Integrated Decision-Making Framework in Industrial Asset Management for Assessing and Managing Emerging Risks

Major challenges for the most used traditional assessment methods of safety risks are the growing complexity and uncertainty of modern systems driven by the digital era which inexorably leads to a rise in emerging risks. These methods for assessing and managing safety risks have their own limits and might not characterize all aspects that affect sociotechnical system safety in the context of new emerging risks. Thus, new tools are needed for the new problems. Hence, the need of building comprehensive decision support methodologies arises from those threats associated with the complexity inherent to industry 4.0 and uncertainty associated with natural disasters, in conjunction with new organizational risks as well as biases in human logic. On these grounds, this paper aims at developing an Integrated Decision-Making Framework in Industrial Asset Management for Assessing and Managing Emerging Technology Risks as well as Extreme, Rare and Disruptive Events. This should account both for traditional and new emerging risk safety management. In this regard, we have chosen a duo of concepts that we consider the best approaches, namely, the Functional Resonance Analysis Method (FRAM) and the System-Theoretic Accident Model and Processes (STAMP). These methods are much more efficient than the conventional ones to engineer complexity and uncertainty in modern socio-technical systems. In fine, to reveal the usefulness of this framework for identifying and managing industrial asset risks, forthcoming targets will be devoted to performing a case study dealing with a complex socio-technological system. The latter is dubbed LineDrone, a Hydro-Quebec (Hydro-Quebec is one of the largest North American companies which generates, transmits, and distributes electricity in Canada) robotic platform designed for inspecting and maintaining energized transmission lines.