Experts' adjustment to model-based SKU-level forecasts: does the forecast horizon matter?

Abstract

Experts (managers) may have domain-specific knowledge that is not included in a statistical model and that can improve short-run and long-run forecasts of SKU-level sales data. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional mean of a time series variable. Analysing a large database concerning pharmaceutical sales forecasts for various products and adjusted by a range of experts, we examine whether the forecast horizon has an impact on what experts do and on how good they are once they adjust model-based forecasts. For this, we use regression-based methods and we obtain five innovative results. First, all horizons experience managerial intervention of forecasts. Second, the horizon that is most relevant to the managers shows greater overweighting of the expert adjustment. Third, for all horizons the expert adjusted forecasts have less accuracy than pure model-based forecasts, with distant horizons having the least deterioration. Fourth, when expert-adjusted forecasts are significantly better, they are best at those distant horizons. Fifth, when expert adjustment is down-weighted, expert forecast accuracy increases.

Keywords:

• model-based forecasts
• expert forecasts
• forecast accuracy
• multi-step-ahead forecasts

Acknowledgements

We thank the pharmaceutical company Organon BV (Oss, The Netherlands) for providing us with the data, and we thank two anonymous referees for their detailed comments.

Notes

1 This model is also used in Franses and Legerstee (2009) for the case h=1.

2 Fildes et al (2009) found that the parameter values could differ across positive and negative values of A. We postpone a detailed analysis of this issue to further work.