Logistics ops · Production
project44 Ocean Visibility — ML-powered container tracking for ONE shipments
The problem
Shippers need real-time visibility into cargo location and movement to identify delays, containers stuck in trans-shipment, and exposure to detention and demurrage fees.
Workflow diagram · grounded in source
1
Shipper initiates tracking
trigger
“track your ONE containers using the MBL / BL Number (Master Bill of Lading) or Container Number”
2
ML updates shipment status
ai_action
“Our machine learning algorithms automatically update shipment status and predictive ETAs in real-time”
3
Predictive ETA generation
ai_action
“predictive ETAs in real-time, providing you and your customers with the most precise information available”
4
Exception identification
output
“swiftly identify container shipments that are delayed or stuck in trans-shipment, incurring D&D fees”
5
Proactive exception management
output
“enable you to take necessary action to address them and ensure that your cargo is being handled appropriately and transported safely and securely”
Reported outcome
project44's Ocean Visibility connects shippers to every carrier and forwarder in their network and uses machine learning to update shipment status and predictive ETAs in real-time, enabling proactive exception management.
Reported stack
Ocean Visibilitymachine learning algorithms
Frequently asked questions
What did this team achieve with this AI workflow?
project44's Ocean Visibility connects shippers to every carrier and forwarder in their network and uses machine learning to update shipment status and predictive ETAs in real-time, enabling proactive exception managem…
What tools did this team use?
Ocean Visibility, machine learning algorithms.
How is this logistics ops AI workflow structured?
Shipper initiates tracking → ML updates shipment status → Predictive ETA generation → Exception identification → Proactive exception management.