The Integration of Lean Manufacturing, Big Data Analytics, and Explainable AI in Modern Industries

Obiora Jeremiah Obiafudo¹, Charles Chikwendu Okpala², Oladapo Babafemi Fakiyesi³

^1,3 Lecturer, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

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

Abstract

Modern production industries face increasing pressure to achieve higher operational efficiency while simultaneously minimizing energy consumption, material waste, and carbon emissions. Although Lean Manufacturing has long provided structured approaches for the elimination of non-value-added activities, its traditional implementations often lack real-time responsiveness in complex Industry 4.0 environments. At the same time, Big Data Analytics enables continuous monitoring and predictive insights from high-volume industrial datasets, yet many AI-driven decision systems remain difficult to trust due to their black-box nature. To address these challenges, this study proposes an integrated Lean-Big Data Analytics-Explainable AI (Lean-BDA-XAI) framework for sustainable intelligent decision-making in modern manufacturing systems. The framework combines lean waste identification tools with real-time analytics pipelines and interpretable machine learning models that are enhanced through SHAP-based explainability. The validation with the application of multi-source industrial datasets demonstrated measurable sustainability improvements. The results indicate a 23–26% reduction in energy waste, a 17–19% decrease in defect-driven scrap, and an approximately 17% reduction in carbon footprint per unit output, alongside a 14.8% improvement in overall equipment sustainability effectiveness. Furthermore, explainability analysis identifies machine idle time, temperature deviations, and scheduling variance as dominant contributors to sustainability inefficiencies, thus enhancing transparency and managerial trust in AI recommendations. The study contributes a scalable roadmap for the integration of operational excellence, data-driven intelligence, and trustworthy AI to support sustainable smart factory transformation across diverse production industries.    

Keywords: Lean Manufacturing, Big data analytics, Explainable AI, Sustainable manufacturing, Industry 4.0, Intelligent decision-making, Energy efficiency

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