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More ocean GRIs; transpac air rates surge on final peak push – December 9, 2025 Update

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More ocean GRIs; transpac air rates surge on final peak push – December 9, 2025 Update

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Published: December 9, 2025

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Weekly highlights

Ocean rates – Freightos Baltic Index

Asia-US West Coast prices (FBX01 Weekly) increased 22% to $2,096/FEU.

Asia-US East Coast prices (FBX03 Weekly) increased 2% to $2,930/FEU.

Asia-N. Europe prices (FBX11 Weekly) stayed level at $2,464/FEU.

Asia-Mediterranean prices (FBX13 Weekly) increased 15% to $3,367/FEU.

Air rates – Freightos Air index

China – N. America weekly prices increased 18% to $7.63/kg.

China – N. Europe weekly prices decreased 12% to $3.64/kg.

N. Europe – N. America weekly prices increased 2% to $2.48/kg.

Analysis

The past week has offered more signs of encouragement for a container market return to the Red Sea. In addition to the Houthis’ release of crew members held in Yemen since a vessel attack in July, CMA CGM and its Ocean Alliance announced some of their services – escorted by French naval vessels – will now transit the Suez Canal for all backhaul voyages, with another also sailing headhaul via the Red Sea.

None of these developments make a Red Sea rebound imminent however, and a full return could still be quite a ways off. But the eventual return of container traffic to the Red Sea will ultimately release a surge of capacity back into a market already struggling with oversupply.

Transpacific container rates to the West Coast hit a low for the year of about $1,400/FEU in early October. Since then, carriers have sought to reduce capacity and introduce GRIs, resulting in a (relatively slow-moving) rollercoaster for prices on these lanes as supply-driven rates rise, retreat and repeat.

Carriers were able to push West Coast rates up in mid-October and again to start November, resulting in an early-November climb to about $3,000/FEU only to see prices fall to $1,700/FEU by the end of the month.

But prices ticked up again to start December – despite volumes projected to be the lowest of the year – with rates to the West Coast up 22% last week to $2,100/FEU. Some carriers are introducing smaller, incremental increases on a weekly basis as opposed to the more typical bi-monthly GRIs in the hopes that the market will accept smaller price bumps more easily than sharper increases. This trend may be reflected in daily rates for this week up another $100/FEU to $2,200/FEU to the West Coast and to more than $3,000/FEU to the East Coast, though once again this month there is skepticism that these prices will hold.

Despite some observers expecting the US market to enter a restocking cycle that would spur ocean volume growth next year, others are less optimistic. The NRF anticipates retailer expectations for negative trade war impacts on consumer behavior will result in double-digit percentage year on year ocean import volume declines through April of next year, with demand lower than 2024 levels as well.

Trade war frontloading is partly to blame for lower US ocean import volumes now, and for the sharply negative year on year comparisons for those expecting weak volumes for Q1 2026. Europe’s ocean imports meanwhile – especially as China has shifted focus away from the US and toward other markets including Europe – have been stronger and more consistent than N. America’s. CTS data shows total ocean imports to Europe eased 2% in month-on-month October, but were still 1% higher than a year ago. Asia – Europe volumes fell 3% year over year in October for the first annual decrease since February.

Despite easing, slow-season volumes, carriers have had more success propping up Asia-Europe rates in Q4 than they have on the transpacific partly due to more aggressive blanked sailings as these lanes enter the home stretch of their annual ocean contract negotiation period.

GRIs starting mid-October have pushed Asia – Europe rates up 40% to $2,463/FEU through last week, though prices have been level since late November. Asia – Mediterranean rates are up 56% to $3,366/FEU including a $500/FEU bump to start December. Some carriers have announced additional GRIs for mid-December, aiming to push N. Europe rates to $3,500/FEU and Mediterranean prices to $4,200/FEU or higher.

As the air cargo market enters its final peak season weeks, Freightos Air Index data suggests transpacific demand strength. China – US rates climbed from around $5.30/kg in mid-October to $6.50/kg to close November, and are now pushing past the $7.50/kg mark – above last year’s $7.30/kg peak – during the end-of-season rush.

Demand is likely stronger on Asia – Europe lanes as China’s e-commerce export focus has also shifted to Europe. But the parallel shift of capacity away from the transpacific and to Asia-Europe lanes – also a factor in the current, higher transpacific rates – has kept Asia – Europe prices from spiking. China – Europe rates eased 12% to $3.64/kg last week and are about on par with a year ago.

The EU and the UK have announced plans to close their de minimis exemptions within the next few years, with several countries planning handling fees for low value imports even before the rule change. US de minimis closures initially led to a sharp decrease in China-US e-comm air cargo volumes. And though volumes remain below April levels, some reports show much of that e-comm demand has returned to the China-US air market as e-commerce platforms have adapted to the new rules.

