Auf der Basis von Marktanalyse und Market observation versucht man durch die Marktprognose die zukünftige Marktsituation vorherzusagen.
Example: The earlier a company can adapt to changes, the more successfully it will assert itself in the market. It is not without reason that different scenarios are developed in the company and possible alternative courses of action are played out as part of a business game. A well-functioning early warning system (e.g. panel evaluations) helps to quickly perceive changes and react. Well-known forecasting methods are for example:
Trend extrapolation: The time series of quantity, value or price are simply updated
Group discussion: Recognize new visions and design new products through the group participants
Expert group: Per Delphi method die Informationen verdichten
AI supported price forecast
Some companies even use automated market forecasting. The high volatility of producer prices has a significant impact on your sales when marketing your raw materials. In order to market more securely despite this volatility and to be able to better assess the future, you need the right data. high quality data is required. Today's immense amount of data, on which the strongly fluctuating prices are based, is overwhelming. It is no longer humanly possible to analyze this flood of information and make reliable, well-founded forecasts from it. The startup Agripreis, for example, uses its AI for a Wheat price forecast.