Page 555 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

• Predictive Demand Forecasting: Machine learning anticipates cargo fluctuations,
enabling proactive truck/container allocation and real-time port slot scheduling, reducing
congestion and idle time.
• Carbon Footprint Monitoring: AI-based emissions tracking provides real-time CO₂
calculations per shipment, offering actionable insights. AI recommends optimal routes, 4.5: Manufacturing
improving load consolidation and carbon KPIs.
• Safety & Traffic Monitoring: AI integrates with truck sensors and port safety systems to
reduce accidents, optimize lane use, and enhance traffic flow. Smart safety scores assess
transporters based on compliance, delivery efficiency, and driving behavior.
• Port AI Support Agents: AI-driven assistants provide predictable wait times, automated
query resolution, and real-time issue handling to enhance the driver-terminal experience.

Standards Development: Regulatory & Industry Guidelines for Digital Logistics

To accelerate the transport industry’s shift towards digitalization, CargoWaves will focus on
developing standardized frameworks that promote seamless technology adoption, AI-driven
communication, and data interoperability across stakeholders.
• E-Payments & Digital Contracts: Encouraging cashless transactions for secure and
seamless trade.
• AI-Powered Communication Protocols: Enabling real-time driver-port interactions for
better slot management and cargo tracking.
• AI-Based Predictive Scheduling: Automating truck deployment based on demand
patterns to optimize transport operations.

By focusing on AI-powered optimization and standardized digital logistics frameworks,
CargoWaves can drive sustainable, efficient, and globally scalable supply chain transformation.

Deployment & Usage

From the Recommendation systems: 15% of the recommendations have been used by the
Beneficiary Cargo Owners for completing their booking.

3 Use Case Requirements

• REQ-1:
It is critical to display a recommended hauler best suited to fulfill the current booking, based
on historical interactions between the BCO and the hauler community. The recommendation
system must prioritize boosting the most relevant hauler. The trained data must be stored
in a relational database and accessed via SQL queries, with the interface to the front end
implemented through REST APIs. It is mandatory that the model is retrained every 15 days and
that all selections made from the recommendations are recorded in the system.

• REQ-2:
It is critical to display a recommended timeslot based on the current port availability and
historical slot usage by the BCO. The recommendations must be computed in real time using
probabilistic techniques and weighted averages.

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