How OSV operators can use AI to cut fuel, OPEX, and maintenance
AI isn't here to replace crews. It's here to do the heavy lifting on data so your teams can make faster, better decisions with the resources they have.
At Riviera's Annual Offshore Support Journal Conference in London, experts showed how AI, remote monitoring, and optimisation software can reduce fuel burn, improve voyage planning, and shift maintenance from reactive to condition-based.
What AI actually helps with
- Pulls together operational and compliance data, then structures it for action. As one executive put it: what matters is how it's applied to real workflows.
- Gives shore and ship teams the same live picture for safety, compliance, and daily decisions.
- Finds discrepancies in reporting or sensor signals before they snowball into bad calls.
- Drives steady performance improvements through clear recommendations and feedback loops.
See AI for Operations for practical examples of optimisation and maintenance planning in maritime fleets.
Fuel and maintenance: where the money is
According to Dipai's leadership, AI can increase fuel efficiency by recommending fewer engines or gensets online when demand allows, and by coaching lower transit speeds when schedule slack exists. It also supports hydrodynamic performance tracking to reduce drag penalties.
With better performance visibility, you can choose the right drydock window for hull and propeller cleaning instead of guessing. Maintenance shifts toward condition-based tasks, cutting unnecessary overhauls and catching issues earlier.
Build the data foundation first
Opsealog's guidance was simple: take time to build the right foundations. Invest in satellite bandwidth so vessels can move the data you actually need. If the feed is patchy, the insights will be, too.
Start with solid Data Analysis: calibrate and validate key sensors, standardise reporting, and stream the right signals so models deliver reliable recommendations.
- Calibrate and validate key sensors (fuel flow, shaft power, RPM, GPS, weather inputs).
- Standardise logbooks and noon reports; align tags and units across vessels.
- Stream data in real time where possible; buffer at the edge when offshore coverage dips.
- Contextualise every datapoint (sea state, load, activity code) and cross-check results so algorithms deliver real value.
Bunkers and fuel: stop the leaks with EFMS
An electronic fuel-monitoring system (EFMS) can alert on anomalies, log bunkering events, and support speed optimisation and onboard security. Onshore teams and charterers get a clear view of consumption patterns and bunker transfers.
As Ascenz Marorka highlighted, operators can see "what the bunker volume is versus what it is meant to be," and flag differences between what the supply vessel reports and what the receiving vessel records.
Practical 90-day rollout plan
- Weeks 1-3: Pick two pilot vessels. Define KPIs (fuel per nm, engine hours vs load, bunkering variance). Confirm satcom capacity. Map data sources and ownership.
- Weeks 4-6: Deploy EFMS and integrate with noon reports. Set up dashboards for bridge and shore. Train crews on data entry and exception notes. Agree playbooks for engine/genset selection and speed bands.
- Weeks 7-9: Enable voyage optimisation with weather/current data. Start daily performance huddles (15 minutes max) to review alerts and actions. Track sensor health and fix drift fast.
- Weeks 10-12: Validate savings and operational impacts with before/after baselines. Lock in condition-based maintenance tasks. Plan hull/propeller cleaning windows based on performance trends.
KPIs operations should track
- Fuel per nautical mile (by activity code: transit, DP, standby, ROV support)
- Engine/genset hours vs power demand; unnecessary running time
- Speed vs schedule adherence; overspeed events
- Idle/loiter time vs tasking; DP power setpoint vs actual conditions
- Hull/propeller fouling indicators (speed-power curve drift)
- Bunkering variance (supplier vs received) and EFMS alert resolution time
- Data completeness and sensor health score
- For applicable fleets: CII trend and improvement actions
Keep people in the loop
Digital Ocean's team stressed that "AI supports collation, structuring, organising and processing of operational and compliance data." The point isn't to automate judgment-it's to give crews and shore staff clearer signals, faster.
Use AI to advise, not to autopilot. Pair recommendations with simple playbooks, and let the team capture what worked so the model keeps improving.
Helpful resources
- IMO carbon intensity (CII) overview for alignment between operations and compliance targets.
- AI upskilling paths by job function to help bridge skills for bridge teams and shore ops.
Bottom line
Focus on clean data, bandwidth, and simple playbooks. Start with fuel, voyage planning, and condition-based maintenance-where the savings show up fast. Then scale across the fleet with the same KPIs and cadence.
Insights referenced from expert discussions at Riviera's Annual Offshore Support Journal Conference.
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