Architecting Resilience in Global Freight Forwarding with a Causal AI Framework.

Area of Science:

• Artificial Intelligence
• Operations Research
• Supply Chain Management

Background:

• Global freight forwarding faces complex, cascading risks.
• Conventional risk management methods are often insufficient.
• Need for advanced analytical tools for systemic risk in logistics.

Purpose of the Study:

• Propose a protocol for a causal AI framework for systemic risk management.
• Develop an interpretable AI tool for freight forwarders.
• Enable a shift from reactive to proactive risk mitigation.

Main Methods:

• Constructing a hierarchical Bayesian Network (BN) as a causal knowledge graph.
• Synthesizing domain expertise and industry reports for BN parameterization.
• Utilizing the Noisy-MAX model for uncertainty management and predictive simulations.

Main Results:

• Market supply and demand identified as the central node of systemic risk.
• Distinct risk fingerprints for Cost (commercial factors), Time (logistical disruptions), and Reliability (cybersecurity threats).
• Sensitivity analysis identifies high-leverage intervention points.

Conclusions:

• The causal AI framework provides an interpretable 'what-if' engine.
• Enables precision-guided risk mitigation in freight forwarding.
• Advances systemic risk management through AI-driven insights.