A two-period sourcing model with demand and supply risks
Bibliographic record
Abstract
In this paper, we study the problem of sourcing a product when the demand and supply may be uncertain. We consider a two-period model where the supplier’s production quantity in the second period is dependent on the amount produced in the first period. This is a common situation in industries where the production capacity cannot be changed in a short period of time such as in the almond industry. In this industry usually a two-year contract between the supplier (farmer) and buyer (handler) is preferred. The buyer can sign two sets of contracts: a production contract where she is responsible for the uncertainty of yield or a purchasing contract where the provided quantity is guaranteed by the supplier at a higher cost. The buyer has to decide about the quantities to buy through the production and purchasing contracts. The buyer has the option to carry excess inventory from the first period to the second. We establish some analytical properties of our proposed model and perform comparative static analysis to study the buyers decisions. In particular, we show under which conditions the buyer may benefit from purchasing contracts. In addition, we shed some light the debate of the merits of inventory carry-over in mitigating the yield risk in the almond industry. To gain some practical insights, we also apply our model to some real data from the California almond industry. Finally, we extend the model to investigate two cases: when the prices in the primary and the secondary markets are functions of yield and when the amount of carry-over is a decision variable.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".