A new method to improve the objectivity of early Six Sigma analysis

Abstract

Purpose A process improvement sampling methodology, known as process variation diagnostic tool (PROVADT), was proposed by Cox et al. (2013). The method was designed to support the objectivity of Six Sigma projects performing the measure-analyse phases of the define-measure-analyse-improve-control cycle. An issue in PROVADT is that it is unable to distinguish between measurement and product variation in the presence of a poor Gage repeatability and reproducibility (R&R) result. The purpose of this paper is to improve and address PROVADT’s sampling structure by enabling a true Gage R&R as part of its design. Design/methodology/approach This paper derives an enhanced PROVADT method by examining the theoretical sampling constraints required to perform a Gage R&R study. The original PROVADT method is then extended to fulfil these requirements. To test this enhanced approach, it was applied first to a simulated manufacturing process and then in two industry case studies. Findings The results in this paper demonstrates that enhanced PROVADT was able to achieve a full Gage R&R result. This required 20 additional measurements when compared to the original method, but saved up to ten additional products and 20 additional measurements being taken in future experiments if the original method failed to obtain a valid Gage R&R. These benefits were highlighted in simulation and industry case studies. Originality/value The work into the PROVADT method aims to improve the objectivity of early Six Sigma analyses of quality issues, which has documented issues.

A six sigma based approach to evaluate the on time performance of Indian railways

“…Proper gauge study analysis plays a crucial role in the quantification of sigma level. Some advances in the area of gauge study can also be seen for instance, Cox et al (2016) proposed a method called as enhanced PROVADT to examine sampling constraints for a Gage R&R study. The authors validated this approach using a simulated manufacturing process followed by case studies.…”