations to align their operations with AI and CE. Moreover, the outcomes of the study will deliver a comprehensive evaluation of CE practices and AI-enabled digital strategies for SC managers, based on the relative indexing obtained through the implementation of the hybrid approach.