A Data-Driven Framework for Agri-Food Supply Chains: A Case Study on Inventory Optimization in Colombian Potatoes Management

Daniel Muñoz Rojas, Jairo R. Montoya-Torres () and Diana M. Ayala Valderrama
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Daniel Muñoz Rojas: Grupo de Investigación en Sistemas Logísticos, Universidad de La Sabana, Km 7 Autopista Norte Bogotá D.C. Chía, Chía 250001, Colombia
Jairo R. Montoya-Torres: Grupo de Investigación en Sistemas Logísticos, Universidad de La Sabana, Km 7 Autopista Norte Bogotá D.C. Chía, Chía 250001, Colombia
Diana M. Ayala Valderrama: Grupo de Investigación GISPA—Gestión Integral de los Servicios y Productividad Agroindustrial, Universidad Santo Tomás Seccional Tunja, Boyacá 150001, Colombia

Logistics, 2025, vol. 9, issue 4, 1-23

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.

Keywords: agri-food supply chain; potato crop; newsvendor model; machine learning (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:9:y:2025:i:4:p:164-:d:1799979

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