Hierarchical Estimation as Basis for Hierarchical Forecasting

In inventory management, hierarchical forecasting (HF) is a hot issue: families of items are formed for which total demand is forecasted; total forecast then is broken up to produce forecasts for the individual items. Since HF is a complicated procedure, analytical results are hard to obtain; consequently, most literature is based on simulations and case studies. This paper succeeds in following a more theoretical approach by simplifying the problem: we consider estimation instead of forecasting. So, from a random sample we estimate both total demand and the fraction of this total that individual items take; multiplying these two quantities gives a new estimate of individual demand.Then our research question is: can aggregation of items, followed by fractioning, lead to more accurate estimates of individual demand? Thirdly, a more practical situation is investigated by means of simulation