Context
A global marine engineering and offshore infrastructure group operates a fleet of highly specialized vessels. Fleet reliability depends on accurate data for 400,000 technical parts in IBM Maximo: engines, pumps, cranes and their maintenance hierarchies.
The group asked FuturWork to lead a one-year technical data quality program.
Challenges
- Inconsistent naming and duplicates. Several naming conventions for the same component and stock fragmented across vessels.
- Broken asset hierarchies. Missing subcomponent links and wrong bills of material made preventive maintenance hard to plan.
- Manual cleansing bottleneck. Data correction relied on many people, including offshore teams, and could not scale.
What we did
- Data governance and taxonomy. Standard naming, an ownership model, validation workflows and one component hierarchy for the fleet.
- AI cleansing agent. Automatic detection of inconsistent names and duplicates, classification proposals and dataset reconciliation.
- Preventive maintenance restored. Bills of material rebuilt and reliable scheduling enabled.
Results
- 400K technical parts harmonized
- About 35% fewer duplicate references
- About 50% less manual cleansing work
From raw industrial data to intelligent asset governance.
Facing a similar challenge? Talk to our team.
[> convert_contact: true]
Lorem ipsum
dolor sit
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
