Logistics ops · Production
HMM container tracking with predictive ETAs via project44 Ocean Visibility
The problem
Shippers tracking HMM containers need real-time visibility into cargo location, estimated arrival times, and potential delays to proactively manage exceptions and avoid demurrage and detention fees.
Workflow diagram · grounded in source
1
Container number input
trigger
“track your HMM containers using the MBL / BL Number (Master Bill of Lading) or Container Number”
2
ML status and ETA update
ai_action
“Our machine learning algorithms automatically update shipment status and predictive ETAs in real-time”
3
Exception and delay identification
output
“swiftly identify container shipments that are delayed or stuck in trans-shipment, incurring D&D fees”
Reported outcome
(not stated)
Reported stack
Ocean Visibilitymachine learning algorithms
Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Ocean Visibility, machine learning algorithms.
How is this logistics ops AI workflow structured?
Container number input → ML status and ETA update → Exception and delay identification.