Demand Detail Page (Fields)
This page is grouped into sections.
• Graph
Demand Settings
The following describes the fields that appear when any forecasting method is selected.
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Field
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Description
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Run Best Fit
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When checked, indicates that Best Fit Analysis has been or will be run on the fly for the SKU.
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Forecast Method
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Indicates the algorithm used to calculate the forecast demand. To run a different forecast method, select it from the drop-down box and click the Process Changes(  button ).This method take precedence over the Stream Forecast Method. Click here for an explanation of each type of forecast method |
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Stream Forecast Method
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The default forecast method assigned to the stream/segment in the stream configuration.
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Updated Date/Time
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The date and time the forecast method was last changed.
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Updated By
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The ID of the user that changed the forecast method most recently.
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# of History Slices
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The number of demand history periods that the AutoPilot is to use in computing the demand forecast and in calculating the standard deviation in demand factored into Safety Stock. Winters and double exponential forecast methods require at least 2 years of history slices. Same as Last Year forecast method requires at least one year of history slices. Other forecast methods can use less than one year of history slices.
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# of Horizon Slices
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The number of periods of forecasting that are projected.
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Seasonal Profile
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The seasonal profile to apply to the forecast. If you have one year of historical demand, you can add a seasonal profile based on it. To do this, click the New button (  ). In the window that opens, type the name of the seasonal profile and click OK. A new seasonal profile will be created based on the historical demand for the selected service/location. |
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History Average
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The average historical demand for all slices used.
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First History Date
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The date of the first slice used for historical demand.
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History Standard Deviation
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A measure of historical demand variability over a specified number of time slices, calculated from either the raw demand history as downloaded from the host system or after being de-seasonalized.
If the forecast parameter Std Deviation - Use de-seasonalized and de-trended history is set to Yes, History Standard Deviation is computed using deseasonalized/detrended history. Otherwise, raw demand history data is used.
HistorySD = SQRT((SUMSQ(values) - (SUM(values)^2)/COUNT(values))/(COUNT(values) - 1))
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Forecast Error Standard Deviation
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A measure of forecast error variability over a specified number of time slices.
For trending and highly seasonal pairs, using Forecast Error Standard Deviation (instead of History Standard Deviation) in Safety Stock calculations generates an optimal Safety Stock value.
If the forecast parameter Std Deviation - Use forecast error is set to Yes, Servigistics will use forecast errors in Forecast Error Standard Deviation calculations. Note that while both History Standard Deviation and Forecast Error Standard Deviation will be calculated, only Forecast Error Standard Deviation will be used in Safety Stock calculations.
If there are less than 3 slices of archived forecast (insufficient forecast history), Forecast Error Standard Deviation will be null and History Standard Deviation will be used in Safety Stock calculations.
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Scaled Percentage
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Scaled Percentage = (History Standard Deviation / History Average ) * Forecast Average
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History Percent Error
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History Percent Error = (History Standard Deviation / History Average) * 100
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Demand To Date
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The actual demand quantity that has been recognized to date within the current time slice.
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MAPE
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Current MAPE only calculated with new period.
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MAD
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Current Mean Absolute Deviation (only calculated with new period). A statistical measure of fit.
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RMSE
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Current Root Mean Square Error (only calculated with new period). A statistical measure of fit.
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Tracking Signal
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A measurement that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. It monitors the forecast to see if it is biased high or low. As used in forecasting, tracking signal is the number of mean absolute deviations that the forecast value is above or below the actual occurrence.
Tracking Signal = (RSFE / count) / MAD
• RSFE = Running Sum of Forecast Errors include all slices, demand and archived forecast
• MAD = Mean Absolute Deviation
• Count = Forecast parameter "# of Slices for Forecast Error Calculation" or Best Fit parameter "Holdout Window".
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Upper Control Limit
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The maximum value of demand for a SKU to be considered as not a spike in demand. Any demand greater than this limit is considered a spike and is flagged as a detected outlier.
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Lower Control Limit
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The minimum value of demand for a SKU to be considered as not a spike in demand. Any demand lower than this limit is considered a spike and is flagged as a detected outlier.
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Upper Control Limit Confidence Level
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The percentage of the time that actual demand is observed to be less than the forecasted demand upper limit. The default is set to 99.73%, with the ability to store up to 3 decimal places.
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Correction Confidence Level
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The percentage of the time that the actual demand is observed to be less than the forecasted demand. The default is set to 99.73%, with the ability to store up to 3 decimal places.
