Smart mining and metals operations

To make strategic decisions for complex and integrated mining and metals assets, such as iron-ore operations, companies should test the impact of different scenarios and changes to their supply chains and evaluate the effects on systems throughput. For instance, key use cases for simulation and optimization software tools might include:

• Developing asset maintenance plans: Generate rail and fixed plant maintenance plans based on high-level requirements and planning rules.
• Designing future mines: Evaluate the impact of new mines on a portfolio and investigate strategies to ramp-up production.
• Operational improvement initiatives: Evaluate the impact of planned initiatives on expected supply chain throughput.
• Pathway to the energy transition: Evaluate the impact of using battery-powered vehicles across the operation.

The challenge

Deloitte Australia was recently engaged by a global mining company to develop a solution that could support decision making in these types of scenarios. Three workstreams were identified:

1. The technical workstream built an ingestion engine in Python and executed functions in the client’s selected cloud platform: AZURE.
2. The modelling workstream built discrete simulation models of the value chain in Python and SimPy, using VS Code, AZURE DevOps and Git. 
3. The ways of working workstream focused on developing the identity of the project through showcase delivery and a single source of truth for project collateral. 

Outcomes and value delivered

The project delivered the following benefits: 

• The client now has a self-service app that enables users to execute simulations without the need for subject matter specialists. 
• Data ingestion is now available in near-real time. Previously, manual data ingestion was lengthy and tiresome.
• Maintenance schedules can be generated in less than 24 hours – a reduction of six weeks. This allows planners to utilize this time for optimization activities.
• The client’s digital teams are trained in Python and SimPy, meaning they are ready for feature enhancements and bug remediation tasks.
• A single source of truth has been created for all integrated system model (ISM) documentation and training.