AI Route Planning Lessons From ONE’s Red Sea Return

A 65% drop in vessel arrivals to the Gulf of Aden (November vs. the prior year) isn’t just a security headline—it’s a demand-signal distortion that breaks “normal” ocean planning. Now, Ocean Network Express (ONE) is stepping back into the Red Sea market with a new Red-Sea China Service (RCS), starting Jan. 15, via a slot charter arrangement and an eight-week rotation that notably doesn’t transit the Suez Canal.

Most companies get this wrong: they treat a route reopening as a simple “turn the old lane back on” decision. The reality is messier. Risk changes faster than contracts, capacity returns in uneven bursts (often via slot deals and smaller vessels first), and customer expectations spike—especially going into Q1 planning and post-holiday replenishment.

This post is part of our AI in Transportation & Logistics series, and ONE’s move is a clean case study: when geopolitics whipsaws a corridor like the Red Sea, AI-driven route optimization and forecasting aren’t nice-to-haves. They’re how you avoid paying for the wrong contingency, on the wrong lane, for the wrong month.

What ONE’s Red Sea service actually signals

ONE’s RCS launch signals that carriers think the Red Sea is “navigable enough” to sell service again—but not stable enough to bet the network on it.

Here are the operational tells embedded in the announcement:

• Slot charter structure: ONE is taking slots on another carrier’s string rather than deploying a fully dedicated service. That’s a classic “test demand / cap downside” move.
• Fortnightly frequency and feeder-size tonnage: Published reports place the SSF Dream around 3,000 TEU, typical for a feeder-type vessel. That points to controlled exposure and flexible redeployment.
• Eight-week rotation with Red Sea port calls: Shanghai → Qingdao → Nansha → Shekou → Jeddah → Sokhna → Aqaba → (back via Jeddah) → Shanghai → Qingdao.
• No Suez transit: That’s the eye-opener. If you’re a shipper, it means you can’t assume “Red Sea = Suez = shortest.” Route design is being driven by a blend of security, insurance, scheduling reliability, and port economics.

From an AI perspective, this is exactly the environment where static routing rules fail. A routing engine needs to treat the corridor as a probabilistic risk surface, not a binary “open/closed” map.

Why carriers re-enter with “option-like” capacity

Carriers tend to re-enter volatile regions with capacity that behaves like an option: limited commitment, measurable downside, and the ability to scale if the market stabilizes.

Slot participation and smaller vessels do three things:

1. Reduce asset concentration risk (you’re not tying up a mega-ship if conditions deteriorate).
2. Preserve schedule integrity (easier to recover from delays with more flexible assets).
3. Match uncertain demand (shippers may remain cautious even if attacks de-escalate).

That logic should sound familiar if you build AI models for logistics: don’t overfit to the last “normal.” Build for volatility.

The Red Sea problem: it’s not just distance, it’s variance

For most networks, the real cost isn’t the average transit time—it’s the variance.

When a corridor becomes high-risk, you get:

• Sudden lead-time expansion (reroutes, convoying, speed changes)
• Port bunching (arrivals cluster after disruption)
• Equipment imbalance (empties trapped in the wrong places)
• Inventory policy whiplash (safety stock inflated, then stranded)

And because it’s December 2025, this matters right now: many shippers are locking Q1 allocations, revisiting buffer stock decisions made during Red Sea disruption cycles, and trying to keep working capital from ballooning.

AI is the practical tool for this moment because it can optimize for risk-adjusted performance, not just theoretical shortest-path routing.

A better objective function: optimize cost and reliability

If your routing logic still optimizes for lowest expected cost, you’re leaving money on the table. High-variance lanes create hidden costs: expedites, OTIF penalties, lost sales, and customer service drag.

The objective function that holds up in 2025 looks more like:

• Minimize total landed cost
• Subject to service-level constraints (OTIF, max late probability)
• Penalize risk exposure (security, insurance, choke points)
• Include network externalities (equipment turns, port congestion)

That’s exactly where AI-driven route optimization performs well: it can evaluate thousands of feasible routings and schedule combinations under uncertainty—then recommend the plan with the best risk-adjusted outcome.

How AI route optimization handles a corridor like the Red Sea

AI doesn’t “predict peace.” It does something more useful: it turns shifting conditions into decisions you can operationalize.

Below is how modern AI in transportation and logistics can be applied to corridors like the Red Sea.