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Forecast Netting Start Date
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Sets the slice to start applying forecast netting.
If no date is specified, forecast netting is applied from the current slice.
This value can be overridden at segment and SKU levels.
Forecast netting must be turned on (Enable Forecast Netting set to Yes) for this field to be used.
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Forecast Netting End Date
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Sets the slice to stop applying forecast netting.
If no date is specified, forecast netting is applied until the horizon.
This value can be overridden at segment and SKU levels.
Forecast netting must be turned on (Enable Forecast Netting set to Yes) for this field to be used.
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When Leading Indicators is selected as forecasting method, you will see this additional field:
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Field
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Description
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SKU Usage Rate
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The failure (or demand) percentage rate currently in use in Leading Indicator forecasting for this SKU. A usage rate can be provided and the forecast determined from the install base, or demand history can be used to determine the usage rate and forecast. The flag to calculate the usage rate is maintained in the parts master.
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When Winters Multiplicative is selected as forecasting method, you will see these additional fields:
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Field
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Description
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Alpha
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A smoothing constant used to smooth the level or the base.
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Beta
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A smoothing constant used to smooth trend estimates.
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Gamma
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A smoothing constant used to smooth seasonality estimates
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Forecast Trend
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The calculated rate of change of the forecast based on demand history.
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Last Smoothing Date
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The date the most recent smoothing operation was initiated on the forecast.
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When Single Exponential Smoothing is selected as forecasting method, you will see these additional fields:
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Field
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Description
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Alpha
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A smoothing constant used in the smoothing equation for updating Level.
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Forecast Base
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The underlying steady state forecast prior to factoring in the trend and multiplying by the seasonal index.
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Last Smoothing Date
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The date the most recent smoothing operation was initiated on the forecast.
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When Double Exponential Smoothing is selected as forecasting method, you will see these additional fields:
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Field
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Description
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Alpha
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A smoothing constant used in the smoothing equation for updating Level.
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Beta
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A smoothing constant used in the smoothing equation for updating Trend.
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Phi
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A dampening factor for trend estimates. The smaller phi is, the more the trend value will be dampened. If phi = 1, then no trend dampening occurs.
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Forecast Base
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The underlying steady state forecast prior to factoring in the trend and multiplying by the seasonal index.
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Forecast Trend
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The calculated rate of change of the forecast based on demand history.
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Last Smoothing Date
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The date the most recent smoothing operation was initiated on the forecast.
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When Croston's Method is selected as forecasting method, you will see these additional fields:
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Field
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Description
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Alpha
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A smoothing constant used in the smoothing equation for updating Level (base) and periods between demand.
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Omega
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A smoothing constant used in the smoothing equation for updating history standard deviation.
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Forecast Base
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The underlying steady state forecast prior to factoring in the trend and multiplying by the seasonal index.
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Periods Between Demand
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The number of periods between demands that is used to calculate intermittency. This value is calculated as slices of zero / (slices of non-zero – 1)
For example:
Consider a demand history of [5, 4, 0, 0, 3, 2, 0, 1, 0, 0, 0]
slices of zero = 6
slices of non-zero = 5
The value of the Periods Between Demand would be 6 / (5 – 1) = 1.5
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Last Smoothing Date
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The date the most recent smoothing operation was initiated on the forecast.
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When Same As Last Year is selected as forecasting method, you will see this additional field:
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Field
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Description
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Trend %
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The percentage to apply to last year's demand to calculate the forecast.
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When Moving Average is selected as forecasting method, you will see these additional fields:
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Field
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Description
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# of Slices
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The number of history slices used for the forecast calculation.
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Weight
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The weight factor applied to the forecast calculation. The weight factor takes a value between 0 and 1 inclusive.
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SKU Demand Details Section
The SKU Demand Details section shows the actual demand history for the selected SKU. The following table describes the fields on the page.
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Field
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Description
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Demand
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The actual demand history for the selected SKU. Also includes rolled up demand from down-chain parts.
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Adjustment
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Any manual adjustments (or overrides) that have been made to the demand history value.
You can All.
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Non-Recurring
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The non-recurring demand adjustment at this stream for the selected SKU.
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Copy Demand
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Lists demand values that have been copied from another SKU.
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Seasonal Profiles
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Seasonal patterns or corrections are applied to adjust for seasonal variations.
