Intelligent Routing for Sparse Demand Forecasting: A Comparative Evaluation of Selection Strategies

Qiwen Zhang

Keywords: sparse forecasting, model router, intermittent demand, meta-learning, supply chain

Abstract:

Sparse and intermittent demand forecasting in supply chains presents
a critical challenge, as frequent zero-demand periods hinder traditional
model accuracy and impact inventory management. We
propose and evaluate a Model-Router framework that dynamically
selects the most suitable forecasting model—spanning classical, ML,
and DL methods—for each product based on its unique demand
pattern. By comparing rule-based, LightGBM, and InceptionTime
routers, our approach learns to assign appropriate forecasting strategies,
effectively differentiating between smooth, lumpy, or intermittent
demand regimes to optimize predictions. Experiments on the
large-scale Favorita dataset show our deep learning (InceptionTime)
router improves forecasting accuracy by up to 11.8% (NWRMSLE)
over strong, single-model benchmarks with 4.67x faster inference
time. Ultimately, these gains in forecasting precision will drive
substantial reductions in both stockouts and wasteful excess inventory,
underscoring the critical role of intelligent, adaptive AI in
optimizing contemporary supply chain operations.

Submission Number: 26