The paper proposes an approach that combines reliability analyses and multi-criteria decision methods to optimize maintenance activities of complex systems. A failure mode, effects, and criticality analysis (FMECA) is initially performed and the fuzzy TOPSIS (FTOPSIS) method is then applied to rank previously identified failure modes. For prioritization, failure modes are assessed against three evaluation criteria that differ from those traditionally involved in risk priority number (RPN) computation (i.e. severity, occurrence and detection). Two criteria refer to the maintenance management reflecting the operational time taken by the maintenance activity performed after the occurrence of a specific fault, and the way such an action is executed. The third criterion reflects the classical frequency of the occurrence of faults. To further develop previous research, the analytic hierarchy process (AHP) is herein applied to weight evaluation criteria and a group of experts is involved with aspects associated with the considered criteria. The approach is applied to a real-world case study, showing that the obtained results represent a significant driver in planning maintenance activities. To test the influence of criteria weights on ranking results, a sensitivity analysis is carried out by varying the vector of criteria weights obtained from the group decision process.

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In a R&D department, several projects may have to be implemented simultaneously within a certain period of time by a limited number of human resources with diverse skills. This paper proposes an optimisation model for the allocation of multi-skilled human resources to R&D projects, considering individual workers as entities having different knowledge, experience and ability. The model focuses on three fundamental aspects of human resources: the different skill levels, the learning process and the social relationships existing in working teams. The resolution approach for the multi-objective problem consists of two steps: firstly, a set of non-dominated solutions is obtained by exploring the optimal Pareto frontier and secondly, based on further information, the ELECTRE III method is utilised to select the best compromise with regards to the considered objectives. The uncertainty associated to each solution is modelled by fuzzy numbers and used in establishing the threshold values of ELECTRE III, while the weights of the objectives are determined taking into account the influence that each objective has on the others

Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of “dependence” among the risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated with input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: When planning interventions of prevention/mitigation, primary importance should be given to (1) supply chain disruptions due to natural disasters; (2) manufacturing facilities, human resources, policies and breakdown processes; and (3) inefficient transport.

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