A Data-Driven Framework for Agri-Food Supply Chains: A Case Study on Inventory Optimization in Colombian Potatoes Management
Bibliographic record
Abstract
Background: Mitigating the negative impacts of climate change and ensuring food security are critical challenges for sustainable development. Potato crops play a key role in global food security, and optimizing their supply chains can improve yields, reduce waste, and stabilize farmer incomes. This study focuses on the potato supply chain in Boyacá, Colombia, aiming to maximize profitability for smallholder farmers through a data-driven approach. Methods: We developed a hybrid framework combining the newsvendor model, Monte Carlo simulation, and machine learning to optimize inventory decisions under uncertain demand and price conditions. Historical data on potato demand and prices were analyzed to fit probability distributions, and simulation scenarios were run for three main potato varieties. Results: The results show that integrating these methods improves inventory decision-making, with the Criolla Colombia variety yielding positive profitability, while the Diacol Capiro and Pastusa Suprema varieties incur losses under current market conditions. The machine learning model enhances predictive accuracy and supports dynamic planning. Conclusions: The findings demonstrate the potential of advanced analytics to reduce waste, support sustainable practices, and inform agricultural policy. The proposed methodology offers a practical decision-support tool for stakeholders and can be adapted to other crops and regions facing similar operational challenges.
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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 | 0.002 | 0.003 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".