Logistics ops · Production
project44 Ocean Visibility: machine-learning container tracking with predictive ETAs for MACS
The problem
Shippers tracking MACS containers risk missing delays or trans-shipment stalls that incur demurrage and detention fees without real-time status updates or predictive arrival estimates.
Workflow diagram · grounded in source
1
Connect via BL or Container Number
trigger
“allows you to track your MACS containers using the MBL / BL Number (Master Bill of Lading) or Container Number”
2
ML status and ETA updates
ai_action
“Our machine learning algorithms automatically update shipment status and predictive ETAs in real-time”
3
Exception identification output
output
“swiftly identify container shipments that are delayed or stuck in trans-shipment, incurring D&D fees”
Reported outcome
Ocean Visibility by project44 provides real-time shipment status and predictive ETAs via machine learning, enabling proactive exception management and a differentiated customer experience.
Reported stack
Ocean Visibility
Frequently asked questions
What did this team achieve with this AI workflow?
Ocean Visibility by project44 provides real-time shipment status and predictive ETAs via machine learning, enabling proactive exception management and a differentiated customer experience.
What tools did this team use?
Ocean Visibility.
How is this logistics ops AI workflow structured?
Connect via BL or Container Number → ML status and ETA updates → Exception identification output.