How AI Fixes Stockpile Uncertainty in Open-Pit Mines

How AI Solves the Stockpile Guessing Game: Real-Time Tracking in Open-Pit Mining

At NTWIST, we’ve seen firsthand what happens when mines rely on outdated assumptions to manage material flow. One of the most costly breakdowns we encounter? Stockpile visibility. Without real-time tracking, planners are forced to guess what’s actually in a given pile - and that guess often leads to value loss, misrouting, and reconciliation issues.

In this article, we break down why stockpile uncertainty persists, what it costs, and how AI-powered tracking systems are helping mines eliminate the guesswork entirely.

The Stockpile Guessing Game

Stockpiles are meant to act as buffers between mine operations and processing plants. But when material is dumped without high-confidence grade verification - or when reclaim happens without reliable context - you’re left managing invisible variability. The orebody you think you’re sending to the mill might not be what actually arrives.

This uncertainty arises from two primary gaps:

• Disjointed data systems: Dispatch, geology, and survey tools often don’t share material updates in real time.
• Delayed measurement methods: Traditional survey workflows may take days or weeks to validate stockpile volume and composition.

By the time the data reaches the planner, the stockpile may already have changed. And if that pile feeds into a precision-sensitive flotation circuit or blend model, the cost of that gap compounds rapidly.

The Role of AI in Real-Time Stockpile Visibility

New advances in AI, drone-based scanning, and digital modeling now allow mining operations to track ore movement with near real-time resolution. These technologies do more than measure volume - they help reconcile what was planned vs what was actually delivered to a stockpile or reclaim feeder.

Drones equipped with LiDAR or photogrammetry sensors capture frequent, high-resolution surveys of active areas. AI algorithms then convert those surveys into digital elevation models (DEMs) and volumetric comparisons, providing instant feedback on stockpile growth, depletion, and movement behavior (Toll Uncrewed Systems, 2024).

When connected to live dispatch data, these AI models can also validate whether the material delivered to a stockpile matches the expected source - and flag inconsistencies. This allows mine planners and metallurgists to intervene early, adjust reclaim plans, or reroute material before performance is affected.

What This Looks Like in Practice

Let’s say an open-pit mine deploys weekly drone flights over three active stockpiles. AI systems convert the point clouds into volume models and automatically compare them against dispatch logs. One pile is growing faster than expected, with trucks tagged as “waste” contributing to its volume.

The AI flags a discrepancy: multiple trucks routed from Block 27 (marginal Cu-Au) were dumped on the high-grade stockpile. Within hours, planners correct future routes, dispatch is re-informed, and a near-miss becomes a closed loop.

Compare that to a traditional operation, where that mistake might not be noticed until mill feed grades begin to drift - and by then, it’s already a loss.

Benefits of Closing the Loop

• More accurate feed forecasting — Real-time reconciliation improves blend control and recovery prediction.
• Fewer rehandling costs — When stockpiles are trusted, trucks move less, and material isn’t double-handled.
• Better decision-making — Metallurgists and mine planners operate on verified, current data.
• Increased accountability — Operators, shovels, and trucks are all part of a connected material flow chain.

3 Takeaways for Mine Operators

1. Visibility is control. If you can’t see what’s in your stockpile right now, you’re not managing risk - you’re hoping for the best.
2. Drones + AI = advantage. Use survey tech to gather, AI to analyze, and dispatch data to complete the picture.
3. Fix misrouting before it becomes a loss. Catch errors while they’re still just inefficiencies - not liabilities.

Conclusion: Eliminate the Guesswork

At NTWIST, we help mines move from reactive stockpile management to proactive decision-making. With AI-powered models, dispatch-integrated intelligence, and automated reconciliation, your teams no longer have to guess what’s in the pile. They know.

Because in modern mining, guessing isn’t just risky - it’s expensive. And with the right tools, it’s also unnecessary.

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