A dynamic ordering policy for a three echelon supply chain with backordering for perishable goods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inventory management considering back-ordering policy is becoming a more effective strategy for balancing limited supply with unpredictable demand. Back-logging of inventory is widespread in numerous businesses, including manufacturing, airline, spare part service, and retail industries. This paper develops a control-theoretical method, Smith predictor, for continuous review inventory systems for perishable items with backordering and multi-supplier supply chain. The proposed model aims to respond quickly to market demand changes and generates different orders to smooth and reduce the bullwhip effect in a reasonable time. This modified control theory eases the flow of information in inventory systems. Finally, the block diagram is used to simulate a three-echelon supply chain for perishable products where backorder is allowed. The proposed model is examined and verified using Normal, Exponential, and Gamma distribution demands in MATLAB’s Simulink. The results illustrate that the proposed model is consistent with the Exponential distribution.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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