Abstract
Manufacturing industries across the globe are undergoing a digital transformation that demands both efficiency and sustainability. Industry 4.0 (I4.0) and Lean 4.0 (L4.0) methodologies have become focal points in these efforts. Despite widespread recognition of the benefits of integrating L4.0 and I4.0, more studies need to address the practical challenges of this integration, especially the key factors that influence its successful implementation. Small and medium-sized enterprises (SMEs) in emerging economies often face significant challenges in integrating L4.0 practices due to resource limitations and complex operational challenges. This study bridges a critical research gap by proposing an integrated framework that combines Artificial Neural Networks (ANN) with fuzzy Interpretive Structural Modeling (FISM) to identify and prioritise the critical success factors (CSFs) for L4.0 adoption. A survey of 216 manufacturing SMEs was used to validate these CSFs through Exploratory Factor Analysis (EFA). The ANN analysis revealed that Process Factors have the highest influence with normalised importance (NI) of 100%, followed by Organizational Factors (NI = 60.46%), Human Factors (NI = 58.93%), Technological Factors (NI = 43.21%), External Factors (NI = 42.13%), and Environmental Factors (NI = 39.63%). Complementary FISM and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analyses further structured these relationships, underscoring the key roles of Change Management, Organizational Culture, Waste Reduction, and Regulatory Compliance. These findings offer both a theoretical advancement in understanding complex CSF interactions and practical guidance for SMEs striving to achieve sustainable manufacturing practices.