Samuel Chan
Co-Founder Supertype.ai

Day 1:

09:00–09:20 : Module 1: Introduction - The Imperative for Data-Driven Mining.
Objective: Set the stage for the training, emphasizing the evolving landscape of the coal mining industry and the critical role of data and AI in navigating future challenges and opportunities.

Key Topics:

• Welcome and Introductions
• The Changing Global Energy Landscape and its Impact on Coal
• Defining "The Mining Revolution": Beyond traditional practices
• The Strategic Value of Data as an Asset
• Overview of the Training Agenda and Expected Outcomes

---

09:20–10:20 : Module 2: Strategic Market Intelligence in Indonesia’s Coal Market.
Objective: Enable executives to appreciate the depth and strategic importance of market intelligence in making informed decisions regarding resource allocation, investment, and market positioning.

Key Topics:

• The Evolving Role of Market Intelligence: From reactive reporting to proactive strategy.
• Decoding Demand and Supply Dynamics:
• Global and regional energy trends influencing coal demand (industrial, power generation).
• Impact of renewable energy growth and climate policies.
• Key supply regions, their capacities, and geopolitical influences.
• Understanding Coal Reserves and Production Nuances:
• Assessing reserve quality and accessibility – strategic implications.
• Global production trends and forecasting challenges.
• The role of technology in reserve estimation and production planning.
• Navigating the Competitive Landscape:
• Identifying key global and regional players (producers, traders, consumers).
• Understanding competitor strategies, strengths, and weaknesses.
• Emerging market entrants and disruptive forces.
• Optimizing the Coal Supply Chain through Intelligence:
• Mapping critical supply chain nodes and vulnerabilities.
• Identifying opportunities for strategic partnerships and vertical integration (drawing parallels with Alamtri's model).
• The role of data analytics in forecasting price volatility and managing risk.
• Case Study Snapshot: Illustrating how market intelligence led to a significant strategic advantage or risk mitigation in a coal mining company.

---

10:20–10:50 : Coffee Break

---

10:50–12:00 : Module 3: Revolutionizing Logistics: AI-Assisted Barging Operations
Objective: Provide executives with insights into how IoT and AI can transform coal transportation logistics, leading to significant cost savings, efficiency gains, and improved supply chain resilience.

Key Topics:

• The Challenge of Coal Logistics: Traditional pain points in barging and transportation (delays, inefficiencies, lack of visibility).
• The Power of IoT in Coal Transportation:
• Real-time tracking of barges, cargo, and equipment.
• Sensor data for monitoring key parameters (e.g., fuel consumption, load status, draft levels).
• Connectivity in remote and challenging waterway environments.
• AI for Intelligent Barging Operations:
• Real-time Monitoring of Key Waterways:
• AI-powered analysis of weather patterns, water levels, and congestion.
• Predictive alerts for potential disruptions.
• Logistics and Supply Chain Optimization through AI:
• Optimized routing and scheduling of barges.
• Improved fleet utilization and turnaround times.
• Dynamic adjustments to schedules based on real-time data.
• Enhanced coordination between mines, ports, and end-users.
• Benefits for Senior Executives:
• Reduced demurrage and waiting times.
• Lower fuel consumption and operational costs.
• Increased throughput and delivery reliability.
• Enhanced decision-making through data-driven insights.
• Illustrative Example: How Supertype's approach with Alamtri (or a similar anonymized case) is leveraging IoT and AI for tangible benefits in barging.
• Discussion: Identifying key opportunities for implementing AI-assisted logistics within their own operations.

---

12:00–13:00 : Lunch Break and Networking

---

13:00–14:00 : Module 4: Predictive Maintenance - Maximizing Asset Uptime and Reducing Costs.
Objective: Equip executives with an understanding of how predictive maintenance, powered by AI and machine learning, can significantly reduce equipment downtime, lower maintenance costs, and improve overall operational efficiency for critical mining assets.

Key Topics:

• The High Cost of Unplanned Downtime in Mining: Impact on production, safety, and profitability.
• Evolution of Maintenance Strategies: From reactive and preventive to predictive.
• Predictive Maintenance in the Coal Industry Context:
• Unique challenges and opportunities for heavy machinery.
• Focus on Critical Assets: Excavators and Haul Trucks Health Monitoring:
• Types of sensors and data collected (vibration, temperature, oil analysis, operational parameters).
• AI and machine learning algorithms for anomaly detection and pattern recognition.
• Failure Prediction and Proactive Intervention:
• Early warnings of impending equipment failures.
• Optimizing maintenance schedules based on actual equipment condition.
• Reducing unnecessary maintenance and associated costs.
• Tangible Benefits of Predictive Maintenance:
• Increased asset availability and utilization.
• Extended equipment lifespan.
• Improved safety by preventing catastrophic failures.
• Optimized spare parts inventory.
• Case Insight: Highlighting the impact of a successful predictive maintenance program in a mining operation (e.g., reduction in downtime, cost savings achieved).
• Strategic Considerations for Implementation: Data infrastructure, skills requirements, and change management.

---

14:00–14:30 : Module 5: Driving the Transformation - Leadership and Strategic Implementation.
Objective: Empower executives to lead the charge in adopting data-driven initiatives by understanding the critical success factors and the role of leadership in fostering a data-centric culture.

Key Topics:

• The Role of Senior Leadership in Championing Data Initiatives: Setting the vision and commitment.
• Building a Data-Driven Culture: Overcoming resistance and fostering innovation.
• Strategic Partnerships for Success: Leveraging expertise like Supertype's experience with Alamtri.
• Key Steps for Initiating High-Impact Data Projects: Identifying pilot projects, securing resources, and measuring ROI.
• Ethical Considerations and Data Governance in Mining AI.

---

14:30–15:00 : Module 6: Q&A, Wrap-up, and Call to Action.
Objective: Address any remaining questions and reinforce the key takeaways, encouraging executives to identify actionable steps for their organizations.

Key Topics:

• Open Q&A session.
• Recap of key insights and strategic imperatives.
• Call to Action: Identifying one key data-driven initiative to explore further.
• Closing remarks.

---