AI-Assisted Sustainability Intelligence and Decision Support in Manufacturing Organizations Using Expert-Validated Indicators Under the Triple Bottom Line Framework

Abstract

Industrial sustainability assessment needs a transparent and operational framework that can manage multidimensional indicators, expert uncertainty, weighting complexity, and managerial interpretation. This research presents the Sustainable Industrial Measurement (SIM) Model, an AI-assisted sustainability intelligence architecture for manufacturing organizations. The model comprises literature-based indicator synthesis, Fuzzy Delphi Technique (FDT), Pareto 80/20 screening, Group Analytic Hierarchy Process (Group AHP), Utility Value Analysis and the web-based AI-enabled decision support system (AI-DSS) under the Triple Bottom Line (TBL) framework. In FDT validation by consensus, threshold and fuzzy score criteria, 33 experts accepted 64 indicators. The Pareto screening reduced the set to 50 high-impact indicators, consisting of 10 economic, 22 social and 18 environmental indicators. The priority weights were derived from the group AHP weighting by 21 experts and checked for consistency. The environmental and economic indicators represent the dominant sustainability priorities. The weighted structure was embedded in the web-based AI-DSS to enable automated scoring, visualization, gap diagnosis and AI-based managerial recommendations. Thirty industrial practitioners reported excellent perceived usability of the SIM Model, with a System Usability Scale score of 86.0. However, the evaluation assessed usability only, not the accuracy, effectiveness, or organizational impact of AI-assisted recommendations for manufacturing sustainability decisions and future implementation.