Green overall equipment effectiveness (GOEE): Theoretical development and simulation-based analysis for sustainable manufacturing

Abstract

Integrating sustainability considerations into equipment-level performance measurement remains a methodological challenge in manufacturing systems. While MES/SCADA platforms enable high-frequency operational monitoring, conventional Overall Equipment Effectiveness (OEE) focuses primarily on productivity losses related to availability, performance, and quality, and does not explicitly account for resource efficiency or environmental impacts at the equipment level. To address this gap, this study presents the theoretical development of a Green Overall Equipment Effectiveness (GOEE) indicator that extends the traditional OEE framework by incorporating a bounded environmental efficiency factor representing normalized resource intensity. The formulation of GOEE is grounded in axiomatic consistency with OEE and introduces a benchmark-based environmental factor to capture deviations in energy, water, and material consumption from expected reference conditions. To examine its theoretical behavior and diagnostic properties, calibrated Monte Carlo simulation (n = 1200 scenarios) was employed to represent controlled manufacturing degradation scenarios, including gradual efficiency drift, stochastic noise, and benchmark uncertainty. Simulation results show that GOEE exhibits markedly stronger discriminatory capability than conventional OEE (Cohen's d = 3.01 for the matrix formulation), while conventional OEE displays negligible separation under matched productivity conditions. The proposed index further demonstrates the mathematical capability to detect progressive resource-efficiency deterioration earlier than conventional lagging indicators under controlled degradation settings. The magnitude of the observed lead time is inherently conditional upon system noise characteristics, degradation trajectories, and detection-parameter settings, and should therefore be interpreted as an analytical sensitivity outcome rather than an empirical constant. These findings constitute a simulation-based proof-of-concept demonstrating the theoretical feasibility and axiomatic consistency of GOEE in controlled manufacturing environments. Empirical validation using live industrial data remains essential to assess infrastructure requirements, robustness, and practical applicability beyond simulation.