Process Capability Indices under Non-Normality Conditions using Johnson Systems

Process capability indices (PCIs) quantify the ability of a process to produce on target and within specifications performances. Basic indices designed for normal processes gives flawed results for non-normal process. Numerous methods have been proposed for non-normal processes to estimate PCIs in which some of them are based on transformation methods. The Johnson system comprising three types that translate a continuous non-normal distribution to normal. The aim of this paper is to estimate four basic indices for non-normal process using Johnson system with single straightforward procedure. The efficacy of the proposed approach can be assessed for all three Johnson Curves (SB, SU, SL) but result for SU is presented in this paper. PCIs for a data set are estimated and percentiles are obtained by our proposed exact method based on selected Johnson density function which was earlier based on approximate methods without any prior knowledge of density function of non-normal process. We compare our results with other existing methods to estimate PCIs for non-normal process. From statistical analysis we have noted that this modification improve process capability indices.

@article{Safdar2019,
  title     = {Process Capability Indices under Non-Normality Conditions using Johnson Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {3},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Suboohi Safdar and Dr. Ejaz Ahmed and Dr. Tahseen Ahmed Jilani and Dr. Arfa Maqsood},
  doi       = {10.14569/IJACSA.2019.0100338},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100338}
}