Theory-Driven Practical Approach to Integrate R&D and Production Planning for Portfolio Management in Agribusiness
Bibliographic record
Abstract
Agribusiness firms, with an eye toward increasing population and evolving weather patterns, are investing heavily into developing new varieties of staple crops that can provide higher yields and are robust to weather fluctuations. In this paper, we describe a multiyear effort at Dow Agrosciences (now Corteva) to manage its seed corn portfolio, which includes several hundred seeds and is valued at more than $1 billion. The effort had two mutually interacting parts: (1) developing a decision-analytic theory to estimate the production yield distributions for new seed varieties from discrete quantile judgments provided by plant biology experts and (2) developing an optimization protocol to determine Dow's annual production plan for the seed portfolio with the flexibility of backup production in South America, under production yield uncertainty. The first part, owned by the research and development (R&D) function, provides yield probability distributions as inputs to the optimization protocol of the second part, which the production function owns. The results of the optimization problem, which include information about the attractiveness of specific future varieties, are returned to R&D. Both parts incorporate contextual details specific to this industry. In this paper, we show the optimality of linear policies for both problems. Additionally, the linear policies have many attractive structural properties that continue to hold for the more complex instances of the problems. A major strength of the theory we developed is that it is implementable in a transparent fashion, providing managers with a user-friendly, real-time decision support tool. The implementation of the theory developed has led to significant monetary and managerial benefits at Dow.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".