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- W2896067230 abstract "Business forecasting frequently combinesquantitative time series techniques with qualitative expert opinionoverrides to create a final consensus forecast. The objective ofthese overrides is to reduce forecast error, which enables safetystock reduction, customer service improvement, and manufacturingschedule stability. However, these overrides often fail to improvefinal forecast accuracy. Process mis-steps include smalladjustments, adjustments to accurate statistical forecasts, andadjustments to match financial goals. At best, these overrideswaste scare forecasting resources; at worst, they seriously impactbusiness performance. This report offers a framework foridentifying overrides that are likely to improve forecast accuracy.A new class of metrics, Dispersion-Scaled Overrides, was developedas an input to the framework. Other inputs included statisticalforecast accuracy and auto-correlation. Classification techniqueswere used to identify whether an override was likely to createforecast value add. These techniques were found to be approximately80% accurate in predicting success. This result suggests that usingDispersion-Scaled Overrides alongside common forecast accuracymetrics can reliably predict forecast value add. In turn, this canmaximize the value that experts add to the business forecastingprocess." @default.
- W2896067230 created "2018-10-26" @default.
- W2896067230 creator A5006131838 @default.
- W2896067230 date "2018-01-01" @default.
- W2896067230 modified "2023-09-24" @default.
- W2896067230 title "Effect of override size on forecast value add" @default.
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- W2896067230 hasPublicationYear "2018" @default.
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