The impact of aggregation level on forecasting performance

Most operations decisions are based on some kind of forecast of future demand. Thus, forecasting is definitely a very traditional area in the operations and inventory management literature. While literature concerning forecast explores the adoption ofvarious qualitative and quantitative methods, this paper tries to design new solutions to improve forecasting accuracy by focusing on the forecasting process that uses such algorithms. In particular, when forecasting demand one should always make clear exactly what he/she is trying to forecast, in terms of the time bucket (i.e., the period oftime over which demand is aggregated), the forecasting horizon, the set ofitems the demand refers to (e.g., forecasting demand for a single item can be much harder than forecasting demand for a group of items), the set of locations the demand refers to (e.g., demand at the single store level is much less predictable than the demand for a whole chain ofstores). Traditionally, these features ofthe final output off orecasting also influence the forecasting process. Indeed, when one wants to forecast demand at single store single item single day level it seems natural to analyse demand and causal factors at the same level of aggregation. On the contrary, in this paper we aim at showing that, first of all often aggregating and/or disaggregating data in the forecasting process can lead to substantial improvements; second, the choice ofthe appropriate level ofaggregation depends on the underlying demand generation process. In addition, most forecasting algorithms tend to focus on a single demand variable. On the contrary, we can analyse analogous time series to improve the effectiveness of the forecasting process. Clustering techniques can be used to identify such homologous time series. Such clusters of homologous time series can provide, on the one hand, the sample size required to gain good statistical confidence and, on the other hand, relatively homogeneous data. In the paper, we use sales data from a food retailer at a very detailed level to test our hypotheses. This claims for relevance for both practitioners and researchers.