Bibliographic record
Abstract
The research focuses on improving supply chain performance under weather related uncertainties. Weather has a significant impact on the demand for various products in many sectors. Firms dealing in those products often face problems in demand management. Hence, managing supply chains under the weather risk is very important and challenging. To improve the performance of supply chains of weather-sensitive products, the main objective of the research is to design new classes of contract that can outperform the widely used supply chain contracts, such as wholesale, buyback, and revenue sharing contracts, and trade credit contracts. To design those new classes of contract, the research investigates: (1) coordination mechanisms of both risk-neutral and risk-averse supply chain members, (2) implementation of weather rebates based on Cooling Degree Days (CDD)/Heating Degree Days (HDD), which are the underlying assets for the temperature-based weather derivatives traded on the financial market, and (3) investigating how weather rebate and financial hedging strategies can be implemented to improve the supply chain performance. The design of new classes of contract involves three aspects: modeling a firm's objective under risk, modeling the payoff from weather derivatives, and designing the supply chain parameters. A firm's objective under risk is measured by using the Conditional Value at Risk (CVaR) approach. The supply chain parameters are designed such that the contracts coordinate with a Pareto-improving solution for both parties involved in the contract. The study has many contributions. Firstly, it fills the gap of the existing domain of literature on weather risk in supply chains. With an increase in climate variations, weather risk in supply chains is a pertinent issue. Secondly, the designed new classes of contract are beneficial to administer for a wide range of products and perform better compared to the regularly used traditional contracts. The designed contracts are analyzed using actual demand data collected from a large retail company in Canada. Thirdly, with trade credits being one of the most common ways of supply chain financing, the study explains how trade credits with weather rebates and financial hedging can effectively coordinate supply chains under weather risk.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".