Evaluation of a novel loss-based process capacity index Spk′ and its applications

Process capability indices (PCIs) are often used to assess process performance. Higher PCIs do not mean lower rejection rates. Thus, loss- based PCIs are better for process capability measurement. This study introduces a new capability index, S-pk ', based on a symmetric loss function for normal process, to include loss into capability analysis. We then estimate PCI S-pk ' employing six standard techniques of estimation and compare their mean squared errors (MSEs) through simulation analysis. For the index S-pk ', asymptotic confidence intervals (ACI), generalized confidence intervals (GCI), and parametric bootstrap confidence intervals (BCIs) are used to construct confidence intervals. Monte Carlo simulation evaluates ACI, GCI, and BCIs average widths and coverage probabilities. Our experiments showed that MPSE produced the smallest width. BCp-boot outperformed its competitors. For most sample sizes and estimation methodologies, P-boot has a greater CP. Two electronic industry data sets are evaluated to demonstrate the accuracy of the suggested estimating methodologies, ACI, GCI, and BCIs.