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How AI Can Help Tame Warehouse Complexity

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How Ai Can Help Tame Warehouse Complexity

Growing Complexity

The complexity of running the warehouse only continues to increase. Supply chain leaders face macro-challenges such as the pressure for sustainability, labor shortages and the effects of inflation on operating margins.

Layer on the daily micro disruptions that every warehouse experiences regardless of size – unexpected or delayed deliveries, inventory shortages, quality issues and scheduled labor or equipment mismatches versus real-time needs—and suddenly running the warehouse becomes unmanageable.

Backwards Solutions

Logisticians and warehouse operators have worked to rise to the challenge; however, there is only so much complexity that a warehouse team can effectively manage using many of today’s siloed, rules- and history-based warehouse management solutions. Too often, the challenge is met by throwing added resources at problems to close the gap. And unfortunately, those resources may not actually add much value.

For example, slotting and picking usually consume more than half of warehouse labor costs. Studies estimate that a typical warehouse picker spends over 50% of their shift just in travel time winding their way across the warehouse to or from a product pick. Warehouses also struggle with being over or understaffed and rarely strike the balance of what is “just right” for the day’s staffing needs.

This is because the data that the warehouse is working on includes historical receipts, demand, picks and shipments. When it does look into the future, it too often uses a forecast or plan that can be weeks, if not months, out of date.

AI Helps Close the Complexity Gap

Acknowledging that warehouse complexity is not going away, what can be done?

We envision AI and Machine Learning as the path to close the complexity gap and move the warehouse to the next level of efficiency and effectiveness.

AI and Machine Learning can take signals from historical data, blend them with real-time updates, future forecasts, predictive learning models and even include signals from other connected solutions. Together, these signals provide the warehouse team with recommendations that can optimize activity across the warehouse for today and into the future.

AI and Machine Learning can be the partner that the warehouse team has been looking for to simplify their complexity. We can already see ways that this can transform warehouse operations.

Agentic AI

Every warehouse team has that one person who understands the warehouse and brings an unrivaled depth of experience that helps the warehouse run.

Now, take that knowledge and expertise and imagine it on an interactive device and in your team’s hands 24/7/365.

Agentic AI can incorporate all those signals and provide recommendations in real-time to your warehouse team on the most effective use of their time. What trailer needs to be worked next and why? What is the best use of your slotting team’s time to reduce pick travel time and improve efficiency?

All while providing your entire team with clear, understandable explanations of why the recommendation was made and how it can help your team and operations.

AI in the Yard

AI and Machine Learning can help to reduce complexity in the warehouse yard.

Today, trailers are often prioritized for unloading mainly to support facility service levels and to avoid detention charges.

With AI and Machine Learning, Warehouse Management can utilize real-time signals to “see” the contents of each trailer, the status of pending orders against those contents and their priority against every other trailer in the yard.

AI and Machine Learning can provide real-time recommendations based on available scheduled resources, select the highest priority trailers to be worked next and anticipate the best dock door to most efficiently slot the product or cross dock for immediate shipment.

All to reduce complexity in the yard and the receiving dock.

AI Within the Facility

Slotting and picking are among the most labor-intensive activities within the facility. Most slotting decisions are made literally in the moment based on available slots. The results are products distributed across the warehouse based on thousands of disconnected slotting decisions.

With the introduction of AI and Machine Learning to the process, warehouse management can analyze each individual slotting decision, including signals such as history, volume estimates, pick affinity, optimal task and travel time, as well as available locations. Then, balance them against configured guardrails such as efficiency and movement costs to determine slotting locations that continuously optimize the warehouse for tomorrow’s needs, instead of just today’s availability.

The result is reduced complexity, which decreases pick travel time and increases pick efficiency since the slotting decisions have been continuously optimized over time to place products for faster picking.

AI and Resource Management

Accurately forecasting and then managing warehouse resources in real-time is a significant part of a warehouse leader’s work.

In today’s warehouse, resource forecasts are typically based on a combination of historical data and demand forecasts that usually do not accurately reflect what is needed in real-time. So, resource forecasting too often results in the warehouse being chronically over or understaffed.

