AI Forecasting for Asia–US Container Rate Volatility

Trans-Pacific container rates are doing that familiar “sawtooth” thing again: a sharp jump after a general rate increase (GRI), followed by a quick slide when capacity and demand don’t line up. Last week, West Coast spot rates fell 6% to $1,963 per FEU, while East Coast rates rose 8% to $3,150 per FEU—yet were still down 15% month over month. Those numbers (and the whiplash behind them) aren’t just market trivia. They’re a signal that planning ocean freight with spreadsheets and static assumptions is officially a liability.

Here’s the thing about the Asia–US route: it’s not just volatile. It’s structurally hard to stabilize because carriers are managing overcapacity, blank sailings, contract-vs-spot dynamics, and policy uncertainty (tariffs) at the same time. For shippers, forwarders, and procurement teams, this matters because ocean decisions taken in December ripple straight into Q1 inventory positions, cash flow, and customer service.

This post is part of our AI in Supply Chain & Procurement series, and I’m going to take a clear stance: rate volatility isn’t the core problem—decision latency is. AI-driven supply chain forecasting and scenario planning reduce that latency, which is why this market setup is basically a flashing neon sign for predictive analytics.

What’s driving Asia–US volatility (and why it won’t “self-correct”)

Answer first: Asia–US rate instability is being driven by a mismatch between capacity additions and inconsistent demand recovery, with carriers using blank sailings and GRIs as short-term tools that don’t fix the underlying imbalance.

The Freightos data highlighted a pattern ocean teams know too well:

• GRIs lift spot rates briefly.
• Shippers shift volume tactically (often chasing spot opportunities).
• Rates sag again because capacity is still there.

One detail worth underlining: about 60% of volume moves under contract and 40% under spot, but that split shifts when capacity tightens. That creates a feedback loop. When a GRI hits, some buyers cover urgent moves on spot, others ride contracts, and the mix changes just enough to make the market look “strong” for a week—until it doesn’t.

Overcapacity isn’t abstract—it’s operational

When analysts talk about “new tonnage in a soft market,” that translates into real operational consequences:

• More sailings competing for the same pool of bookings
• More temptation to discount to fill vessels
• More blank sailings announced (then adjusted) to engineer utilization

Blank sailings are a rational tool, but they’re also a blunt instrument. They can improve carrier economics while increasing schedule risk for shippers. If your procurement strategy is built on the assumption that weekly service strings remain stable, you’re planning against a world that doesn’t exist.

Tariff uncertainty makes demand forecasting messy

The article points to a meaningful wildcard: some U.S. importers may be pausing imports based on expectations around potential changes to emergency tariffs, with legal and political timing pushing clarity into January or later.

For supply chain planning, this is the worst type of uncertainty:

• It’s binary (tariffs stay vs. tariffs fall)
• It’s time-bound (decision likely soon)
• It influences large purchase decisions (not minor routing tweaks)

Static forecasts hate this. Scenario-based forecasts handle it.

Why Asia–Europe looks steadier (and what that teaches procurement)

Answer first: Asia–Europe rates have held GRIs more effectively because demand signals are firmer (early Lunar New Year ordering), and longer lead times—often linked to Red Sea diversions—push shippers to commit earlier.

In the Freightos recap, Asia–Mediterranean rates held at $3,342 per FEU after a 15% early-month climb, marking multiple successful GRIs since mid-October. Asia–North Europe stayed around $2,449 per FEU, still well above the mid-October low.

What’s the practical lesson for shippers and procurement leaders? When lead times lengthen and uncertainty rises, buyers pay for reliability. That dynamic makes it easier for carriers to sustain rate moves.

If you manage procurement for ocean freight, you can use this as a playbook:

• When lanes become schedule-risky, contracting behavior changes.
• When contracting behavior changes, spot markets become less “true.”
• When spot is less true, index-only buying decisions get riskier.

This is one reason I’m skeptical of teams that treat “spot rate down” as a green light. A lower spot rate paired with higher rollover risk can be a net loss.

The AI opportunity hiding in GRIs and blank sailings

Answer first: AI helps carriers and shippers anticipate rate moves and capacity shifts earlier by combining booking signals, sailing changes, and external risk data into predictive models—turning reactive planning into proactive planning.

Most companies get this wrong: they treat GRIs, blank sailings, and rate indexes as facts to react to, rather than signals to forecast from.

A modern AI forecasting approach treats the market like a living system with leading indicators.

