Transform your maintenance strategy with a cognitive plant model that understands equipment behavior, predicts failures, and prescribes action, just like your best reliability engineer.
In complex industrial plants, fragmented data and disconnected workflows often lead to delayed root cause analysis (RCA), reactive maintenance, and suboptimal reliability.
This webinar explores how process traceability, powered by Agentic AI, can revolutionize maintenance, enabling:
- Rapid fault tracing across sensors, SAP, and engineering documents
- Automated root cause investigation with full process lineage
- Prescriptive maintenance actions and work order recommendations
- Risk-based maintenance prioritization for reliability optimization
- Autonomous planning with context-rich decision support
By building a Process Traceability Graph, organizations can connect maintenance records, sensor events, SAP orders, and engineering documents to create a living digital thread of the plant.
Agentic AI agents can then traverse this graph to deliver explainable, self-learning, and prescriptive maintenance workflows.
Key Takeaways:
✔️ How process traceability accelerates fault-to-fix cycles and RCA
✔️ Building a maintenance knowledge graph from sensors, SAP, SOPs, and work orders
✔️ Automating maintenance planning, fault tracing, and risk-based decision-making with Agentic AI
✔️ Real-world use cases: recurring equipment failures, risk prioritization, prescriptive maintenance suggestions
✔️ The future of reliability: self-healing plants and continuous process-aware AI
- Agenda:
- Maintenance and reliability challenges in modern industrial operations
- Architecture, contextualization, and process lineage for maintenance
- Fault tracing, RCA, prescriptive maintenance, and risk-based planning
- Recurring faults, downtime analysis, and automated maintenance recommendations
- Discussion and pathways to adoption
