Lean techniques impact evaluation methodology based on a co-simulation framework for manufacturing systems
Bibliographic record
Abstract
Lean implementation plays a major role in optimizing productivity and reducing waste. Applying the adequate integration of Lean Techniques (LT) can ensure a higher profitable benefit. Many companies face difficulties in choosing the LT that best suit their situations to reach their objectives. In this study, we propose the simulation of specific modeled industrial contexts and check the impact of implementing LT simultaneously. Market fluctuation, demand diversification, and uncertainty of resources contexts are studied to perceive how LT behaves accordingly. Four KPIs (Key Performance Indicators) are retained for the analysis: Work in Progress, Lead-time, Production Throughput, and Defect Rate. An aeronautical company is modeled and experiments are performed to demonstrate the usefulness of a developed co-simulation framework to perceive the sensitivity of LT to some industrial contexts. The results showed that Poka Yoke and 5S are context-free LT valid in any industrial context. Pull, SMED, and Cross training are contextual and deserve careful applicability regarding the simulated context. Cross training, suitable for uncertainty of resources, does not show any significant improvements when the company was exposed to market fluctuations and demand diversification contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".