DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting

TL;DR

DriftGuard addresses the silent degradation of supply chain forecasts under concept drift by proposing a five-module end-to-end framework that integrates hierarchical drift detection, SHAP-based root-cause diagnosis, and cost-aware selective retraining. The approach combines an ensemble of four detectors with hierarchical propagation analysis to localize drift, uses SHAP to diagnose root causes across SKU-store-region levels, and optimizes retraining to balance accuracy gains against compute costs. Evaluated on the M5 dataset with ~30k time series, DriftGuard achieves 97.8% detection recall within 4.2 days and can deliver up to 417× ROI by retraining only the most affected models. This work provides a production-ready solution that bridges academic drift detection methods with practical MLOps requirements in retail forecasting.

Abstract

Supply chain forecasting models degrade over time as real-world conditions change. Promotions shift, consumer preferences evolve, and supply disruptions alter demand patterns, causing what is known as concept drift. This silent degradation leads to stockouts or excess inventory without triggering any system warnings. Current industry practice relies on manual monitoring and scheduled retraining every 3-6 months, which wastes computational resources during stable periods while missing rapid drift events. Existing academic methods focus narrowly on drift detection without addressing diagnosis or remediation, and they ignore the hierarchical structure inherent in supply chain data. What retailers need is an end-to-end system that detects drift early, explains its root causes, and automatically corrects affected models. We propose DriftGuard, a five-module framework that addresses the complete drift lifecycle. The system combines an ensemble of four complementary detection methods, namely error-based monitoring, statistical tests, autoencoder anomaly detection, and Cumulative Sum (CUSUM) change-point analysis, with hierarchical propagation analysis to identify exactly where drift occurs across product lines. Once detected, Shapley Additive Explanations (SHAP) analysis diagnoses the root causes, and a cost-aware retraining strategy selectively updates only the most affected models. Evaluated on over 30,000 time series from the M5 retail dataset, DriftGuard achieves 97.8% detection recall within 4.2 days and delivers up to 417 return on investment through targeted remediation.

Figures (4)

• Figure 1: End-to-end drift lifecycle framework: 5-module pipeline from baseline forecasting through automated correction.
• Figure 2: Detection latency-recall tradeoff across methods. Our ensemble achieves superior performance (97.8% recall, 4.2 days) through complementary signal fusion, operating within the target region where both metrics are optimized. Pareto frontier (dashed) shows the efficiency boundary.
• Figure 3: SHAP-based diagnosis showing regional drift impact (CA worst affected).
• Figure 4: Forecast accuracy degradation and recovery across 3 drift severities.