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Deseasonalized History
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Deseasonalized History = Demand History / Seasonal Profile (or index).
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Total
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The total demand history for the selected SKU at this stream.
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Effective Part Population
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[only displayed for streams using leading indicator forecasting method]
The number of parts installed upon which you are basing your usage rate calculation.
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Slice Usage Rate
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[only displayed for streams using leading indicator forecasting method]
The calculated usage rate for the current slice.
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Actual Usage Rate
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[only displayed for streams using leading indicator forecasting method]
The actual usage rate for one time slice.
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Accumulated Part Population
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[only displayed for streams using leading indicator forecasting method]
When demand is zero, the accumulated effective part population for each time slice the part has had no demand. It is zeroed when there is demand and when the user enters a Usage Rate.
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Slice MTBF
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[only displayed for streams using leading indicator forecasting method]
The Mean Time Between Failure rate calculated by Servigistics for informational purposes only.
When Leading Indicators forecasting is in use, Servigistics may be able to provide valuable feedback to manufacturing by calculating the actual MTBF experienced in the field. On the part record, if both the current forecast period's Effective Part Population of this part and its demand are positive, the MTBF is calculated as Usage Hours Per Month (which defaults to 720) / (demand / EPP). This number is the same as the Calculated MTBF shown on the Parts page.
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Demand Across Locations
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This row appears for streams forecasted using Leading Indicator forecasting method. Displays the total demand across all locations, which is useful when validating a calculated part usage rate.
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Normal
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Displays external demand
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NormalInternal
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Displays aggregated external demand from all child locations from the begin date to the start of the current slice. External demand history from the current slice is also included.
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Forecast Detail Section
The Forecast Detail section shows the forecasted demand for the selected SKU, broken out by scheduled and non-recurring demand. The following describes the fields on the page.
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Field
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Description
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Total Net Forecast
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The sum of the netted forecasts for all bins for the selected SKU at this stream.
You will only see this row if bins are used for the SKU. This row is considered a forecast netting row, and it can be shown or hidden using the Show/Hide Forecast Netting Rows button (  ) |
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Net Forecast
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The netted forecast for the selected SKU at this stream, which is dependent upon the parameters that are set for the forecast netting scheme governing the SKU.
If bins are used for the selected SKU, you will see a Net Forecast row for each bin. This row is considered a forecast netting row, and it can be shown or hidden using the Show/Hide Forecast Netting Rows button ( ) |
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Forecast
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The forecasted demand at this stream for the selected SKU.
[for causal forecasting only] In the Forecast row, click the All link to open the Causal Forecast Detail page, which displays causal forecast data for a particular part-forecast stream including the contract, product, and install site names; product quantity; part population; and failure rate.
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Scheduled
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Represents user adjustments to the forecast that (in contrast to non-recurring adjustments) will not have an effect on demand history once the date is in the past.
Profile adjustments are also saved in this slice. User adjustments is the priority. Hover over the note icon to confirm whether it is a profile or user adjustment.
You can click All.
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Non-Recurring
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The non-recurring forecast adjustment at this stream for the selected SKU.
You can All.
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Returned
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This row is displayed in the Forecast Details grid if the part is repairable.
Returned = Forecast Amount x (1 - Returned Wash Rate) * (1 - Not Repair This Station Rate)
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Repaired
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This row is displayed in the Forecast Details grid if the part is repairable.
Repaired = Returned Quantity x (1 - No Fault Found Rate) * (1 - Repaired Wash Rate) = Forecast Amount * (1 - Returned Wash Rate) * (1 - Not Repair This Station Rate) x (1 - No Fault Found Rate) * (1 - Repaired Wash Rate)
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No Fault Found
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This row is displayed in the Forecast Details grid if the part is repairable.
No Fault Found = (Returned Quantity) x No Fault Found Rate = Forecast Amount * (1 - Returned Wash Rate) x (1 - Not Repair This Station Rate) * No Fault Found Rate
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Demand
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The total forecasted demand for the selected SKU at this stream.
If bins are used for the selected SKU, you will see a Demand row for each bin. This row is considered a forecast netting row, and it can be shown or hidden using the Show/Hide Forecast Netting Rows button ( ) |
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Usage Rate
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[only displayed for streams using leading indicator forecasting method]
The usage rate used to calculate the leading indicators forecast.
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Forecast Part Population
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[only displayed for streams using leading indicator forecasting method]
The forecasted population of parts upon which the forecast is based.
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