Forecasting Methodology


Data sources for the forecast include historical order or ship demand, firm and planned sales orders, EDI releases, blanket orders, inter-company orders, and point of sale data. The Forecasting algorithms form the basis of the the DemandCaster Forecasting and Planning process.

Forecasting comprises of 5 key components:

  1. Import historical sales data: Automatically via Data Integration, or manually via a tab-delimited text file uploaded in the upload portal.
  2. Analyze Quality of Historical Data: Determine the type of historical data to determine the best forecasting approach.
  3. Create statistical forecasts: Forecasts can be generated as often as required (most clients run new forecasts weekly or monthly).
  4. Import customer forecasts: Data Integration can connect to external data sources where such data is shared. Customer or external sales forecasts can also be uploaded using Excel spreadsheets converted to text files.
  5. Review and edit forecasts: Numerous metrics are included to test and evaluate results. DemandCaster includes confidence indices, numerous error measures, trend indices, hold-back testing, and other analytics to test the reliability and continually improve the forecasting process.

Forecast Algorithms

Forecasting models accommodate seasonal demand, product hierarchies, product promotions, slow-moving items, causal variables, outliers and much more:

  • Expert Selection: The built-in expert system analyzes your data, selects the appropriate forecasting technique, builds the model and calculates the forecasts.
  • Exponential Smoothing: Numerous Holt-Winters exponential smoothing models are provided to accommodate a wide range of data characteristics.
  • Croston's: A number of Croston's intermittent demand models are provided to accommodate low volume and "sparse" data (i.e., data where the demand is often zero and volumes are low).
  • DemandCaster Intermittent Demand Model (Bootstrapping): A model that calculates an items reorder point by sampling historical demand. The traditional approach to calculating intermittent demand order points is by using Croston's method with a calculated safety stock. In this case, there is no safety stock applied. In certain situations, this methodology provides a more reliable result.
  • Event Models: Event models extend exponential smoothing by providing adjustments for special events such as promotions, strikes or other irregular occurrences. You can adjust for events of several different types such as promotions of varying sizes or types, or movable holidays.
  • Multiple-level Aggregate Models: With the Advanced Planning add-on, multiple-level models allow you to aggregate data into groups that can be reconciled using a top-down, middle-up, or bottom-up approach to produce consistent forecasts at all levels of aggregation. Seasonal and event indexes can be extracted from the higher-level aggregates and applied to lower-level data.
  • New Product Forecasting: Includes methods for forecasting new products, including forecasting by analogy and item supercession.
  • Simple Methods: Moving average and percentage overrides.


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