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Container Shipping Overcapacity & Rate Outlook 2026

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Container Shipping Overcapacity & Rate Outlook 2026

Published: January 27, 2026

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Container freight is poised for a downcycle – putting downward pressure on rates and carrier revenue – starting in 2026 as an unprecedented wave of new vessel capacity enters the market. But despite signs of overcapacity in 2025, carriers continue ordering new vessels and holding onto older ships.

In a recent Freightos market update webinar, Parash Jain, Managing Director, Global Head of Transport & Logistics Research at HSBC shared his analysis of this state of affairs: This seemingly counterintuitive strategy reflects carrier lessons learned from recent disruptions and longer-term strategic positioning, at the cost of rate and revenue challenges for carriers in the coming years.

Key Takeaways

There’s a reason vessels aren’t being retired. Despite overcapacity concerns, carriers are maintaining older vessels as insurance against unpredictable disruptions, the “known unknowns” of global shipping – like COVID and the Red Sea crisis –- for which available capacity has helped carriers keep containers moving and maximize volumes and revenue.

Pandemic-era profits have both allowed carriers to pay down vessel debt – reducing pressure to scrap ships – and enabled them to prepare for the future now via newbuilds

Individual carriers have to make vessel purchase decisions based on their own needs and strategies, not on the aggregate capacity level in the market – likewise contributing to vessel order growth despite industry overcapacity

Expect a cyclical pattern of sharp rate dips followed by periods of recovery through capacity management in the near term, though overall rate levels will likely trend lower than 2025 through the downcycle. In the long-term, the larger fleets will make the market more resilient (should carriers choose to activate them when the going gets tough).

An oversupplied market: Trends in overcapacity

One of the biggest factors likely to impact container rates in 2026 is the growing global fleet.

Since 2021, carriers have been plowing their record profits earned from record revenues during the pandemic years into a record number of orders for new vessels – some of which started being delivered in 2023. According to S+P, an estimated ten million TEU of container ship capacity – the size of a third of the current active fleet – is now on order and will be delivered over the next few years.

Source: S+P in JOC.com

As demand eased post-pandemic and new vessels started being delivered, Freightos Baltic Index spot rates fell sharply with transpacific pricing to the West Coast (FBX01) dipping below $1,000/FEU in March of 2023. When the Red Sea crisis began however, the longer sailing times for Asia – Europe voyages and the extra vessels deployed to maintain departure schedules on these lanes absorbed that excess capacity, pushing freight rates up to their highest levels since COVID.

But new vessels continued to enter the market in 2024 and 2025. And even with Red Sea diversions continuing throughout 2025, the growing supply pushed East – West long haul rates down by 45% year on year, with transpacific rates slipping to $1,400/FEU in October 2025.

Check out our Container Bytes podcast for a bitsize weekly freight update

Driving a Downcycle

The current orderbook size means the fleet will continue to grow significantly over the coming few years, such that even with demand growth, most observers project a container market downcycle: capacity is expected to outpace volumes putting persistent downward pressure on freight rates, reducing carrier revenues and even spurring losses.

Carriers maintain that they will pull all the capacity management levers – blanked sailings, idled vessels, service suspensions, slow steaming and scrapping – to balance supply with demand and minimize or avoid periods of losses. But despite the current signs of overcapacity, the current idle fleet is minimal and very few older ships have been scrapped. What’s more, carriers continue to order more vessels to join the already overstocked fleet.

Why no scrapping? The “Known Unknown”

Lessons learned and profits earned in the last few years may be motivating carriers to hold on to older ships even at the risk of oversupply.

More Capacity for Better Resilience

Though it may not have seemed that way as delays mounted and freight rates spiked, the slack capacity available during the pandemic did help carriers keep containers moving. Post-COVID, as noted above, overcapacity was one factor to loss making rates at times in 2023. But by December, carriers were diverting away from the Red Sea, and vessels that had just been considered oversupply were now key to carriers (mostly) maintaining departure schedules despite the much longer voyages. Available capacity was key to helping shippers keep their orders coming while also allowing carriers to maximize volumes and revenues even with the disruption.

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And the list of examples of disruptions for which having excess capacity available has helped carriers adjust –- the Russia-Ukraine war, Panama Canal drought, Baltimore bridge collapse, port strikes, and tariff frontloading – since the pandemic is a long one. This list makes a compelling argument that the next unpredictable disruption – the “known unknown” – is out there, and makes keeping older, extra vessels active despite the overcapacity risk make sense.

Pandemic-era carrier profits are also playing a part in the decision not to scrap older vessels. In previous downcycles, carriers have been incentivized to scrap vessels and use the proceeds to pay down debt or cover losses from sinking revenues. This time though, carriers have already used those record profits to pay down almost all debt on their vessels over the last few years and still have cash on hand to cover losses if they arise.

