Case study
Our client was one of the largest manufacturing companies in Central and Eastern Europe, operating in the FMCG segment. The organization manages a broad portfolio of brands and distribution channels, generating significant volumes of sales data. The company was looking for ways to improve its sales forecasting and production planning processes, while reducing the risk of excess inventory and stock shortages.
The goal of the project was to develop a solution leveraging Machine Learning (ML) and Artificial Intelligence (AI) to automate sales forecasting for selected product categories.
The system aimed to improve forecast accuracy on a weekly basis, at the product and market level, taking into account seasonal variability, market trends, and the specifics of individual distribution warehouses.
We developed a set of Machine Learning models for sales and distribution forecasting. The models were based on historical data and current trends, incorporating seasonal and market factors.
Forecasts are generated automatically on a weekly cycle and cover:
By automating the analytical process, the solution significantly reduced the workload related to manual forecast preparation and lowered the risk of incorrect production and logistics decisions.
The project concluded with a successful PoC outcome, confirming the feasibility of effective sales forecasting automation based on analytical data. The company plans to further scale the solution across additional product categories and markets.
As part of the project, we delivered:
The project resulted in a significant improvement in performance indicators:
68,7%
accuracy of weekly sales forecasts
95,2%
accuracy of distribution forecasts
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