Concise process improvement definition with case studies

Abstract

Purpose

The purpose of this paper is to examine the efficiency and objectivity of current Six Sigma practices when at the measure/analyse phase of the DMAIC quality improvement cycle.

Design/methodology/approach

A new method, named process variation diagnostic tool (PROVADT), demonstrates how tools from other quality disciplines can be used within the Six Sigma framework to strengthen the overall approach by means of improved objectivity and efficient selection of samples.

Findings

From a structured sample of 20 products, PROVADT was able to apply a Gage R&R and provisional process capability study fulfilling the pre-requisites of the measure and early analyse phases of the DMAIC quality improvement cycle. From the same sample, Shainin multi-vari and isoplot studies were conducted in order to further the analysis without the need of additional samples.

Practical implications

The method was tested in three different industrial situations. In all cases PROVADT's effectiveness was shown at driving forward a quality initiative with a relatively small number of samples. Particularly in the third case, it lead to the resolution of a long standing complex quality problem without the need for active experimentation on the process.

Originality/value

This work demonstrates the need to provide industry with new statistical tools which are practical and give users efficient insight into potential causes of a process problem. PROVADT makes use of data needed by quality standards and Six Sigma initiatives to fulfil their requirements but structures data collection in a novel way to gain more information.

Citation

Cox, S., Garside, J., Kotsialos, A. and Vitanov, V. (2013), "Concise process improvement definition with case studies", International Journal of Quality & Reliability Management, Vol. 30 No. 9, pp. 970-990. https://doi.org/10.1108/IJQRM-03-2012-0029