期刊
INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT
卷 34, 期 5, 页码 598-625出版社
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/IJQRM-07-2015-0096
关键词
Six Sigma; Lean Six Sigma; Lean management; Research publications
类别
Purpose - The purpose of this paper is to extend previous studies to a hybrid analysis of three business improvement practices of Lean, Six Sigma and Lean Six Sigma (LSS) within last two decades and identify the research gaps and focusses in more comprehensive and robust classification framework. Design/methodology/approach - A secondary data collection and a literature review were conducted to collect information about peer-reviewed journal articles under six dimensions of a tested classification framework. The frequency and distribution analysis was conducted followed by Pearson's x(2) test to analyse any relationship between dimensions of framework in order to identify the gap. Findings - Despite a relatively great deal of regular research outputs about Six Sigma, Lean and LSS, academic journal articles have been found mainly limited to a few industries, themes and countries. General manufacturing, healthcare, automotive and electronic industries as sectors; and tools and techniques, benefits and success factors as key themes have been mostly approached by LSS, Six Sigma and lean management articles. It was also found that there is still a great disparity amongst researchers and journals to publish about these three business improvement practices. Research limitations/implications - The research publications for LSS, Six Sigma and lean management should have wider approach towards various manufacturing and service sectors, countries and journal publications. A greater level of research/enterprise activities has been found in relation to LSS and Six Sigma articles compared to lean management articles. Originality/value - This research aims to identify the gaps in research publications during last two decades about three major business improvement practices in one package and through more comprehensive robust classification framework and also through comparative analysis.
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