Concise process improvement definition with case studies

Abstract

(2013) 'Concise process improvement denition with case studies.', International journal of quality and reliability management., 30 (9). pp. 970-990. Further information on publisher's website:http://dx.doi.org/10.1108/IJQRM-03-2012-0029Publisher's copyright statement:This article is c Emerald Group Publishing and permission has been granted for this version to appear here http://dro.dur.ac.uk/17091/. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract PurposeThis paper examines the efficiency and objectivity of current Six Sigma practices when at the Measure/Analyse phase of the DMAIC quality improvement cycle. Design/methodology/approachA 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. FindingsFrom 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 implicationsThe 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/valueThis 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.

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

“…There are instances were both methods have been used in conjunction to take advantage of their respective strengths (Cox et al, 2012(Cox et al, , 2013Sharma and Chetiya, 2009). (Steiner et al, 2007) goes further, stating elements and tools from the Shainin System 'should be incorporated into other process improvement methodologies'.…”

“…The PROVADT methodology enables a user to move a Six Sigma improvement project from its measure through its analyse phase with a single sampling strategy (Cox et al, 2012(Cox et al, , 2013. This is achieved by utilising principles from the Shainin System.…”

“…This is achieved by enabling a true Gage R&R to be calculated as part of the enhanced PROVADT design. This paper is structured to provide: a review of current literature of quality improvement in low-volume process in section 2; an outline of where PROVADT fits in a quality improvement initiative and the tools it implements to achieve this in section 3; the sampling constraints required by PROVADT to perform statistically valid analysis are defined in section 4; an enhanced approach is suggested in section 5 addressing the poor Gage R&R issue of the original PROVADT proposed in (Cox et al, 2012(Cox et al, , 2013; in section 6 a worked example is provided which directly compares the original and enhanced approaches; two industrial case studies of PROVADT's implementation are detailed in section 7; and the paper is concluded in section 8.…”

“…A new concise process improvement sampling methodology, known as Process Variation Diagnostic Tool (PROVADT), was proposed by Cox et al (2013). The PROVADT method was designed to support the objectivity of Six Sigma projects, which follow the Define-Measure-Analyse-Improve-Control (DMAIC) cycle in low-and mediumvolume manufacture.…”

“…This was achieved by sampling from a process, in a structure that enables the application of the required statistical metrics from Six Sigma's measure and analyse phases. The sampling structure, also, allowed the use of graphical analysis tools from the Shainin System (Cox et al, 2012(Cox et al, , 2013.…”