Use of IoT & AI in Mining Industry for Inventory & Consumables Tracking

How IoT (Internet of Things) and Artificial Intelligence (AI) are used in the mining industry for inventory and consumables tracking, including system architecture, components, and benefits:

🏗️ 1. Background and Importance

Mining operations rely on a wide range of consumables and spare parts, such as:

• Blasting materials (explosives, detonators)
• Drill bits, lubricants, and cutting fluids
• Wear parts for crushers, mills, conveyors
• Safety gear, PPE kits, filters
• Diesel, hydraulic oil, greases
• Conveyor belts, cables, and electrical spares

Poor inventory visibility leads to:

• Stockouts, causing unplanned downtime
• Overstocking, tying up capital
• Loss or theft, especially for high-value or hazardous materials
• Inefficient procurement and delays

📶 2. IoT Use in Inventory & Consumables Tracking

A. RFID (Radio-Frequency Identification) Tags

• Passive RFID tags attached to consumables and spare parts
• Readers at entry/exit points (e.g., stores, workshops, tool cribs)
• Automates tracking of movement, usage, issuance, and returns

B. BLE (Bluetooth Low Energy) & LoRa Sensors

• For tracking high-value assets or tools in real-time over wide areas
• Used to monitor tool usage in workshops, mining sites

C. Weight Sensors & Load Cells

• Measure remaining quantity in:
  • Diesel tanks
  • Lubricant drums
  • Chemical dosing tanks
  • Explosives magazines

D. Barcode / QR Code Integration

• Smart handheld scanners used by warehouse staff for:
  • Stock checks
  • Issuance and returns
  • Batch and expiry tracking

E. Smart Cabinets & Vending Machines

• Lockable units with IoT control
• Used for automatic dispensing of PPE, tools, or consumables
• Access logged via RFID badges or biometrics

🧠 3. Artificial Intelligence Applications

A. Demand Forecasting

• AI uses historical usage data, production schedules, and equipment health data to:
  • Predict future consumption
  • Plan reordering automatically
  • Prevent emergency procurement

B. Inventory Optimization

• Machine learning models determine:
  • Optimal reorder points (ROP)
  • Economic order quantity (EOQ)
  • Just-in-time (JIT) stocking strategies

C. Anomaly Detection

• AI flags:
  • Unusual withdrawals (potential theft or hoarding)
  • Sudden spikes in consumption (possible equipment issues)
  • Expired stock being issued

D. Automated Procurement Triggers

• AI-powered systems generate purchase requests (PRs) when:
  • Inventory falls below thresholds
  • Predicted lead time and consumption risk stockout

E. Usage Pattern Recognition

• AI identifies:
  • Which crews or shifts consume more
  • Which tools or spares fail more frequently (linked to operator behavior)

🧩 4. System Architecture

🧾 5. Use Case Example: Lubricant Inventory Tracking

Problem:

Frequent stockouts of hydraulic oil disrupted underground mining equipment operations.

Solution:

• IoT load cells installed under lubricant tanks
• AI forecasted consumption patterns based on equipment hours and maintenance logs
• Automated PRs sent to procurement 5 days before depletion
• Alerts set for unauthorized withdrawals

Outcome:

• 25% reduction in unplanned downtime due to consumable shortage
• Optimized inventory levels with no overstocking

📊 6. Key KPIs Tracked

🔐 7. Security and Safety Integration

• Explosives and chemical tracking via RFID to meet DGMS and safety regulations
• Tamper alerts for unauthorized access to controlled items
• AI compliance dashboards for audit trails and safety reporting

✅ 8. Benefits of IoT & AI in Inventory Management