The aim of this research article is to model the Maintenance 4.0 implementation barriers in manufacturing industries by employing an integrated approach that combines structural equation modelling (SEM) and Bayesian network (BN) framework.
A comprehensive literature review was undertaken to systematically identify key barriers to the implementation of Maintenance 4.0 within manufacturing industries. The preliminary model is developed, and the correlations between variables are evaluated using exploratory factor analysis (EFA), followed by model validation through confirmatory factor analysis (CFA). In addition, the impact of barriers on Maintenance 4.0 implementation is investigated using a Bayesian network framework, with sensitivity analysis performed to assess the model findings’ reliability and robustness.
The structural equation modelling model, which was developed using expert survey responses, shows high reliability and validity. It categorizes the Maintenance 4.0 barriers into five categories: implementation barriers, finance- and policy-related barriers, technological barriers, data-related barriers and people-related barriers.
The proposed hybrid approach making it slightly complicated and extensive but generates precise and validated framework for the BN model that results in a more accurate and reliable BN model.
Findings from this research will be extremely beneficial for manufacturing industries in understanding the most influential barriers to increase rate of Maintenance 4.0 implementation.
The combined SEM-BN approach modelling complex systems, including Maintenance 4.0 implementation barriers, ensuring both statistical rigour and data-driven insights. The study provides valuable guidance for industry practitioners and policymakers to systematically address critical barriers, thereby improving the success rate of Maintenance 4.0 implementation.
