Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach
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Bibliographic record
Abstract
One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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