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Demand sensing is a forecasting method that leverages new mathematical techniques and near real-time information to create an accurate forecast of demand, based on the current realities of the supply chain. Gartner, Inc. insight on demand sensing can be found in its report, "Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven." ^[1]

Traditionally, forecasting accuracy was based on time series techniques which create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. However, past sales are frequently a poor predictor of future sales. Demand sensing is fundamentally different in that it uses a much broader range of demand signals (including current data from the supply chain) and different mathematics to create a more accurate forecast that responds to real-world events such as market shifts, weather changes, natural disasters, consumer buying behavior etc.

Reference: Wikipedia.org