SmartMRO - Maintenance & Repair Operations with Agentic AI

Our AI automates machine maintenance and repairs by detecting risks, planning tasks, and streamlining parts sourcing—reducing failures, optimizing resources, and maximizing uptime with smart agents.

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Project Story: Reimagining Machine Maintenance through Manufacturing Digital Ops with Agentic AI

🔧 About the Project

Machine downtime and inefficient maintenance are long-standing challenges in manufacturing. Failures are often detected only after they occur, requiring time-consuming root cause analysis across disconnected data sources. Operator training remains static, with limited exposure to real-time fault scenarios. Spare part stockouts and unclear repair costs further delay recovery.

This project began with one question:

What if machines could tell us when and how they’re likely to fail—before they actually do?

We set out to reimagine Maintenance & Repair Operations (MRO) by combining Agentic AI with Manufacturing Digital Ops—reducing unplanned downtime, enabling predictive maintenance, and streamlining spare part logistics.

🌟 Inspiration

Business Problems Identified

  • Failure risk is detected too late, requiring multiple systems and manual effort to identify root causes.
  • Operator training is rigid and lengthy, with conflicting schedules and limited exposure to real scenarios.
  • No visibility into prior failure actions, or the full impact of repair-related downtime.
  • Spare parts are hard to locate, making it difficult to assess total recovery cost or lead time.

🛠️ How We Built It

Agentic AI Workflows

The system uses intelligent agents to improve equipment safety and maintenance efficiency.

-Stage 1, tools extract risks, parameters, and past issues from SOPs and logs. -Stage 2 optimizes maintenance operations under budget/labor constraints , followed by scheduling and executive reporting. -Stage 3 handles part replacement by checking inventory, finding suppliers, and recommending the best option. Each agent automates key decisions—risk detection, maintenance planning, and procurement—to reduce failures and optimize resources.

AI-Powered Diagnostics

  • Agents analyze machine logs and process parameters to detect anomalies.
  • SOPs are scanned to extract steps, parameters, and potential failure points.

Simulation Engine

  • Agents simulate production processes to recommend step-by-step paths for achieving optimal yield and throughput.
  • Learnings include thresholds, parameter interdependencies, and cause-effect chains.

Predictive Maintenance Planning

  • Agents recommend action plans based on historical data, repair impact, and available labor/resources.
  • Maintenance tasks are optimally scheduled to minimize cost and downtime.

Spare Parts Optimization

  • Real-time tracking of spare inventory and supplier options.
  • Recommendations to restock, reorder, or reroute parts based on urgency and cost.

🚧 Challenges We Faced

  • Data fragmentation across machines, SOPs, and inventory systems.
  • Limited labeled data for failure prediction and causal analysis.
  • AI explainability was needed for operator trust and adoption.
  • Integration barriers with both legacy systems and modern IoT devices.

🎓 What We Learned

  • Agent-based AI doesn’t just automate—it augments.
  • AI agents that can sense, simulate, and act bring a fundamental shift to manufacturing ops.
  • Deep collaboration with domain experts was critical to shaping agents that could understand real-life shop-floor variability.
  • Virtual operator training powered by these agents shortens ramp-up time and brings exposure to real failure cases.

🚀 Impact & Outlook

Future State Achieved:

  • Failures predicted, not just detected.
  • Operator training virtualized, shorter, and scenario-based.
  • Spare part procurement optimized through real-time tracking and smart sourcing.
  • Maintenance becomes strategic, not reactive.

This solution sets the foundation for self-healing manufacturing systems—where intelligent agents continuously monitor, decide, and act at the edge.

What's next for Reimagine Maintenance & Repair Operations with Agentic AI

We're excited to scale this system across larger plants with more complex operations. Our next steps:

  • [ ] Integrate real-time sensor data for even faster detection and intervention
  • [ ] Add self-learning capabilities to adapt based on results over time
  • [ ] Extend the platform to support multi-line coordination and global spare part logistics

Built With

  • cloud-run
  • cloud-shell
  • gemini-api
  • gemini-models
  • google-agentic-development-kit
  • google-cloud
  • python
  • streamlit
  • vertex-ai
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