1) Dynamic route planning using real-time risk signals

A credible approach combines multiple signal classes:

• Security and incident signals: event frequency, proximity, severity
• Commercial signals: blank sailings, spot rate volatility, premium surcharges
• Operational signals: port dwell, schedule reliability, missed connections
• Insurance and compliance signals: underwriting constraints, war-risk changes

The output shouldn’t be a single “go/no-go.” It should be route policies:

• Preferred routing when risk is low
• Approved alternates when risk rises
• Trigger thresholds for switching (and switching back)

If you’re managing ocean allocations, this avoids the common mistake of rerouting too late—after everyone else does and capacity disappears.

2) AI-driven ETA forecasting where schedules are unreliable

Schedules are promises. ETAs are probabilities.

On volatile lanes, you need ETA models that learn from:

• carrier-specific schedule performance
• port congestion patterns and berthing delays
• transshipment reliability by hub
• seasonal port productivity dips

A strong model doesn’t just produce an ETA—it produces a confidence band (for example, “70% chance arrival between Jan 28–Feb 2”). That’s what inventory planners can actually use.

3) Contingency planning that doesn’t explode your cost base

ONE’s choice to re-enter via slot agreements mirrors what shippers should do in procurement: treat capacity like a portfolio.

AI-based procurement optimization can help you:

• split volume across carriers and routings to reduce single-point exposure
• reserve limited “option capacity” for high-margin SKUs
• decide when to pay for premium services vs. hold buffer inventory

A practical rule I’ve found works: pay for optionality only where lateness is expensive (promotions, seasonal items, production-stopping parts). AI helps you quantify “expensive” instead of arguing about it.

What shippers and 3PLs should do in Q1 2026 planning

The fastest wins aren’t exotic models—they’re disciplined decisions driven by better inputs.

Build a risk-aware routing playbook (not a one-time reroute)

Document routing decisions as if they’re policies, with triggers:

• If risk index exceeds X for Y days → shift Z% volume to alternate
• If schedule reliability drops below A% on a string → change transshipment hub
• If premium surcharges exceed $B/container → re-optimize mode mix

Even if you don’t have a full AI platform yet, the playbook structure forces clarity. If you do have AI tooling, you can automate these triggers.

Segment freight by tolerance for delay

Not all containers deserve the same routing rules. Create three buckets:

1. Zero-late freight: production-critical, promotional, contractual OTIF
2. Managed-late freight: can take mild variance with buffer stock
3. Flexible freight: lowest priority, route on price

Then let AI route optimization apply different penalties for lateness and risk per bucket.

Rebalance inventory policy using probability, not fear

The instinct after disruption is to add safety stock everywhere. That’s how you end up with a warehouse full of slow movers by April.

A better approach:

• Use probabilistic ETAs to set safety stock only where the late-risk is real
• Reduce safety stock when stability returns (and do it deliberately)
• Align reorder points to confidence bands, not advertised schedules

If ONE’s re-entry holds and other services follow, the biggest financial upside for many shippers will be releasing working capital, not shaving a day off transit.

“People also ask” about Red Sea routing and AI

Why would a Red Sea service avoid the Suez Canal?

Because route choice isn’t purely geographic. Carriers can design rotations to manage risk, insurance constraints, port economics, and schedule recovery—sometimes at the expense of the theoretically shortest corridor.

Does AI route optimization replace human planners?

No—and it shouldn’t. AI is best at evaluating many scenarios quickly and consistently. Humans are best at interpreting business context (customer commitments, margin priorities, relationship realities) and making the final call.

What data do you need to start using AI for ocean route planning?

Start with what you already have: historical shipment legs, carrier performance, dwell times, cost components, and exception reasons. Add external risk and congestion signals next. The early wins typically come from cleaning internal data and defining decision rules.

The real lesson from ONE: resilience is a design choice

ONE’s return to the Red Sea via a slot-based, controlled-frequency service is a reminder that resilience is built into the network by design. It’s not a crisis response. It’s how you structure capacity, routing, and inventory so your business doesn’t swing wildly with every geopolitical headline.

For teams investing in AI in transportation and logistics, the Red Sea case is as clear as it gets: AI-driven route optimization should be measured by how well it reduces variance, not how clever the algorithm sounds.

If you’re planning your 2026 ocean strategy, the next step is simple: map your highest-impact lanes, define your risk triggers, and build a routing portfolio that can flex without chaos. What would change in your network if you treated route reliability as a first-class KPI—not an after-the-fact apology?