Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods...

Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which...

Judgmental adjustments to statistically generated forecasts have become a standard practice in demand forecasting, especially at a stock keeping units level. However, due to the subjective nature of judgmental interventions this approach cannot guarantee optimal use of available information and...

The paper shows that due to the features of SKU (stock-keeping unit) demand data wellknown error measures previously used to analyse the accuracy of adjustments are generally not advisable for the task. In particular, percentage errors are affected by outliers and biases arising from a large...

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...