This paper is concerned with identifying an effective method for forecasting the lead time demand of slow-moving inventories. Particular emphasis is placed on prediction distributions instead of point predictions alone. It is also placed on methods which work with small samples as well as large samples in recognition of the fact that the typical range of items has a mix of vintages due to different commissioning and decommissioning dates over time. Various forecasting methods are compared using monthly demand data for more than one thousand car parts. It is found that a multi-series version of exponential smoothing coupled with a Pólya (negative binomial) distribution works better than the other twenty-four methods considered, including the Croston method.