Integrated mining operations center using Physical AI to connect mine, plant, maintenance, logistics, energy, water, and safety operations while specialized AI agents coordinate recommendations under human supervision.

心を動かす会話

From Recommenders to an Agentic IROC

Transforming the integrated operations center into a Physical AI system that understands the end-to-end operation, coordinates specialized agents, and turns insight into governed action.

会議のスケジューリング

Local optimization is not the same as operational optimization.

WHY THIS AGENTIC IROC CHALLENGE?

Mine, plant, maintenance, logistics, energy, and water systems can each recommend a locally optimal action while operators reconcile competing priorities under pressure.

The next evolution of the IROC is a governed decision layer that understands context, evaluates trade-offs, and coordinates action around one shared operating objective.

主なメリット

One operation needs more than disconnected intelligence

An Agentic IROC connects domain decisions so the operation can reason across constraints, consequences, and total value.

Give every decision the wider context

Connect production, asset health, utilities, safety, and logistics.

Resolve trade-offs across domains

Evaluate local recommendations against shared objectives and hard constraints.

Increase autonomy with boundaries

Progress from copilot to local agent, cross-domain coordination, and orchestration.

Let the operating model learn

Replan from overrides, exceptions, outcomes, and feedback.

NTTデータのサポート体制は？

01

Find where coordination breaks down

Map critical decisions, recommender overrides, and cross-domain pain points.

Mining operations specialists reviewing an integrated view of mine, plant, maintenance, logistics, energy, and water operations to identify conflicting recommendations, decision overrides, and coordination gaps.

02

Define what the operation is optimizing for

Establish the critical-decision catalog, global objective, domain boundaries, constraints, and autonomy levels.

03

Build intelligence by domain then orchestrate it

Design specialized agents and a master orchestrator for cross-domain trade-offs.

Mining operations specialists collaborating around a detailed physical model of an end-to-end mining operation, examining how mine, plant, maintenance, logistics, energy, and water decisions interact.

04

Connect reasoning to the operating environment

Link perception, context, knowledge, reasoning, action, and learning to existing systems.

Mining operations specialists collaborating around a detailed physical model of an end-to-end mining operation, connecting operational knowledge and decisions across mine, plant, logistics, energy, and water systems.

05

Put governance inside autonomy

We don’t treat autonomy as unlimited authority. Confidence thresholds, vetoes, escalation, traceability, and feedback define where action is allowed.

Mining operations and safety specialists discussing operational decisions in the field, combining site expertise and real operating conditions to define where autonomous action is permitted and where human intervention remains required.

Mining operations specialists collaborating around a detailed model of the end-to-end mining value chain, bringing mine, processing, logistics, energy, and water operations into one coordinated view.

実証済みの効果

One system view. One operating objective. Governed intelligence across the value chain.

重要な結果

The target is coordinated decision-making across the full mining system.

Connected events to operational context

One system view across the value chain.

Aligned competing recommendations

One objective for resolving domain trade-offs.

Defined graduated autonomy

L1-L4 governed autonomy according to evidence and trust.

Built learning into operations

Continuous replanning through shift-level and periodic feedback loops.

Connect local intelligence to one shared operating objective.

Shape the IROC into a governed orchestration layer for the entire operation.

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