10 Ways AI Is Quietly Changing Voyage Planning
📊 Subscribe to the Ship Universe Weekly Newsletter
AI has already slipped into voyage planning screens in ways that most crews and even some shore teams barely notice. Behind the scenes it is quietly steering routes, nudging speeds, reshuffling port calls and turning bunker and carbon cost into live inputs instead of once-a-voyage guesses. So, where is AI live at scale and where is it emerging? Find out below.
#1 AI route and Speed Optimisation Status: live on many fleets
Instead of planning a route once and hoping it holds, AI engines now keep adjusting route and speed as weather, currents and port windows change. The goal is to hit the best mix of fuel cost, arrival time, safety and carbon exposure for each leg, not just follow the shortest line on the chart.
Mechanism & Purpose
- Combines weather, waves, currents and vessel performance data into one live model.
- Suggests route and speed profiles that reduce fuel burn while keeping schedules realistic.
- Replans during the voyage when forecasts, port slots or congestion change.
Current Status
- Already in active use on many deep sea fleets through voyage optimisation and weather routing tools.
- Most common on containers, tankers and bulkers with performance monitoring in place.
- Next step is adding full carbon cost and CII impact alongside fuel and time in the optimisation target.
Practical takeaway: treat AI routing as a second opinion that runs all the time. The gains are usually a few percentage points per leg, which add up across a year of sailings.
#2 Continuously updated, high-accuracy ETAs Status: live and scaling
The old pattern was one ETA at departure and a stream of apologetic updates. AI models now refresh ETAs constantly using live AIS, port congestion, weather and vessel behaviour, so planners and customers see a time of arrival that actually tracks reality rather than a guess from last week.
Mechanism & Purpose
- Combines AIS tracks, historical voyage patterns and port call data into a prediction model for each ship and trade.
- Ingests weather, currents and known delays at pilot stations, anchorages and terminals to adjust arrival times.
- Feeds updated ETAs into planning, chartering and customer systems so decisions use the latest view, not static notices.
Current Status
- Already standard on many liner and short sea services where schedule reliability is critical.
- Spreading across tramp and project trades through third party platforms and port community systems.
- Next wave is linking ETA accuracy to service level guarantees, laytime planning and just-in-time arrival programs.
Practical takeaway: treat ETA as a live signal. The more systems you connect to AI-driven ETAs, the fewer surprises you get at the berth and with customers.
#3 Just-in-time Arrivals and Port Vall Optimisation Status: live in key corridors
Instead of racing to the pilot station and paying to wait at anchor, AI tools now coordinate ship speed with real berth readiness. They read port lineups, tug and pilot slots, and terminal schedules, then slow the ship down so it glides in when the berth is actually open.
Mechanism & Purpose
- Combines live port arrival data, berth plans, tug and pilot schedules and terminal constraints into a single planning view.
- Recommends speed profiles that match the arrival window, cutting idle time at anchor and reducing bunker use and emissions.
- Shares updated arrival plans between ship, port and terminal so everyone can adjust to delays or early departures on the day.
Current Status
- Already live in pilots at major hubs that support just-in-time arrival and digital port call platforms.
- Adopted first on liner, car carrier and energy trades where schedules and port partners are stable.
- Scaling challenge is data sharing and trust across owners, terminals, agents and port authorities, not the core AI itself.
Practical takeaway: every hour you do not sit at anchor is fuel and emissions saved. The fastest gains come when your voyage tools and the port’s systems talk to each other in real time.
#4 Smarter Weather Routing Status: mature, now AI-enhanced
Weather routing is not new, but AI and stronger optimisation engines are changing what it can do. Instead of a simple “green route, red route” view, planners now see route and speed options that factor in detailed swell, wind, currents and vessel behaviour to reduce risk and cut fuel at the same time.
Mechanism & Purpose
- Uses high-resolution weather and ocean data combined with a performance model for each hull and engine combination.
- Runs thousands of route and speed combinations to find tracks that avoid the worst conditions while staying commercially viable.
- Can target different objectives: lowest fuel use, lowest risk, schedule protection or a weighted mix that suits the voyage.
Current Status
- Widely used on deep sea trades for tankers, bulkers and containers, often integrated into existing weather routing services.
- AI models are gradually replacing static polar curves with learned ship behaviour based on real voyage data.
- Next step is tighter links to safety management, so heavy weather limits, crew comfort and cargo constraints feed directly into route choices.
Practical takeaway: think of AI weather routing as a way to turn “avoid bad weather” into a quantifiable trade-off between comfort, safety, fuel and schedule on every leg.
#5 Bunker Planning and Procurement Status: live in tools, still maturing
Bunker lifts used to be driven by habit, local contacts and last minute emails. AI supported platforms now treat them as a data puzzle, matching voyage plans, price curves, quality, risk and carbon cost to decide where and when you should actually stem.
Mechanism & Purpose
- Pulls in live and historical bunker prices, availability and quality by port, along with expected consumption from voyage plans.
- Ranks candidate bunkering ports by all-in economics, including deviation, waiting time, barge fees and likely price moves.
- Surfaces recommended lift plans and alternative options so operators see the cost and risk trade-offs before fixing.
Current Status
- Already embedded in some voyage optimisation and bunker procurement platforms used by larger owners and trading houses.
- Most advanced on standardised deep sea trades where fuel use and port patterns are predictable and data history is rich.
- Still emerging for smaller fleets and ad hoc trades, where data quality, internal processes and human trust remain the main barriers.
Practical takeaway: the more bunker and voyage history you can centralise, the more value AI can add. Treat each stem as a data point that trains your next buying decision.
#6 Carbon-cost-aware routing for EU ETS and FuelEU Status: early live use, fast emerging
With EU ETS already covering shipping and FuelEU Maritime phasing in, route choice now changes your carbon bill as well as your bunker bill. New tools are starting to treat allowances, penalties and intensity scores as inputs to the voyage plan, not just something the compliance team tidies up months later.
Mechanism & Purpose
- Calculates expected CO₂ emissions for each candidate route and speed profile based on ship data and load.
- Translates emissions into a euro cost using allowance price assumptions and FuelEU penalty levels.
- Optimises voyages on combined fuel plus carbon cost, instead of distance alone, and flags routes that hurt CII or intensity scores.
Current Status
- Already appearing in specialist emissions and voyage optimisation platforms used by larger European-focused fleets.
- Most developed on trades with heavy EU exposure where allowance costs are material and data flows are mature.
- Still emerging for global tramp fleets, where gaps in activity data, contract wording and internal carbon pricing slow adoption.
Practical takeaway: even simple internal carbon prices can change which routes and speeds make sense. Building carbon cost into routing now is cheaper than retrofitting it once ETS and FuelEU bills start to bite.
#7 Port Congestion and Network Choices Status: live on major trades
Instead of guessing which port will be clogged next week, AI systems now read AIS, satellite and port call data to flag building queues in near real time. That lets planners flip rotation order, pick alternate ports or adjust speed before congestion turns into lost days and blown schedules.
Mechanism & Purpose
- Tracks live vessel counts, anchorage times and berth occupancy around ports using AIS and historical port call data.
- Uses pattern recognition to spot early signs of congestion and predict likely waiting times for different ship types.
- Feeds this into network and voyage planning so schedulers can change port rotation, switch discharge ports or adjust speed in advance.
Current Status
- Already used by larger liner, tanker and dry bulk operators through analytics dashboards and congestion alerts.
- Most developed on busy global hubs and chokepoints where data coverage is strong and delays are costly.
- Emerging use is linking congestion scores directly into routing, bunker planning and contract discussions with cargo owners.
Practical takeaway: congestion is no longer just a port problem. When your voyage tools see delays coming, you can redesign the network instead of reacting at the anchorage.
#8 Guidance from Digital Twins Status: live on advanced fleets
High-frequency data from engines, props and hull sensors now feeds digital twins that act like a second pair of hands on the bridge. Instead of relying only on rules of thumb, crews and shore teams get live suggestions for speed, power and manoeuvring that reflect how the ship is behaving in real conditions.
