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AOM Implementation

· 3 min read
Suresh Ketha
Suresh Ketha
CEO Rise Corp

Implementing Asset Operations Management (AOM) in an iron ore mining "pit-to-port" value chain requires a holistic, phased approach. AOM unifies asset performance, process control, and operational efficiency under a composable digital twin and industrial AI agent framework.

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Unlike traditional Asset Performance Management (APM), AOM bridges maintenance, control, and operations domains – ensuring asset health improvements translate into real operational gains. Using a “Bowling Strike” approach (think big, start small, and scale fast), mining companies can sequence high-impact use cases across open-pit mining, processing, stockyards, rail haulage, and port operations.

AOM Vision: Think Big for Unified Pit-to-Port Operations​

The 3–5 year vision is a unified pit-to-port operations command center where AI co-pilots/auto-pilots and digital twins span the entire mining value chain.

Key Elements of the Vision​

  • Holistic Situational Awareness: Real-time visibility of mine equipment, conveyors, stockpile levels, trains, and port operations in one unified view.
  • AI-Powered Decision Making: AI “autopilots” recommend haul routes, optimize processing plant setpoints, and propose maintenance schedules.
  • Seamless OT/IT Integration: Data from OT (FMS, SCADA, MES, CMMS) and IT (production and maintenance planning) systems is fused into a single source of truth.
  • KPIs Aligned with Business Outcomes: Maximizing throughput, uptime, safety, and sustainability, while minimizing cost and resource/energy use (ESG oriented).

This ambitious vision ensures leadership can align technology and workforce transformation toward a unified end-state.

Head-Pin Use Cases: Start Small with High-Impact Pilots​

To demonstrate early value, AOM begins with focused “head-pin” use cases that deliver quick wins and measurable ROI.

Use Case 1: Predictive Maintenance & Smart Dispatch for Haul Trucks​

  • Domain: Maintenance + Operations
  • Scope: Fleet of open-pit haul trucks
  • Objective: Reduce downtime and improve fleet utilization
  • Approach: Digital twin integrates telemetry (FMS) and maintenance logs (CMMS). AI predicts failures and suggests maintenance windows, while dispatch integrates re-routing.
  • Outcome: Increased truck uptime, smoother shift productivity, fewer breakdowns, and reduced unplanned maintenance costs.

Use Case 2: Intelligent Crushing Plant Optimization​

  • Domain: Control + Maintenance
  • Scope: Primary crusher and stockpile feed system
  • Objective: Optimize throughput and prevent downtime
  • Approach: Digital twin monitors SCADA data (vibrations, power draw, ore properties). AI optimizes feed rates and predicts failures (e.g., bearing wear-out).
  • Outcome: Increased tons/hour, improved reliability, reduced stoppages, and safer operations.

Use Case 3: Rail Asset Health and Schedule Optimization​

  • Domain: Maintenance + Operations
  • Scope: Heavy-haul rail network and locomotives
  • Objective: Improve rail throughput, reliability, and energy efficiency
  • Approach: Digital twin integrates locomotive telemetry, wayside sensors, and traffic control data. AI forecasts equipment failures and adjusts schedules accordingly.
  • Outcome: Higher uptime, fewer delays, better cycle times, and reduced fuel consumption.

Use Case 4: Port Stockyard and Shiploader Optimization​

  • Domain: Operations + Maintenance
  • Scope: Port reclaimers, shiploaders, and stockpiles
  • Objective: Streamline ship loading and maximize asset availability
  • Approach: Digital twin combines SCADA, inventory, and vessel scheduling data. AI optimizes reclaiming, detects asset anomalies, and schedules shiploader maintenance.
  • Outcome: Reduced ship delays, lower demurrage, improved asset reliability, and alignment of supply chain with vessel scheduling.

From Pilots to Scale: The Bowling Strike Approach​

Each pilot demonstrates practical AOM value by unifying maintenance, control, and operations. Starting with high-impact “head-pin” use cases builds trust and momentum, while the ultimate vision remains a fully integrated pit-to-port command center powered by digital twins and industrial AI agents.