Bridging industry 5.0 and circular economy in Indian SMEs: an integrated ML–MCDM framework to prioritize cross-sectoral barriers and enablers

Abstract

Circular economy (CE) adoption is crucial for Indian small and medium enterprises (SMEs), which represent 99% of firms and act as critical tier I/II suppliers. Effective CE transition, however, demands coordinated action across a diverse ecosystem—from policymakers to SME managers and technology providers—making it inherently a multi-stakeholder endeavor. This study equips these diverse actors with a shared understanding of cross-sectoral barriers, enablers, digital readiness, and policy pathways across five Indian SME sectors: manufacturing, textiles and apparel, waste management, reverse logistics, and business services. To achieve this, we develop a three-stage analytical pipeline: (i) Grey Analytic Hierarchy Process (G-AHP) prioritizes twenty CE barriers across cultural, financial, regulatory, technological, and knowledge dimensions; (ii) Stochastic VIKOR generates sector-specific rankings of ten Industry 5.0 enablers under uncertainty; (iii) XGBoost refines these rankings by incorporating uncertainty patterns and digital-readiness features. This framework advances theory by bridging prescriptive decision-analysis with predictive analytics through a unified logic of uncertainty propagation. Guided by 20 experts across the five sectors, the analysis reveals that financial and regulatory barriers are paramount across sectors, while technological bottlenecks are severe in waste management, and cultural and knowledge barriers dominate in business services. Regarding enablers, IoT-based maintenance and blockchain for supply chain management consistently rank highest. The framework was validated through rigorous robustness checks. The study concludes with targeted policy interventions and managerial recommendations.