Judgmental Adjustments of Previously Adjusted Forecasts

Improving Reliability of Judgmental Forecasts

All judgmental forecasts will be affected by the inherent unreliability, or inconsistency, of the judgment process. Psychologists have studied this problem extensively, but forecasters rarely address it. Researchers and theorists describe two types of unreliability that can reduce the accuracy of judgmental forecasts: (1) unreliability of information acquisition, and (2) unreliability of inform...

Models for Product Demand Forecasting with the Use of Judgmental Adjustments to Statistical Forecasts

Measuring the Accuracy of Judgmental Adjustments to SKU-level Demand Forecasts

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 number of low actual demand values and correlation between forecast errors and actual outcomes. It is al...

Do 'big losses' in judgmental adjustments to statistical forecasts affect experts' behaviour?

The behaviour of poker players and sports gamblers has been shown to change after winning or losing a significant amount of money on a single hand. In this paper, we explore whether there are changes in experts’ behaviour when performing judgmental adjustments to statistical forecasts and, in particular, examine the impact of ‘big losses’. We define a big loss as a judgmental adjustment that si...

Estimating Demand Uncertainty Using Judgmental Forecasts

Measuring demand uncertainty is a key activity in supply chain planning. Of various methods of estimating the standard deviation of demand, one that has been employed successfully in the recent literature uses dispersion among experts’ forecasts. However, there has been limited empirical validation of this methodology. In this paper we provide a general methodology for estimating the standard d...