International Journal of Forecasting 20 (2004) 375 – 387 www.elsevier.com/locate/ijforecast
A new approach to forecasting intermittent demand for service $ parts inventories
Thomas R. Willemain *, Charles N. Smart, Henry F. Schwarz
Smart Software, Inc., Belmont, MA 02478, USA
Abstract A fundamental aspect of supply chain management is accurate demand forecasting. We address the problem of forecasting intermittent (or irregular) demand, i.e. random demand with a large proportion of zero values. This pattern is characteristic of demand for service parts inventories and capital goods and is difficult to predict. We forecast the cumulative distribution of demand over a fixed lead time using a new type of time series bootstrap. To assess accuracy in forecasting an entire distribution, we adapt the probability integral transformation to intermittent demand. Using nine large industrial datasets, we show that the bootstrapping method produces more accurate forecasts of the distribution of demand over a fixed lead time than do exponential smoothing and Croston’s method. D 2003 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords: Accuracy; Bootstrapping; Croston’s method; Exponential smoothing; Intermittent demand; Inventory; Spare parts; Service parts
1. Introduction A fundamental aspect of supply chain management is accurate demand forecasting. We address the problem of forecasting intermittent (or irregular) demand. Intermittent demand is random demand with a large proportion of zero values (Silver, 1981). Items with intermittent demand include service (spare) parts and high-priced capital goods, such as heavy machinery. Such items are often described as ‘slow moving’. Demand that is intermittent is often also ‘lumpy’,
PII of linked article S0169-2070(03)00034-7. * Corresponding author. Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY...