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From Data to Decisions: Revolutionizing Supply Chain Management with Demand Sensing and Forecasting

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From Data To Decisions: Revolutionizing Supply Chain Management With Demand Sensing And Forecasting

For demand sensing and forecasting in the supply chain, the ability to quickly ingest and analyze data, and subsequently make strong business decisions is crucial. While this is true across all aspects of supply chain management, it is especially important when tracking actual demand versus projected demand. This crucial need can be slowed down or impeded by issues such as a lack of end-to-end supply chain visibility, antiquated data management processes, or even inaccurate data.

Significant disruptions along the supply chain from external factors such as geopolitical events, supplier capacity issues, poor network inventory visibility, and constant changes in buyer behavior, make synchronizing demand and supply very difficult. This is further complicated by inaccurate data from dozens of disparate applications and enterprise systems within the organization, its partners, and its suppliers. Traditional forecasting methods struggle to keep up with rapid changes in global supply chains, often failing to predict demand accurately during volatile periods.

Companies have traditionally relied on historical data and internal systems for demand forecasting, but this approach is limited in its ability to respond to sudden market shifts. The ability to sense demand disruptions in real time and improve forecasting in this environment is difficult to achieve, especially if you want a high degree of customer satisfaction, and it also highlights the responsiveness needed to adapt quickly to unexpected changes. Companies that leverage demand sensing can emerge stronger and better positioned after disruptions.

An Introduction to Demand Sensing and Forecasting

Demand sensing and demand forecasting are both crucial aspects of optimizing supply chains, but they do have slightly different functions in their approach and focus. Demand sensing uses real-time data and analytics to identify and respond to immediate demand fluctuations, while demand forecasting uses historical data to predict future demand over a longer period (months or years). Different methods, such as statistical modeling and machine learning, are used to enhance the accuracy and adaptability of these processes. Both areas are crucial for companies when it comes to projecting sales, managing inventory, and coordinating replenishment. In the end, the goal is to accurately predict customer demand by using predictive models to forecast future demand.

From a metrics standpoint, companies need to accurately measure forecast versus actual sales, inventory turnover, stockout rates, inventory obsolescence, order fill rates, and on-time in-full percentage. When forecasting, it is important to predict demand for a particular product to avoid excess inventory and stockouts. Advanced analytics and AI tools provide granular insights into sales activities, inventory levels, and financial metrics, supporting more precise decision-making.

Recognizing the growing complexity of these demands, InterSystems surveyed 450 senior supply chain practitioners and stakeholders to examine key supply chain technology challenges, trends, and decision-making strategies across five key use cases: fulfillment optimization; demand sensing and forecasting; supply chain orchestration; production planning optimization; and environmental, social, and governance (ESG). This blog is Part 2 in our Optimizing Supply Chain Performance with Unified Data series, with a focus on demand sensing and forecasting.In the unified data survey, respondents were asked how they currently integrate and prepare disparate information for decision-making. Not surprisingly, 42% of respondents use manual methods, including spreadsheets, to integrate and prepare disparate information for decision-making. While spreadsheets can be incredibly useful and are clearly used by a lot of companies for planning purposes, they also have limitations.

As the picture above shows, spreadsheets are not a useful tool when it comes to decision intelligence. Decision intelligence is focused on improving decision-making by understanding how decisions are made and using AI and machine learning to optimize outcomes. In supply chain, an AI-enabled decision intelligence platform can optimally manage disruptions when and before they occur so companies can react faster and ensure that products are available when companies need them, while also monitoring engagement to improve sales outcomes.

Current State of Demand Sensing and Forecasting

One of the biggest issues with demand sensing and forecasting is that human intervention is often required. This is because AI often lacks the nuances to fully understand the complexity of demand patterns. So, while human intervention is required to bridge that gap, it can be both time-consuming and error-prone, especially if the data a company is relying on is bad. According to the survey results, when asked how they currently forecast demand, 36% of respondents indicated that they have several solutions that require staff input. Aside from the aforementioned issues with human input, the use of multiple systems often leads to disjointed, disparate data silos. When different systems are unable to communicate, decisions take longer to make and are usually not as accurate, leading to errors in demand sensing and forecasting. To maintain data accuracy and relevance, it is crucial that data is updated and transferred regularly.

The harsh reality is that the use of intelligent data platforms is not widespread. The survey revealed that only 27% of respondents have an intelligent data platform. This is most notable in logistics and transport (18%) and pharmaceuticals (19%) where less than one-fifth of companies are currently using an intelligent data platform. For these platforms to be effective, it is essential that all data is validated before being used in forecasting models to ensure consistency and accuracy.

Demand Sensing and Forecasting Challenges with External Demand Signals

According to the survey, the top demand sensing and forecasting challenges are related to issues with data: its collection, visibility, and analysis. It’s no surprise that all of these issues are directly tied to data inconsistencies. Clean data is essential to ensure accuracy and consistency, especially when integrating external datasets.

