A Discrete Stress–Strength Interference Theory-Based Dynamic Supplier Selection Model for Maintenance Service Outsourcing
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Bibliographic record
Abstract
Maintenance service outsourcing is a strategic driver for asset intensive industries pursuing the enhancement of supply chain performance. Maintenance service supplier selection plays a relevant role in this premise since its significant impact on equipment availability, and hence, on business success. To periodically review suppliers' performances and update the outsourcing contract, a discrete stress-strength interference (DSSI) theory-based dynamic supplier selection model is presented for maintenance service outsourcing process. Taken account of the influence of randomness and uncertainty, a novel and universal evaluation criterion, demand fulfillment level (DFL) is introduced based on the DSSI theory. DFL is relevant to two random variables, which are random user's service order quantity (stress) and random supplier's service fulfillment quantity (strength), and DFL is defined as the probability that the latter (strength) is larger than the former (stress). Based on DFL, the proposed model can help users outsource the corresponding maintenance service to the most suitable supplier (maximum supplier reliability) at different periods. The decision rule can be described as a dynamic 3-D diagram, according to which decision makers can periodically review suppliers' performances and update the outsourcing contract. A case study on maintenance service supplier selection problem for a steel company illustrates the effectiveness of the proposed model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it