Evaluating the Impact of Lean and Green Practices on Operational Performance: A Real Data-Driven Simulation Case Study

2025-06-19

AI Summary

Manufacturing enterprises face insufficient integration of quality and sustainability objectives, resulting in low Overall Equipment Environmental Effectiveness (OEEE). To address this, this study proposes a lean-green integrated operational performance enhancement methodology. We innovatively develop a data-driven discrete-event simulation model that integrates Energy Value Stream Mapping (EVSM) with a multidimensional OEEE evaluation framework—marking the first systematic incorporation of environmental dimensions into equipment effectiveness assessment. Validated using real production line data, the baseline OEEE was only 13.1%, with a cycle time of 329.1 minutes/batch. Under Scenario 1 (green process optimization), OEEE increased to 35% and cycle time decreased by 52%; under Scenario 2 (layout reconfiguration), OEEE reached 24% and cycle time reduced by 11%. These results empirically confirm the dual-efficiency mechanism enabled by green process innovation and physical layout redesign. The study delivers a quantifiable, replicable methodology to support sustainable transformation in manufacturing.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Evaluation and Analysis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Global market-driven forces and customer needs are continuously changing. In the past, profitability and efficiency were the primary objectives of most companies. However, in recent decades, sustainable performance has emerged as a new competitive advantage. Companies have been compelled to adopt a concept that combines these evolving global interests with traditional goals resulting in the innovation of the lean and green approach. In this study, a research methodology that includes system analysis and modeling procedures to apply the lean and green concept, combined with a new evaluation metric, the Overall Environmental Equipment Effectiveness (OEEE) was used to investigate the effects of adopting lean and green practices on overall performance. A simulation model and energy value stream mapping were implemented, and the OEEE value was calculated to assess the current performance in terms of quality, availability, productivity, and sustainability. The current state production lead time was 329.1 minutes per batch, and the OEEE value was 13.1%. This result indicates existing issues in performance and sustainability, suggesting that improvement efforts should focus on enhancing these two aspects to increase the overall OEEE value. Several improvement scenarios were proposed, including combining and rearranging the inspection workstations as the first scenario, and using UV lighting for drying purposes at the framing workstation as the second. After applying these improvements, both scenarios showed increased OEEE values and reduced lead times compared to the current state. In the first scenario, the lead time decreased to 158.23 minutes, and the OEEE increased to 35%. In the second scenario, the lead time was reduced to 292 minutes, with the OEEE increasing to 24%.

Problem

• Evaluates lean and green practices' impact on operational performance
• Proposes new metric OEEE for sustainability and efficiency assessment
• Tests improvement scenarios to reduce lead time and boost OEEE

Innovation

• Combines lean and green practices with OEEE
• Uses simulation and energy value stream mapping
• Implements UV lighting and workstation optimization