Sense–Predict–Act: A Big-Data Playbook for Smarter Maritime Supply Chains

Why This Matters Now

Two structural shocks have made Sense–Predict–Act urgent.

First, global rerouting and chokepoint pressure have pushed up costs and stretched schedules. UNCTAD’s 2024 review tied higher freight rates to longer distances and congestion, warning that sustained shipping cost inflation can ripple into consumer prices. The remedy it highlights is more digitalization, standards, and data-driven coordination across the chain.

Second, volatility has returned to ocean schedules. Sea-Intelligence’s 2025 updates show global schedule reliability hovering in the mid-60s and slipping again late in the year. That is better than the crisis years, but still fragile for just-in-time importers. A playbook that anticipates slippage and pre-positions decisions is the difference between premium freight and planned flow.

Sense: Build a Shared Picture that Operations can Trust

Sensing starts with Standardized, machine-readable events. Container shipping now has a practical backbone for this in the Digital Container Shipping Association’s Track & Trace standards, which define common event names, timestamps, and APIs for tracking a container's location and what happened to it. When carriers, forwarders, and ports emit the same verbs, we can stitch inland legs to vessel milestones without custom mapping for each partner.

At the port and terminal level, sensing expands beyond status messages. Gate telemetry, yard equipment data, berth utilization, tug and pilot dispatch, rail EDI, and truck appointment compliance form a real-time fabric. Cyber telemetry belongs in the same fabric because it now affects schedule and safety. The Port of Los Angeles reports blocking tens of millions of hostile probes per month, and documented 750 million intrusion attempts in 2023 alone. Treating cyber health as an operational signal enables early mitigation, so that TOS, gates, and documentation flows are not the next bottleneck.

Two practical rules keep sensing honest. First, schema first: agree on data models before dashboards. Second, provenance and quality: track the source of every field and its reliability. Teams that do this find they can reuse the same data for safety cases, emissions reporting, and customer service rather than running three parallel projects.

Predict: Convert Streams into Forward-looking Features

The predictive layer converts raw signals into features that explain risk and cost. A few high-leverage ones:

• ETA slippage velocity tracks how quickly ETA moves compared with the last update. It flags deteriorating routes long before a vessel appears late at anchor.

• Berth contention index blends approaching vessel ETAs, historical berth productivity, and planned turn times to forecast queue formation days.

• Probable speed loss uses weather, currents, and vessel performance curves to estimate speed degradation on the next leg.

• Gate dwell predictor uses live gate rates, appointment compliance, and yard fill to forecast truck turn times and prevent platoons from peaking at the same hour.

Academic and industry work on port congestion prediction from AIS features has matured enough to be useful. These models move beyond counting ships at anchor to leading indicators based on trajectories and port process data. The output is not a pretty heat map. It is an earlier decision about where to call first, whether to swap rotations, or when to slow steam without risking a miss.

Prediction is not only about time. It is also about safety and sustainability. One recent analysis found that AI-assisted navigation, by reducing close encounters and unnecessary maneuvers, could cut global shipping emissions by roughly 47 million tons per year and save about one hundred thousand dollars in fuel per vessel. Even if your fleet captures a fraction of that, the business case is obvious.

Act: Wire Predictions into the Systems that Move the Work

The hardest step is action. The prediction that lives in a slide deck does not save a dollar. An action engine pushes decisions into the tools frontline teams already use.

• Voyage and network control: When ETA slippage velocity crosses a threshold, the network control center receives a re-sequencing suggestion with cost and service impacts. If the berth contention index points to a queue, the system proposes a swap and pre-books tugs and pilots for the alternative call.

• Terminal operations: When the gate dwell predictor shows a spike for the afternoon, the TOS triggers micro-windows for truck appointments and staggers rail pulls.

• Mariner decision support: When probable speed loss exceeds a safe envelope for lashing design or crew hours, the conning display offers speed and heading envelopes that stay inside limits, with a simple rationale for the bridge team.

