Data-driven life cycle assessment of the automobile industry in Indonesia: Identifying circular supply chain enablers

Abstract

This study constructs and develops a valid hierarchical structure for life cycle assessments (LCA) of circular supply chain (CSC) enablers within the automobile industry in Indonesia. Prior studies have focused on developing LCA of CSC enablers metrics; however, there is insufficient focus on integrated assessments of materials, parts, and product design in conjunction with waste management, aimed at enhancing the entire life cycle from raw material extraction and sourcing to end-of-life management. This study adds to the LCA literature by integrating the essential circular materials, parts and product design to optimize waste management, while enhancing the hierarchical measures of CSC enablers for improved policy recommendations and decision-making process, employing a data-driven approach to develop the structure. This data-driven approach that utilizes both qualitative and quantitative techniques integrates content and bibliographic analyses, the entropy weighted method, the fuzzy Delphi method, exploratory factor analysis, and fuzzy decision-making trial and evaluation laboratory to construct a hierarchical LCA structure by analyzing the interrelationships among attributes. The findings indicate that prioritizing assessments of waste management and circular materials, parts and product design are essential for improving CSC. Prioritize product refurbishment, eco-design, recycling process improvement, information management systems, and risk assessment management are as key CSC enablers for policy recommendations.