Adaptive supplier selection framework for sustainable and resilient additive manufacturing supply chains

Abstract

Supplier selection has become a key strategic decision in the area of supply chain management, especially after the advent of the COVID-19 pandemic. Supply chain managers worldwide are being tested for their ingenuity, resilience, and adaptability as they seek to keep their organization’s essential activities operating smoothly in the face of the massive disruption that COVID-19 has brought to supply networks around the globe. This research, thus, proposes a supplier selection framework encompassing resilience and sustainability-enhancing attributes for an additive manufacturing incorporated supply chain. However, since supplier selection problems are influenced by cognitive and stochastic uncertainties, which cannot be dealt with traditional approaches, therefore, Grey relational theory (GRA) has been employed in this research work. Using a real-world case study of a maintenance, repair, and overhaul (MRO) supply chain with five different suppliers, grey possibility values are computed based on which all the prospective suppliers are prioritized. To validate the applicability of the proposed framework, check the efficacy of the GRA technique and comprehend the extent of our performance, the study’s findings have also been compared to the analytic hierarchy process (AHP) method. By enabling informed, traceable, and data-driven supplier decisions under uncertainty, the study contributes to the industrial information integration literature. It demonstrates how intelligent decision-support systems can aid in managing digital manufacturing ecosystems, thereby supporting industrial digitization, integration, and supply chain agility in increasingly volatile environments.