Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications

TL;DR

This paper introduces a feature-based framework for combining forecasts in intermittent demand scenarios, improving accuracy and offering insights for inventory management.

Contribution

It proposes a generalized, flexible framework for forecast combination in intermittent demand, enhancing accuracy and interpretability over existing methods.

Findings

Improved point and quantile forecast accuracy

Enhanced inventory decision support

Efficient computational performance

Abstract

Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study aims to examine the empirical outcomes of some existing forecast combination methods and propose a generalized feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions.