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This study aims to develop a risk prioritization process using failure mode and effect analysis (FMEA) in association with composition of probabilistic preferences (CPP) and weighting the risk analysis criteria. It seeks to develop decision-making considering the fast response necessary to achieve project objectives in complex scenarios, such as the pandemic of COrona VIrus Disease 19 (COVID-19).

After identifying the risks, the prioritization process was applied to a project in the oil and gas area, in which a focus group assessed these risks. This evaluation took place employing traditional FMEA, FMEA with CPP by axes considering four points of view and FMEA with CPP by weighted sum with the use of a multicriteria method to weight the criteria. These approaches were compared to understand their differences and benefits, with a flow chart being developed, consolidating the procedure.

The methodologies that showed the greatest benefits were FMEA with CPP by axes PO (progressive-optimistic) and by weighted sum. Essentially, this was mainly related to the interrelationship between risks and to the importance of prioritization.

This procedure can consider company's views on what is critical and the interrelationship between risks. It provides a clear segmentation of what should and should not be prioritized. It was also developed in a practical case, showing a possible alternative to support fast responses in decision-making.

The purpose of this paper is to discuss the application of a method for combining multiple criteria based on the transformation of numerical evaluations into probabilities of preference. It is applied to compare failure risks and to measure efficiency in the retail trade sector.

The main conceptual aspect of the method employed is taking into account uncertainty. Its other important feature is allowing for the combination of evaluations in terms of joint probabilities. This avoids the need of assigning weights to the criteria. In the context of failure modes and effects analysis (FMEA) it provides a probabilistic derivation for priority scores. An application of FMEA to the sector of services is discussed. Another area of application investigated is the assessment of efficiency.

Details of the application of the probabilistic composition in the evaluation of modes of failure and in the comparison of operational efficiencies of retail stores are evidenced.

The study is limited to the retail market. Other factors might be considered in the reliability analysis and other inputs and outputs might be added to the productivity evaluation. The extension of the study to other cases and sectors is straightforward.

Features of the evaluation of modes of failure and of productivity in the retail sector are revealed.

The main contribution of this paper is showing how to use a probabilistic framework to measure efficiency in services management.

Data mining registers of transactions allows for benchmarking customer's evaluation strategies. The purpose of this paper is to provide information on the application of different approaches to explore this kind of data.

Traditionally, heuristics based on variables such as recency, frequency, and monetary (RFM) value of transactions are used to determine the best customers. In this paper, a new form of directly combining the values of these variables is compared to an approach based on fitting a stochastic model. This last model is a mixture of a model for the number of transactions and another for the value spent. The new direct form of evaluation is based on computing the joint probability of maximizing quality indicators.

Good fit of the different models tested to the series of individual data as well as coherent predictions are registered. Patterns found provide empirical confirmation of results that theoretically should be expected.

These results are valid for a particular supermarkets network in a Brazilian city. The inner consistency of the results, nevertheless, and the coherence of the results obtained with what was expected, encourage application to other places and sectors of activity.

The results obtained show clearly the effectiveness of the approach based on RFM value measurement.

The models studied are applied for the first time for the kind of data treated, where determination of which customers remain active is a problem of special interest.

The purpose of this paper is to propose a method, derived from numerical evaluations on the criteria of security, frequency and detectability, of Failure Modes and Effects Analysis (FMEA), a probabilistic priority measure for potential failures; and to evaluate the use of this method when combined with subjective evaluations to decide on improvement actions.

The method proposed is based on treating the numerical initial measurements as estimates of location parameters of probability distributions. This allows for objectively taking into account the uncertainty inherent in such measurements and to compute probabilities of each potential failure being the most important according to each criterion. These probabilities are then combined into a global quality measure, which can be interpreted as a joint probability of choice of the potential failure.

The results obtained in the cases studied show the suitability of the changes proposed. Thresholds levels proposed for the discretization of the probabilistic scores are also shown to be able to allow for an efficient combination with experts' evaluations.

The approach here developed allows for the introduction of statistical parameters in the first stage of FMEA modeling. Employing a more complete model leads to greater reliability of the methodology.

The more precise modeling asks for a certain degree of practical knowledge of the factors effectively introducing variability in the measurements. This need may be surpassed by the choice of distributions such as those here employed.

The evaluation of the potential failures by the probability of being the most important, according to each criterion, is new in FMEA.