When forecasting resources with AI and ML, solutions can incorporate historical data but also the latest real-time forecasted data to more accurately project resource needs days or months in advance, improving staffing and scheduling at a much more granular level.

But even the most intelligent resource forecast needs to be adjusted when today’s emergency intrudes. With AI-powered visibility, the warehouse management solution can ingest all these various signals, see the priority of workflows across the entire building and then shift available resources in real time from lower-priority work to mitigate today’s emergency.

The warehouse management solution is continuously updated and can re-prioritize tasks in real time, ensuring that all work across the facility stays on track and reducing the complex firefighting work that warehouses complete daily.

All these recommendations can be reviewed and approved by the warehouse team based on clear and understandable explanations from the solution’s AI.

Conclusion

All of this sounds like science fiction. But it is all very real.

Software providers are seeing more logistics and supply chain clients that need and inquire about AI and Machine Learning capabilities as part of their warehouse management or other solution sets.

To address the growing complexity and macro- and micro-disruptions, the warehouse team needs a trusted partner that can learn, improve and understand their facility and its capabilities.

A trusted partner that can help to reduce complexity and keep the facility working efficiently, not just today but every day.

AI and Machine Learning is ready to be that partner.

Steve Ross is part of Blue Yonder’s Solution and Industry Marketing team focusing on Supply Chain Execution.

With almost 30 years of operations, logistics, and e-commerce experience. Steve is an advocate for how technology can help free up the warehouse and store teams from the obstacles that legacy processes, solutions, and thinking have put in their path.

Steve believes in that Blue Yonder’s industry-leading supply chain platform and cognitive capabilities can be the partner the warehouse team needs to simplify their workload and improve efficiency.

Before joining Blue Yonder, Steve held multiple leadership roles, serving as the “translator” between the Logistics, Operations, Planning, and Buying teams. Steve has led various omnichannel implementations and has a background in Lean Six Sigma and project management.

Steve has an MBA from Roosevelt University, and a BA from the University of Arizona.

In his spare time, you will often find him in his kitchen baking sourdough bread or something sweet.

The post How AI Can Help Tame Warehouse Complexity appeared first on Logistics Viewpoints.

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The Digital Backbone of the Warehouse: Trends Shaping the 2026 WMS Market

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The Digital Backbone Of The Warehouse: Trends Shaping The 2026 Wms Market

The Warehouse Management Systems (WMS) market continues to grow, driven by e-commerce growth, increasing fulfillment complexity, faster delivery expectations, and the need for real-time operational visibility. Organizations are investing in WMS to improve inventory accuracy, throughput, and responsiveness to customer demand. Suppliers are driving WMS progress by implementing capabilities that allow customers to see their warehouse operations digitally, respond to disruptions more quickly, and address labor shortages before they arise.

WMS is shifting from a transactional system of record to a coordination layer across warehouse execution, orchestrating workflows across people, automation, and digital systems. This reflects broader changes in supply chain execution, where integration with robotics, AI, and adjacent systems is now a baseline expectation. ARC research reinforces this view: WMS providers are increasingly expected to manage both manual and automated processes holistically, rather than operate in isolation from material handling systems or automation layers.

Key Trends Redefining the WMS Landscape

Automation as a Core Requirement: Warehouse automation is no longer an add-on; it is a central requirement shaping WMS development. Systems must integrate with robotics, autonomous mobile robots (AMRs), and material handling equipment while balancing human and machine workflows. Learning from past decisions, recommending new ones, and looking into the future to identify anticipated disruptions before they occur.
AI-Driven Execution and Decision Support: AI is increasingly embedded into WMS platforms to support predictive analytics, dynamic slotting, and operational decision-making. In many cases, this includes agent-based tools that help diagnose issues and simulate potential outcomes. Chatbots and agents allow warehouse operators to access information and data faster, reducing the time spent making decisions. Increasingly, companies are releasing solutions on a low-code platform that can be easily customized to an organization’s specific needs.
Convergence Across Supply Chain Execution, WMS is increasingly part of a broader execution ecosystem that includes transportation, yard, labor, and order management. Vendors are positioning their solutions as part of integrated platforms rather than standalone applications. AI is playing a role in the de-siloing of systems. When systems are unified and data is accessible, AI can perform traditional processes, such as stock-out scenarios, which require the ability to see into multiple systems, such as inventory, shipping, and warehousing, much faster than a supply chain planner.

