Graph Neural Forecasting Is Finally Beating Classical Supply Chain Baselines
How temporal graph transformers cut backorders for omnichannel retailers
Supply chain teams have been chasing a holy grail for years: forecasts that react quickly to regional demand shocks without overfitting noise. This winter I joined a pilot with a national retailer exploring temporal graph transformers (TGTs), and we finally saw meaningful lift over ARIMA, Prophet, and even DeepAR baselines. The key insight is to treat stores as nodes in a dynamic graph connected by transferability signals—web traffic correlation, regional promos, and even weather systems—then let attention uncover the diffusion structure.
Building the Graph
- Nodes: Each store SKU combination. We embedded static factors such as footprint size, demographics, and omnichannel maturity.
- Edges: Learned similarities from rolling-window Granger causality tests, plus engineered edges for logistics lanes and shared promo calendars. Edge weights are updated weekly.
- Temporal Signals: 730 days of unit sales, web visits, and competitor price indices, aligned into 15-minute buckets for fast-moving categories.
Model Architecture
The winning setup combined a TGT encoder (think Graph Attention Networks meets TimeSeries Transformer) with two heads:
- Demand Head: Predicts 1-, 3-, and 7-day horizons simultaneously.
- Inventory Head: Suggests safety stock adjustments conditioned on predicted lead times.
We trained with a quantile loss to optimize service level targets and used hierarchical reconciliation to ensure the sums matched regional planning totals.
What Changed in Operations
- Promo Shock Responsiveness: When a flash promotion hit the Southeast, edge attention weights spiked around Atlanta and propagated to adjacent states within hours, giving planners early warning to re-route inventory.
- Cold Snap Resilience: Weather-driven edges lit up before sales surged, enabling proactive redistribution of heaters and winter apparel.
- Explainability Wins: Integrated gradients on the attention scores gave planners tangible narratives (“Memphis web traffic drove Nashville demand two days later”).
Tooling Stack
We orchestrated the pipeline in Databricks with Delta Live Tables, trained models on NVIDIA L40 GPUs, and deployed them through Ray Serve for low-latency scoring. A Feast-backed feature store kept transformations consistent across training and inference.
Why This Is a Trend to Watch
GNN-based forecasting is crossing the chasm because retailers now have the data granularity, GPU budgets, and MLOps maturity to support it. Expect to see more hybrid graph/time-series models powering replenishment and dynamic pricing before year-end.
For practitioners: start by mapping your signal graph and benchmarking a lightweight GCRN. The interpretability you gain from attention scores will make stakeholder buy-in far easier than a black-box sequence model ever could.
Citation
@misc{tolone2025,
author = {{Ryan Tolone}},
title = {Graph {Neural} {Forecasting} {Is} {Finally} {Beating}
{Classical} {Supply} {Chain} {Baselines}},
date = {2025-02-05},
langid = {en-GB}
}