When asked to identify their top challenges in demand sensing and forecasting, respondents cited the following: no real-time visibility along the supply chain (41%), current processes are too manual (39%), inaccuracies in data within the organization, partners, and suppliers (37%), and no real-time sensing of demand and supply changes (34%). Understanding demand and supply shifts, and reacting accordingly, is at the heart of demand sensing and forecasting. From the demand side, shifts are the result of changing consumer preferences, brand loyalty, or economic factors. From the supply side, these market shifts are tied to raw material pricing or availability, labor shortages, or new entrants to the market. For those companies that cannot sense shifts in real-time, their forecasting accuracy suffers, thus leading to lost sales and higher cost of goods sold.

Supply chain visibility has been a hot topic over the last few years, but most people think of it only from a shipment standpoint. Point-to-point tracking solutions have seen billions of dollars in venture capital investments, but supply chain visibility goes well beyond these solutions. Supply chain visibility enables companies to track the location and status of products, components, and materials as they move through the supply chain. However, it also encompasses the entire end-to-end supply chain, from the sourcing of raw materials to the final delivery to the end consumer. At the core of supply chain visibility is access to real-time data for inventory optimization, tracking, and potential disruptions. To respond effectively to demand changes, companies must be able to adjust inventory levels quickly in response to market volatility and shifting consumer demand.

The second challenge identified by respondents is reliance on manual processes. More and more often, we hear about the autonomous supply chain. Automated demand sensing processes leverage real-time data and advanced analytics to predict short-term demand fluctuations, while manual methods rely on human interpretation of data, which can be time-consuming and prone to errors.

A third challenge highlighted by respondents is inaccuracies in data from within the organization, partners, and suppliers. As far back as 1957, computer scientists have referred to this as “garbage in, garbage out.” In a syndicated newspaper article about US Army mathematicians and their work with early computers, Army Specialist, William D. Mellin explained that computers cannot think for themselves, and that “sloppily programmed” inputs inevitably lead to incorrect outputs. A lot has changed since then, but the underlying principle is the same. Inaccurate data will lead to errors in demand sensing and forecasting, which will impact inventory management, supply chain operations, and profitability.

Demand Sensing and Forecasting Capabilities to Improve Forecast Accuracy

According to the survey, the capabilities respondents believe would most improve their ability to accurately forecast demand correlate with their biggest challenges. The top capability survey respondents said would improve their ability to forecast demand is the ability to ingest and analyze real-time data from many sources in disparate formats (27%). InterSystems Supply Chain Orchestrator is a data platform that ingests all relevant data from the sources that matter, both internally and externally, including geopolitical events, information on supply chain product integrity issues, supplier fulfillment discrepancies, and much more. Harmonizing and normalizing all this information to provide accurate data in real time, the platform simulates your business processes and then applies embedded AI and ML capabilities. With no “rip-and-replace” needed, companies gain accelerated implementation of powerful new capabilities, while lowering total cost of ownership in a way unmatched in the industry today.

The second capability identified by respondents is integrated inventory management with enterprise resource planning (ERP) and electronic point of sale (EPOS) to automate demand-sensing and forecasting (24%). Supply Chain Orchestrator enables organizations to adjust forecast plans with high levels of accuracy to successfully navigate sudden events, disruptions, or trends that affect demand, transforming fulfillment optimization. By leveraging demand sensing, organizations can increase output by adjusting production schedules in response to predicted demand, ensuring they meet customer needs effectively. Organizations can integrate more advanced sensing and forecasting capabilities with their point-of-sale, ERP systems, or applications, achieving faster time-to-value.

Final Thought on Demand Sensing and Forecasting

To be agile and competitive, organizations must be capable of extracting critical insights in near real-time. This remains a significant challenge when so many businesses lack end-to-end visibility or rely on manual data analysis and ad hoc provisioning and integration of different solutions. For demand sensing and forecasting, a reliance on manual data analysis, especially given the current state of disparate data streams, can be catastrophic. If companies are unable to understand the reasons behind supply shifts, they will be unable to adjust their demand forecasting accurately, which will lead to improper inventory availability, lost sales, and higher cost of goods sold.

Demand sensing and forecasting efficiency requires unified, trusted, and harmonized data. As an intelligent supply chain decision intelligence platform, InterSystems Supply Chain Orchestrator provides a complete view of an organization’s supply chain, harmonizing and normalizing disparate data from applications, suppliers, manufacturers, distributors, retailers, and consumers. It uses AI and ML to uncover what is currently happening, predicts what is likely to happen next, and uses prescriptive insights to outline the best options, ensuring maximum effectiveness and minimum delay.

Read the full report here.

Chris Cunnane is the Supply Chain Product Marketing Manager at InterSystems. In this role, he is responsible for developing and executing marketing strategy and content for the InterSystems supply chain technology suite. Chris has 20+ years of supply chain expertise, leading the supply chain practice at ARC Advisory Group, as well as holding various sales, marketing, and operations roles in the wholesale, retail, and automotive parts markets. He holds a BA in Communications from Stonehill College and an MA in Global Marketing Communications from Emerson College.

The post From Data to Decisions: Revolutionizing Supply Chain Management with Demand Sensing and Forecasting 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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