Why shipping is finally a candidate for AI
Liner shipping operates 24 hours a day, 365 days a year. Most of the day-to-day work is labour-intensive and repetitive. AI doesn't get distracted, tired or negligent — it keeps processing, learning and executing. As the process ages, the AI's instructions get more specific and more detailed.
The real benefit is reducing avoidable human error — the kind that leads to costly delays. Repetitive digital processes are constantly analysed by AI, and anomalies are flagged so corrections can be made before mistakes are made. The same constant analysis provides cost-competitive and operational advantage, enhances service reliability, and boosts customer satisfaction.
SEDGE is a cloud-based AI analytics platform that brings clarity and insight to data. Data-led decisions become possible — with confidence — for vessel and port operations, sales and marketing, product development, fleet and service deployment, finance and accounting, and procurement of inland logistics.
“Shipping lines that fail to embrace AI miss the opportunity while competitors realise the benefits.”
Port Cost Reduction
SEDGE evaluates historical port data to avoid busy or congested periods at each port or terminal. It enables timely decisions on alternative routings backed by data — not by hunch. Liner operators can optimise port costs by sequencing calls intelligently and avoiding the overtime and anchorage charges that compound on the wrong day of the week.
- Avoid expensive weekend overtime labour charges
- Avoid additional anchorage charges due to vessel delays
- Compare port costs with those of competitors
Bunker Hedging
Bunker fuel pricing has become one of the most volatile inputs in liner economics. SEDGE analyses crude and petroleum futures contracts traded at NYMEX and IPE alongside spot bunker prices in Rotterdam and Singapore. Regional supply and demand data feed into the same model, so purchasing decisions can be made on the full picture instead of the last quoted price.
- Constant updates on future and spot rates
- Flexible, informed response to bunker price changes
- Considerable cost savings through agile bunker-purchasing decisions
Asset Deployment
SEDGE ensures that the right vessels and equipment are deployed to each specific service. It analyses historical data and runs predictive algorithms across seasonal port constraints, weather and tidal patterns, optimal stowage and vessel speeds, surplus and deficient equipment scenarios. The result is a deployment plan grounded in years of operational data, not last quarter's spreadsheet.
- Informed decisions about vessel deployment within services
- Real-time data for equipment planning and repositioning
- Optimised vessel performance across the network
Box Repair Costs
Container repair costs vary by port and by supplier depot. Estimating manually requires considering container type, labour cost and hours, location, currency, type of repair, and material used. SEDGE compares the repair estimate against historical depot data — months and years back — using a regression model that produces a strong negotiating platform against the depot's quoted price.
- Save considerable research time evaluating multiple historical cost factors per depot
- Data provides strong bargaining position to negotiate lower repair costs
Cargo Demand & Forecasting
Matching cargo demand to capacity supply involves so many variables that demand forecasting genuinely needs deep learning. SEDGE processes sample data across a multitude of port-pair combinations, groups each pair on a weekly basis, and runs the data through Long Short-Term Memory (LSTM) deep-learning programs. Patterns are analysed, defined, and projected forward — giving operators concrete demand estimates for future weeks rather than seat-of-pants guesses.
- Leverages LSTM deep learning to optimise future vessel planning and deployment
- Cost savings from better demand–capacity matching
Intermodal Operations
Once cargo arrives in port the inland movement matters as much as the sea leg. SEDGE analyses data from ports, railways, storage facilities and transport companies to identify potential landside congestion and cost fluctuations. The output is operational planning visibility — for shoreside transportation, special equipment, reefer plugs, reefer monitoring, and forward contracts with trucking and freight-rail operators.
- Time saved knowing the best mode of inland transportation
- Landside transport contracts negotiated from a data-backed position
- Cost savings on equipment detention and demurrage