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The purpose of this paper is to evaluate the performance of a modified EWMA control chart (γEWMA control chart), which considers data distribution and incorporate its correlation structure, simulating in-control and out-of-control processes and to select an adequate value for smoothing parameter with these conditions.

This paper is based on a simulation approach using the methodology for evaluating statistical methods proposed by Morris et al. (2019). Data were generated from a simulation considering two factors that associated with data: (1) quality variable distribution skewness as an indicator of quality variable distribution; (2) the autocorrelation structure for type of relationship between the observations and modeled by AR(1). In addition, one factor associated with the process was considered, (1) the shift in the process mean. In the following step, when the chart control is modeled, the fourth factor intervenes. This factor is a smoothing parameter. Finally, three indicators defined from the Run Length are used to evaluate γEWMA control chart performance this factors and their interactions.

Interaction analysis for four factor evidence that the modeling and selection of parameters is different for out-of-control and in-control processes therefore the considerations and parameters selected for each case must be carefully analyzed. For out-of-control processes, it is better to preserve the original features of the distribution (mean and variance) for the calculation of the control limits. It makes sense that highly autocorrelated observations require smaller smoothing parameter since the correlation structure enables the preservation of relevant information in past data.

The γEWMA control chart there has advantages because it gathers, in single chart control: the process and modelling characteristics, and data structure process. Although there are other proposals for modified EWMA, none of them simultaneously analyze the four factors nor their interactions. The proposed γEWMA allows setting the appropriate smoothing parameter when these three factors are considered.

This paper aims to determine the stage of the life cycle where the trucks of a waste collection fleet from a Colombian city are located through a reliability approach. The reliability analysis and the evaluation of curve of operational costs allow to know the moment in which it is necessary to make decisions regarding an asset, its maintenance or possible replacement.

For a dataset presented as maintenance work orders, the time to failures (TTFs) for each vehicle in the fleet were calculated. Then, a probability density function for those TTFs was fitted to locate each vehicle in a region of the bathtub curve and to calculate the reliability of the whole fleet. A general functional analysis was also developed to understand the function of the vehicles.

It was possible to determine that the largest proportion of the fleet was in the final stage of the life cycle, in this sense, the entire fleet represent critical assets which in most of cases could be worth replacement or overhaul.

In this study, an address is exposed for the identification of critical equipment by reliability and statistical analysis. This analysis is also integrated with the maintenance management process. This is a broadly interested topic since it allows to support the maintenance and operational decision-making process, indicating the focus of resource allocation all over the entire asset life cycle.