Artificial Intelligence-Enabled Lean Six Sigma: A Multi-Industry Longitudinal Analysis of Operational Performance and Sustainable Digital Transformation

Okechukwu Chiedu Ezeanyim¹, Charles Chikwendu Okpala², Chukwudi Emeka Udu³

¹ Associate Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria. 

³ Research Scholar, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria. 

Abstract

Organizations are under increasing pressure to align operational excellence with measurable sustainability outcomes in an era that is characterized by accelerated digital transformation. While Lean Six Sigma (LSS) has traditionally improved quality and efficiency, its integration with Artificial Intelligence (AI) offers new opportunities for predictive optimization and environmental performance enhancement. This study developed and empirically validated an AI-Enabled Lean Six Sigma (AI-LSS) framework with the application of a five-year longitudinal dataset which comprises 214 firms across manufacturing, healthcare, logistics, and energy sectors. With a hybrid methodological approach that integrates fixed-effects panel regression, structural equation modeling, and explainable machine learning (random forest with SHAP analysis), the findings demonstrate that AI-LSS adoption significantly improves operational performance (β = 0.48, p < 0.001) and sustainability performance (β = 0.29, p < 0.001). Over a three-year post-adoption period, the firms achieved an average of 18.7% reduction in carbon intensity, 16.2% reduction in energy consumption per unit output, and 23.4% reduction in material waste. Digital transformation maturity partially mediates the AI-LSS–sustainability relationship, which indicates that organizational digital readiness amplifies environmental gains. Machine learning results (R² = 0.63; 87% predictive accuracy) identified predictive maintenance and real-time energy monitoring as the most influential drivers of sustainability improvement. Through the conceptualization of AI-LSS as a dynamic operational capability, this study provided rigorous, cross-industry evidence that structured AI integration within DMAIC routines can serve as a scalable pathway towards sustainable digital transformation. The findings contribute to operations management, sustainability science, and digital transformation literature through the demonstration of quantifiable environmental impact through data-driven process innovation.    

Keywords: Lean Six Sigma, Artificial intelligence, Sustainability performance, Digital transformation, Longitudinal analysis, ESG metrics, Explainable machine learning

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