Of all the levers in voyage economics, speed has the strongest physics: fuel consumption scales roughly with the cube of speed, so a 10% speed reduction saves close to 30% in propulsion energy — more than most route changes ever earn. The shipping industry knows this; slow steaming is an established practice. What AI adds is the hard part: finding the speed profile — not just 'slower' — that meets the ETA, respects the weather, and spends the least.
That turns speed from a fixed operational decision into a continuously optimized variable — the difference between 'steam at 14 knots' and a profile that varies with every forecast.
Key findings
- Fuel-vs-speed is roughly cubic — speed is the highest-leverage variable in voyage economics. Established
- Speed optimization is constrained by the ETA obligation — the profile must still make the laycan, not just save fuel. Established
- The AI's advantage is the profile — variable speed against the forecast, not a fixed rate. Research demonstration
- Speed and route are coupled — the optimal speed depends on the route the weather allows; optimizing one without the other leaves value on the table. Established
- Engine limits bound the range — the AI optimizes within the engine's efficient envelope, not the theoretical one. Established
Why speed is the biggest lever
Propulsion power scales approximately with speed cubed, which means the marginal fuel cost of each knot grows steeply. Slow steaming is the crude application of this physics; the AI's contribution is precision — the speed that meets the ETA with margin to spare, adjusted segment by segment as the forecast moves, rather than a conservative constant held for the whole passage. The savings live in the variance. Established
The constraint: the ETA is owed
Speed optimization is bounded by obligation — the charterparty's laycan, the berth window, the cargo's market timing. The AI doesn't minimize fuel; it minimizes fuel subject to arriving on time, which is a different problem: sometimes the optimal answer is to speed up early (buy the ETA margin cheaply in calm weather) to preserve the option to slow later. The optimizer's job is sequencing speed against the forecast, not just picking a number. Research demonstration
Speed and route are one problem
The optimal speed depends on the route — and the optimal route depends on the speed, because arrival-time constraints interact with weather along each candidate path. Optimizing them separately is the common shortcut and the common loss: the AI's job is the joint optimization, testing speed profiles against route alternatives simultaneously. The coupled problem is harder computationally but it's the honest one — the decoupled version optimizes a fiction. Established
The envelope is mechanical
The optimization operates inside the engine's real constraints — minimum efficient load, sustained-power limits, the load profile the machinery prefers. An AI that optimizes speed as a free variable produces profiles the engine can't efficiently follow; the practical system optimizes within the engine's operating envelope, which narrows the range but makes the answer executable. The physics of the fuel curve sets the value; the physics of the machinery sets the bounds. Established
What we know
- Speed's cubic relationship to fuel makes it the dominant optimization variable.
- The constraint is the ETA — optimization is fuel minimization subject to arrival.
- Speed and route must be optimized jointly, not sequentially.
What we don't
- The marginal return of AI speed profiles vs good human practice in live fleets.
- How to certify an AI-recommended speed profile for operational authority.
- Whether speed optimization's gains survive hull-fouling uncertainty.
Being tested
- Continuous speed-profile optimization on live vessels.
- Joint speed+route optimizers against ensemble forecasts.
- Engine-envelope-aware speed recommendation.
Maritime-GPT analysis
Speed optimization is the highest-leverage, lowest-glamour AI application in voyage economics — the physics is known (cubic), the constraint is commercial (ETA), and the AI's job is precision over a variable the industry already knows to move. The real product insight is that 'optimal' isn't a speed, it's a profile — and the human's job isn't setting the throttle, it's deciding whether the ETA obligation the AI is optimizing against is the right one. Maritime-GPT analysis
This is a research feature, not operational guidance. Simulation results are reported as simulation; demonstrations are not deployments; company claims are attributed as such. Verify safety-critical decisions against official nautical publications and flag-state requirements.
Sources & evidence
Recent research on fuel/speed optimization under forecast uncertainty.
The fuel-vs-time-vs-safety trade-off speed sits in.