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Judah Levine

Head of Research, Freightos Group

Judah is an experienced market research manager, using data-driven analytics to deliver market-based insights. Judah produces the Freightos Group’s FBX Weekly Freight Update and other research on what’s happening in the industry from shipper behaviors to the latest in logistics technology and digitization.

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The New Economics of Logistics Visibility

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Knowing that a shipment will arrive six hours late is information. Knowing it early enough to reschedule labor, protect a customer commitment, avoid detention, or change an inventory decision is economic value.

That distinction is becoming central to the logistics visibility market. The earlier argument that exceptions are becoming the real unit of work explains why visibility economics depend less on event volume than on whether the organization can convert important events into timely resolution.

The first era of visibility was largely about answering a basic question: Where is my shipment? The next era is about a harder question: What should I do because its state has changed?

Visibility Is Not the Outcome

Location and status data can be valuable, but they are intermediate products. A business does not earn a return because a dot moved across a map more accurately. The return appears when information changes an operational decision. A useful way to think about visibility is as a chain: signal -> interpretation -> decision -> intervention -> economic outcome. If any link is missing, much of the potential value disappears.

A Signal Has to Arrive Inside the Decision Window

Timing matters.

A delay discovered after the customer has already missed production is history. The same delay identified early enough to expedite an alternate shipment may be actionable. An ETA update received after warehouse labor has reported for a shift may have less value than the same update received while the schedule can still be changed.

This means visibility quality is not only about accuracy. It is about whether the signal arrives with enough lead time to support an intervention.

Not Every Exception Deserves Attention

As visibility improves, organizations often discover a new problem: too many exceptions. A network with thousands of shipments will always contain delays, deviations, missed scans, changing ETAs, and incomplete data. If every deviation creates an alert, planners become the bottleneck. The more important capability is prioritization.

Which late shipment threatens a high-value order? Which delay creates a stockout? Which container risks demurrage? Which arrival change will disrupt a dock schedule? Which event is likely to self-correct without intervention?

Visibility becomes intelligence when the system can distinguish operational consequence from mere deviation.

ETA Is a Decision Input

Estimated time of arrival is a good example of how the economics are changing. ETA was once primarily a customer-service or tracking metric. Increasingly it can influence warehouse scheduling, yard planning, labor, inventory, customer promises, and downstream transportation. That makes ETA a shared operating variable.

The value increases when the prediction is connected to the systems that can respond. A changing ETA that remains trapped in a visibility dashboard creates less value than one that can trigger a workflow or decision elsewhere.

Dwell, Detention, and Demurrage Make the Economics Visible

Some visibility use cases have direct financial consequences. Better awareness of arrival, dwell, free-time windows, and container status can help organizations manage detention and demurrage exposure. Yard visibility can reduce unnecessary trailer search and moves. Earlier exception detection can protect delivery appointments and reduce costly service recovery. These cases make an important point: visibility value is often realized outside the visibility platform itself.

More Visibility Can Increase Work

This is the uncomfortable side of digital transparency. If a company exposes ten times as many events but does not improve prioritization or workflow, it may create ten times as many things for people to inspect. The result can be an expensive monitoring layer sitting on top of the same manual decision process.

That is why visibility and autonomous exception management are converging. The system must increasingly help decide which events require action, assemble context, recommend a response, and automate routine resolution where appropriate.

Measure Intervention, Not Just Coverage

Visibility programs are often measured by tracking coverage, data completeness, ETA accuracy, or number of connected carriers. Those are necessary operating metrics, but they do not fully describe business value.

Organizations should also ask: How many material exceptions were identified early enough to act? How quickly were they resolved? How often did intervention protect service or avoid cost? How many alerts required no useful action? How much planner time was consumed per exception?

Those measures connect visibility to economics.

The Market Is Moving Toward Action

This shift has strategic implications for technology providers. Pure visibility is becoming less differentiated as location and event data become more widely available. The higher-value layer is interpretation and action: understanding what an event means to a specific operation and helping execute the appropriate response.

That pushes visibility platforms toward orchestration, workflow, decision intelligence, and AI. It also pushes TMS, WMS, and other execution systems toward richer external event awareness.

The Bottleneck Moves

For years, logistics organizations complained that they could not make better decisions because they could not see what was happening. Increasingly, they can see more.

The bottleneck is moving.

When a network can identify exceptions continuously, the constraint becomes the speed and quality with which the organization can interpret and resolve them. That is precisely the environment in which AI agents become interesting—not because logistics needs another conversational interface, but because it needs more capacity to do operational work.

