Centralized data and machine learning enabling granular logistics planning across thousands of SKUs.

Master’s logistics planning was constrained by fragmented information. Demand data was spread across multiple silos, resulting in a highly aggregated, macro-level view of sales. This lack of granularity limited SKU-level inventory and production management, creating imbalances—excess stock for some items and shortages for others.
The challenge was to introduce intelligence into the process, enabling each product in the portfolio to have its own dedicated demand forecasting curve.

DB identified that the solution required more than a new algorithm—it demanded a shift in the data infrastructure. The assessment pointed to a centralized Data Lake as the foundation to ensure data integrity across sources.
To handle the scale of thousands of SKUs, the machine learning model needed a scalable architecture (Databricks) capable not only of generating predictions, but also serving them in real time to decision-makers.

We built an end-to-end data pipeline connecting Master’s Data Lake to a predictive model. The solution leveraged a time-series machine learning model trained to capture SKU-level demand patterns with high granularity.
To operationalize insights, we implemented Databricks Model Serving, turning the model into a high-availability service that delivers real-time forecasts. This unified demand visibility, eliminated inconsistencies across fragmented data sources, and established a reliable foundation for logistics and supply planning.