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

TL;DR

This paper assesses how lean and green practices impact operational performance using a data-driven simulation, introducing the OEEE metric to evaluate improvements in sustainability and efficiency.

Contribution

It develops a novel evaluation framework combining system analysis, modeling, and the OEEE metric to measure lean and green practice effects on performance.

Findings

Improved OEEE values after implementing scenarios

Reduced lead times with proposed improvements

Demonstrated benefits of lean and green integration

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%.