AI-ENHANCED MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR LEAN MANUFACTURING EFFICIENCY AND ENERGY-CONSCIOUS PRODUCTION SYSTEMS

Abstract

Manufacturing enterprises increasingly operate under the dual imperative of enhancing operational efficiency while simultaneously reducing energy consumption. However, conventional improvement approaches often remain siloed—addressing either productivity or sustainability—thereby constraining opportunities for reproducible and system-wide optimization. This study aims to develop and empirically evaluate an AI-enhanced, multi-objective optimization framework that aligns lean manufacturing principles with energy-conscious operations. Using a quantitative, cross-sectional, case-based design, enterprise cloud data pipelines were established to integrate Manufacturing Execution System (MES) and Supervisory Control and Data Acquisition (SCADA) historians with sub-metered energy logs across multiple discrete-part production lines in three industrial plants. The primary variables included Lean Practice Intensity (measured through a five-point Likert instrument), Overall Equipment Effectiveness (OEE) and its components, and Energy Intensity (kWh per good unit). Additional operational moderators such as peak kW, demand variability, product mix, machine age, and shift patterns were also captured to account for contextual heterogeneity. The analysis pipeline consisted of scale reliability testing, descriptive and inferential statistics, correlation analysis, and hierarchical multiple regression incorporating plant- and shift-level controls. Furthermore, mediation analysis tested the role of efficiency as an intervening variable, while moderation analysis assessed the conditional effects of demand variability. To complement empirical inference, a surrogate-assisted evolutionary optimization algorithm was employed to generate Pareto-optimal fronts and knee solutions, benchmarked against baseline production schedules. Key results demonstrate that Lean Practice Intensity exhibits a positive association with OEE and a negative association with energy intensity, confirming efficiency as a significant mediator with a residual direct effect of lean practices on energy outcomes. The influence of lean practices was found to diminish under high demand variability, indicating a boundary condition for implementation. The optimization results further revealed that knee-point policies typically improved OEE by approximately 2–5 percentage points, reduced energy intensity by 6–15%, and curtailed peak demand by around 6%, all without compromising delivery performance or schedule adherence. From a managerial and systems integration perspective, the findings highlight the importance of viewing lean routines as actionable energy levers rather than ancillary practices. The study recommends institutionalizing variability-aware and tariff-aware production planning, and adopting a governed digital stack that links key performance indicators (KPIs), digital-twin telemetry, and AI-driven optimization engines.