Digital Shipping and Maritime AI Advantage Course – Barcelona, 23–27 Nov 2026

Overview

Digital Shipping and Maritime AI Advantage Course is a one-week practitioner course for shipping managers, maritime operations teams, port professionals, freight leaders, and commercial teams, who leave with a Digital Shipping AI Advantage Roadmap. Participants use vessel, voyage, port-call, fuel, maintenance, cargo, and customer-visibility data to assess AI-supported decisions, platform choices, value, and risk. The course connects maritime use cases to business evidence. Agile Leaders Training Center provides training in digital shipping and maritime AI advantage.

Who Should Attend

• Shipping managers responsible for operational and commercial performance
• Maritime operations teams responsible for vessel and voyage decisions
• Port and terminal teams responsible for port-call and turnaround coordination
• Freight and logistics leaders responsible for cargo visibility and reliability
• Commercial teams responsible for digital shipping use cases and value

The course assumes participants work with maritime operations or commercial data and leaves out navigation instruction, autonomous-vessel engineering, software development, and general supply-chain optimization.

Departments and Industries

The course supports digital and AI decisions across maritime transport and port services.

• Fleet operations, marine operations, and vessel performance
• Ports, terminals, harbor services, and port-call coordination
• Freight forwarding, liner operations, and cargo planning
• Chartering, commercial operations, and customer service
• Technical management, maintenance, sustainability, and data teams

Learning Objectives

By the end of this course, participants will be able to:

• Analyze maritime data sources and decision readiness
• Apply AI use cases to voyage and port performance
• Evaluate fuel, emissions, maintenance, cargo, and reliability signals
• Compare platform, data-sharing, and visibility choices
• Prioritize use cases by value, risk, and feasibility
• Build a digital shipping AI advantage roadmap

Course Agenda

Day 1: Maritime Data and Digital Readiness

• Vessel, Voyage, Port, and Cargo Data Map
• Manual Report and Sensor Data Comparison
• Data Quality and Timeliness Checklist
• Ship-to-Shore Decision Information Flow
• Digital Shipping Readiness Scorecard

Day 2: Voyage and Vessel Performance

• Route and Schedule Reliability Use-Case Canvas
• Speed, Weather, and Consumption Decision Grid
• Fuel and Emissions Performance Indicator Set
• Voyage Exception and Alert Register
• Human Review and Operational Boundary

Day 3: Port Calls and Cargo Visibility

• Port-Call Milestone and Turnaround Map
• Estimated Arrival and Berth Data Exchange
• Delay Cause and Port Exception Taxonomy
• Cargo Status and Customer Visibility Dashboard
• Port and Partner Data-Sharing Agreement Card

Day 4: Maintenance, Value, and Platform Choice

• Condition Signal and Predictive Maintenance Concept
• Failure Risk and Maintenance Priority Matrix
• Digital Platform Capability Comparison
• Use-Case Value, Cost, and Risk Assessment
• Implementation Feasibility and Dependency Review

Day 5: Digital Shipping Advantage Practice

• Suggested Exercise: Map Maritime Decision Data
• Suggested Exercise: Assess Voyage and Port AI Uses
• Suggested Exercise: Review Maintenance and Cargo Signals
• Suggested Exercise: Prioritize Value and Risk
• Capstone Exercise: Digital Shipping AI Advantage Roadmap

Practical Exercises

The course uses suggested activities to convert maritime data and AI opportunities into reviewable decisions.

• Suggested activity: map vessel, voyage, port, cargo, customer, and partner data with quality and timeliness constraints
• Suggested activity: evaluate route, schedule, fuel, emissions, port-call, turnaround, and exception use cases
• Suggested activity: interpret condition, maintenance, cargo-status, and customer-visibility signals with human review
• Suggested activity: compare platforms and prioritize use cases by value, cost, risk, feasibility, and dependencies

FAQs

Who suits digital shipping and maritime AI training?

Digital shipping and maritime AI training suits shipping managers, maritime operations teams, port professionals, freight leaders, commercial teams, and technical managers who work with maritime operational or commercial data.

How does digital shipping AI differ from general supply-chain analytics?

Digital shipping AI concentrates on vessel, voyage, port-call, fuel, machinery, cargo, schedule, and customer-visibility decisions. General supply-chain analytics spans sourcing, inventory, warehousing, production, distribution, and wider logistics networks.

Which maritime data supports AI-assisted shipping decisions?

Useful data may include vessel position, speed, weather, consumption, machinery condition, voyage schedules, port milestones, berth information, cargo status, delays, exceptions, and customer-service records, subject to quality and permission checks.

How can AI support port-call and voyage performance?

AI can support pattern detection, forecasts, exception alerts, comparisons, and decision options for arrivals, schedules, routes, fuel, delays, and turnaround, while accountable professionals retain operational judgment.

How should digital shipping AI use cases be prioritized?

Use cases should be prioritized using business value, data readiness, decision impact, risk, human oversight, platform fit, implementation effort, dependencies, measurable outcomes, and an accountable owner.

Conclusion

Participants take back a Digital Shipping AI Advantage Roadmap linking maritime decisions, data, use cases, platforms, value, risks, dependencies, owners, and measures. It makes digital shipping priorities visible and comparable. It supports phased investment in AI uses that have suitable data, defined oversight, and credible operational or commercial value.