From Data to Bottlenecks: Enhancing Workshop Performance Via Discrete-Event Simulation and Operator Modelling
Bibliographic record
Abstract
This article investigates the use of dynamic discrete-event simulation (DES) to assess and improve the performance of industrial maintenance workshops, with a focus on the fine modelling of operators. Using three distinct industrial cases, the study analyses the impact of operator skills, availability, and allocation rules on the identification of bottlenecks. Case A (maintenance of railway bogies) illustrates an iterative sizing approach in the design phase, while Case B (maintenance of aero-engines) highlights the importance of modelling individual skills to plan training in the medium term. Case C (watch case maintenance) demonstrates the value of detailed modelling based on real data for reallocating flows in the short term. The DES models incorporate specifications such as hourly schedules, skill levels (technical and product), and task variability (deterministic or stochastic). Results show that performance indicators, such as operator occupancy rates, skills, or dual skills, can be used to identify bottlenecks and emphasise the importance of detailed operator modelling for effective human resource management in complex industrial 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".