Hong KongLogistics and transport2018 – ongoingOrient Overseas Container Line
Machine-learning predictions for vessel schedules and berth activities
OOCL processed over 30 million vessel data records monthly for machine-learning predictions, according to Microsoft
Problem
OOCL needed to turn large volumes of vessel data into information for shipping operations. Predicting schedules and berth activity was an existing application of its machine-learning work.
Approach
OOCL had used machine learning for several years and processed vessel data monthly for predictive analytics. Microsoft described this operational use when announcing a separate research partnership on shipping-network optimisation.
Results
Takeaway
OOCL used machine learning for schedule and berth predictions. The expected savings from the separate network-optimisation partnership were not measured results.
Categories
- Size
- Large enterprise
- Industry
- Logistics and transport
- Business function
- Logistics and supply chain
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Built in-house
- Evidence
- Vendor case study
Sources
Source summaries
Microsoft described OOCL's existing use of machine learning for schedule and berth predictions. It separately announced research cooperation, with prospective operating savings rather than a measured financial result.
Full details are in the original sources.
