Methodology for estimating capacity indices in processes for non-normal data
DOI:
https://doi.org/10.21754/tecnia.v24i1.32Keywords:
Approximation to frequency distributions, Process capability indices, Normality, data transformations, SimulationAbstract
Globalization has intensified competition in many markets. To remain competitive, the companies look for satisfying the needs of customers by meeting market requirements. In this context, Process Capability Indices (PCI) play a crucial role in assessing the quality of processes. In the case of non-normal data there are two general approaches based on transformations (Box-Cox and Johnson Transformation) and Percentiles (Pearson’s and Burr’s Distribution Systems). However, previous studies on the comparison of these methods show different conclusions, and thus arises the need to clarify the differences between these methods to implement a proper estimation of these indices. In this paper, a simulation study is made in order to compare the above methods and to propose an appropriate methodology for estimating the PCI in non-normal data. Furthermore, it is concluded that the best method used depends on the type of distribution, the asymmetry level of the distribution and the ICP value.
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