Modern fulfillment systems—particularly in patient-specific cell therapy and high-volume e-commerce—face intense pressure to respond dynamically to real-time disruptions. Traditional planning systems lack the responsiveness needed for exception handling, particularly when timing and traceability are critical. This paper presents a cross-industry framework for real-time decision analytics that transforms live operational signals into actionable logistics decisions. Drawing on use cases from Amazon-style fulfillment and regulated cell therapy delivery chains, the framework combines live data ingestion, KPI-driven alerting, rule-based reprioritization, and human-in-the-loop decision nodes. Simulation and real-world benchmarks show reductions of up to 18% in SLA misses and 10–12% in average turnaround time [1][2]. This paper contributes a reference architecture and implementation roadmap for supply chain leaders aiming to integrate signal-aware orchestration into mission-critical logistics systems.