A new approach to forecasting intermittent demand for service parts inventories

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 good...

Full description

Saved in:
Bibliographic Details
Published in:International journal of forecasting 2004-07, Vol.20 (3), p.375-387
Main Authors: Willemain, Thomas R., Smart, Charles N., Schwarz, Henry F.
Format: Article
Language:English
Subjects:
Accuracy
Bootstrapping
Croston’s method
Exponential smoothing
Intermittent demand
Inventory
Service parts
Spare parts
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0169-2070
1872-8200
DOI:10.1016/S0169-2070(03)00013-X