Forecast Analysis - Advanced
This dashboard is designed to help you analyze things like:
• The over or under forecasting of segments, parts, and locations
• The forecast error by understanding the tracking signal trend and accuracy trend over time
• The trend of the forecast method counts and forecast adjustments
The information on this dashboard is grouped into the following mini-dashboards:
Forecast Error Analysis Summary
This mini-dashboard helps you analyze the over or under forecasting of critical segments, parts, and locations.
Widget | Description |
|---|---|
# SKU-Streams | The count of SKU-Streams that are planned in the system, with External Indicator = yes and Use In Total = yes |
# Over | The count of SKU-Streams that are flagged as over-forecasted using Tracking Signal < -0.5 |
% Over | The percentage of over-forecasted SKU-Streams. (# Over) / (# SKU-Streams * 100) |
# Under | The count of SKU-Streams that are flagged as under-forecasted using Tracking Signal (over Lead Time) > 0.5 |
% Under | The percentage of under-forecasted SKU-Streams |
Forecasting Categories by Tracking Signal | A pie chart of the count of SKU-Streams in the following groupings: • Over (Tracking Signal < -0.5) • Under (Tracking Signal >0.5) • Accurate (Tracking Signal between -0.5 and 0.5 • Not Calculated |
Forecast Error Analysis Graph
This mini-dashboard helps you analyze the forecast error by showing the tracking signal trend and accuracy trend over time.
Widget | Description |
|---|---|
Tracking Signal (Over forecast, -ve | A chart of the Tracking Signal (lead time) of only over-forecasted SKU-Streams, used to calculate a system level tracking signal by using History Average as the weight. T.S. (Over) = [Σ( T.S. * HAvg) of all the SKU-Streams that are over-forecasted] / [Σ(HAvg) of all the SKU-Streams that are over-forecasted] |
Tracking Signal (Under forecast, +ve) | A chart of the Tracking Signal (lead time) of only under-forecasted SKU-Streams, used to calculate tracking signals at the system level by using History Average as the weight. T.S. (Under) = [Σ( T.S. * HAvg) of all the SKU-Streams that are under-forecasted] / [Σ(HAvg) of all the SKU-Streams that are under-forecasted] |
Forecast Accuracy | A chart of the Tracking Forecast Accuracy (lead time) at the system level, calculated by using Forecast Accuracy of all the SKU-Streams and their History Average as the weight. Forecast Accuracy = [Σ(Forecast Accuracy *HAvg) of all the SKU-Streams] / [Σ(HAvg) of all the SKU-Streams] |
Forecast Error Analysis Grid
This mini-dashboard helps you understand the over-forecast, under-forecast and forecast accuracy for location , part type, and forecast methods.
 | To return to the Forecast Analysis - Advanced dashboard after clicking a hyperlink, click Forecast Analysis - Advanced on the Servigistics breadcrumb trail. |
Widget | Description |
|---|---|
Location (Over Forecast) | The average Tracking Signal at the Location level, calculated using the Tracking Signal and History Average of all the over-forecasted SKU-Streams of that Location. Σ(Tracking Signal * History Average) / Σ(History Average) Click a Location hyperlink to open the SKU Forecast Analysis - Advanced dashboard, populated with the selected location for all part numbers. |
Location (Under Forecast) | The average Tracking Signal at the Location level, calculated using the Tracking Signal and History Average of all the under-forecasted SKU-Streams of that Location. Σ(Tracking Signal * History Average) / Σ(History Average) Click a Location hyperlink to open the SKU Forecast Analysis - Advanced dashboard, populated with the selected location for all part numbers. |
Location (Forecast Accuracy) | The average Forecast Accuracy at the Location level, calculated using the Forecast Accuracy (lead time) and History Average of all the SKU-Streams of that Location. Σ(Forecast Accuracy * History Average) / Σ(History Average) Click a Location hyperlink to open the SKU Forecast Analysis - Advanced dashboard, populated with the selected location for all part numbers. |
