Lean tools selection for mining : an occupational health and safety approach
Bibliographic record
Abstract
The implementation of lean principles is a well-known subject in the manufacturing industries. There are several publications regarding the impact of this implementation on productivity and workers’ occupational health and safety (OHS) in this sector. In mining industries, however, the link between lean integration and OHS is missing and the publications regarding this issue are scarce. \n \nIn this thesis, an attempt was made to address this issue and investigate more about lean mining in Canada. For that purpose, a literature review followed by an expert elicitation study were conducted. The main objectives of this thesis were to find out about the proper lean tools to be implemented in the mining industry and to investigate about their links with an important productivity indicator (i.e. daily advance rate) and OHS indicators (i.e. body reaction and struck by an object) in the Canadian underground gold mining. \n \nBased on the results of this thesis, a lean mining lifecycle framework with four different phases and a set of lean tools (i.e. VSM, 5S, Kaïzen, TPM, SMED and LIC) were proposed for the mining sector. Furthermore, according to the expert elicitation (7 Canadian experts), 5S and TPM could have positive impacts on daily advance rate and kaïzen could potentially enhance the miners’ safety by reducing the rate of struck by an object risks at their workplace. A lean mining preliminary road-map was proposed based on these findings. \n \nThis study’s results can be used as a stepping stone in future studies to gain a better understanding about lean mining integrating OHS issues in Canada.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".