Importaciones PP: AI-powered demand forecasting
Importaciones PP is a wholesale wine and spirits distributor with distribution centers in Mexico City, Cancún, Los Cabos and Puerto Vallarta. We developed a machine learning model that forecasts demand for every product and a portal that brings purchase recommendations, sales history and inventory turnover together in one place.
The challenge
- The replenishment policy did not always respond to actual business demand.
- Thousands of products across several distribution centers made it hard to decide what to buy, how much and for which warehouse.
- Excess inventory created extra costs, and stock-outs meant lost sales.
- Sales, product and warehouse data lived in the ERP, with no view to support purchasing decisions.
The solution
A three-part solution: the predictive model, a reporting portal and the client's digital presence.
Odoo integration
The model automatically pulls sales orders, the product catalog and warehouses from the ERP.
Data cleaning and preparation
Validation and standardization of dates and quantities, with statistical outlier treatment so isolated events don't distort the forecast.
Machine learning model
A gradient boosting model (XGBoost) that estimates weekly demand per product from sales history, moving averages, prices, discounts, lead times and ABC classification.
Predictive stock portal
Purchase recommendations by product, distribution center and supplier, with monthly sales history, inventory turnover by days without movement and report export.
Monitoring and retraining
Tracking of performance and data changes to decide when to retrain the model.
Landing page
A commercial website to attract wholesale clients, with online quote requests.
Results
- A machine learning model in production that generates expected weekly demand per product.
- Objective purchase recommendations for each distribution center, supporting the purchasing team's decisions.
- Visibility into inventory turnover to spot products without movement in each warehouse.
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