Key Takeaways

  • MineMax by NTWIST is explicitly AI-driven — it sits as a supervisory layer over existing mine and plant systems, continuously learning from operational outcomes to make real-time blend and feed recommendations.
  • Deswik Blend, BOLT, and GO are the leading optimization-based planners for stockpile management, multi-commodity blending, and joint mine-to-market feed decisions.
  • Dynamic blend-consistency dispatching (routing trucks to maintain a target feed blend in real time) has been reported to raise truck cycle efficiency by roughly 11% in deployed systems.
  • MineMax’s four core models — OreMax, DynaMax, PlanMax, MillMax — cover ore tracking, stockpile intelligence, feed forecasting, and optimization as one connected decision layer.
  • All three items here (stockpile, plant feed, and blending strategy) are really one problem viewed from three points in the material flow, and increasingly solved by the same platforms.

TL;DR

Layer an AI optimization platform (MineMax/NTWIST, or Deswik’s Blend/BOLT/GO suite) on top of your existing mine and plant systems to continuously recommend stockpile allocation, plant feed mix, and dispatch blending targets from real-time ore-tracking data, rather than planning blends on a fixed weekly/monthly schedule.

How Do I Optimize Ore Blending, Stockpile and Plant Feed Strategy With AI?

Stockpile strategy, plant feed strategy, and blending strategy are really the same optimization problem — how do you combine the ore you have (varying in grade, hardness, clay content, deleterious elements) to hit a consistent plant feed target — viewed at three different points: what goes onto which stockpile, what gets drawn to feed the plant, and how trucks get routed to build the right blend as it’s mined.

Deswik’s Blend, BOLT, and GO products cover the planning end of this: Deswik Blend models material flow to simplify managing and blending from multiple deposits or stockpiles; BOLT is a multi-commodity blending, stockpiling, and logistics optimizer that generates mathematically optimal plans across the full value chain; Deswik GO uses fast multi-pit optimization to jointly solve mining-to-marketing feed decisions for NPV. These are optimization-based planning tools — you define constraints and objectives, and they solve for the mathematically best blend/feed schedule, typically on a planning cadence (shift, week, month) rather than continuously.

MineMax, built by NTWIST, is the more explicitly AI-native option and operates differently — it sits in a supervisory layer on top of your existing mine and plant systems rather than replacing them, combining ore tracking, stockpile intelligence, and feed forecasting into a single pit-to-plant decision layer that continuously learns from operational actions and outcomes as conditions change. Its four core models (OreMax, DynaMax, PlanMax, MillMax) split the problem into ore characterization, dynamic scheduling, planning, and mill/plant optimization, giving geologists, mine engineers, and metallurgists a shared real-time view rather than each working from separately-updated plans.

The execution layer is dispatch: routing individual truckloads to maintain your target blend as material is actually mined, rather than only planning the blend on paper. Dynamic blend-consistency dispatching — where the fleet management system actively routes trucks to hit a blend target in real time, informed by grade sensing at the shovel — has been reported to raise truck cycle efficiency by around 11% in deployed systems, because trucks spend less time being rerouted or queued for the “right” stockpile.

Try It With Geocluster

Getting real value from any of these platforms depends on the quality of the ore-tracking data feeding them — grade, hardness, and mineralogy estimates need to be reconciled across your geological model, grade-control data, and plant feed records. That reconciliation work is exactly what Geocluster is built to support if you’re setting up a blend/feed optimization pipeline.