Context-Aware Decision Intelligence Engines for Personalized Cell Therapy Logistics

The supply chain for autologous cell therapy is highly complex due to its personalized, patient-specific nature and regulatory requirements. Traditional logistics and planning systems lack the granularity, real-time adaptability, and contextual awareness needed for such high-stakes biological treatments [1]. This paper proposes a novel architecture for a context-aware Decision Intelligence Engine (DIE) specifically tailored for cell therapy logistics. The engine integrates real-time data from manufacturing, quality assurance, transportation, and patient readiness systems to orchestrate decisions on scheduling, resource allocation, and risk mitigation. A multi-layer architecture including context ingestion, predictive modeling, optimization, and stakeholder feedback is described. Implementation of such systems shows significant potential in reducing lead time, improving chain-of-identity compliance, and enhancing patient outcomes through timely delivery [2].