Developing effective spare parts estimations results in improved system availability
Bibliographic record
Abstract
Production and manufacturing firms are under great pressure to continuously reduce their production costs in order to stay competitive. Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating cost. For instance, the cost of maintenance in the highly mechanized Kiruna underground iron ore mine in Sweden is 30-50% of the total operating cost. Spare parts availability, an issue of the maintenance process, is studied in this paper. Simply stated, production can be enhanced by the increased availability of functional machinery and the subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is a pragmatic method to improve supportability; it can guarantee non-delay in spare parts logistics which can ultimately improve production output. This study uses an improved statistical-reliability (S-R) approach which incorporates system/machine operating environment information in systems reliability analysis. It selects a multiple regression type of analysis based on Cox's proportional hazards modeling (PHM). It considers a parametric approach with a baseline Weibull hazard function and time independent covariates and analyzes the influence of operating environment factors on this model. Based on the results of analyses, a mathematical model for spare parts prediction in component level for non-repairable parts is developed and the findings are validated through a case study in the Swedish mining industry. The study finds that the outputs represent a significant difference in the required spare parts estimation when considering the influence of the system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management; this can enhance the availability of parts and consequently of machines resulting in economical operation and cost savings.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".