A model for selecting the appropriate level of aggregation in forecasting processes

Abstract

Demand forecasting is a major issue in several industrial sectors. A relevant choice for companies is the proper level of forecast aggregation. Forecasters need to properly identify what is the object of the forecasting process, in terms of time bucket (e.g., forecasts are produced on a daily level or on weekly one), set of items the demand refers to (e.g., single item or group of items), set of locations the demand refers to (e.g., single store or chain of stores). Managers can follow two basic approaches: on the one hand they can adopt a detailed forecasting approach, i.e., they can forecast demand for the item at the store by simply looking at the demand for the specific item/store; on the other hand they can adopt an aggregated forecasting approach. In this paper, we aim at figuring out what is the balance between the strengths and weaknesses of these two options, and to identify the contingent variables that might lead managers to adopt one approach rather than the other. In this paper we study the aggregation across locations by evaluating the components of forecasting error under the assumption of stationary demand. Finally, we suggest metrics that one can adopt to support the choice of the appropriate forecasting process, thus providing help to managers in defining the proper level of aggregation for a specific situation.