Introduction: The power industry, as one of the most critical infrastructures underpinning economic, social, and industrial development, plays a decisive role in the sustainability of public services and economic growth. To cope with disruptions caused by climate change, natural disasters, technical failures, human errors, and cyber threats, this industry requires a high level of resilience. At the same time, the emergence of Industry 4.0 technologies has created new opportunities for intelligent monitoring, optimal control, predictive decision-making, and the reduction of network instabilities. Despite these capacities, the existing literature has not adequately addressed the precise mechanisms through which these technologies affect network resilience. Moreover, no structured study has been conducted to examine the impact of improving the maturity level of each of these technologies on network resilience. Therefore, the main objective of this study is to model and analyze the impact of key Industry 4.0 technologies on the resilience of the electricity distribution network and to assess the probability of resilience improvement under different technology maturity enhancement scenarios.

Methods: In the present study, a hybrid approach combining Fuzzy Cognitive Mapping (FCM) and Bayesian Belief Networks (BBN) was employed. In the first stage, the causal model of the effects of Industry 4.0 technologies on the resilience of the electricity distribution network was identified, and subsequently, probabilistic and scenario-based analyses were conducted. Following a literature review and the development of an interview protocol based on the FCM procedure, the technologies influencing the resilience of the distribution network and the causal mechanisms of their effects were extracted through experts’ mental maps. After integrating the individual maps, the final Fuzzy Cognitive Map, with certain modifications, was used as the causal model for the Bayesian network, and posterior probabilities were calculated. Finally, the probabilistic behavior of network resilience under changes in the maturity levels of technologies was analyzed through six scenarios. In addition, sensitivity analysis was conducted to determine which variables had the greatest influence on changes in the probability of resilience.

Findings:The findings indicate that technologies such as the Internet of Things (IoT) and Big Data Analytics, by providing accurate and real-time data, as well as Artificial Intelligence (AI), through predicting critical situations and enabling rapid decision-making, play a decisive role in improving resilience. Furthermore, Robotics, by facilitating automated repairs and reducing dependence on human intervention during crises, demonstrated the greatest impact on recovery speed. The scenario analysis results reveal that independently improving the maturity level of any one of the technologies—IoT, Big Data Analytics, AI, or Robotics—cannot produce a significant change in the level of network resilience. However, Bayesian network analysis under the scenario of simultaneous maturity enhancement of all technologies showed that their synergy, compared with the isolated development of each technology, has a greater effect on improving key resilience indicators. As a result, the probability of the network operating at “medium” and “high” resilience levels increases significantly compared with the baseline scenario.

Conclusion: Overall, this study demonstrates that improving the resilience of the electricity distribution network requires the simultaneous and coordinated development of Industry 4.0 technologies, and that isolated investment in a single technology does not provide sufficient effectiveness. The proposed framework can serve as a tool for the intelligent prioritization of technological investments and strategic planning aimed at enhancing network resilience, and may be utilized by managers and policymakers.