A Study on Data Federation-Based AI Forecasting for Small E- Commerce Merchants and the Architecture of Warehouse as a Service (WaaS)

Small e-commerce merchants face critical operational vulnerabilities due to data fragmentation, severe demand signalnoise, and inefficient inventory allocation. Unlike enterprise platforms that leverage massive centralized data streams tooptimize supply chains, small merchants suffer from data sparsity, leading to AI model hallucinations and overfitting indemand forecasting. This study presents a comprehensive technical and business architecture that integrates a DataFederation pipeline with a Warehouse as a Service (WaaS) framework. By leveraging the Law of Large Numbers, ourproposed federated data pipeline aggregates noise-filtered clickstream, cart activity, and UX behavioral signals acrossdistinct small merchants without exposing sensitive business insights. This paper outlines the theoretical modeling,systemic pipeline design, and an evaluation framework with target metrics designed to achieve up to a 63.1% reduction indemand forecast error (MAPE), a 54.7% decrease in days of inventory, and a 35.5% reduction in last-mile fulfillment costsunder synthetic modeling conditions.