ML models such as regression, time series analysis, and deep learning can be trained on historical data to predict future demand accurately. These models take into account various factors like seasonality like holidays, trends, promotional activities, macro indicators, and external events that influence demand.

Demand sensing techniques leverage real-time data such as point-of-sale transactions, as well as external and enterprise data that helps in creating demand forecasts predictions close to the actual expected demand.

AI-driven demand forecasting systems can identify uncertainties in demand patterns, such as sudden spikes or drops in demand, supply chain disruptions, or changes in consumer preferences. This enables businesses to proactively mitigate risks and adapt their demand planning strategies accordingly.