A five-layer AI architecture for resilient global supply networks — integrating multi-source risk intelligence, graph AI, digital twins, multi-objective optimization, and governed agentic decision systems.
Contemporary procurement systems were designed for stable, predictable supply environments. Disruptions that materialize in hours — geopolitical conflicts, sanctions cascades, climate shocks — expose the fundamental inadequacy of quarterly-review paradigms.
ARPS proposes a unified conceptual architecture integrating multi-source risk signals, graph-based contagion modeling, digital twin simulation, multi-objective optimization, and governed agentic decision systems — addressing eight critical research gaps identified in the literature.
ARPS does not replace Coupa, SAP Ariba, or JAGGAER. It sits on top of them — providing the risk monitoring, scenario simulation, and agentic decision support that workflow systems fundamentally cannot provide.
"From reactive to predictive — ARPS closes the intelligence gap between disruption and decision."
Conceptual demonstration of all five intelligence layers with synthetic data and LLM integration.
Live data feeds, graph database integration, real supplier registry, and validated scenario models.
Multi-tenant architecture, role-based access, API integrations with Coupa/Ariba, and pilot deployments.
Production-grade federated learning, quantum-ready optimization, SOC2 compliance, and SLA guarantees.
Enter the intelligence system to explore all five layers — from multi-source risk fusion to governed agentic decision-making.