Part Type (Over Forecast) | The average Tracking Signal at the Part Number level, calculated using the Tracking Signal and History Average of all the over-forecasted SKU-Streams of that Part Type. Σ(Tracking Signal * History Average) / Σ(History Average) Do the following to view more details: • Click  to display the Part Numbers associated with the Part Type.• Click a Part Type hyperlink to open the Part Forecast Analysis - Advanced dashboard in Servigistics, populated with the selected part type for all part numbers in that part type. • Click a Part Number hyperlink to open the Part Forecast Analysis - Advanced dashboard in Servigistics, populated with the selected part number. |
Part Type (Under Forecast) | The average Tracking Signal at the Part Number level, calculated using the Tracking Signal and History Average of all the under-forecasted SKU-Streams of that Part Type. Σ(Tracking Signal * History Average) / Σ(History Average) Do the following to view more details: • Click to display the Part Numbers associated with the Part Type.• Click a Part Type hyperlink to open the Part Forecast Analysis - Advanced dashboard in Servigistics, populated with the selected part type for all part numbers in that part type. • Click a Part Number hyperlink to open the Part Forecast Analysis - Advanced dashboard in Servigistics, populated with the selected part number. |
Part Type (Forecast Accuracy) | The average MAPEat the Part Number level, calculated using the Forecast Accuracy (lead time) and History Average of all the SKU-Streams of that Part Type. Σ(Forecast Accuracy* History Average) / Σ(History Average) Do the following to view more details: • Click to display the Part Numbers associated with the Part Type.• Click a Part Type hyperlink to refresh the widget with the data specific to that part type. • Click a Part Number hyperlink to open the Forecast Review page in Servigistics, populated with the selected part number for all locations. |
Forecast Method (Over Forecast) | The average Tracking Signal at the Forecast Method level, calculated using the Tracking Signal and History Average of all the over-forecasted SKU-Streams of that Forecast Method. Σ(Tracking Signal * History Average) / Σ(History Average) |
Forecast Method (Under Forecast) | The average Tracking Signal at the Forecast Method level, calculated using the Tracking Signal and History Average of all the under-forecasted SKU-Streams of that Forecast Method. Σ(Tracking Signal x History Average) / Σ(History Average) |
Forecast Method (Forecast Accuracy) | The average MAPE at the Forecast Method level, calculated using the Forecast Accuracy (lead time) and History Average of all the SKU-Streams of that Forecast Method. Σ(Forecast Accuracy * History Average) / Σ(History Average) |
MAPE vs COV | A scatter plot using the MAPE and COV of all the SKU-Streams. COV = HistorySD / History Average Click a bubble on the Part Type to open the pop-up menu and select HyperLink. Then select one of the following options: • Part Number, to open the Forecast Review page in Servigistics, populated with all locations for the selected part number. • Location, to open the Forecast Review page in Servigistics, populated with all part numbers for the selected location. |
Demand Forecast Analysis - Advanced
This mini-dashboard helps you understand the trend of the forecast method counts and forecast adjustments.
Widget | Description |
|---|---|
Demand and Forecast (Units) | A line graph that compares Total Demand and Forecast Units for the selected Location, Part Number or for the system. |
Demand and Forecast ($ Values) | A line graph that compares The Total Demand and Forecast Values ($) for the selected Location, Part Number or for the system. |
Forecast Method Count | A chart that shows a time-series view of Forecast Methods used for forecasting. |
Forecast Adjustment | A line graph that shows a time-series count of Total SKU-Streams and the % of SKU-Streams that have forecast adjustments. |
MAPE vs COV - SKU Stream
This mini-dashboard helps you understand the part, location, and forecast stream details by showing circles on the graph that correspond in size with the amount of annual forecast.
Widget | Description |
|---|---|
MAPE (LT) vs COV | A scatter plot that uses MAPE(lead time) and COV of all the SKU-Streams. The size of the bubble is proportionate to $ Annual Forecast. COV = HistorySD / History Average |