This study investigates the effects of three different types of correlation on forecasting and stock control of intermittent demand items. Applying appropriate forecasting and stock control methods to theoretically generated compound Poisson demand data we show that correlation in intermittent demand does play a role in forecast quality and stock control performance. Negative autocorrelation levels lead to higher service levels than positive values, while cost does not significantly change. Our results also show that high intermittency levels intensify these changes in service level. We also show that cross-correlation produces results in the opposite direction of autocorrelation in size or intervals; that is, positive (negative) cross-correlation leads to higher (lower) service levels.