ETA Confidence Score

The score is a statistical probability (0-100%) that a shipment will arrive within a defined time window, not a deterministic guarantee. It is derived from Bayesian inference models that continuously update prior beliefs with real-time evidence.

• Prior Probability: Based on historical carrier performance on a specific lane.
• Likelihood: Updated with real-time signals like GPS speed, traffic, and weather.
• Posterior Probability: The final confidence score, representing the updated belief.

Unlike a static ETA, the confidence score is dynamically recalibrated with every new data point ingested by the control tower. A truck stopping for an unplanned 30 minutes will cause an immediate, mathematically sound drop in the score.

• Event-Driven Updates: IoT sensor pings, geofence crossings, and port congestion alerts trigger recalculation.
• Temporal Decay: Confidence naturally decays as the prediction horizon extends further into the future.

The score explicitly quantifies aleatoric uncertainty (inherent randomness like a flat tire) and epistemic uncertainty (lack of knowledge, like a missing GPS signal). This distinction is critical for determining the appropriate response.

• Wide Confidence Intervals: Indicate high epistemic uncertainty, signaling a need for more data.
• Low Score with Narrow Intervals: Indicates high aleatoric uncertainty, signaling a truly volatile situation.

The raw score is mapped to operational thresholds that trigger automated playbooks. A score of 95% requires no action, while a drop below 70% might automatically initiate an Autonomous Resolution Agent.

• Green (>90%): On-track, no intervention needed.
• Yellow (70-90%): Monitor closely, alert the Predictive Milestone Engine.
• Red (<70%): High risk of SLA Breach, trigger a What-If Simulation for re-routing.

The score is a composite metric generated by a model that fuses diverse data streams. It is not based solely on GPS location. Key inputs include:

• Carrier Historical Reliability: On-time performance data from the Supply Chain Graph.
• Real-Time Telematics: Speed, heading, and engine status from IoT Sensor Fusion.
• External Context: Weather forecasts, port congestion indices, and geopolitical risk signals from a Key Risk Indicator (KRI) feed.

To enable trust and rapid human decision-making, the score must be explainable. The system provides the top factors contributing to a low score, such as 'Carrier is 2 hours behind schedule' or 'Severe weather warning on planned route'.

• Feature Attribution: Uses SHAP or LIME values to show the impact of each factor.
• Natural Language Justification: An NLQ interface can answer 'Why is the confidence score for PO #12345 so low?'

The underlying machine learning model that generates the ETA Confidence Score. It forecasts the completion time of critical supply chain events—such as vessel arrivals, customs clearance, or final-mile delivery—by analyzing historical transit patterns, real-time telemetry, and contextual variables like weather and port congestion.

• Ingests streaming IoT and AIS data
• Outputs a probabilistic time window, not just a single timestamp
• Continuously retrains on recent outcomes to adapt to seasonal shifts

An algorithm that consumes the ETA Confidence Score to autonomously adjust inventory safety stock and time buffers. When confidence in an inbound shipment's arrival drops below a defined threshold, the system preemptively increases buffer stock at the destination node to prevent a stockout.

• Links probabilistic lead time to inventory policy
• Reduces the bullwhip effect by dampening reaction to low-confidence signals
• Operates in a closed loop with the Supply Chain Control Tower

A specialized application of the ETA Confidence Score that identifies shipments at high risk of violating service level agreements. It calculates the probability that the predicted arrival time will exceed the contractual delivery window, triggering preemptive alerts.

• Uses the confidence interval, not just the point estimate
• Enables proactive customer communication before the failure occurs
• Feeds into Automated Playbook Execution for rerouting or expediting

A simulation technique that uses low ETA Confidence Scores as input signals to map how a localized delay cascades through interconnected nodes. A low-confidence arrival at a component supplier propagates downstream, quantifying systemic risk to final assembly.

• Models second-order effects of a single unreliable ETA
• Visualizes Value-at-Risk across the supply chain graph
• Informs strategic decisions on near-shoring or supplier diversification

The real-time stream processing backbone that correlates raw events to compute the ETA Confidence Score. CEP engines ingest disparate data—GPS pings, port status APIs, weather feeds—and detect meaningful patterns that degrade or improve arrival probability.

• Filters noise from high-frequency telemetry streams
• Identifies causal event patterns (e.g., port strike + vessel loitering = low confidence)
• Publishes scored events to the Cognitive Control Tower dashboard