The Challenge: Evaluating a Blurred Market

As these trends converge, the WMS market is becoming more difficult to define and evaluate:

Functional overlap between WMS, WES, robotics platforms, and planning systems
Increasing variation in how vendors describe similar capabilities
Expansion of WMS into adjacent execution domains

This creates a disconnect between traditional market analysis and how buyers actually evaluate solutions. From ARC’s perspective, many of the legacy ways of analyzing the market, such as segmentation by tier or deployment type, do not fully explain how solutions differ in real-world performance or how they are evolving. In response, ARC is shifting its research methodology to better reflect how buyers evaluate technology today. Rather than focusing primarily on market size, segmentation, and historical growth, the approach is placing greater emphasis on:

Functional capabilities (e.g., receiving, picking, optimization, labor management)
Technical architecture (modularity, scalability, cloud readiness, interoperability)
Integration with automation and execution systems
AI capabilities and data utilization
Execution quality and measurable performance impact

This approach aligns with ARC’s internal research scope for WMS, which includes both core execution processes (receiving, put-away, picking, shipping) and add-on modules such as labor management, analytics, and optimization. The shift reflects a broader goal: moving beyond describing the market to understanding solution performance and differentiation at a deeper level.

The Role of the ARC Market Map

To support this shift, ARC has introduced the Market Map as a core analytical framework. The Market Map provides a structured, visual representation of supplier positioning in the WMS market, enabling more consistent and transparent evaluation across vendors.

Evaluation Framework

Suppliers are assessed across two primary dimensions:

Solution Capabilities (Execution Today)
Includes:

Functional capabilities across warehouse processes
Technical architecture (cloud, scalability, interoperability)
Integration with automation and adjacent systems
Execution quality and support services

Strategic Vision (Future Positioning)
Includes:

Product roadmap and innovation strategy
Corporate direction and ecosystem alignment
Customer base and growth trajectory

These dimensions are equally weighted and supported by a structured scoring model that incorporates multiple sub-criteria across both capability and strategy dimensions. The Market Map reflects ARC’s view that the WMS market is no longer defined solely by functionality; it is defined by how well solutions integrate across the warehouse ecosystem. WMS solutions are being compared on their ability to support automation and AI-driven execution, and how well the vendors are prepared for future supply chain demands. As markets grow and technology progresses, we also need to develop new ways to analyze and understand market dynamics. By combining both current capabilities and long-term strategy, the framework provides a more complete view of vendor positioning than traditional market rankings.

Vendor Outreach

ARC has been conducting market research for over 30 years, and we, too, have changed and adapted with the times and technology. From pen and paper to an online market analysis platform that allows for dynamic visualizations. We have adapted and progressed alongside the clients we serve, which is why we are looking forward to delivering our first batch of Market Maps this summer.

We are currently speaking with Vendors in the Warehouse Management System market. Learning about each solution’s differentiators, functional capabilities, and much more. If you’d like to be added to our vendor list and included in our WMS Market Map research, please reach out to (gsimon@arcweb.com).

Manhattan Associates
Blue Yonder
Oracle
SAP

Körber (HighJump / Infios)
Infor
Microsoft (Dynamics 365)
NetSuite

Epicor
Acumatica
Tecsys
Made4net

Mecalux
Generix Group
Deposco
Logiwa

ShipHero
3PL Central (Extensiv)
Infoplus
Cadre Technologies

The post The Digital Backbone of the Warehouse: Trends Shaping the 2026 WMS Market appeared first on Logistics Viewpoints.

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Help Shape the Supply Chain Decision Intelligence Market Map

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Help Shape The Supply Chain Decision Intelligence Market Map

As AI, visibility, planning, risk, and orchestration platforms converge, Logistics Viewpoints is developing an analyst-defined Market Map to clarify where decision-making value is emerging — and supplier participation is now welcome.

Supply chain technology markets are becoming harder to evaluate. Established software categories still matter, but they no longer explain where much of the new differentiation is emerging. Planning systems are adding orchestration. Visibility platforms are moving into exception management and recommendation engines. Risk platforms are becoming operating signal layers. Enterprise application vendors are embedding AI across broader suites. Specialized providers are using external data, event intelligence, and analytics to help companies respond faster to disruption.

