Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts

Andrey Davydenko and Robert Fildes

International Journal of Forecasting, 2013, vol. 29, issue 3, 510-522

Abstract: Forecast adjustment commonly occurs when organizational forecasters adjust a statistical forecast of demand to take into account factors which are excluded from the statistical calculation. This paper addresses the question of how to measure the accuracy of such adjustments. We show that many existing error measures are generally not suited to the task, due to specific features of the demand data. Alongside the well-known weaknesses of existing measures, a number of additional effects are demonstrated that complicate the interpretation of measurement results and can even lead to false conclusions being drawn. In order to ensure an interpretable and unambiguous evaluation, we recommend the use of a metric based on aggregating performance ratios across time series using the weighted geometric mean. We illustrate that this measure has the advantage of treating over- and under-forecasting even-handedly, has a more symmetric distribution, and is robust.

Keywords: Judgmental adjustments; Forecasting support systems; Forecast accuracy; Forecast evaluation; Forecast error measures (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (77)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:29:y:2013:i:3:p:510-522

DOI: 10.1016/j.ijforecast.2012.09.002

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