Requirements, challenges and impacts of Lean Six Sigma applications – a narrative synthesis of qualitative research
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify, present and categorise the main requirements, challenges and impacts of Lean Six Sigma (LSS) applications. Emphasis is given to the soft impacts of LSS applications, which are intangible in nature and difficult to quantify and measure, highlighting the most frequently cited ones. Design/methodology/approach A qualitative synthesis of the studies using the narrative synthesis approach is adopted to descriptively summarise and categorise the requirements, challenges and impacts of LSS applications. The studies were searched by using the following keywords: “LSS applications,” “LSS requirements,” “LSS challenges” and “LSS impacts” in almost all major electronic databases such as Emerald, Taylor and Francis, ScienceDirect and Wiley. A total of 116 articles published between 2007 and 2017 in 41 academic journals were collected and reviewed. Consideration was also given to a number of substantial publications in 2006, 2018 and 2019. Findings In addition to its process efficiency and financial impacts, LSS was found to have another impact category related to individual and organisational behaviours. Management commitment, training and organisational culture were concluded to be amongst the most important and required categories for successful LSS applications. It was also found that the lack of awareness of LSS tools and benefits and the lack of change management and resistance to change were amongst the most cited categories of implementation challenges. Research limitations/implications The studies published between 2007 and 2017 are mainly considered in this paper. It is believed that 10-year publication period considered in this research is sufficient to study the evolution, benefits, limitations and future trends of a particular research topic. However, the exclusion criteria used in the search process with respect to the articles’ year of publication and search terms and keywords may limit the generalisation of the research findings. In addition, the qualitative nature of this research study and the lack of empirical data to support its findings is another limitation that future research should consider. Practical implications This research paper may serve as a valuable source of information for LSS researchers as it will provide them with useful and new insights and directions for further research in LSS. It will also increase the awareness of LSS practitioners about the kind of impact LSS has, and therefore, achieve a better utilisation of its tools by ensuring availability of application requirements and overcoming application challenges. Originality/value This study differs from previous research studies as it focusses attention on the soft impacts of LSS applications and highlights them. The study identifies and prioritises LSS application impacts, requirements and challenges. The study on these aspects was found to be limited and lacking in previous research studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".