Logic-driven, simulation-based risk engineering to ensure the sustainability of productive processes even with data scarcity

Designing and operating production systems and keeping them up to date at the speed of innovation to meet competition, consumer trends and sustainability requirements is a challenging task. The problem becomes even more challenging when the production system is highly uncertain and data-poor. This complexity is increasingly recognised, as is the value that risk engineering can bring to the design, operation, maintenance and upgrading of production systems to avoid compromising their viability and ensure their continued efficiency. The manuscript is guided by this challenge and has a twofold objective. First, it analyses and explains the importance of adopting a logic-driven and simulation-based risk engineering approach to support decisionmaking. This objective is achieved by going to the root of the methodological constructs that characterise the methodologies available in the literature and by highlighting the main limitations. Second, it applies the HoRAM method to the use case of food banks to verify its suitability. The use case of food banks was chosen because it is characterised by more and unusual uncertainties compared to conventional production systems (such as uneven labour and raw material availability). The main contributions of this research are as follows. First, it provides a critical analysis of currently available risk assessment methodologies. It highlights the weaknesses and explains the implications of these weaknesses for decision support. Second, it concerns the development of a complete risk engineering process, offering an approach that goes beyond conventional risk assessment approaches that stop at the identification of the critical elements associated with the problem at hand. It explains the importance of closing the risk analysis loop by quantitatively verifying the efficiency of the identified mitigation solutions, thus moving from risk assessment to risk engineering. The results suggest that the proposed approach is suitable for supporting complex decision-making processes characterised by high uncertainty and data scarcity. Furthermore, due to the logic-driven nature of the proposed approach, non-experts can be involved and contribute to the analysis and engineering of risks. This allows to increase the situational awareness of decision makers and consequently their efficiency in making complex decisions.