This study was conducted with the aim of presenting a structural model for implementing Maintenance 4.0 technologies with an emphasis on sustainability in Iran's oil and gas industry. To analyze the relationships between variables, machine learning techniques and artificial neural networks were employed. Data were collected through a researcher-designed questionnaire and included variables such as Maintenance 4.0 technologies, organizational digital readiness, implementation challenges, and organizational sustainable performance. The results indicated that the most influential factors on sustainable organizational performance were data mining and the Internet of Things while technical infrastructure and employee skills also had indirect effects. The model's accuracy was confirmed with a coefficient of determination of 0.89, a mean squared error of 0.057, and a mean absolute percentage error of 2.6%. The final model can be used as a decision-support tool in formulating smart and sustainable maintenance policies in Iran's energy sector.
Falehi et al. (Fri,) studied this question.