A multi-level production-inventory-distribution system under mixed make to stock, make to order, and vendor managed inventory strategies: An application in the pulp and paper industry
Bibliographic record
Abstract
In today's competitive environment, most companies tend to adopt a hybrid manufacturing strategy to provide more reliable services to a wide range of customers. Based on a real-world case study, we investigate a customer-wise production-inventory-distribution system in a multi-level supply chain. Within this system, at a tactical planning level, the decision concerning the production strategies including make to stock (MTS), make to order (MTO), or vendor managed inventory (VMI) strategy, to choose for each customer is made, and then at the operational planning level, the production, inventory, and distribution activities are coordinated according to the strategy assigned to the customers. The objective is to optimize the total cost while the VMI customers are timely satisfied and the target service level for MTO customers is respected. Furthermore, the purchase acceptance rate for MTS customers can be controlled to enhance capacity utilization. We develop a rolling horizon planning approach to deal with the dynamics of customers' demands under different forecasting scenarios. We perform numerical experiments under both static and rolling horizons based on adapted data from a real-world application from a Canadian pulp and paper industry. The results reveal that customers placing smaller orders are classified within the VMI strategy to mitigate potential delays. Conversely, MTS customers are predominantly characterized by larger order quantities. A sensitivity analysis of key problem parameters demonstrates that the backorder cost for MTO customers can effectively influence the strategy allocation. Moreover, our results underscore the notable influence of sales prices on the distribution of customers among strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".