Inventory control in a two-level supply chain with learning, quality and inspection errors
Bibliographic record
Abstract
A common measure of quality for a buyer or a vendor is the defect rate. Defects may represent an attribute, a dimension or a quantity. They may be classified as product quality defects or process quality defects. Product quality defects may be caused by human error which can de due to fatigue, lack of proper training, or other reasons. For example, an inspector may misclassify a defective fuel tank of a car as good. On the other hand, process quality defects maybe caused by a machine going out-of-control. While many researchers assume that the screening processes which separate the defective items are error-free, it would be realistic to consider misclassification errors in this process. Beside inspection errors, learning is another human factor that brings in enhancement in the overall performance of a supply chain. Learning is inherent when there are workers involved in a repetitive type of production process. Learning and forgetting are even more important in manufacturing environments that emphasize on flexibility where workers are cross-trained to do different tasks and where products have a short life cycle. Inventory management with learning in quality, inspection and processing time will be the focus of this thesis. A number of models will be developed for a buyer and/or a two level supply chain to incorporate these human factors. The key findings of this work may be summarized as 1. Inspection errors significantly affect the annual profit. 2. An increase in the unit screening cost reduces the annual profit to a great extent at slower rates of learning. 3. For the two-level supply chain we investigated, learning in production drops the annual cost significantly while the learning in supplier's quality results in a situation where there are no defectives from the suppliers. 4. Type II error may seem to be beneficial for a two level supply chain as the order/lot size goes down and thus affects the costs of ordering, production and screening. 5. Consignment stocking policy performs better than conventional stocking when holding costs go higher than a threshold value.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".