Mechanism & Purpose
- Builds a virtual copy of each vessel using design data, sea trial results and real-time sensor feeds from engines, hull and propeller.
- Compares live performance with expected curves to recommend optimal speed and shaft power for current draft, trim, wind and sea state.
- Provides guidance for harbour manoeuvres and tight passages, helping avoid wasteful bursts of power, thruster overuse and hard turns.
Current Status
- Already deployed on parts of large tanker, bulk and LNG fleets with strong performance monitoring and data infrastructure.
- Often used in advisory mode today, with the bridge team and shore-based operators deciding which recommendations to follow.
- Next step is deeper links to maintenance and safety, so the same twin informs engine health, hull cleaning windows and risk checks.
Practical takeaway: digital twins turn years of performance data into simple speed and power advice. Even small changes per leg compound into meaningful fuel and emissions savings over a year.
#9 Stowage and Capacity Planning Status: live in containers, emerging elsewhere
Stowage used to be treated as a separate problem from the voyage plan. Now smarter planning tools link bay plans, weights and stack limits back into routing, speed and port choices so you see the real impact of bookings and load plans on draft, stability, port time and fuel.
Mechanism & Purpose
- Reads bookings, container types, weights, reefers and hazardous cargo limits along with ship constraints and class rules.
- Builds stowage plans that minimise rehandles, balance stability and keep key boxes in easy positions for each port call.
- Sends expected draft, trim, crane time and restow counts back into voyage and network planning so speed, rotation and bunker plans are based on how the ship will actually be loaded.
Current Status
- Already standard in large container lines as part of centralised stowage centres and planning software.
- Feedback from stowage into voyage plans is growing, with planners using expected port time and draft to refine schedules and fuel use.
- Early work is extending similar ideas to RoRo, car carriers and some bulk trades where loading plans drive time alongside the berth.
Practical takeaway: bay plans are not just for the terminal. When stowage data flows into voyage tools, you get more realistic schedules, better bunker estimates and fewer last minute surprises alongside.
#10 Integrated Voyage Management Status: emerging, rolling out now
Chartering, ops, bunker, technical, finance and ESG used to sit in different systems and spreadsheets. New AI platforms are starting to sit in the middle as a shared “voyage brain”, pulling data from each team and suggesting the next best action for the voyage instead of leaving everyone to work in separate views.
Mechanism & Purpose
- Connects AIS, noon reports, weather, bunker prices, ETS data, port costs, charter parties and technical limits into one model per voyage.
- Surfaces options such as route changes, speed adjustments, bunker ports or schedule tweaks, with clear cost, revenue and carbon impacts.
- Pushes simple prompts to each team (ops, bunker, chartering, ESG) so decisions line up with one shared view of the voyage P&L and risk.
Current Status
- Live today in early forms inside some voyage optimisation suites and in-house control rooms at larger owners and operators.
- Often runs as decision support, not autopilot: humans still choose, but they see joined-up numbers instead of siloed reports.
- Next phase is tighter integration with contracts, finance and emissions reporting so every fixture and routing choice is reflected instantly in cash flow and compliance views.
Practical takeaway: a voyage “brain” pays off fastest when you connect it to real decisions. Start with one fleet and a few use cases, then expand as trust in the shared numbers grows.
AI is not here to replace your voyage team, it is here to sit beside them and keep score on every leg in a way humans cannot. The tools in this guide are already changing how routes are chosen, how fuel is bought, how port calls are planned and how carbon cost shows up in day to day decisions. The owners who benefit most will not be the ones with the fanciest dashboard, but the ones who pick a few concrete use cases, clean up their data and let AI suggestions flow into real chartering and ops calls. If you treat AI as a quiet partner in voyage planning, not a black box or a one-off project, every voyage becomes a little more visible, a little cheaper and a little cleaner, and that edge compounds over the life of the fleet.
We welcome your feedback, suggestions, corrections, and ideas for enhancements. Please click here to get in touch.
