Demand sensing

Demand sensing – what is it?

Demand sensing, or real-time demand forecasting, is an advanced method that uses supply chain data, AI, and machine learning to create short-term demand forecasts based on current market signals. It differs from traditional forecasting based on historical sales data by analyzing current information such as POS transactions, weather trends, social media sentiment, and changes in consumer behavior. In the food industry, it helps minimize food waste, optimize inventory levels, and respond to fluctuations in demand, such as those caused by promotions or weather conditions.

FAQ

How does demand sensing differ from traditional demand forecasting?

Traditional forecasting is based on time series from years of historical data, assuming repeatable seasonality, while demand sensing integrates real-time data from multiple sources, improving the accuracy of short-term forecasts by up to 40%. This method reduces response time to market events such as sudden trends or supply disruptions.

How does demand sensing support the food industry?

In the food sector, demand sensing analyzes data from e-commerce, distributor inventory levels, and macroeconomic indicators, enabling precise production planning, replenishment, and inventory allocation, which reduces stockouts and surpluses of perishable products such as fresh fruit. Food companies gain agility, for example, by dynamically adjusting production schedules to actual demand.

What technologies does demand sensing use?

The key technologies are AI, machine learning, and big data analytics for processing signals such as real-time sales, weather forecasts, and competitive data; for example, self-learning models update forecasts automatically. In the context of Foodcom, this supports the effective management of food raw material inventories.