Global supply chains still run on uncertainty—nearly half of ocean shipments arrive late, leaving companies to manage delays with costly buffers and guesswork. But what if you could see disruptions before they happen?
In collaboration with MIT CTL, a leading global logistics provider developed an AI-powered approach that goes beyond fragmented forecasts to deliver true end-to-end visibility across maritime journeys. By analyzing entire voyages, not just individual legs, this model brings a new level of accuracy and foresight to global shipping.
The result? Smarter routing decisions, fewer surprises, and a more resilient supply chain.
Download the case study to see how AI is transforming maritime logistics.
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From research to real-world impact:
How MIT CTL is using AI to forecast maritime shipping delays and improve global supply chain reliability
The Challenge
Traditional forecasting methods analyze each shipping leg in isolation, focusing on short-term predictions at specific ports or segments of the journey. While useful, these approaches do not provide a consistent end-to-end view across multi-port voyages. As a result, shippers lack a reliable way to predict overall transit times when planning shipments, forcing them to over-buffer inventory or risk supply chain disruptions.
Key Insights
- 50-55% average on-time arrival rate in 2024* , leaving half of all shipments unreliable
- 80% of global trade moves by maritime shipping
- 50% of global trade value comes from maritime shipping
- 5.32 days average schedule delay for late-arriving vessels
The Research
MIT CTL’s Supply Chain Design Lab, directed by Dr. Milena Janjevic, recognized that maritime shipping's low reliability stems not from a lack of data, but from the absence of end-to-end visibility. While traditional models predict individual legs with reasonable accuracy, they do not translate into a coherent view of the full journey. Working with a global leader in maritime logistics, the team developed a neural network-based model designed to predict vessel waiting and transit times across entire voyages. Rather than analyzing isolated segments, the model leverages deep learning to detect complex patterns in historical shipping data and dynamically adjusts predictions using real-time inputs such as port congestion and weather conditions. The result is a fundamentally more accurate ETA forecasting system that captures the full end-toend complexity of global shipping.
Meet the ETA Neural Network Model:
An AI-powered predictor for maritime shipping reliability.
With the neural network model, shippers can:
- Predict ETAs with 94% accuracy, explaining past variability across complex, multi-port journeys
- Achieve average error rates of less than 1.7 days for vessels visiting up to 4 different ports on a given journey
- Select routes before booking based on predicted reliability, enabling proactive service selection
- Dynamically reroute shipments in-transit using continuously updated predictions from real-time conditions
The Impact
For supply chain companies, the implications are powerful:
- Improve planning of downstream operations such as unloading vessels and forwarding cargo
- Reduce buffer stocks and bottlenecks by replacing uncertainty with data-driven confidence
- Improve delivery consistency and transport capacity utilization across global shipping networks
Model results:
- 94% accuracy rate in explaining past shipping variability
- <1.7 days average prediction error for end-to-end transit times of vessels visiting up to 4 different ports on a given journey
- ≤0.51 days average prediction error for vessel transit times on individual legs
