Data-Driven Lean Six Sigma for Complex Systems: The Integration of Artificial Intelligence for Quality, Resilience, and Sustainability

Somkenechi Chinwe Okpala¹, Charles Chikwendu Okpala²

¹ Paediatrician, Department of Paediatrics, University of Nigeria Teaching Hospital, Ituku/Ozalla, Enugu, Nigeria.

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

Abstract

The growing complexity of modern socio-technical systems presents significant challenges for organizations who seek to improve quality while simultaneously enhancing resilience and achieving sustainability objectives. Traditional Lean Six Sigma (LSS) methodologies, although effective in stable and well-defined environments, often struggle to address the nonlinear dynamics, data intensity, and environmental pressures that are characteristic of contemporary operational systems. This study proposes a Data-Driven, Artificial Intelligence–enabled Lean Six Sigma (DD-AI-LSS) framework that was designed to extend the traditional DMAIC cycle through the systematic integration of machine learning, systems analytics, and sustainability performance indicators. Using large-scale empirical data from manufacturing, healthcare, and energy systems, the framework is evaluated through a multi-case comparative analysis against conventional LSS implementations. The results demonstrate that DD-AI-LSS delivers significant and simultaneous improvements across three critical performance dimensions. Quality performance improved through substantial reductions in defects and service errors, while system resilience was enhanced through shorter recovery times and improved adaptive response to disruptions. Importantly, the framework achieved measurable sustainability benefits, including notable reductions in energy consumption and carbon emissions intensity, without introducing trade-offs between operational efficiency and environmental performance. Through the integration of artificial intelligence and sustainability metrics directly into the structure of Lean Six Sigma, the study advances quality management theory and provides a scalable, data-driven pathway towards resilient and sustainable operational excellence in complex systems. The findings offer valuable insights for researchers, practitioners, and policymakers who aim for integrated solutions at the intersection of quality, digital transformation, and sustainability.    

Keywords: Lean Six Sigma, Artificial intelligence, Sustainability, Complex systems, Resilience, Quality management, Data-driven decision making 

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