But why are more container ships on the orderbooks in 2026?

The above factors make a case for keeping paid off vessels in circulation, but if these also increase the risk of overcapacity, why are carriers continuing to order vessels after the 2021 to 2024 spending spree?

Because even if the industry is oversupplied, individual carriers can’t make ordering decisions from a market perspective. One carrier’s capacity gain doesn’t address another’s needs. So one investing in new vessels doesn’t mean a competitor won’t continue to order too, even if in the aggregate it pushes the market (further) into oversupply.

Different carriers have had different fleet renewal strategies and – especially given the low rate of new vessels ordered from 2016 to 2020 – some carriers are still playing catch up in a market where shipyard capacity is limited and vessels take a long time to build. Finally, the COVID profits mean carriers have the opportunity now to invest in new, more efficient and lower carbon ships and prepare for the next twenty-five years, even if it means contributing to a downcycle.

Can capacity management prevent downcycle losses?

Much to the surprise of long time observers, in recent years carriers have demonstrated the ability to manage capacity effectively and keep rates up in times of demand collapses – first during the initial volume drop in the first months of the pandemic, and more recently during the month and a half in 2025 when US tariffs on China stood at 145%.

If carriers kept rates level when demand evaporated, why can’t they do the same when capacity grows?

When demand collapses were abrupt, like in 2020 and 2025, carriers were able to make a proportionate response – in many cases just simply keeping vessels wherever they were at the time – and keep rates level.

But when the imbalance is structural, gradual and sustained – like in a supply-drive downcycle – the process of rebalancing can be much more challenging and prolonged. As the examples of the supply-driven rate slides in 2023 and late Q3 through October of 2025 show, it is harder to maintain that discipline when the drivers are a trend instead of a shock. And since incremental costs of taking on additional containers decrease once a vessel is already mostly booked, the economics of container shipping can also sometimes help push carriers into low or loss making rate environments.

But both instances of extremely low spot rates in 2023 and 2025 were followed by periods of rate recovery through capacity reductions even as demand continued to ease, and further price increases as seasonal demand picked up.

This pattern is likely the one we’ll see repeated over the coming years as capacity continues to grow: overall downward pressure on rates with levels likely lower than in 2025, and periods of very low spot prices followed by rate recoveries via capacity management or increases in demand.

All things being equal, this scenario should be a big driver of rate and revenue levels in the container market until a rebalance of supply and demand spurs the next upcycle.

On to the next known unknown?

But of course, the known unknowns that will shake up this pattern are out there: It is known that carriers – at some point – will resume Red Sea transits, which will at first trigger congestion that will absorb capacity, but then release even more supply once the delays unwind, increasing the overcapacity challenge. And geopolitical disruptions that could close shipping lanes, or sudden trade war shifts that could drive sudden demand spikes (or collapses) are all too plausible.

If these or other disruptions arise in the next few years, shippers will lament higher prices, but also be grateful that carriers have the available capacity to keep containers moving nonetheless.

You can catch our Global Freight Outlook webinar every month, or sign up for our weekly international freight update, here.

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 post Container Shipping Overcapacity & Rate Outlook 2026 appeared first on Freightos.

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Warehouse Orchestration: Solving the Daily Breakdown Between Plan and Execution

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Warehouse Orchestration: Solving The Daily Breakdown Between Plan And Execution

In most warehouses today, the problem is not whether work gets done; it is how much effort it takes to keep everything aligned and on track. Every day, there is a breakdown between the plan and executing the plan. Labor plans, inbound schedules, picking priorities, and automation all operate from valid assumptions, but not always the same ones. The gaps between them are filled in real time by supervisors and teams, making constant adjustments. That is what keeps operations running, but it is also what makes them fragile.

It is a challenge many operations recognize. Even with modern systems in place, execution still depends heavily on human coordination. Warehouse orchestration is the shift from managing tasks independently to coordinating the entire operation and ensuring decisions across the system stay aligned as conditions change. The best way to understand what that means in practice is not through a system diagram, but through the lens and experience of the people running the floor.

Consider Maria, a warehouse supervisor responsible for keeping a high-volume operation on track. She is experienced, practical, and steady under pressure, but what she is really managing is not just work; it is complexity.

At any given moment, she balances labor availability, work queues, inbound variability, equipment status, and shifting order priorities. Those inputs are not wrong. They are just not aligned. It is her job to bridge that gap in real time.

A shift that starts “normal” … until it does not

Maria arrives before the floor fully wakes up. Her first stop is not the dock or the pick module; it is yesterday’s reality. What shipped? What did not? Where did the backlog form? Which waves did not behave as the plan assumed? She is not looking for blame; she is looking for drift. Drift is what turns into firefighting later.