What data actually improves ocean freight forecasting

You don’t need magical data. You need consistent data with the right cadence. The strongest inputs tend to be:

• Booking velocity by lane (week-over-week changes)
• Capacity signals (blank sailings, alliance changes, new service strings)
• Port performance (dwell time, berth productivity, congestion proxies)
• Intermodal constraints (rail service reliability, chassis availability, dray capacity)
• Policy risk signals (tariff announcements, legal timelines, enforcement signals)
• Geopolitical disruption indicators (Red Sea risk posture, insurance pricing, rerouting behavior)

AI models thrive when they can compare what’s happening now against thousands of prior “market setups” and learn which combinations usually precede a rate spike, a collapse, or a schedule failure.

A concrete use case: “Should we chase spot or lock contract?”

Procurement teams ask this every quarter, and the wrong answer costs real money.

AI decision support can frame it as expected value:

• Expected landed cost = ocean rate + accessorials + delay cost + inventory carrying cost
• Expected service level = probability of on-time sailing + probability of rollover + buffer needed

If a model estimates that blank sailings are likely to increase over the next 3–4 weeks, then a slightly higher contract rate can be cheaper than “cheap spot” once you price in expedited freight, stockouts, and safety stock.

Another use case: optimizing allocations across ports and services

On Asia–US, small choices compound:

• West Coast vs. all-water East Coast
• Direct call vs. transshipment
• Faster transit vs. more reliable schedule

AI-enabled route optimization isn’t just about shortest path. It’s about best outcome under uncertainty, especially in peak planning around Lunar New Year when factories close and shippers rush.

A practical playbook for shippers and logistics teams (Q1 2026 planning)

Answer first: Build an AI-supported “ocean control tower” workflow that refreshes forecasts weekly, runs scenarios for tariffs and capacity, and converts signals into booking actions.

Here’s what I’ve found works when teams want results without a 12-month transformation program.

1) Stop forecasting demand as a single number

Replace the single forecast with three scenarios:

• Base case (current demand trend)
• Upside case (pre-Lunar New Year pull-forward accelerates)
• Downside case (tariff uncertainty delays ordering)

Then attach operational triggers:

• If upside signals hit X threshold → lock additional allocations
• If downside persists for Y weeks → reduce premium services, renegotiate space

2) Treat blank sailings as a probability, not a headline

Instead of “Carrier announced blank sailing,” track:

• Frequency of blanking by carrier/service string
• Lead time of announcements (2 weeks vs 5 days matters)
• Historic follow-through rate (some announcements get reversed)

AI can score blank-sailing likelihood and feed it into ETAs and safety stock calculations.

3) Add “delay cost” into procurement decisions

If your procurement KPI is only ocean rate, you’ll systematically underinvest in reliability.

Simple starting point:

• Assign a cost per day of delay by SKU family (or by container)
• Include it in bid evaluation and routing guides

This is where AI in supply chain procurement becomes real: it connects freight decisions to inventory and revenue outcomes.

4) Align contracts to the 60/40 reality

With roughly 60% contract / 40% spot as a baseline, consider a deliberate posture:

• Contract enough to protect service levels and budget
• Keep a controlled “spot flex” bucket
• Use AI forecasts to decide when to deploy the flex bucket (not gut feel)

5) Put tariff uncertainty into the same model as freight

Too many teams treat tariffs as a separate legal/compliance thread. Operationally, tariffs are a demand lever.

A clean approach:

• Model “tariff outcome” as a scenario variable
• Estimate ordering behavior shifts under each outcome
• Link to booking forecasts and inventory coverage

People also ask: what should we watch right now?

Answer first: Watch Lunar New Year pull-forward signals, carrier capacity discipline, and policy timelines—then convert those signals into booking and contract actions.

• Lunar New Year ordering: If bookings accelerate earlier than usual, rate pressure appears before the calendar tells you it should.
• Capacity discipline: Blanked sailings that don’t move rates are a sign overcapacity remains the dominant force.
• Tariff timeline: When buyers expect policy changes, you’ll see unusual pauses or sudden surges.
• Red Sea risk posture: Even the expectation of disruption can trigger inventory building and longer lead-time planning.

What to do next (if you’re tired of whiplash)

Asia–US ocean freight rate volatility isn’t going away just because it’s Q4. Overcapacity, GRIs that don’t stick, and tariff uncertainty are the recipe for more abrupt moves—especially as Lunar New Year planning ramps up.

The better way to approach this is operationally simple: use AI-driven demand forecasting and route optimization to shorten the time between signal and decision. When your team can run scenarios weekly (not quarterly) and quantify delay risk (not just rate), you stop being surprised by the market.

If you’re mapping your 2026 supply chain analytics roadmap, here’s the question I’d pressure-test: Which ocean freight decisions are you still making “by habit” that should be made by forecast?