Related Logistics Viewpoints research

The New Architecture of Logistics
Systems Engineering in Logistics
2026 Autonomous Exception Management Market Map
The Economics of Decision Latency
Previous in this series: Transportation Is Becoming Computational

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If your organization is assessing connected execution, orchestration, AI, observability, decision velocity, or selective autonomy, I would be glad to provide the complete client edition and discuss the implications for your logistics operating model and technology architecture.

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The post The New Economics of Logistics Visibility appeared first on Logistics Viewpoints.

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o9 Solutions Uses a Knowledge Graph to Connect Supply Chain Decisions

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Supply chain decision-making is difficult partly because the relevant information is distributed across products, locations, suppliers, orders, capacities, policies, and external events. A system may have access to all of those records and still struggle to understand the relationships among them quickly enough to support a consequential decision.

o9 Solutions addresses that problem through its Digital Brain architecture, including an enterprise knowledge graph and in-memory modeling designed to connect demand, supply, inventory, planning, and operating context. That semantic layer is important because it gives analytics and AI a structured representation of how supply chain entities relate to one another rather than treating the environment as a collection of independent tables and documents.

The company combines this architecture with integrated planning, optimization, scenario modeling, machine learning, and human oversight. The strategic direction is toward a continuous decision environment where changes can be interpreted quickly, alternatives can be modeled, and recommendations can be traced back to the assumptions, events, and constraints that produced them.

As with any broad planning and intelligence platform, the value depends on implementation quality. Knowledge models need strong data governance, entity resolution, process ownership, and clear decision rights. A sophisticated model of the supply chain is useful only if the organization can keep it current and use it consistently in real operating workflows.

o9 Solutions appears in the Logistics Viewpoints Supply Chain Decision Intelligence MarketMap and Autonomous Exception Management MarketMap. The two MarketMaps highlight the relationship between integrated decision intelligence and the faster exception-response capabilities now developing around it.

The post o9 Solutions Uses a Knowledge Graph to Connect Supply Chain Decisions appeared first on Logistics Viewpoints.

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AI Will Not Transform the Supply Chain Until the Architecture Around It Catches Up

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Executive thesis. The model is becoming the least durable layer of the AI stack. Sustainable advantage will come from the operating architecture around AI: authoritative context, governed tools, permissions, observability, and connection to enterprise workflows.

The model is only one component

Supply chain AI discussions often begin with model capability: prediction accuracy, reasoning quality, computer vision performance, or the fluency of a generative system. Those capabilities matter, but operational value depends on everything around the model. The system still needs authoritative context, enterprise tools, permissions, workflow, observability, and a reliable path from recommendation to action.

Different AI patterns solve different problems

Prediction, optimization, generative AI, vision, and agents should not be treated as interchangeable technologies. Forecasting demand, selecting a route, extracting information from a document, interpreting an image, and executing a multi-step workflow require different evidence, control, and performance measures. A mature architecture starts with the decision or task and chooses the AI pattern that fits it.

Context is the operating fuel

An AI system can produce a plausible answer while using stale or incomplete operational context. In logistics, that can be dangerous because the truth may reside across orders, inventory, rates, carrier status, warehouse state, supplier records, and policy documents. Retrieval, master data, identity resolution, and system access therefore become part of the AI architecture, not secondary data-engineering concerns.

Tool access turns intelligence into consequence

The moment an AI system can create a shipment, change an order, contact a carrier, release inventory, or approve an exception, governance becomes an operational requirement. Tool permissions, financial limits, approval gates, idempotency, retries, and rollback are the mechanisms that separate an interesting demonstration from a dependable production workflow.

Measure workflow performance

A fluent response is not the right success metric for operational AI. Supply chain leaders should measure decision latency, manual context gathering, exception closure, override behavior, error recovery, tool failure, and the business outcome being improved. That measurement discipline also creates a rational basis for expanding autonomy as evidence accumulates.

The Logistics Viewpoints AI in Logistics: Use Cases, Architecture, and Implementation Guide separates the major AI patterns and connects them to data, tools, governance, workflow, observability, and ROI—the architecture required to move from model capability to operating value.

Executive implication

AI strategy should separate model selection from control architecture and measure value through workflow performance, decision quality, and operational outcomes.

Go deeper: provides the durable buyer, architecture, and implementation reference for this topic. AI & Advanced Analytics connects this analysis to the broader Logistics Viewpoints research architecture.

Related Logistics Viewpoints research

Download: AI in the Supply Chain — From Architecture to Execution
The New Architecture of Logistics

Go Deeper

Read the full AI in Logistics: Use Cases, Architecture, and Implementation Guide.

Explore the broader AI & Advanced Analytics domain for related Logistics Viewpoints research and analysis.

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