Forecast History Type: Select either the customer order ship date or original order due date (Actual) as the basis for history used for forecasting. Applied in Item Forecasting, S&OP, and Advanced Planning.
Auto Remove Outliers: The number of iterations specifies how many times the system will attempt to smooth history. The more iterations the smoother the history and the lower the standard deviation becomes. If the use of this feature is desired, we do not recommend more than two iterations. Setting the value to 0 disables the outlier setting. If S&OP or Advanced Planning is enabled, the outlier may be applied to demand planning and will also be used to remove spikes for safety stock calculations. With Item Forecasting (No S&OP or Advanced Planning) the outliers are used for both the item forecast and safety stock settings.
Sigma Threshold: Sets the number of standard deviations for DemandCaster to identify an outlier.
Forecast History Length (months): The number of months of history to use for forecasting as a default. Default is 48 months for demand planning. The Advanced Planning app has an additional functionality that automatically sets the proper history length by removing leading 0's for forecasting.
Ignore last N buckets: Provides the capability to ignore a set number of the most recent forecasting buckets. This is at times necessary when the most recent sales data is not available when running a new forecast. By ignoring the last period, for example, your forecast will not be influenced by a very low demand period. At present this option is an all or nothing setting meaning it is applied to all forecasts.
ADI Threshold: The threshold set to establish the historical demand profile based on the measure of frequency of demand. To learn more read the article Forecast Structure.
CoV Threshold: The threshold set to establish the historical demand profile based on the measure of volatility of demand. To learn more read the article Forecast Structure.
Legacy S&OP Specific System Forecast Settings
The following measure is unique to the legacy S&OP application. Those measure covered above but not visible below are not applicable.
Recent Period Weight (in months): Used as part of the aggregation calculation. The distribution of the aggregate forecast is weighted by the periods selected. Applicable only when S&OP and Item Forecasting are enabled.
Item Forecasting Specific System Forecast Settings
Bucket Size Selection: Applicable to Item Forecasting. The default historical period aggregation basis for item based forecasting. The options are weeks or months.
- Weekly Buckets Logic: The historical buckets are all weeks and conform to the following rules. Nevertheless which day of the week the forecast is generated, the data series start date is the Sunday (1st day of the week) of that week. A starting zeros check is performed. In case there are one or more weeks with zeros (no invoices) in the beginning of the history data period, then these weeks are removed and the first week which has invoices becomes the first week of the history data period. The end date is chosen to be the Saturday of the last week (the week before the current week when the forecast is generated)
- Monthly Buckets Logic: The historical buckets are all calendar months and conform to the following rules. Nevertheless which day of the week the forecast is generated, the data series start date is the 1st day of each month. A month is defined by the type of calendar selected in system settings: calendar, 4-4-5, or 5-4-4. Starting zeros check is performed. In case there are one or more months with zeros (no invoices) in the beginning of the History data period then these months are removed and the first month which has invoices becomes the first month of the forecast history basis. The end date is chosen to be the last day of the last month (the month before the current month when the demand analysis is generated)
Re-forecast frequency (in months): Sets how often new forecasts are generated to ensure the forecast time frame is long enough to cover cumulative lead team.