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Special Trade War Update – US Court Ruling: Analysis and Freight Impact

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Special Trade War Update – US Court Ruling: Analysis and Freight Impact

A recent U.S. court ruling orders the removal of key Trump-era tariffs, creating short-term relief for importers but raising new questions about future trade policy and supply chain stability.

May 29, 2025

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The US Court of International Trade ruled on Wednesday that President Trump wrongly invoked the International Emergency Economic Powers Act (IEEPA) to apply reciprocal tariffs on a long list of countries and other tariffs on Mexico, Canada and China targeting fentanyl smuggling.

The ruling instructs the administration to remove the 10% global tariff, the 25% tariffs on Canada and Mexico and the 30% tariffs on China within ten days. Tariffs on steel, aluminum, vehicles and automotive parts will remain in effect as they are not based on the IEEPA.

The White House is appealing the decision and could ask the Supreme Court to keep the tariffs in place during the lengthy appeals process.

If tariffs are suspended, the administration could seek to restore or apply tariffs through other trade laws – like Section 301 used to apply tariffs on China in Trump’s first administration, and Section 232 used in 2018 and for steel, aluminum and vehicle tariffs this year – though these could take more time and can require congressional approval.

For supply chains, the development adds even more uncertainty to the mix, but may not drastically change the recent trade war-driven trends in logistics.

US importers had already started frontloading peak season goods since the China-US deescalation on May 12th saw tariffs on China drop to 30%, pushing transpacific ocean volumes and rates up. And with no guarantee that these tariffs won’t be restored or other tariffs introduced soon, shippers are likely to keep frontloading – or even increase shipping activity – while they know tariffs are low.

For air cargo, the ruling likely will nullify the US’s May 2nd suspension of de minimis eligibility for Chinese goods which has led to a big drop in B2C e-commerce volumes moving from China to the US via air cargo. If the ruling restores de minimis for China we may see some rebound in these volumes.

But as there was bi-partisan support for limiting de minimis for China even before Trump took office, this exemption is likely to be closed to China at some point by other means. And as platforms like Temu and Shein have already increased their ocean logistics and domestic fulfillment capabilities for the US market, we may not see a full reversal of the drop in air cargo volumes even in the interim.

Timelines and Tariff Alternatives

The administration paused its reciprocal tariffs in early April and set a July 9th deadline after which – if the US does not reach trade agreements with the targeted countries – those tariffs would be restored. Similarly, on May 12th the US reduced tariffs on China from 145% to 30% and set an August 14th deadline to come to new trade terms with China, after which it could raise tariffs once again.

The court’s decision reduces the likelihood that these deadlines are still valid and the White House’s leverage in these negotiations. And even though only the UK had come to a tentative agreement with the US so far in any case, the ruling could slow the progress in negotiations even further. At the same time, the aluminum, steel and auto tariffs that remain in effect could motivate countries where the manufacture of these goods plays a significant role in their economies – like Canada, Mexico, Japan and the EU – to continue negotiations in any case.

A Supreme Court emergency order could quickly reinstate the tariffs canceled by the trade court’s decision. But barring a Supreme Court intervention the appeals process that could potentially restore the IEEPA tariffs would be lengthy. The process would start in federal appellate court and, if that court upholds the ruling, it could continue to the Supreme Court.

In the meantime, there are other trade acts at the White House’s disposal that could be used to introduce tariffs. But none are quite as broad as those attempted via the IEEPA, and each requires processes that would make it hard for new tariffs to be introduced immediately.

The other avenues to tariffs include Section 232 which Trump used to tariff steel and aluminum in his first administration and to tariff these as well as vehicles and automotive parts this year. Trump relied on Section 301 for 7.5% to 25% tariffs on nearly $400B of Chinese imports in 2018 and 2019 and could potentially use this law again, and the president used Section 201 for tariffs on washing machines in 2018.

Each of the above laws require some form of an investigation of the trade issue by a federal agency, and often a comment or review period before the president can take action. For some, congressional approval is also required once the president decides to introduce tariffs.

Other options include Section 122 which can be used to apply 15% tariffs on imports for 150 days to address issues related to payments and currencies, and Section 338 which allows the introduction of 50% tariffs on a specific country, but has not been used since the 1940s.

However, though most of these options usually take weeks or months, Trump has already requested and received reports from federal agencies for most of the trade issues that the IEEPA tariffs were being used to address.

Trump directed agencies to research and make recommendations on trade imbalances, fentanyl smuggling and other issues on his first day in office and again in March, with most of those findings meant to be delivered in April. He has also already initiated seven other investigations looking into the state of US trade in lumber, minerals and pharmaceuticals.

Using the above trade acts take time and are likely more difficult to leverage for rapid tariff introductions or levies on 100% of a target country’s exports. But the fact that many investigations that could support new tariff roll outs are already complete or underway, could shorten the timeline for implementation.

Implications for Freight

Ocean Freight

The May 12th deescalation between China and the US has driven a sharp rebound in ocean freight demand that had slumped while US tariffs on China were at 145%. In the last two weeks, many shippers were already starting to pull peak season orders forward to move goods before the deescalation’s August expiration date.

Hapag-Lloyd estimates that China-US container demand dropped by 20% from early April to mid-May. By last week, volumes had already rebounded by 50% from April/May lows, pushing container levels to low double digit percentage gains compared to before the April tariff rollout – even before the court’s ruling.

The combination of April’s canceled or paused shipments and a build up of goods manufactured during that stretch is contributing to the speed at which container demand has picked up, though estimates of ready-to-load containers in China range widely from 180k to as much as 800k TEU. And Freightos Baltic Index transpacific benchmark saw container rates increase by about 25% since the May 12th tariff reduction.

This week’s ruling may therefore intensify but not change current trends in the container market too drastically. If the IEEPA tariffs indeed remain suspended during the appeals process shippers may still prefer to frontload now when these tariffs are removed, instead of waiting until more typical start of peak season territory of July or August by which time those tariffs could be restored on appeal or through the use of other trade acts. Likewise, the decision could increase the strength of the pull forward and recent jump in container demand as some shippers deterred by 30% tariffs start frontloading as well.

Air Cargo

For air cargo, the ruling likely will remove the US’s suspension of de minimis eligibility for Chinese goods. The suspension, which has been in place since May 2nd, has led to a big drop in B2C e-commerce volumes moving from China to the US via air cargo.

We’re likely to see some rebound in these volumes and in transpacific freighter capacity if the ruling restores de minimis eligibility for Chinese goods, and the tariff reduction may also spur some increase in demand and rates in the spot market from general cargo as well.

But as there was bi-partisan support for reducing or closing the de minimis avenue to Chinese imports even before Trump took office – the USTR under the Biden administration announced proposed rule changes to de minimis at the very end of Biden’s term – this exemption is likely to be closed to China at some point by other means, and possibly soon.

And as platforms like Temu and Shein have already started to shift away from air cargo by increasing their ocean logistics and domestic fulfillment capabilities for the US market, we may not see a full reversal of the drop in air cargo volumes in the interim.

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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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.

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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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