Demand shifted over the weekend, but the pick face still reflects last week’s reality. One area is short-staffed; another has idle labor. When the team built the labor plan, it made sense, but the day had already moved on. The team scheduled inbound; however, it is not predictable. Every ETA is a best guess, and how trailers show up rarely matches how they appear on a screen.

Individually, nothing here is catastrophic, but warehouses do not fail all at once. They gradually lose alignment between plan and execution. The team compensates in real time by moving people, reprioritizing work, working around automation delays, and making judgment calls. And the shift “works,” but there is a cost:

Overtime, which did not need to happen.

Detention fees, which show up later.

Service misses, driven by wrong priorities rather than a lack of effort.

Leaders who spend more time reacting than improving.

These challenges are the reality across many operations. Execution is strong, but coordination is fragile.

The real bottleneck: decisions are fragmented

Most warehouses are not short on tools. They have WMS, robotics systems, labor tools, and planning solutions. Each one does its job well, but they do not make decisions together. Each system optimizes its scope based on different priorities or timings. The gaps between them are filled manually by people like Maria. In an environment with less variability, that might work, but in most cases:

Demand changes faster and more frequently.

Labor is less predictable.

Automation introduces new dependencies.

Customer expectations continue to rise.

Under these conditions, static plans, especially labor plans and wave structures, can drift out of sync before the shift is halfway through. That is when the operation starts relying on “manual heroics.” Experienced supervisors keep things running. It is hard to scale, and even harder to sustain.

AI-driven warehouse orchestration: keeping the operation aligned

Warehouse orchestration and the power of AI address this gap. Because it is not just about executing tasks, it is about coordinating decisions across the operation and using intelligence to see, analyze, and recommend actions with full visibility to all the variables. Instead of managing isolated activities, intelligent orchestration continuously aligns:

Labor to demand.

Inbound and outbound priorities.

Work sequencing across zones.

Automation with human workflows.

It does this in real time, as conditions change. Variability is constant, and it is not realistic to eliminate. The goal is to see the risk earlier, respond faster and more consistently, and prevent disruption.

Back to Maria: when the system helps carry the load

Now imagine Maria running that same Monday, but operations now behave like a connected ecosystem, not a collection of islands. Before the shift even starts, she is not just reviewing what happened yesterday. She is looking at a forward-facing view that is already adjusting based on incoming signals. She is getting visibility into risk early before it is a problem. Inbound appointments are not just a schedule; they are a ranked set of trade-offs that balance urgency, detention risk, inventory needs, and outbound commitments. Her decisions are clearer because the system prioritizes them, reflecting business impact. Slotting does not rely on disruptive, periodic re-slot projects that leave the pick face to decay. Instead, optimization and learning continuously shape placement, folding the highest value moves into natural replenishment windows and explaining the “why” in business language.

And during the shift, when one area starts falling behind, Maria does not have to guess the best move. She can see the impact of her options:

Shifting labor.

Reprioritizing tasks.

Adjusting sequencing.

Instead of relying on instinct and experience alone, she has visibility into how decisions affect the entire operation. She is still in control, but the system is helping her avoid problems instead of chasing them. And that changes how the shift feels. It is not static; it is dynamic, but stable.

The key ingredients: unified data, SaaS, AI & ML, connected systems

Behind the scenes, this comes down to unified data, SaaS, AI, ML, and systems that work together. When you connect your warehouse systems, add real-time operational signals and visibility to systems outside of the warehouse, and apply AI and ML for speed and precision, you are working from a single source of truth and an interconnected ecosystem of systems. As a result, users make decisions with a broader context. Then the operation starts to learn; outcomes inform future decisions, improving how the system responds over time. And now, humans are not the only thing holding the performance together.

Why this matters right now

For supply chain leaders, this is not only about efficiency. It is about operating in a world where volatility is constant. Across industries, the specifics vary, but the challenges are consistent:

Handling demand swings without inflating labor costs

Scaling operations without scaling complexity

Maintaining service levels under pressure

The operations that succeed are the ones that do not just react faster; they are the ones that operate in alignment.

The shift ahead

A single, modern technology will not define the future of warehouse management. It will be defined by how well operations coordinate across people, systems, and workflows in real time. That is what intelligent warehouse orchestration enables. It turns the warehouse from a collection of well-run processes into a connected system that can adjust continuously. Because in the end, the goal is not just to execute the plan. It is to keep the plan from breaking when the shift starts.

By Tammy Kulesa
Senior Director, Solution & Industry Marketing, Blue Yonder

Tammy is the Senior Director of Solution and Industry Marketing, leading go-to-market strategy and thought leadership for Blue Yonder Cognitive Solutions for Execution, and the LSP Industry. With over 20 years of experience in technology marketing and nearly a decade focused on retail, logistics, and supply chain, Tammy brings a deep understanding of the operational and strategic challenges facing today’s supply chain leaders. A passionate advocate for innovation and collaboration, Tammy has a proven track record of connecting market needs with transformative solutions.

