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    Eucatex

    Eucatex enhances demand forecasting with artificial intelligence

    The solution enabled greater accuracy in projections, optimizing decision-making and boosting operational efficiency.

    Eucatex enhances demand forecasting with artificial intelligence
    ClientEucatex
    Category
    Machine Learning
    IndustryRetail

    The challenge

    Faced with the need to improve sales predictability and optimize strategic planning, Eucatex struggled to produce accurate demand projections. The traditional process showed a high average error, directly impacting resource allocation and the efficiency of the commercial team.

    The solution

    To address these challenges, MadeinWeb developed a forecasting solution based on machine learning, leveraging historical sales data for advanced modeling and high-accuracy demand projections.

    The main steps of the project included:

    • Data mapping and ingestion: collection and automation of relevant information sources.

    • Predictive model development: implementation of machine learning to analyze patterns and forecast future trends.

    • Interactive dashboards: creation of panels to visualize metrics and monitor model performance.

    • Continuous optimization: adjustments and improvements to maximize forecasting accuracy and efficiency.

    By implementing the demand forecasting solution with MadeinWeb, we are beginning a significant transformation in our inventory management. With this partnership, we hope to forecast demand more accurately, ensuring our products are always available at the right time and in the ideal quantity.

    — Eliezer Ferraz, Business Intelligence Supervisor – Eucatex

    With this new approach, we are confident we can optimize our processes, better meet market demands, and make our operations increasingly efficient and agile. MadeinWeb's expertise gives us the assurance of a positive and sustainable impact on our business.

    — Marcio Roberto Crespo Candido, Corporate IT Manager – Eucatex

    Generated impact

    7.9%average forecast error (down from 19.5%)
    1,584hours dedicated to the project
    100+variables created for the models
    90models tested and validated

    Want to achieve results like these in your company?

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