Lean Mining, Productivity and Occupational Health and Safety: An Expert-Elicitation Study
Bibliographic record
Abstract
The implementation of lean tools in the Canadian mining industry is still in its beginnings. To the best of our knowledge, published information and articles on this subject are scarce. Consequently, the impacts of using lean tools on productivity and workers’ health and safety in this field are still unclear and need more investigations to better integrate the technical aspects of lean with Occupational Health and Safety (OHS). Therefore, this study aims to provide insights about lean mining in Canada. The objective of this paper is to propose a preliminary road-map for lean implementation considering OHS concerns in Canadian underground gold-mining. To meet this objective, a set of lean tools (i.e. VSM, 5S, Kaizen, TPM, SMED and LIC) as independent variable, and OHS indicators (i.e. “struck by an object” and “body reaction” risks) and an economic indicator (i.e. daily advance rate) as dependent variable were selected. An expert-elicitation study was conducted recruiting 7 experts from academia and practitioners active in the mining sector. Results show that the majority of experts agreed on a possible positive impact on a mine’s daily advance rate after implementing 5S and TPM, and a reduction of the risk rate of “struck by object” among workers by implementing Kaizen.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".