2018年-德勤全球_Deloitte_and_NORCAT_9页_7mb

Future of Mining with AI: A Summary

Core Content

The document outlines the transformative potential of artificial intelligence (AI) in the mining industry, emphasizing its role in driving operational efficiency, improving health and safety, and reducing environmental impact. It presents a multi-step framework for successfully implementing AI technologies, highlighting the importance of strategic planning, data preparation, analytical modeling, insight communication, and operationalization.

Main Points

Why AI is Relevant to Mining

• Legacy Technology: Most mines still rely on outdated systems, making it difficult to achieve cost and productivity gains.
• Operational Challenges: Declining ore grades, longer haul distances, and volatile commodity prices are increasing the need for innovative solutions.
• Workforce and Talent Management: Changing demographics and perceptions of mining as a career are pushing the industry to rethink how it manages talent.
• AI's Role: AI technologies, including machine learning and deep learning, are enabling mining companies to become insight-driven, using data to make better decisions.

Benefits of AI in Mining

• Faster and More Accurate Decisions: AI accelerates data processing and decision-making, reducing human error.
• Improved Health and Safety: AI helps monitor worker behavior and automate risky tasks, enhancing safety.
• Boosted Efficiency: AI-driven systems improve productivity and reduce downtime.
• Smaller Environmental Footprint: AI optimizes energy use and reduces waste, contributing to sustainability goals.

Key Use Cases

• RockMass Technologies: Uses real-time sensors and machine learning to identify rock failure planes, enabling faster and more accurate risk assessments.
• ThoroughTec: Implements AI-driven simulator-based training that identifies behavioral trends and recommends targeted training, improving workforce performance.
• Ionic Engineering: Applies deep learning to image recognition, significantly reducing errors in copper grade identification.
• Shyft Inc.: Uses AI to forecast energy peaks and optimize ventilation systems, lowering energy costs.
• Wipware: Leverages neural networks to provide real-time material sizing data, enabling automated process control and reducing downtime.
• Praemo: Engages employees in the development and testing of AI solutions, ensuring alignment with operational needs and fostering adoption.

Challenges in AI Implementation

• Data Quality and Testing: High-quality, consistent data is essential for training AI models, but gathering it can be difficult.
• Industry Culture: Mining companies are often risk-averse and resistant to change, which can hinder AI adoption.
• Limited Understanding: Many professionals lack a clear understanding of how AI can be deployed and what financial returns it can deliver.
• Capacity Gaps: There is a need for combining traditional mining expertise with AI and data science skills, which can be challenging for organizations.

Framework for AI Implementation

Omnia AI, Deloitte Canada's AI practice, provides a 5-step framework for deploying AI in mining operations:

1. Discovery: Align business goals with the future of mining, identify key users and use cases.
2. Data Preparation: Transform raw data into a usable format, ensuring data quality and designing contingency plans.
3. Analytical Modelling: Develop data models using machine learning and deep learning, tailored to the complexity of the business case.
4. Insight Communication: Use intuitive data visualization to communicate insights and their operational impact.
5. Operationalization: Implement AI solutions with structured guidelines, ensuring scalability and adaptability.

Conclusion

The mining industry is at a pivotal moment, with AI offering a pathway to overcome long-standing operational and strategic challenges. While the adoption of AI is not without hurdles, the document argues that the right approach—centered on collaboration, clear communication, and a phased implementation—can lead to sustainable and impactful change. The future of mining lies in leveraging AI to create safer, more efficient, and environmentally responsible operations.

Key Partners and Organizations

• Deloitte Canada: Offers consulting and AI solutions to help mining companies navigate the digital transformation.
• NORCAT: A non-profit innovation center that supports the development and testing of new mining technologies in real mine environments.
• Omnia AI: Deloitte's AI practice, providing a comprehensive framework for AI deployment in the mining sector.

Contact Information

• Andrew Swart: Consulting, Deloitte Canada – aswart@deloitte.ca
• Don Duval: NORCAT – dduval@norcat.org
• Shak Parran: Omnia AI – sparran@deloitte.ca

About the Partnership

Deloitte and NORCAT collaborate to provide insights into emerging mining technologies and innovation trends, combining practical experience from the NORCAT Underground Centre with Deloitte’s global market knowledge.

About Omnia AI

Omnia AI is a core part of Deloitte’s AI practice, with over 350 experts in AI, machine learning, data integration, and analytics. It focuses on aligning AI with business strategy and ensuring optimal value capture.

Special Thanks

• Technology Companies: Hard-Line Solutions Inc, Ionic Engineering, Praemo, Rockmass Technologies Inc., SHYFT Inc., ThoroughTec Simulation, and WipWare Inc.
• Contributors: Magesh Pillay, Wade Sahni, Jean Dauvin, and Mayesha Tashnil.

Publication Series

Further articles from Deloitte and NORCAT explore topics such as "Human Centred Design" and the "Future of Work" in mining, available at Deloitte and NORCAT - Collaborating to explore the future of mining.