This white paper presents a practical architecture for operational demand forecasting and basket prediction in e-commerce logistics environments. It provides a modular, production-oriented framework that enables logistics service providers (3PLs) and e-commerce operators to forecast SKU-level demand over short horizons (1–14 days) and predict co-purchase patterns that support pre-kitting, box preparation, and packaging planning. The paper outlines a layered system design that combines robust statistical baselines with bounded corrections for peak events and external context such as promotions, holidays, and weather. It further describes how co-purchase prediction can be used to generate operational box templates and improve packing efficiency. The document focuses on implementable practices rather than specific vendors or cloud platforms. It covers data contracts, forecasting pipelines, peak detection, context enrichment, co-purchase modeling, backtesting procedures, deployment patterns, and governance safeguards required for reliable operational use. The objective is to provide engineering and operations teams with an executable blueprint for building forecasting capabilities that improve warehouse efficiency, transportation planning, and resource utilization while reducing operational waste. Audience: IT leaders, engineers, data analysts, operations managers, and solution architects working in e-commerce and logistics operations.