Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand

TL;DR

This paper introduces a two-model approach for forecasting lumpy and intermittent demand, combining demand occurrence prediction with demand size estimation, and proposes a new evaluation criterion for such models.

Contribution

It presents a novel two-fold modeling framework and a new evaluation metric tailored for lumpy and intermittent demand forecasting.

Findings

Global classification models excel at predicting demand occurrence.

Simple Exponential Smoothing performs best for demand size estimation.

The approach was validated on real-world automotive demand data.

Abstract

Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Sparse demand data usually results in lumpy or intermittent demand patterns, which have sparse and irregular demand intervals. Usual statistical and machine learning models fail to provide good forecasts in such scenarios. Our research shows that competitive demand forecasts can be obtained through two models: predicting the demand occurrence and estimating the demand size. We analyze the usage of local and global machine learning models for both cases and compare results against baseline methods. Finally, we propose a novel evaluation criterion of lumpy and intermittent demand forecasting models' performance. Our research shows that global classification models are the best choice when predicting demand event occurrence. When predicting demand sizes, we achieved the best results using Simple Exponential Smoothing forecast. We tested our approach on real-world data consisting of 516 three-year-long time series corresponding to European automotive original equipment manufacturers' daily demand.