Six sigma for gamma-distributed processes: a case study in oil and gas

Identification of asset life cycle stage: case study in heavy-duty truck fleet

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Six Sigma quality evaluation of life test data based on Weibull distribution

Purpose

While Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.

Design/methodology/approach

In the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.

Findings

In this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.

Originality/value

The theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.