Lean Six Sigma and Industry 4.0 combination: scoping review and perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current market is characterised by a high level of customisation, leading to aggressive competition between companies to respond to customer needs. To keep their competitiveness, companies search continually to improve their efficiency, flexibility, and performance. With the advent of Industry 4.0 (I4.0), new technologies are developed to increase connectivity and automate processes. Lean Six Sigma (LSS) is known for its capacity to solve complex problems using statistical methods. I4.0 affects almost everything including LSS. Thus, a new combination is emerging LSS4.0 aiming at further increasing the operational excellence for companies. Therefore, the aim of this paper, as the first scoping review in this field, is to present the results of existing studies on the LSS and I4.0 integration, find the gaps in the literature, and provide directions for future studies. The paper proposes a framework that categorises the findings into three main discussed directions, which are relationship, implementation, and impact on performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it