It is more imperative than ever for companies to make accurate forecasts of sales, delivery times or raw material costs in order to plan production more efficiently and use resources effectively.
Forecasts that do not take into account expertise or market conditions are unreliable and lead to and lack of predictability.
Working with domain experts, the necessary source data is identified, analyzed for suitability, pre-processed, and integrated into system environments. Based on the forecast objectives and the underlying business processes, appropriate target variables and suitable metrics are developed for validating the forecasts. Forecast results are continuously checked for plausibility and anomalies. The entire modeling process is documented and thus made traceable. Resulting models are continuously maintained and adjusted if necessary.
Customized forecasts, which are not only generated from standard data, allow better and more resilient predictions, as explicit knowledge about individual business processes and a variety of source data are taken into account. They are more robust and more accurate, relevant influencing factors can be determined, specifics can be better modeled and target metrics can be defined more precisely.
White-box approaches are used. The forecasts are therefore traceable and can be quickly adapted to new conditions.
AI-supported forecasting models