Company processes cover a broad range of rule-based and knowledge-based routine work through many roles in the mining sector that do not bring much value to the main business or the end-users doing them.
FREMONT, CA: Mining operations globally are 28 percent less productive today than they were a decade earlier. Declining ore grades, rising prices, shortage of skilled labor, market volatility, and strict regulatory compliance have been driving the mining sector to look for ways to improve efficiency and productivity. Company processes cover a broad range of rule-based and knowledge-based routine work through many roles in the mining sector that do not bring much value to the main business or the end-users doing them.
Robotic Process Automation (RPA) provides an approach using software robots for automating tedious and non-value-adding functions. The RPA platform's further convergence with an AI platform helps robots increasingly take on decision automation tasks using cognitive computation and machine learning.
A significant portion of this work can be automated for the mining industry using RPA to make and realize touch-less business processes. Repetitive tasks such as downloading, uploading, validating or encoding data, conducting measurements, and others can be done by software robots with increased speed and precision in a repeatable way 24x7, increasing the overall process performance.
Things a Mining Firm Must Think of Automating
The mining industry must define the main processes and practices for the full benefit that can be taken under the RPA's supervision. Some of the fields where RPA has demonstrated advantages related to efficiency, consistency, and cycle time reduction improvements include:
• Corporate back-office systems, such as the procure to pay cycle, AR/AP processes, month-end financial reporting.
• Conversion of life of mine plan to business plan along with ore reserve estimation and validation processes
• Data aggregation from various processing units to provide tonnage, consumables and services for production monitoring and reconciliation.
• Data aggregation and interpretation of maintenance readiness and facilitating workflows from enterprise asset management systems to production information systems.
• Metals balancing measurements from manufacturing processes and laboratory knowledge to eventual integrations and workflows into metals accounting using data aggregation.
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