AI cost reduction and productivity program for dry bulk shipping
From a global dry bulk fleet to a learning operating network.
Berge Bulk already has the ingredients for high-value AI: scale, vessel data, procurement workflows, voyage optimization, decarbonisation ambition, and document-heavy operations. The opportunity is to turn those assets into auditable decisions that save fuel, reduce purchasing waste, accelerate claims, and make carbon performance commercially visible.
Fleet AI command view
90+ vessels
Fuel-4.8%
ETA+12h risk
CO2eauto report
01 / Business and digital baseline
What Berge Bulk does, and why AI can now compound its existing digital work.
Berge Bulk is a Singapore-headquartered dry bulk owner, operator and manager serving miners, steel mills, charterers and commodity supply chains. Its cost base is shaped by fuel, vessel condition, spares, port delays, claims, carbon rules and market volatility.
A
Core business: global dry bulk transportation
The fleet spans major bulk carrier classes, including Capesize, Newcastlemax and very large ore carrier profiles. The commercial reality is asset-heavy and decision-dense: every voyage carries trade-offs between speed, bunker, berth timing, weather, charterparty exposure, carbon cost and customer service.
Iron ore
Coal
Steel value chain
Global chartering
B
Procurement baseline
SERTICA and Moscord already digitise procurement, inventory, spare parts and supplier collaboration. This creates a strong foundation for AI-driven price anomaly detection, demand forecasting, supplier scoring and invoice matching.
C
Voyage and performance baseline
Voyage optimization tools such as Wayfinder indicate that data-driven routing is already accepted. The next step is a fleet-level performance loop that connects recommendations, execution, fuel impact and carbon reporting.
D
Decarbonisation baseline
Wind-assisted propulsion, ammonia-ready thinking, biofuel, solar and carbon-capture trials show a broad decarbonisation agenda. AI can make these investments measurable, explainable and commercially useful for customers with Scope 3 pressure.
02 / Prioritised AI value pools
Three practical AI opportunities with clear ROI and adoption paths.
The recommended entry points are practical: they connect to current systems, can be piloted with bounded data, and create value that finance, operations, technical management and commercial teams can verify.
AI Procurement & Spares Optimisation
Turn requisitions, quotations, inventory and invoices into a recommendation engine for lower cost and fewer urgent purchases.
- Financial lever
- Purchase savings, lower inventory, fewer emergency orders
- Efficiency lever
- Automated quote review and three-way matching
Fleet Energy & Carbon Copilot
Fuse routing, weather, vessel condition, fuel curves and carbon rules into a fleet-wide optimisation cockpit.
- Financial lever
- Bunker savings plus EU ETS and FuelEU exposure management
- Efficiency lever
- Automated exception detection and closed-loop performance reviews
Charterparty & Operations Document AI
Read charterparties, SOFs, invoices, survey reports and emails to surface claims, risks and actions.
- Financial lever
- Recovered claims, faster billing, fewer overpayments
- Efficiency lever
- Document extraction, evidence packs and approval acceleration
03 / Workflow learning path
Explore three short business workflows that show how AI can support daily decisions.
Each workflow gives a business role, a queue of work, a decision to make and an AI-generated business output. The goal is to make the operating change tangible before discussing technology and rollout.
Understand the operating baseline
Start with the fleet, procurement, voyage-performance and decarbonisation context.
Pick a value pool
Select the opportunity that matches the audience in the room: procurement, operations or legal/finance.
Complete a business task
Enter the workflow page and make the decision from the perspective of the business user.
Review ROI and rollout
Use the generated result to discuss data availability, pilot scope and adoption risks.
01
02
03
Role: Procurement buyer
Process RFQs and create a PO approval pack
Choose a requisition, review supplier risk, select a buying strategy and generate a finance-ready decision note.
Role: Fleet operations manager
Send an explainable voyage instruction
Select an active voyage, choose the commercial objective, accept AI actions and see fuel, CO2 and value impact.
Role: Operations / legal / finance reviewer
Generate an evidence-backed action pack
Review extracted evidence, choose claim, dispute or compliance action and create the business output.
04 / Implementation and adoption roadmap
Move from business validation to controlled pilot and scalable operating model.
The recommended path starts with measurable business value, validates data readiness and keeps human approval in the loop before scaling AI-supported decisions across the fleet and shore teams.
Value and data readiness workshop
Align procurement, technical, operations, chartering, finance and ESG teams on target workflows, available data and success metrics.
Data proof and workflow prototype
Use bounded, anonymised data to validate recommendation quality, workflow fit and baseline ROI before broader integration.
Operational pilot
Deploy one selected opportunity across 5-10 vessels, a port supply region, or a high-volume document workflow with adoption metrics.
AI operating model
Create governance, human review controls, data owners, model monitoring and monthly value reviews so AI becomes a management rhythm.
Public sources used for the business baseline
Business assumptions were shaped by the Berge Bulk website, official fleet list, company handbook, SERTICA customer case and Digital Ship coverage of Wayfinder. ROI numbers are indicative and should be recalibrated with Berge Bulk operational data during the value and data readiness phase.