Promoting Expert Knowledge for Comprehensive Human Risk Management in Industrial Environments

This research develops a methodological approach aimed at promoting expert knowledge and, as a consequence, exploiting the strength of practical experience in industry in a scientifically rigorous way. In particular, we make use of Fuzzy Cognitive Maps (FCMs) to organise in a flexible way human knowledge about decision-making problems focused on the topic of human risks management. FCMs enable the representation of causal relations among relevant decision-making elements on the basis of spontaneous human brainstorming stimulated from a single expert or a decision-making team. After leading a comprehensive literature review, aimed at identifying industrial human risks in the most exhaustive way, a FCM will be built to define relations among risks. Specifically, the calculation of the total effect will be supported by means of the use of the Nonlinear Hebbian Learning (NHL) algorithm, something that will be useful to make the approach even more reliable. Risks will be later prioritised by means of a modified Failure Modes, Effects and Criticality Analysis (FMECA): the total effect previously calculated will be considered as an additional criterion for risk prioritisation, apart from the ones traditionally considered by FMECA. Moreover, an alternative calculation way for the Risk Priority Number (RPN) will be proposed: the Multi-Criteria Decision-Making (MCDM) approach making use of the TODIM (an acronym in Portuguese standing for interactive and multi-criteria decision-making) method under Z-environment will be used to perform the final ranking. This technique is indeed useful to deal with the psychological behaviours of decision-makers. A real case study is eventually implemented to provide useful implications for risk management.