Optimizing asset performance is a strategic priority in the mining and metals industry

KPIs are used to measure asset performance because they provide clear, quantifiable insights into factors such as monitoring equipment efficiency, reliability and even profitability.

Effective KPI data governance relies on rigorous validation at every stage, from data collection to its final use in decision-making. To ensure data quality and reliability, each step must be guided by well-defined processes and clear roles.

1.1. Transferring data, from source to performance reports

Data comes from a variety of sources, including sensors (PLCs), manual quality test inputs (LIMS), control systems (SCADA), operational monitoring platforms (MES/WMS) and even financial systems (ERP).

These datasets, often vast and complex, must be collected, filtered and centralized before they can be used effectively. To optimize this process, it’s best to centralize data within a scalable and modular infrastructure. Such an approach enables efficient processing of both local operational data and corporate data, supporting strategic decision-making.

Centralizing data provides an overview of asset-specific metrics and indicators for easier access while eliminating information silos. What's more, it strengthens collaboration among teams and enables the implementation of advanced analytics KPIs to support strategic decisions.

1.2. Data access: A four-level structure

1. Operations level (data characterization)
   Collected data must be categorized properly and organized by operators. At this level, it’s crucial to ensure clear and accurate data entry, including checking formats and adding appropriate metadata.
2. Supervisory level (data validation)
   Supervisors play a key role in data validation. They ensure that the information collected complies with defined standards and accurately reflects operational realities. At this stage, automatic or manual verification tools can be used to detect anomalies or inconsistencies, ensuring the accuracy of the data used in more complex analyses.
3. Continuous improvement level (data utilization)
   After the data is validated, it becomes a powerful lever for continuous improvement. For example, it can be used to identify trends, optimize processes or justify CAPEX investments in new and more efficient equipment.
4. Decision-making level (data analysis and governance)
   At this level, data is transformed into clear and actionable reports (KPIs and dashboards) for the management team. This team uses the information to guide strategic decisions, set priorities and steer the organization toward its future goals. Effective governance also means establishing feedback mechanisms to adjust strategies as needed.

The goal is to ensure that all data contributes to improved performance tracking, reduced efficiency losses and informed decision-making.

Failing to adhere to this structure can lead to fragmented data, biased strategic decisions and increased risk of regulatory non-compliance, ultimately compromising the organization’s operational performance and competitiveness.

Stay tuned for the next two steps!

Would you like to dive deeper into these topics with other industry leaders in mining and metallurgy? Attend our next virtual round table to exchange best practices, share experiences and discuss pitfalls to avoid. This is an excellent opportunity to explore actionable solutions together to improve asset performance.

Sign up now for our waiting list by emailing MES@bba.ca. You’ll receive updates on event dates and an exclusive invitation to this collaborative event. Don't miss this unique networking and learning opportunity!