Results of demand planning serve as the basis of every planning activity in a demand-supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals, that are propagated and magnified over the network, becomes the crucial cause of ineffective operation plans. With the globalization of demand-supply networks and the desire for a more integrated operation plan, demand planning is now one of greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating two interrelated demands. We show that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, we further explore the properties of the aggregated time series and provide guidelines for practitioners to determine proper aggregation and forecasting approaches. A very important finding of our research is that demand aggregation is far more effective than statistical forecasting in operations planning for any two demands with low positive correlation or negative correlation.