AI Shift Management Boosts Productivity in Eurasia's Largest Coal Mine
In the previous two posts we broke down why dispatching exists and why the traditional model hits a ceiling. Now — how we think shift management should actually work.
Today's typical dispatcher spends most of their shift on routine: tracking equipment, rerouting trucks, filling in logs, reacting to things that have already gone wrong. Proactive management — finding inefficiencies before they compound — gets maybe 10–15% of their attention. That's exactly where the real gains are.
At one of our flagship operations — largest coal mine in Eurasia — we built it differently. The core idea: take the cognitive load of managing hundreds of small situations per shift off the dispatcher, and hand it to the system.
An AI balancer continuously recalculates truck assignments and sends directives straight to drivers' tablets in the cab. Not through the dispatcher, not over the radio — directly. The driver sees the route, confirms, and moves. No more calling eighty people one by one.
For everything else, we built MAS: a multi-agent system of thirty-plus AI agents, each responsible for its own zone, each taking a decision all the way to the specific person who needs to act on it.
🟢 Excavator losing productivity? The agent identifies the cause and sends the mine supervisor a specific task. 🟢 Trucks slowing on a haul road? The agent generates a maintenance task automatically — no calls, no waiting.
🟢 Driver stretching a refueling stop? A reminder goes straight to his tablet.
And so it goes — for every deviation, every piece of equipment, around the clock.
The results:
✅ +17% excavator productivity
✅ 15–25% downtime
✅ $900K/month in savings
These numbers didn't appear because we replaced dispatchers. They appeared because we gave them the space to do what actually matters. The dispatcher of the future isn't someone replaced by AI. It's someone AI gave their time back to.
In the previous two posts we broke down why dispatching exists and why the traditional model hits a ceiling. Now — how we think shift management should actually work.
Today's typical dispatcher spends most of their shift on routine: tracking equipment, rerouting trucks, filling in logs, reacting to things that have already gone wrong. Proactive management — finding inefficiencies before they compound — gets maybe 10–15% of their attention. That's exactly where the real gains are.
At one of our flagship operations — largest coal mine in Eurasia — we built it differently. The core idea: take the cognitive load of managing hundreds of small situations per shift off the dispatcher, and hand it to the system.
An AI balancer continuously recalculates truck assignments and sends directives straight to drivers' tablets in the cab. Not through the dispatcher, not over the radio — directly. The driver sees the route, confirms, and moves. No more calling eighty people one by one.
For everything else, we built MAS: a multi-agent system of thirty-plus AI agents, each responsible for its own zone, each taking a decision all the way to the specific person who needs to act on it.
🟢 Excavator losing productivity? The agent identifies the cause and sends the mine supervisor a specific task. 🟢 Trucks slowing on a haul road? The agent generates a maintenance task automatically — no calls, no waiting.
🟢 Driver stretching a refueling stop? A reminder goes straight to his tablet.
And so it goes — for every deviation, every piece of equipment, around the clock.
The results:
✅ +17% excavator productivity
✅ 15–25% downtime
✅ $900K/month in savings
These numbers didn't appear because we replaced dispatchers. They appeared because we gave them the space to do what actually matters. The dispatcher of the future isn't someone replaced by AI. It's someone AI gave their time back to.