For buyers, the result is a more complicated evaluation environment. For suppliers, the challenge is positioning. Many companies now use similar language — AI, orchestration, control tower, resilience, visibility, automation, intelligence — while solving different problems at different layers of the operating model.

That is why Logistics Viewpoints is developing the Supply Chain Decision Intelligence Market Map, an analyst-defined view of one of the most important emerging layers in supply chain technology.

Supplier participation is now welcome. If your company is listed below, or if your company is active in supply chain decision intelligence, AI-enabled decision support, orchestration, event intelligence, risk, resilience, control towers, visibility, planning intelligence, or related areas, this is the time to engage. Participation helps ensure that your capabilities are understood accurately before the Market Map is finalized.

The Market Map is designed to clarify the layer above and across core supply chain systems where data is interpreted, signals are connected, tradeoffs are evaluated, and better operating decisions are made. This is not intended to be another logo landscape. The purpose is to define the market, establish boundaries, organize the provider landscape, and create a more disciplined basis for buyer and supplier conversations.

Why Decision Intelligence Matters

For decades, supply chain technology was organized around familiar application categories: ERP, WMS, TMS, planning, procurement, order management, visibility, and execution platforms. Those systems remain essential. But they do not fully explain where value is moving.

The most important shift is the emergence of an intelligence layer that helps companies understand what is changing, why it matters, what options are available, and what action should be taken. That is the practical meaning of Supply Chain Decision Intelligence.

The category includes technologies that materially improve how supply chain decisions are made across planning, execution, coordination, disruption response, risk management, logistics, sourcing, fulfillment, and multi-enterprise operations. It is broader than a single application category, but it is not a catch-all for every vendor using AI language.

The governing test is straightforward: does the technology improve decision quality in a meaningful supply chain operating context?

A dashboard is not decision intelligence. A transactional execution system is not decision intelligence simply because it stores operational data. A generic AI platform is not automatically part of the category unless it is materially tied to supply chain decision-making. The Market Map is intended to hold that boundary.

Providers Currently Under Review

The Supply Chain Decision Intelligence Market Map is being developed around a curated set of providers whose capabilities appear to intersect with this emerging intelligence layer. Providers currently under review include:

Altana
Blue Yonder
Coupa
e2open
Everstream
FourKites
Interos
Kinaxis
Manhattan
o9
Oracle
Overhaul
project44
SAP

These companies do not all compete in the same way. That is precisely why the market needs structure.

Some are associated with planning, scenario analysis, and decision optimization. Some are stronger in logistics visibility, event data, transportation intelligence, or control tower capabilities. Some focus on supplier risk, trade intelligence, resilience, or multi-enterprise network coordination. Some are broad enterprise application providers extending intelligence across large installed bases. Others are more specialized providers focused on risk signals, shipment intelligence, orchestration, or external operating context.

The analytical value of the Market Map comes from making those differences visible. A buyer evaluating supply chain decision intelligence should not treat all of these providers as interchangeable. Nor should suppliers be forced into legacy categories that obscure their actual role in decision support.

Why Suppliers Should Participate

Supplier participation matters because this market is still being defined.

Many providers have capabilities that cross legacy category lines. A company may be known for visibility but now offer decision automation. A planning vendor may increasingly support cross-functional orchestration. A risk platform may function as an operating intelligence layer. A network provider may support decision-making across parties, geographies, and systems.

If those distinctions are not understood clearly, suppliers risk being positioned too narrowly, grouped with adjacent providers that solve different problems, or evaluated only through outdated category labels.

Participation gives suppliers an opportunity to clarify:

How their platform improves supply chain decision-making
Where their capabilities sit relative to planning, execution, visibility, risk, and orchestration
What data, AI, analytics, workflow, or network capabilities support decision quality
Which use cases best demonstrate enterprise value
How their solution differs from adjacent providers that may sound similar in the market

This is especially important in a category where language has become crowded. “AI,” “control tower,” “visibility,” “orchestration,” “resilience,” and “decision intelligence” can mean very different things depending on the provider. The Market Map process is intended to separate substance from terminology.

