Towards a novel tiered approach to assess the resilience level in the safety domain

Resilience is the system ability to adjust its functioning prior to, during, or following changes and perturbations. Resilience Engineering represents a new paradigm to improve safety, focusing on how to create resilience in systems. The resilience measurement supports decision making processes, but it is not a trivial task. Therefore, the objectives of this paper are: (1) to critically analyze the literature about quantitative resilience assessments in the industrial safety domain, and (2) to propose a novel three-tier approach for measuring and assessing the resilience potential in any organization in the same domain. To achieve our objectives, we performed a narrative literature review about the existing approaches, frameworks, and methods quantifying and ranking resilience indicators, and/or estimating an overall resilience score. Multi-Criteria Decision Making and Bayesian Network approaches are frequently employed for such purposes. The results gathered through the narrative review represent a key source for developing a novel tiered approach. We propose an approach able to quantitatively assess the resilience potential in the industrial safety domain that consists of three tiers. A knowledge-driven tier assesses resilience by using the knowledge of decision makers through techniques involving judgements, a knowledge and data-driven tier incorporates methods considering both expert knowledge under uncertainty and objective data, while a data-driven tier includes models performing resilience assessments entirely based on data provided by devices and information systems in the organization.