• Cyber playbooks: If a port partner’s telemetry shows elevated threat levels, the documentation cutovers move to a pre-agreed backup path, avoiding last-minute delays at cargo release.

• A good pattern is to build a small catalogue of playbooks that combine trigger, decision, and integration target. Example: “If BCI > 0.7 and next-port slack < 6 hours, propose rotation swap and simulate customer impact.” Each playbook has an owner on sea and shore, so operations do not wait for a steering committee.

Architecture that scales

Most organizations converge on a similar stack:

• Open ingestion. Stream AIS, engine telemetry, weather, tides and currents, TOS events, WMS/TMS events, rail and truck feeds, plus cyber telemetry.

• Standard semantics. Adopt DCSA event vocabularies for container flow, and consistent naming for port and berth events, so every stakeholder reads the same timeline.

• Feature store. Centralize features such as slippage velocity, contention index, and dwell predictors so models share the best-engineered signals rather than reinventing them.

• Model factory. Time series for ETA and dwell, classification for encounter risk, reinforcement learning for routing under constraints.

• Decision integrations. APIs into voyage planning, TOS, appointment systems, and NOC tools.

• Learning loop. After action, compare predicted vs actual, adjust features, and improve triggers.

Governance underpins the stack. Treat models as operational assets with change control and rollback. Capture the why behind a recommendation so a master, pilot, dispatcher, or planner can accept it with confidence.

Where Returns Appear First

• Avoided queues and reshuffled calls. Even a few hours of earlier warning about emerging berth contention enables rotation swaps that protect service windows. Sea-Intelligence’s mid-60s reliability environment leaves value on the table for those who can buy time with early changes.

• Fuel and emissions. AI-assisted navigation that reduces sharp maneuvers and close-quarters risk lowers fuel burn and CO₂. That helps near-term budgets and positions teams for tightening decarbonisation rules and customer scorecards.

• Cyber-resilient operations. Port communities that operationalise cyber telemetry reduce the risk that a gate, TOS, or documentation system becomes a surprise bottleneck. LOSA-style “near-miss” tracking for cyber events is emerging as a shared KPI. Evidence from Los Angeles shows the sheer scale of attempted intrusions and the value of shared defences.

• Customer experience. When a forecast is paired with a concrete plan, customer conversations change. Instead of “your box is delayed,” the message is “we anticipate a two-day delay at X; we have moved your cargo to Y with an inland plan that preserves delivery.”

Lessons from the Front Line

From my years across ship command and logistics leadership, three lessons repeat.

• Make features first. The biggest wins came when teams stopped polishing dashboards and started crafting robust features, such as approach instability or dwell predictors. Good features feed multiple models and survive vendor changes.

• Integrate with authority. The moment predictions triggered actual bookings, rotation swaps, and appointment nudges, trust rose. If the system only suggests changes and nobody owns them, the value evaporates.

• Explainability beats magic. Bridge teams and terminal planners accept guidance when they see the drivers. A one-line reason code with the recommendation is often enough.

What ‘Good’ Looks Like in 18 Months

• Shared event language. Partners publish DCSA-style events across booking, track-and-trace, and verified gross mass, so all parties read the same clock.

• Playbooks in production. Five to ten Sense–Predict–Act playbooks operate every day across voyage control and terminals, each with an owner and a KPI.

• Operational cyber telemetry. Security health is visible to operations and used to route documentation paths during incidents.

• Measured decarbonization. AI navigation support is tied to fuel and CO₂ metrics, not just safety narratives, aligning with the regulatory vector and customer commitments.

• Sense–Predict–Act is not another rebrand. It is a discipline for converting noisy, multi-party supply chains into reliable outcomes at sea and ashore. The technology is ready. The standards exist. The data is already flowing. The advantage accrues to the operators who wire predictions into decisions and let their teams act with confidence when minutes still matter.