Modelling Maintenance 4.0 enablers in manufacturing: a PLS-SEM–Bayesian network approach

Purpose The research proposes an integrated hybrid partial least squares structural equation modelling (PLS-SEM) and Bayesian network (BN) approach to analyse the crucial enablers to Maintenance 4.0 adoption in manufacturing industries. Design/methodology/approach A detailed review of the literature is executed to identify the crucial enablers for maintenance adoption in the manufacturing sector. An initial hypothesised model is developed, factors loadings are evaluated, and model fit is confirmed using a PLS-SEM. In addition to that, a BN is modelled to examine the influence of enablers on the adoption of Maintenance 4.0 in the manufacturing sector. Further, to verify the robustness of the outcome of the study, sensitivity analysis is conducted. Findings The analysis reveals that the proposed PLS-SEM model is accurate and valid, categorising the enablers into four groups: organisation-related enablers, digital infrastructure and system support–related enablers, data-related enablers and people-related enablers. A machine learning-based BN technique was implemented to investigate the impact of the identified enablers on the adoption of Maintenance 4.0 in the manufacturing sector. Additional sensitivity analysis confirmed the accuracy of the BN model. Research limitations/implications The proposed methodology, adopted slightly complicated and laborious, provides an accurate and reliable framework for the BN model, which provides a more accurate and clear understanding of the enablers. Practical implications The findings of this research will be highly beneficial to manufacturing industries, enabling them to identify the most influential enablers for successful Maintenance 4.0 adoption and, consequently, improve the probability of success. Originality/value A hybrid framework combining PLS-SEM and machine learning is employed to examine complex systems such as Maintenance 4.0 enablers, allowing both statistical robustness and data-driven insights. The framework offers useful direction for policymakers and industry practitioners by clarifying how critical enablers can be utilized to improve the success rate of successful Maintenance 4.0 adoption.