Adaptive Demand Forecasting with Automated Drift Detection for Inventory Optimization in Micro-Fulfillment Networks

Abstract

AI-driven demand forecasting models deployed in micro-fulfillment networks degrade silently when the statistical relationship between inputs and demand outcomes shifts over time, a phenomenon known as concept drift. This paper presents the Adaptive Demand Forecasting with Automated Drift Detection (ADF-ADD) framework, a production-oriented architecture that continuously monitors forecast accuracy, detects distributional shift using a multi-signal ensemble of error-rate and feature-space detectors, and triggers targeted model retraining in response to confirmed drift events. The framework formalizes three inventory-specific drift types, demand-shock drift, behavioral drift, and network topology drift, and addresses the label latency challenge inherent in retail demand environments. Experimental evaluation across three simulated micro-fulfillment scenarios shows a 48% reduction in average peak Mean Absolute Percentage Error (MAPE) compared to a static baseline and a 43% reduction compared to industry-standard periodic retraining, achieved with 63% fewer retraining events. The ADF-ADD framework provides a practical blueprint for self-correcting inventory intelligence at the scale of modern distributed fulfillment operations.