Demand Sensing
Demand forecasts built entirely on historical data work well when demand is stable — but markets rarely are. Demand sensing in sedApta Suite combines historical sales data with live signals such as point-of-sale data, order patterns, and external market indicators, then applies AI and machine learning to generate a much shorter, more accurate view of what demand is doing right now. The result is a forecast that responds to what is actually happening in the market, not just what happened last month or last year — and planning teams that can act sooner, with greater confidence.
How Demand Sensing works
First, the exogenous data sources must be identified and analysed. It is crucial to dedicate sufficient time to this phase, as it not only narrows down the potentially vast field of variables of interest but also requires finding data sources that provide values for these variables in the format and timing needed for the forecasting process.
Next, select the ML algorithms to apply to estimate the effect that one or more exogenous variables, or a combination, have on sales.
Then, we will train and test the ML algorithms on historical data to select the exogenous variables whose effect on demand forecasting can be predicted with sufficient accuracy and is significant.
Finally, it enables data flows to systematically acquire the predicted values of these variables so they can be actively used to support the forecasting process.
Demand Sensing in sedApta sofware
sedApta's demand sensing uses machine learning to measure the impact of external variables — such as macroeconomic indicators, promotional activity, or sector-specific signals like dietary trends in food and beverage — on sales demand. Once the relevant variables are identified, ML algorithms are trained on historical data to determine how much the baseline forecast should be adjusted in response to changes in those variables. The result is a forecast that responds to what is happening now, not just what happened last year.
Combining ML-based demand sensing with sedApta's statistical forecasting gives manufacturers: more accurate short-term forecasts; the ability to revise forecasts quickly when conditions change; and better insight into which external factors are actually driving demand — which feeds directly into budgeting and rolling forecast processes.
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Value creation across the planning horizon
Accurate demand management gives manufacturers better visibility of what customers will need and when — reducing the stock imbalances and reactive purchasing that come from getting the forecast wrong. Find out how sedApta Suite helps planning teams improve forecast accuracy and reduce the cost of demand uncertainty.