Demand Forecasting
Forecasting or Demand Prediction is the process of estimating future demand in order to assist better-informed business decisions. Forecasting is used across consumer goods brands to optimize inventory, allocate resources intelligently and maximize revenue. In reality, companies differ widely in their forecast accuracy.
According to the Institute of Business Forecasting & Planning (IBF), the retail industry averages a 30% error rate when forecasting products one month in advance.
In addition the Institute defines “Best-in-Class Forecast Errors” as “The average forecasting errors of ‘best-in-class’ companies, which may be the average of top 25 or 30 performing companies.” Cogniata builds on best practices to help you build a more accurate forecast to support short-term (S&OE) and long-term (S&OP) planning.
A best-in-class forecast is not built from sell-in data or even actual sell-out, but rather from unconstrained demand. This is how much product would be sold if the company never ran out of stock in any channel. To properly calculate “true demand,” both sell-through and out-of-stocks must be examined.
As true unconstrained demand is assessed, the outliers in the data can be understood and accounted for to create a baseline forecast. The aim of a baseline forecast is essentially to be a “normal” forecast, not counting promotions or other special events. A key part of this baseline will be seasonality which will be the regular fluctuations in demand that occur quarterly, monthly, weekly or even daily.
To plan for demand, the baseline forecast will be needed to be adjusted to reflect changes that impact the business, whether they come internally or externally. The last part of best-in-class forecasting is thus sizing the impact of these events, to be used in the forecast accordingly.
At Cogniata, we’re commited to making demand prediction seamless by integrating with your software systems to facilitate continual forecast updates.