AI Meets Lean Six Sigma:

Abstract

This article explores the use of Artificial Intelligence (AI) within the framework of Lean Six Sigma (LSS) through the DMAIC approach and its five stages, namely Define, Measure, Analyze, Improve, and Control for an improvement of quality management. In addition to increasing productivity, combining AI’s predictive powers with LSS’s methodical approach ensures that most, if not all, quality control standards are achieved. This leads to continuous improvement in a variety of industries where operational effectiveness is essential for sustainability and success. However, several companies tried to apply LSS, only a few of them have been successful in improving their operations to achieve the expected results. This research focuses on how AI -through Machine Learning (ML) models and data-driven approaches- improves each phase of LSS, from problem identification and data mining to root cause analysis and process optimization. In particular, neural networks, anomaly detection, ML algorithms, and digital simulations are described. It also outlines the issues that are associated with it (such as data privacy constraints, specialized skills requirements, and technical compatibility). To sum up, it confirms that applying AI to LSS enhances industrial process advances in efficiency, quality, and sustainability