Deep Dive

Traditional Shipment Management

Prior to AI integration, rerouting transportation relies heavily on manual processes and static contingency plans. Traditional rerouting typically involves phone calls, emails, or manual data entry into logistics systems, with significant reliance on human judgment. This results in slow response times, increased operational costs, and limited agility in rapidly changing circumstances. Key challenges in this approach include:

• Reactive rather than proactive management of disruptions.
• Limited visibility into alternative options and their trade-offs.
• Inability to rapidly evaluate cost, service, or emissions impacts of rerouting decisions.

Dynamic Shipment Routing

Integrating AI, especially generative AI (large language models) and advanced machine learning, significantly enhances transportation rerouting capabilities, driving improved outcomes across the supply chain.

Comprehensive data integration

An AI-driven rerouting platform built in HASH can aggregate real-time data and near real-time data from multiple sources, including:

• Transportation Management Systems (TMS)
• IoT sensors tracking vehicles and cargo conditions
• GPS and telematics data
• External data feeds such as weather forecasts, traffic updates, port congestion reports, and geopolitical events

Advanced AI platforms like HASH convert this diverse data into actionable insights using highly-trustable knowledge graphs, providing a robust, continuously updated representation of products moving through the logistics network.

Predictive & prescriptive analytics

Machine learning models assess transportation risks continuously, predicting disruptions and prescribing optimal rerouting strategies. These models:

• Evaluate alternative routes and modes (e.g., switching from ocean to rail or air freight).
• Predict and quantify potential impacts on transit times, cost, emissions, and service levels.
• Automatically recommend and execute rerouting actions before disruptions escalate.

Generative AI Copilots for manufacturing teams

AI copilots empower logistics planners to interact with the rerouting platform naturally, accelerating decisions. Planners can:

• Pose queries such as, "Given port delays, recommend alternative ports and modes that maintain delivery schedules."
• Instantly receive comprehensive scenario analyses and actionable rerouting options.

These capabilities accelerate response times and reducing manual workload.

Autonomous Execution Capabilities

Agentic AI embedded within the rerouting tool can autonomously execute approved rerouting decisions, further reducing response times. Autonomous agents:

• Continuously scan for disruptions and dynamically initiate rerouting.
• Communicate rerouting decisions automatically with transportation partners and update logistics systems.

Benefits of AI-Driven Rerouting

• Improved resilience and agility, rapidly adapting to disruptions.
• Reduced transportation costs and minimized transit delays.
• Enhanced service reliability with higher On-Time-In-Full (OTIF) performance.
• Visibility into CO₂ and ESG impacts, enabling sustainable transportation choices.

Implementation & Enhancement

Selecting A Solution

Key considerations for deploying predictive control models include:

• Data Quality & Latency: Ensure robust real-time data integration for accurate rerouting.
• Change Management: Train logistics planners and build trust in AI-generated recommendations.
• Cybersecurity: Maintain secure integration with logistics partners, enforcing strict data governance and permissions.
• Scalability & Flexibility: Opt for cloud-based platforms for ease of maintenance, scalability, and integration with existing systems.
• Incremental Adoption: Pilot AI-driven rerouting in specific high-risk or high-impact transportation scenarios first, demonstrating quick wins and building internal confidence.

HASH provides an open-source, AI-native platform that seamlessly integrates structured and unstructured data. HASH enables dynamic, intelligent, and automated decision-making for transportation rerouting, enhancing supply chain agility, resilience, and sustainability. To find out more about our platform, visit hash.ai or contact us at hash.ai/solutions to learn more about how our technology and services can support your supply chain.

Roadmap To Value

A typical dynamic routing model built on HASH involves:

1. Data Integration & Baseline: Connect TMS, IoT tracking, telematics, and external disruption data.
2. Real-Time Visibility: Activate dashboards providing immediate visibility into transportation statuses and risks.
3. Predictive & Prescriptive Analytics: Implement ML-driven forecasting of disruptions and recommendations for rerouting.
4. Autonomous Rerouting: Deploy autonomous agents that proactively manage and execute rerouting.
5. ESG & Sustainability Insights: Integrate analytics tracking CO₂ emissions and sustainability impacts of rerouting decisions.
6. Continuous Learning & Improvement: A self-improvement/feedback loop update AI models based on real-world outcomes, reinforcing adaptive intelligence and decision-making capability.