Forecasting Intermittent Demand by Hyperbolic-Exponential Smoothing

TL;DR

This paper introduces Hyperbolic-Exponential Smoothing, a new hybrid method combining Croston's approach with Bayesian inference, designed to improve demand forecasting by reducing bias and handling obsolescence effectively.

Contribution

It presents a novel hybrid forecasting method that addresses bias and obsolescence issues in intermittent demand prediction, outperforming existing techniques.

Findings

Performs well in experimental evaluations

Decays hyperbolically during obsolescence

Unbiased on non-intermittent and stochastic demand

Abstract

Croston's method is generally viewed as superior to exponential smoothing when demand is intermittent, but it has the drawbacks of bias and an inability to deal with obsolescence, in which an item's demand ceases altogether. Several variants have been reported, some of which are unbiased on certain types of demand, but only one recent variant addresses the problem of obsolescence. We describe a new hybrid of Croston's method and Bayesian inference called Hyperbolic-Exponential Smoothing, which is unbiased on non-intermittent and stochastic intermittent demand, decays hyperbolically when obsolescence occurs and performs well in experiments.