Accurate automatic demand forecasts calculated using state of the art statistical and machine learning techniques. Easy to use solution with no expert knowledge required
Accurate demand forecasts are the foundation of making right business decisions. ProLogistica Trend automatically forecasts the demand, taking into account such phenomena as trends and seasonality, among others. The right forecasting method and its parameters are chosen automatically, which allows for creating forecasts in a non-labour-consuming way and does not require users to have experience in forecasting
Using state of art statistical and machine learning techniques allows for a significant increase of the accuracy of demand forecasts calculated in companies. With an automatic selection of the best algorithm and forecast calculation, the system can be used without any expert knowledge. Demand forecasts for thousands of SKUs are created within several minutes
Our forecasts are calculated with trends, seasonality, the influence of external factors (such as prices or weather), and the dependency of demand for similar products taken into consideration. The system automatically calculates to what extent the seasonality affects the sale of different products and presents a report concerning products for which the trend has changed
We have created an original solution for forecasting demand for new products, including forecasting demand with regard to features, taking into consideration substitutes and the phenomenon of cannibalism. This method also allows to effectively plan works in a newly created branch or a point of sale
ProLogistica allows for planning promotional campaigns or setting a price policy and verifying forecast influence of individual demand forecast scenarios. With an extensive simulation panel, users can freely change the planned selling prices and enter information on expected marketing campaigns, and the system will illustrate how the demand would change considering different business scenarios
The application automatically detects and eliminates outliers that can distort the demand forecast. Users can define events which have caused such an observation (e.g. industry fairs). Such an event, when occurring in the future, will be then included in forecasts calculated by the system
