Data-Driven Decision Making in Mining Operations: Turning Insights into Actions

In the ever-evolving landscape of mining operations, the role of data has transcended from mere measurement to becoming a foundational pillar in strategic and operational decision-making. By leveraging data, mining companies can enhance efficiency, reduce costs, and ensure sustainability. In a recent discussion with Andre Naude and Sean Mowatt from Business Science Corporation’s Productivity Science division, we delved into the intricacies of data-driven decision-making and its transformative impact on mining operations.?

Defining Data-Driven Decision Making in Mining?

According to Sean Mowatt data-driven decision-making is about making objective, scientific-based decisions underpinned by data, rather than relying on gut feelings or anecdotal evidence. "It involves the whole process of collecting data, analysing it, beneficiating it, interpreting it, and then using that in decision-making across operational, technical, and strategic levels," Sean explains.?

In mining, the types of data can be quite diverse—ranging from geological data and sensor metrics to financial data. These data points are crucial for optimizing processes, driving efficiency, reducing costs, managing energy and mineral resources, and improving safety. Andre Naude concurs, emphasizing that the applicability of data-driven decision-making in mining is vast and transformative, touching every aspect of operation and strategy.?

The Importance of Data Governance?

The complexity and scale of mining operations necessitate stringent data governance measures to ensure data quality and integrity. Andre Naude points out, "Mining involves hundreds of pieces of equipment and personnel at any stage of production, which generates a vast amount of data." Effective data governance is essential to manage this complexity and prevent underutilization, which has been a challenge in past years.?

He suggests a top-down strategy, starting with high-level KPIs and ensuring that data is aligned and measured against these performance targets. This approach creates a foundation where stakeholders can easily comprehend and utilize the data, fostering organic understanding and insight into the business. Andre shares that success often starts small, using high-quality data to provide quick, actionable insights, which builds confidence and demonstrates the power of data governance.?

Effective Data Integration Techniques?

Integrating data from various sources is another critical aspect, especially in mining where cost management and production efficiency are paramount. Andre Naude advises focusing on high-level cost and production data, such as those from ERP systems like SAP or high-level production data from systems like Modular Mining and PI historians.?

The approach should be top-down, beginning with high-level data to understand the business operations and challenges. This helps in identifying where to focus more detailed data integration efforts. Given the multitude of IoT devices and data sources in mining, it's essential to link integration efforts with specific operational issues rather than pursuing data integration for its own sake. This targeted strategy ensures that the data integration efforts result in meaningful, actionable insights.?

Predictive Maintenance and Operational Efficiency?

Predictive maintenance offers significant benefits for improving operational efficiency. Sean Mowatt explains that proactive maintenance reduces downtime by ensuring that necessary spares and operators are available, thereby minimizing delays. "By ensuring the right parts and technicians are on-site, we can significantly reduce idle times and extend equipment lifespan," Sean notes.?

He shares a success story involving tire tread prediction, where predictive modelling was used to anticipate tire wear and schedule proactive maintenance. This approach not only reduced idle time but also saved costs, illustrating how predictive maintenance can transform the maintenance schedules and overall efficiency of mining operations.?

Optimizing Supply Chains?

Data-driven decision-making is crucial for optimizing supply chains, which have become increasingly complex in recent times. Sean Mowatt highlights several strategies that mining companies can employ, including demand forecasting, inventory management, route optimization, and supplier benchmarking. Predictive analytics can help forecast mineral demand and optimize inventory levels to prevent overstocking or understocking.?

Sean also emphasizes the importance of real-time tracking and benchmarking suppliers to ensure efficient supply chain management. A case study involving a lithium producer showcased how data modelling helped optimize working capital and manage a complex supply chain extending from mines in Australia to smelters and refineries in China.?

Converting Data to Actionable Intelligence?

Turning vast amounts of data into actionable intelligence is no small feat. Andre Naude describes this process as more of an art than a science. The key is taking a top-down approach to identify strategic opportunities and a bottom-up approach for implementation. This involves building models to link strategic objectives with operational actions, ensuring that every initiative is impactful and aligned with the mine’s goals.?

For instance, if optimizing hauling performance is identified as a strategic objective, the actionable intelligence would involve specific levers to pull—such as increasing truck availability by improving the supply chain for maintenance parts. Creating and validating detailed models is crucial for describing how these changes will impact the overall performance, making the intelligence truly actionable.?

Promoting Sustainability Through Data?

Data-driven decision-making also plays a critical role in promoting sustainability in mining operations. Andre Naude points out that optimizing existing assets can significantly reduce energy consumption and operational costs. This approach makes future Environmental, Social, and Governance (ESG) transitions more feasible by lowering both current and future capital costs associated with energy use.?

Andre shares a success story where simulation models were used to identify and apply initiatives that improved efficiency and aligned with long-term sustainability goals. This proactive approach ensures that mining companies are prepared for ESG transitions and can implement sustainable practices effectively.?

The Future Outlook?

Looking ahead, Sean Mowatt and Andre Naude foresee significant advancements in data-driven decision-making through technologies like AI, machine learning, digital twins, and autonomous equipment. These innovations will enhance exploration, safety, efficiency, and sustainability in mining operations.?

Sean Mowatt highlights AI and machine learning's potential to uncover complex correlations and optimize operations. He emphasizes that as mining operations become more complex, with deeper, lower-grade ore bodies, leveraging these technologies will be crucial for successful exploration and mining.?

Andre Naude is particularly excited about the potential of digital twins and customized equipment. By using data to create a precise operational fingerprint, mining companies can work with OEMs to develop assets and services tailored to their specific needs. This level of customization promises to revolutionize how mining equipment is designed and utilized, ensuring higher efficiency and better performance.?

Additionally, augmented reality (AR) and virtual reality (VR) are expected to play a significant role in training, visualization, and safety. These technologies can enhance operator training, facilitate complex concept visualization, and improve change management processes.??

Embracing the Future of Mining with Data?

The journey from data collection to actionable insights and strategic implementation is complex, multi-faceted, and transformative. Business Science Corporation continues to lead the charge, helping mining companies harness the power of data to achieve operational excellence and sustainability.?

As the industry navigates this data-driven future, the emphasis will be on maintaining high data quality, fostering a robust data-driven culture, and developing talent with strong data analytics and interpretation skills. The successful adoption of these principles will ensure that mining companies are well-positioned to face the challenges of the future, leveraging data to drive strategic success and sustainable growth.?

Learn more: https://bscglobal.com

About Business Science Corporation (BSC):

Business Science Corporation (BSC) is a leader in innovative business solutions, committed to transforming industries through cutting-edge technologies and expert consulting. Founded with a vision to drive sustainable growth and operational excellence, BSC specializes in providing comprehensive services in productivity science, data analytics, and digital transformation. Our dedicated team of professionals collaborates with clients across diverse sectors, including mining, manufacturing, and finance, to deliver tailored solutions that address complex challenges and foster long-term success.

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