The PO bootstrap approach for comparing process incapability applied to non-normal process selection

ABSTRACT

Process selection has been a focal task in operation management. This research focuses on finding alternatives to the current process that have to be at least as capable as the current process. Having multiple alternative processes available enables the manufacturers to have better resource utilization and scheduling flexibility. However, selecting the right process under non-normal data remains a challenge. Quality loss is a popular criterion because of its direct relationship with cost objectives. In this research, we propose the C_pp-based PO bootstrap approach to evaluate candidate processes based on quality loss by utilizing the incapability index. The C_pp index represents Taguchi’s Loss function k(x – T)², which is suitable for the nominal-the-best type of quality characteristic. It measures production loss caused by process inaccuracy and imprecision. The experiments show that the proposed method can loosen up the reliance on normal assumption by controlling type I error and providing higher power compared to the extended method from the literature. The application to amplifier circuits manufacturing showed that the proposed method is effective to identify the inferior processes despite the severe departure of data from normal, while the opposed method built under normality assumption fails to do so.

KEYWORDS:

• Production loss
• inaccuracy
• imprecision
• resampling
• Bonferroni adjustment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST 109-2221-E-155-021].

Notes on contributors

Florence Leony

Florence Leony received her bachelor’s degree in Industrial Engineering from Universitas Kristen Maranatha, Indonesia, and an MS degree in Industrial Engineering and Management from Yuan Ze University, Taiwan. Her research focuses on process selection and multiple comparisons.

Chen-ju Lin

Chen-ju Lin is an Associate Professor in the Department of Industrial Engineering and Management at Yuan Ze University, Taiwan. She received her BS degree in Industrial Engineering and Management from the National Chiao Tung University and MS and PhD degrees in Industrial and Systems Engineering from the Georgia Institute of Technology. Her research interests include multiple comparison techniques, quality control, and spatiotemporal statistics.