For suppliers, the benefit is not promotional placement. It is accurate market understanding. A well-informed Market Map helps buyers better understand the provider landscape — and helps suppliers avoid being misread by the market.

Inclusion and Exclusion Logic

The Market Map will focus on technologies that contribute directly to better supply chain decisions.

Relevant capabilities include decision-support layers, orchestration and coordination tools, AI and advanced analytics tied to operating decisions, control towers with real decision depth, context and event intelligence, scenario modeling, cross-functional intelligence environments, and selected enabling infrastructure where the connection to decision quality is explicit.

This includes technologies that help enterprises interpret signals from internal systems and external operating environments. Shipment delays, supplier risk, demand shifts, geopolitical events, inventory constraints, transportation disruption, port congestion, regulatory exposure, and weather events become more useful when they are connected to decisions.

Clear exclusions are equally important. Core systems of record are not included simply because they are important. ERP, WMS, TMS, planning, procurement, and asset management systems belong in the discussion only when they demonstrate a meaningful intelligence layer above the transactional core.

Pure execution tools without decision depth also remain outside the center of the category. The same applies to horizontal BI tools, generic enterprise AI platforms, and narrow point solutions with limited strategic relevance.

These technologies may be useful. Some may even enable decision intelligence. But enablement is not the same as category membership. The objective is not to reward every AI message in the market. The objective is to identify where real decision-making value is emerging.

Why This Is Commercially Important

Decision intelligence is becoming one of the more important ways to understand the next stage of supply chain technology. The market is not moving simply toward more software. It is moving toward more interpretation, more coordination, more contextual awareness, and more decision support across fragmented operating environments.

That shift has implications for both buyers and suppliers. Buyers need a better way to compare providers whose capabilities cut across traditional categories. Suppliers need a more disciplined way to explain where they fit and why they matter. Analysts need a framework that can separate category substance from marketing language.

The Supply Chain Decision Intelligence Market Map is designed to provide that structure.

It will not answer every selection question. No market map can. But it can help buyers ask better questions, compare providers more intelligently, and understand which capabilities are truly central to decision improvement. It can also help suppliers understand how their market position may be perceived within a broader, analyst-defined framework.

Participation Is Welcome

Logistics Viewpoints welcomes supplier participation in the Supply Chain Decision Intelligence Market Map process.

If your company is listed above, participation can help ensure that Logistics Viewpoints has the most accurate understanding of your capabilities, positioning, and role in the market. If your company is not listed but is active in supply chain decision intelligence, AI-enabled supply chain decision support, orchestration, event intelligence, resilience, control tower capabilities, planning intelligence, visibility, supplier risk, trade intelligence, or related areas, we welcome the opportunity to understand where you fit.

Participation does not mean guaranteed positioning, endorsement, or favorable treatment. The value of the Market Map depends on analytical discipline. But supplier input can materially improve the quality of the research, sharpen category boundaries, and ensure that relevant capabilities are understood before the map is finalized.

For suppliers active in this market, non-participation carries a practical risk: your company may still be evaluated based on available information, but without the benefit of your most current explanation of strategy, capability depth, roadmap direction, and customer value proposition.

Next Step

Logistics Viewpoints is developing the Supply Chain Decision Intelligence Market Map as part of a broader Market Maps portfolio for supply chain technology buyers and providers.

To request the Executive Summary, discuss the Supplier Selection Guide, or explore participation in a Supplier Spotlight, contact Logistics Viewpoints.

If you are one of the suppliers listed above, or if your company is active in this market, we welcome your participation in the process.

The post Help Shape the Supply Chain Decision Intelligence Market Map appeared first on Logistics Viewpoints.

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Hormuz tension keeps pressure on rates; Section 122 invalidated – May 12, 2026 Update

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Hormuz tension keeps pressure on rates; Section 122 invalidated – May 12, 2026 Update

Published: May 12, 2026

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Weekly highlights

Ocean rates – Freightos Baltic Index

Asia-US West Coast prices (FBX01 Weekly) increased 4%.

Asia-US East Coast prices (FBX03 Weekly) increased 1%.

Asia-N. Europe prices (FBX11 Weekly) increased 10%.

