Mathieu Pourqué |
Aroma Performance: Marie PereiraSpp: Cecile BaillyNovecare: Jean-Baptiste CercueilPeroxides: Benoit HornyakSilica: Philippe Deloro/ Jose Pablo LeyvaSpecial Chem: Anne Vanesse Soda Ash & Derivatives: Fabien Piret |
| Forecast Error: Bias & Tracking Signal (NFM) | |
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Forecast error measurement should serve a purpose. From a demand forecasting perspective, the purpose is to understand the planning process capability and identify products that are systematically the biggest error contributors and set actions to offset the effects of them. | |
The objective is to introduce a framework for using Solvay’s forecast error metrics according to a structured process:
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Measure Forecast Deviation Forecast Bias % (PE) = (sum(Final Forecast)- sum(Gross History))/Sum(Gross History) x 100 When to use: The conversion of forecast error into percentages, allows for comparison across products with different magnitudes of demand volumes. It is also used to indicate underforecast or overforecast. When the computed error is greater than 100% in either direction, the measurement result should be shown as it appears. | Diagnose the forecast error and root causes: Normalized Forecast Metric = (Final Forecast - Gross History)/ (Gross History + Final Forecast)) When to use: as a diagnostic measure for finding forecast line items with the most significant forecast bias in a diverse group of products. It can be used regardless of their scales of volume or units of measure. |
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| Technical Documentation | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Source: DYNASYS / APO (WP1) / APO (PF1) - QV_BW_QRY_MVDYN11_0005Update: Monthly full reload (history included) on the night of the 6th and the 7th (minor corrections in Gross History may still occur from one month to another)Technical BW Documentation: BW - DP - Forecast Accuracy (Core Query)Naming convention by Source System (Dynasys vs APO)
Dimensions: Global Overview of Dimensions
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