Identificación de demanda en la empresa Argentto por ciudad utilizando algoritmos k-Means

Abstract

Una gestión eficiente del inventario es clave para evaluar el desempeño de las ventas y optimizar el uso de los recursos en la producción. Actualmente, Argentto SAS enfrenta desafíos en la administración de su inventario y el análisis de sus ventas, lo que dificulta la planificación estratégica y la distribución óptima de sus productos. Para abordar esta problemática, el proyecto implementará Machine Learning mediante el algoritmo K-Means, con el objetivo de identificar los productos más vendidos por ciudad. Este modelo analítico facilitará la toma de decisiones en el manejo del inventario, dependiendo cuales son los productos con mayor demanda. Argentto SAS es una empresa consolidada en el mercado textil colombiano, especializada en la fabricación de medias diseñadas para mejorar la comodidad de los integrantes de las Fuerzas Armadas de Colombia, quienes operan en condiciones extremas. Sus productos se comercializan en ciudades clave como Barranquilla, Popayán, Rionegro, Cali y Bogotá. Como parte del proyecto, se estructurará el inventario existente, utilizando códigos SKU, además, se desarrollará una prueba piloto para analizar tendencias de demanda por ciudad de los productos ofrecidos. Los resultados permitirán mejorar la planificación y manejo de inventario, la compra de producto terminado y la distribución estratégica de productos. Además, la segmentación geográfica contribuirá a un análisis más preciso del rendimiento comercial por región, fortaleciendo la competitividad de Argentto SAS en el mercado.

An efficient inventory management system is key to evaluating sales performance and optimizing resource use in production. Currently, Argentto SAS faces challenges in managing its inventory and analyzing its sales, which hinders strategic planning and the optimal distribution of its products.

To address this issue, the project will implement Machine Learning using the K-Means algorithm, with the goal of identifying the best-selling products by city. This analytical model will support decision-making in inventory management by highlighting the products with the highest demand.

Argentto SAS is a well-established company in the Colombian textile market, specializing in the production of socks designed to enhance the comfort of members of the Colombian Armed Forces, who operate under extreme conditions. Its products are marketed in key cities such as Barranquilla, Popayán, Rionegro, Cali, and Bogotá.

As part of the project, the existing inventory will be structured using SKU codes. In addition, a pilot test will be carried out to analyze demand trends by city for the offered products. The results will enable improved inventory planning and management, purchasing of finished products, and strategic distribution. Furthermore, geographic segmentation will contribute to a more accurate analysis of commercial performance by region, strengthening Argentto SAS’s competitiveness in the market.

Efficient inventory management is key to evaluating sales performance and optimizing the use of resources in production. Currently, Argentto SAS faces challenges in managing its inventory and analyzing its sales, which hinders strategic planning and optimal product distribution. To address this issue, the project will implement machine learning using the K-Means algorithm to identify the best-selling products by city. This analytical model will facilitate decision-making in inventory management, based on which products are in greatest demand. Argentto SAS is a well-established company in the Colombian textile market, specializing in the manufacture of socks designed to improve the comfort of members of the Colombian Armed Forces, who operate in extreme conditions. Its products are sold in key cities such as Barranquilla, Popayán, Rionegro, Cali, and Bogotá. As part of the project, the existing inventory will be structured using SKU codes. A pilot test will also be conducted to analyze demand trends for the products offered by city. The results will improve inventory planning and management, finished product purchasing, and strategic product distribution. Furthermore, geographic segmentation will contribute to a more precise analysis of sales performance by region, strengthening Argentto SAS's competitiveness in the market.