Using Machine Learning to evaluate Industry 4.0 Maturity: A comprehensive analysis highlighting Lean's impact on Digital Transformation

Abstract

The rise of digital technologies in manufacturing and industries, known as the fourth industrial revolution, has created both opportunities and challenges for businesses. To succeed in this era of "Industry 4.0," companies need to assess their digital maturity. Through this study, we analyze the global state of Industry 4.0 maturity, identifying industry-specific trends, challenges, and potential growth. Leveraging advanced machine learning techniques, including data analysis, prediction, and recommendations. The study explores the complexities and evolution of Industry 4.0. Additionally, we show how machine learning plays a pivotal role in this analysis, contributing to enhanced insights and decision-making capabilities. Our research aims to not only assess the current state but also forecast future roadmaps while providing tailored recommendations for enhancing maturity levels.  We aim to evaluate various machine learning based approaches for addressing these inquiries, focusing on Decision Tree, Support Vector Machine, and Random Forest models. We will choose the best performing model for our scenario. Initially, we use unsupervised learning through Hierarchical Clustering for grouping data, followed by data expansion. Subsequently, we employ supervised learning techniques, particularly Decision Tree, for descriptive, predictive, and perspective analysis. Among our recommendations for enhancing Industry 4.0 maturity levels, we advocate for extensive interventions, but exclusively for companies meeting predetermined criteria delineated within the decision tree node. Furthermore, we examine the influence of Lean on digital transformation. Through this interdisciplinary approach, our findings contribute to a deeper understanding of Industry 4.0 evolution and offer practical insights for strategic decision-making in the era of digitalization.