The post Warehouse Orchestration: Solving the Daily Breakdown Between Plan and Execution appeared first on Logistics Viewpoints.

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How Operational AI Turns Supply Chain Recommendations into Action

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Supply chain AI cannot stop at better insight. To create operational value, AI recommendations must connect to workflows, execution systems, approval paths, and measurable outcomes.

Artificial intelligence is quickly becoming part of the supply chain technology conversation. Vendors are adding copilots, recommendation engines, autonomous agents, and predictive analytics to planning, transportation, warehousing, procurement, and visibility applications. The promise is clear: better decisions, faster responses, and more adaptive operations.

But there is a critical distinction that supply chain leaders need to keep in view. An AI system that identifies a problem is not the same as an AI system that helps solve it.

A demand-planning model may identify a likely stockout. A transportation model may flag a lane disruption. A supplier-risk model may detect a deteriorating delivery pattern. Those are useful insights. But unless the system can connect that insight to an action pathway, the burden still falls on the planner, transportation manager, procurement team, or customer service group to decide what happens next.

That is where many AI deployments will either create real value or stall out.

For a deeper look at the architecture behind operational AI, including A2A, MCP, RAG, Graph RAG, and connected decision systems, download the full white paper: AI in the Supply Chain: From Architecture to Execution.

Insight Is Not Execution

Supply chains do not run on insight alone. They run on orders, shipments, purchase orders, inventory moves, carrier tenders, production schedules, warehouse labor plans, customer commitments, and exception workflows.

A recommendation that remains in a dashboard is not yet operational AI. It is decision support. Decision support can be valuable, but it does not fundamentally change the operating model unless it becomes part of the execution process.

The question is not simply, “Can the AI make a recommendation?” The better question is, “Can the organization act on that recommendation in a controlled, auditable, and timely way?”

For example, if an AI system predicts that a regional distribution center will run short of inventory, several action pathways may be available. The company might expedite inbound supply, rebalance inventory from another facility, substitute a product, modify customer allocation rules, or adjust promised delivery dates.

Each action has a cost, a service implication, and a governance requirement.

Operational AI must understand those pathways. It must also know which actions it can recommend, which it can execute automatically, and which require human approval.

The Execution Layer Matters

This is why integration with core execution systems is so important. AI cannot operate effectively if it sits outside the systems where work is actually performed.

For supply chain AI to become operational, it must connect to transportation management systems, warehouse management systems, order management systems, ERP, procurement platforms, supplier portals, customer service workflows, and control tower environments.

Without these connections, AI may diagnose problems faster, but it will not necessarily resolve them faster.

The difference is material. An AI assistant that says, “This shipment is likely to miss its delivery appointment,” is useful. An AI-enabled workflow that identifies the delay, calculates downstream service risk, recommends a carrier alternative, checks cost thresholds, initiates an approval workflow, and updates customer service is much more powerful.

That is the move from analytics to operational intelligence.

Human-in-the-Loop Still Matters

This does not mean every AI recommendation should become an automated action. Supply chain decisions often involve tradeoffs among cost, service, risk, inventory, and customer relationships. Many require judgment.

The more practical model is tiered autonomy.

Low-risk, high-frequency actions may be automated. Moderate-risk decisions may require planner approval. High-impact exceptions may require escalation to a manager or executive.

This is not a weakness. It is a design requirement.

A well-architected operational AI system should know when to act, when to recommend, and when to escalate. It should also capture the outcome so the system can learn whether the decision improved performance.

Closed-Loop Learning Is the Real Prize

The most important capability may not be the first recommendation. It may be the feedback loop that follows.

Did the expedited shipment prevent the stockout? Did the alternate supplier meet the delivery date? Did the inventory transfer protect service without creating a shortage elsewhere? Did the customer accept the revised promise date?

These outcomes should not disappear into operational noise. They should feed back into the intelligence layer.

That is how AI becomes more than a static recommendation tool. It becomes a learning system embedded in the daily operating rhythm of the supply chain.

What This Means for Buyers

Supply chain leaders evaluating AI-enabled software should press vendors on action pathways. The relevant questions are straightforward.

Can the system connect recommendations to execution workflows? Can it distinguish between automated, approved, and escalated actions? Can it operate across functions, not just inside one application? Can it create an audit trail? Can it learn from outcomes?

The vendors that answer these questions well will move beyond AI features. They will become part of the operating architecture.

The next phase of supply chain AI will not be won by the tool that produces the most impressive recommendation. It will be won by the systems that help companies act faster, with more control, better context, and measurable outcomes.

The post How Operational AI Turns Supply Chain Recommendations into Action appeared first on Logistics Viewpoints.

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