Analyzing the impact of opportunistic maintenance optimization on manufacturing industries in Bangladesh: An empirical study
Bibliographic record
Abstract
The study investigates the impact of opportunistic maintenance (OM) optimization on manufacturing industries, especially in Bangladesh, to reduce maintenance costs. To that end, OM strategies have been proposed and optimized for multi-unit manufacturing systems, whereas most of the existing research is for single- or two-unit systems. OM strategies in this research cover one of the three policies: preventive replacement, preventive repair, and a two-level maintenance approach. The proposed two-level maintenance approach is a combination of lower-level maintenance, known as preventive repair, and higher-level maintenance, known as preventive replacement. Simulation optimization (SO) techniques using Python were utilized to evaluate the strategies. Historical data from two of Bangladesh's most promising and significant sectors, the footwear and railway industries, was used as the case study. Compared to the currently utilized corrective maintenance approach, the two-level maintenance approach is the most effective for both case studies, demonstrating cost savings of 16.9 % and 22.4 % for the footwear and railway industries, respectively. This study reveals that manufacturing industries can achieve significant cost savings by implementing the proposed OM strategies, a concept that has yet to be explored in developing countries like Bangladesh. However, the study considered the proposed approaches for major components of the system, and more significant benefits can be achieved if it is possible to apply them to all critical components of the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".