Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework

We study the relative effectiveness of top-down (TD) and bottom-up (BU) strategies for forecasting the aggregate demand in a production planning framework. The aggregate demand series is composed of several correlated subaggregate components (or items), each of which is assumed to follow a stationary time series process, which is correlated over time. As is common in a production planning environment, it is assumed that exponential smoothing is used as the forecasting technique under both strategies. We analytically show that there is no difference in the relative performance of TD and BU forecasting strategies when the time series for all of the subaggregate components follow a first-order univariate moving average [MA(1)] process with identical coefficients of the serial correlation term. We then perform a simulation study to examine the case when the coefficients of the serial correlation term for the subaggregate components are not identical. It is found from the simulation study that the difference in the performance of the two forecasting strategies is relatively insignificant when the correlation between the subaggregate components is small or moderate.