Case study 03 · Mining
Industrial Data Platform for Predictive Mining Analytics
Sensor and historical operations data powering anomaly detection, predictive maintenance, safety analytics and drill-and-blast optimisation.
The challenge
A large diversified mining company was using machine telemetry and long-term operational history to improve how mines are run. The programme required dependable data architecture and pipelines for multiple applications operating on data from haul trucks, shovels, loaders, road conditions, blast events and drilling operations.
Business use cases supported
- Detect anomalies in heavy equipment behaviour and support predictive-failure analysis.
- Monitor road quality and safety conditions across mine sites.
- Give supervisors data-driven visibility into mining operations.
- Analyse more than a decade of blast and drill information to optimise future operating parameters.
- Reduce explosive usage and operating cost by improving drill-and-blast decisions.
Delivery scope
The engagement covered data architecture and pipelines across multiple applications, data-engineering leadership, and migration of pipelines from GCP to Azure.
Engineering for a multi-application industrial programme
Operational data flow
Engineering scope
- Designed data architecture patterns for a portfolio of industrial analytics applications.
- Built ETL pipelines on GCP and orchestration workflows with Airflow / Cloud Composer.
- Developed data-processing and metrics services with Python, Spark, Docker and Kubernetes.
- Implemented Databricks workloads in Azure and supported migration of existing pipelines from GCP to Azure.
- Provided data-engineering leadership within a large multidisciplinary programme.
Programme impact
Operational leverage
The data foundation enabled predictive, safety and optimisation applications to use telemetry and historical mine data as part of day-to-day decision support.
Wider initiative
The client's project record states that the wider initiative helped reduce client costs by up to USD 1B per year.
Note: the USD 1B figure describes the impact of the wider client initiative and is not attributed solely to the data-engineering work presented in this case study.
Technology
- GCP
- Azure
- Databricks
- BigQuery
- Airflow
- Docker
- Kubernetes
- Python
- Spark
Commercial takeaway
Industrial analytics depends on a reliable data layer that can handle operational telemetry, historical context and multiple downstream products. The platform work connected those inputs to applications that operators and engineering teams could use in the field.
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Operational data you can build on
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