supply chain planning
and demand management
FrePPLe uses advanced time series algorithms to extrapolate the demand history, and gives users across the company the capability to review and adjust the sales forecast.
System proposed forecast
FrePPle’s algorithms examine the demand history and extrapolate it as predicted future sales.
Advanced time series forecasting algorithms recognize the demand pattern (intermittent, constant, trend, seasonal, …) and automatically apply the correct forecasting method.
Forecast review and editing
Capture demand information from the sale team
Your planners and sales team know more than any forecast algorithm can predict.
Users review and adjust the forecast proposed by the system.
- The demand history in past periods needs adjustments to filter out exceptional demand outliers which won’t repeat in the future.
- The sales forecast in future periods needs adjustments to reflect information that isn’t visible from the historical demand pattern: economy growth, market trends, impact of competitors, etc…
Item, location, customer and time dimensions
Demand management is a multidimensional problem with different levels of aggregation.
- An account manager can edit and review the sales forecast for his accounts.
- A regional sales manager can edit and review the sales forecast for his region and all products by quarter.
- A product manager can edit and review the sales forecast for his products across all locations.
- A production planner might want to review the sales forecast for the next month in weekly buckets.