Logistics & supply-chain data: shipments, ports and freight rates

Supply chains are global, fragmented and increasingly volatile, and the organisations that run them depend on external data to see beyond their own four walls. This guide covers the logistics and supply-chain data that exists, how it is generated, and how to source and deliver it reliably.

Why supply-chain data matters now

Disruption has become the norm rather than the exception, and visibility is the difference between reacting and anticipating. External logistics data lets organisations track shipments, anticipate port congestion, benchmark freight costs and spot supplier risk before it becomes a stoppage.

The logistics data landscape

• Shipment and container movements: events, milestones and dwell times.
• Port and terminal activity: congestion, throughput and vessel schedules.
• Freight rates: ocean, air and road pricing benchmarks.
• Inventory and warehouse signals: stock and occupancy indicators.
• Risk and disruption: supplier-risk indicators and disruption alerts.

How it is generated

Sources include carrier and forwarder systems, AIS vessel-tracking signals, port community systems, telematics, customs-related datasets and commercial rate indices. Coverage, latency and licensing vary widely, so feasibility assessment matters.

Common use cases

Typical applications include shipment visibility and exception management, port and route planning, freight procurement and rate benchmarking, inventory optimisation, and supplier-risk monitoring across multiple tiers.

Sourcing considerations

Identifiers rarely line up across carriers and systems, so harmonising references is central. Freight-rate data needs clear methodology, and some datasets carry redistribution limits. Combining sources, for example vessel tracking with port schedules, is where much value lies.

Delivery and cadence

Visibility use cases want near-real-time events via API; planning and procurement use scheduled batches in analytical formats. Most programmes combine both, with a unified model built around the organisation's systems.

Governance

Most logistics data is non-personal, but provenance and licensing documentation remain essential for procurement and tenders, and security practices aligned with NIS2 and ISO/IEC 27001 principles support critical-supply-chain use.

Where supply-chain data comes from

Logistics data is assembled from many systems, and knowing the origin tells you its reliability. Carrier and forwarder systems provide shipment milestones; AIS gives vessel positions; port community systems describe terminal activity; telematics tracks trucks and temperature; and customs and trade data reveal flows. Each differs in latency, coverage and licensing. The integration challenge is that identifiers, container numbers, booking references, shipment IDs, rarely line up across systems, so resolving them into a single shipment view is the core work.

Turning events into visibility

Raw position and milestone data only becomes useful once it is turned into the answers operators need: an accurate ETA, an alert when a shipment dwells too long, a flag when a route deviates. That derivation, fusing AIS, port schedules and milestones into predicted arrivals and exceptions, is where most of the value sits, and it depends on clean, time-aligned inputs across sources.

A logistics data checklist

• Which systems feed the data, and what is each source’s latency and coverage?
• How are identifiers reconciled into a single shipment or container view?
• Are derived events (ETA, dwell, exceptions) provided, or just raw positions?
• What are the redistribution terms for carrier or AIS-derived data?
• Does the use case need near-real-time visibility or batch analysis?

Sources & further reading

• Eurostat: international trade and transport statistics.
• UNCTAD: review of maritime transport and freight indicators.
• European Commission: customs and the EU single window for logistics.
• EUR-Lex: Regulation (EU) 2023/2854 (Data Act).