Asia-Mediterranean prices(FBX13 Weekly) decreased 5%.

Air rates – Freightos Air Index

China – N. America weekly prices stayed level.

China – N. Europe weekly prices decreased 3%.

N. Europe – N. America weekly prices decreased 3%.

Analysis

The US paused its Operation Freedom, designed to support vessel transits out of the Strait of Hormuz – and which sparked renewed US-Iran exchanges of fire as well as Iranian missile attacks on Gulf states last week – less than two days after its launch.

Even amid sporadic military engagement, US-Iran negotiations continue, though the sides remain far apart, with President Trump stating that he may restart the operation if negotiations stall. In the meantime, Iran announced the creation of a Persian Gulf Strait Authority through which vessels are required to request permission – and possibly pay – to pass through the strait.

Maersk CEO Vincent Clerc estimates that elevated fuel prices due to the closure has the carrier facing $500M per month in additional costs. He also reports that Maersk has so far been able to pass those costs on to customers via higher freight rates.

Freightos Baltic Index container price behavior has varied by lane, however, with transpacific rates up about $1,000/FEU compared to before the war, while Asia – Europe prices that climbed by a few hundred dollars per FEU in March have mostly slipped back to pre-war levels. Asia – N. Europe rates climbed by 10% last week to $2,850/FEU, but prices so far this week are trending down, similar to rate behavior to the Mediterranean earlier this month.

Carriers are planning additional, likely modest, increases for mid-month. In preparation, they are stepping up blanked sailings – with reports of east-west service space getting tight and some containers being rolled – to support higher spot rates during what is still a low demand stretch, and hoping peak season demand picks up to support prices later in the year.

The latest National Retail Federation US ocean import volume report projects June arrivals to be 2% lower than May, with volumes increasing 4% month on month in July before easing slightly in August and further in September. If these estimates materialize, transpacific peak season will be a muted one relative to recent years, with the July peak 8% lower than last year’s tariff driven burst, but also 6% lower than the August peak in 2024.

The NRF suggests that this relative weakness reflects importer caution due to current economic uncertainty. Maersk’s Clerc also suggests that a coming downturn in ocean demand due to higher consumer prices is possible and could make this year’s H2 challenging and possibly loss-making for carriers still facing elevated fuel costs.

Elevated jet fuel prices are contributing to global air cargo rates that are 30% higher than before the war and year on year. Higher costs are pushing some volumes away from the skies when feasible, including some Asia – Europe shippers opting for ocean-air services via West Coast US ports.

Overall though, the market is stabilizing as air space closures decrease and capacity from Gulf carriers continues to recover. Jet fuel prices have also leveled out after coming down from April highs as the market has shifted sourcing for jet fuel – and energy exports more generally – to the extent possible to account for the Persian Gulf export drop, and as demand for fuel has also eased as carriers scrap unprofitable flights.

Freightos Air Index rates decreased slightly or were level on most major lanes last week. Prices out of China were stable at $5.47/kg to N. America and dipped 3% to $5.16/kg to Europe. While China – US rates are now back to pre-war levels, prices to Europe remain 50% higher, but down 15% from their peak in April. S. Asia – Europe rates were stable at $4.66/kg last week – a level 80% higher than in February – but down 10% from a month ago. SEA – Europe prices meanwhile were up double digits last week to a new high of $5.74/kg.

In trade war news, President Trump and China’s Xi Jinping are set to meet in Beijing later this week for a summit aimed at stabilizing the US-China trade relationship – whose status quo will expire in November – but complicated by the Iran war.

US tariffs on China are lower at the moment than before the US Supreme Court invalidated Trump’s IEEPA-based tariffs in February. The White House replaced IEEPA duties with a 10% global tariff based on Section 122 that is set to expire in late July, with the administration working to replace the 122 duty with Section 301-based IEEPA-like tariffs by then.

Last week though, the US Court of International Trade ruled that the president’s use of Section 122 was invalid. The ruling and the court-required refunds were limited to the specific plaintiffs in the case, but open the door for other businesses to sue as well. The White House has appealed the ruling and asked that the tariffs stay in place during the appeals process or until they expire, but these developments do set the stage for another possible widespread tariff refund.

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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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