Abstract
To be able to thrive in the grand challenges of the current historical moment, which includes important driving phenomena such as climate change, digitalization and energy transition, the organizations need a comprehensive understanding of the organizational and technical aspects that may pose opportunities and risks.
This paper presents a novel approach to identify weak organizational and technical factors within the context of the energetic transition challenge. To accomplish this, a Machine Learning system is proposed, that integrates, as input features, escalation and mitigation factors related to the risks that may arise in relation to the energetic transition. The target variable is an indicator concerning the possible increase in the probability of accidents and near misses, which is selected as an effective detector of potential weaknesses in the system. The primary objective is to uncover organizational aspects that influence the mitigation, or enhancement of technological risks during the energetic transition. By analysing the interplay between organizational and technical factors and their role on preventive and mitigating barriers, this paper aims at identifying critical areas that require Attention and improvement to ensure a smooth and successful energetic transition process. A reference case-study is presented to demonstrate the actual capability of the presented framework. The findings of this study have practical implications in the definition of organizational priorities in managing the energetic transition; the identified weaknesses can serve as a basis for targeted interventions and strategic decision-making, allowing for more effective risk management and improved outcomes during the energy transition.
