Solution / Mining & Metallurgy

LiDAR Cargo Volume & Material Recognition

Measure actual truck/wagon/conveyor material volume and detect anomalies using LiDAR and machine vision.

Business context

What this capability solves

LiDAR Cargo Volume & Material Recognition converts fragmented operational signals into an accountable digital workflow so teams can detect risk earlier, optimize resources and improve safety or productivity without losing human operational control.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

3D Measurement

Estimate load geometry and volume.

Vehicle/Wagon Recognition

Associate measurement with the correct asset/event.

Under/Overload

Detect configured loading deviations.

Conveyor Vision

Monitor material flow and foreign objects.

Event Evidence

Store measurement, image/context and timestamp.

Reconciliation

Compare measured volume with dispatch/production records.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Field / Asset Layer

Vehicles, equipment, sensors, cameras, tags, infrastructure and operational systems.

Connectivity & Edge

GNSS, industrial wireless/wired networks, gateways and local processing.

Analytics Platform

Telemetry normalization, event fusion, AI/computer vision, scoring and rules.

Operations Layer

Dispatch, command center, workflow, alerts, enterprise integration and management reporting.

Controls & governance

Authorized and purpose-bound data collection
Safety-first operational boundaries and human override
Role-based access and asset ownership
Data-quality and calibration controls
Event/audit history and investigation evidence
Cybersecurity for devices, edge and enterprise integration

Priority use cases

Truck load verification
Rail wagon measurement
Conveyor protection
Material reconciliation

Key deliverables

Operational/process assessment
Reference architecture
Pilot design/configuration
Integration and data map
SOP / escalation model
KPI baseline and scale roadmap

Integration considerations

LiDAR
Cameras
Dispatch
Weighbridge
Conveyor control
BI

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. BaselineMap assets, routes/process, data, risk and current KPIs.

2. InstrumentConnect selected devices/data and validate data quality.

3. OperationalizeConfigure analytics, alerts, workflows and dashboards.

4. ScaleExpand fleet/site coverage, tune models and institutionalize performance management.

Outcome and KPI framework

Measurement errorUnderload eventsForeign-object detectionReconciliation variance