Enhancing Decision-Making Efficiency Through Production Process Diagnostics

In response to fragmented approaches in green manufacturing research, this study proposes an integrated decision-support framework that unifies production process diagnosis, multi-resource optimization, and data-driven analytics to enhance sustainability in complex manufacturing systems. Combining theoretical modeling (e.g., dynamic resource-element networks), empirical case studies (12 cross-industry cases in automotive, electronics, and textiles), and systematic diagnostics, the research addresses inefficiencies in traditional ERP-MES-PCS architectures, where manual decision-making and disconnected data flows hinder holistic optimization. Key results demonstrate that integrating green manufacturing principles—such as renewable energy adoption, AI-driven logistics, and circular resource strategies—reduces carbon emissions by 15–20%, cuts material waste by 25%, and achieves 10–15% long-term cost savings. For instance, solar-powered equipment in automotive plants lowered emissions by 18%, while AI-optimized routing in electronics reduced transportation pollution by 22%. The framework establishes actionable benchmarks (e.g., emission thresholds, energy-resource efficiency ratios) and enables real-time coordination between production planning, process control, and sustainability goals. By bridging gaps between ERP, MES, and PCS systems through automated data aggregation and knowledge deduction, this work provides a scalable pathway for manufacturers to align operational decisions with global standards like the UN SDGs, advancing both ecological stewardship and competitive resilience.