Abstract
Only two forecasting methods have been designed specifically for intermittent demand with possible demand obsolescence: Teunter–Syntetos–Babai (TSB) and Hyperbolic-Exponential Smoothing (HES). When an item becomes obsolete the TSB forecasts decay exponentially while those of HES decay hyperbolically. Both types of decay continue to predict nonzero demand indefinitely, and it would be preferable for forecasts to become zero after a finite time. We describe a third method, called Exponential Smoothing with Linear Decay, that decays linearly to zero in a finite time, is asymptotically the best method for handling obsolescence, and performs well in experiments on real and synthetic data.
Highlights
Inventory management is of great economic importance to industry, but forecasting demand for spare parts is difficult because it is intermittent : in many time periods the demand is zero
We described a new Croston variant called Linear-Exponential Smoothing (LES) for handling obsolescence, shown to be unbiased on stochastic intermittent demand
LES has a feature that we consider to be an advantage over the two other variants TSB and Hyperbolic-Exponential Smoothing (HES) designed to handle obsolescence: when obsolescence occurs its forecasts decay to zero in a finite time
Summary
Inventory management is of great economic importance to industry, but forecasting demand for spare parts is difficult because it is intermittent : in many time periods the demand is zero. A recent review of the literature on intermittent demand can be found in [14] Another difficult feature of some inventories is obsolescence, in which an item has no demand at all after a certain time period. The authors of this paper know of an inventory company who were obliged to modify Croston’s method, artificially forcing its forecasts to zero after a certain number of periods without demand This is a pragmatic but inelegant solution, and obsolescence has been neglected in the literature. In this paper we describe a new Croston variant whose forecasts decay linearly to zero in a finite time, a feature we believe will appeal to practitioners We compare it empirically and analytically with other forecasters and show that it is unbiased, handles obsolescence better than other methods, and competitive in experiments with intermittent demand.
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