Benefits for Manufacturing

VIA by Vitria improves the monitoring, management, and optimization of manufacturing operations management.  It enables real-time situational awareness, proactive response, predictive maintenance, and faster time to issue resolution.


VIA can ingest streaming IoT data, at millions of events per second and process this data in combination with relational data stores in real time.  It supports the operationalization of complex machine learning models to enable process automation that delivers productivity gains.

  • Gain visibility to complex manufacturing processes in real time
  • Manage processes by exception in real time
  • Take automated or semi-automated actions based on advanced analytic results
  • Predict equipment failure and take preventative action
  • Reduce physical equipment inspections with real-time performance data
  • Lower time to diagnose, improve time to respond and increase the first-time fix rate with equipment service incidents

Use Cases

Improve operational performance though insight and analytics that enable faster response and better decisions.

  • Real Time Monitoring of Complex Operational Processes

    Define and continuously refine key performance indicators for complex operational processes based on insights gained from advanced analytics. Ingest and analyze streaming data, data from relational data stores, and other required sources to monitor performance indicators in real time to spot anomalies, determine root cause, and take corrective action.

  • Predictive Maintenance

    IoT sensors on critical assembly equipment can deliver data in real-time that enable managers to make decisions rapidly and trigger actions to maintain production lines at maximum capacity. Leverage machine learning algorithms and predictive analytics to determine when to best schedule maintenance to minimize downtime, reduce the number of manual inspections and potentially extend asset life.

  • Accelerate Incident Resolution

    Reduce human involvement in diagnosing and accelerating response to equipment service incidents through machine learning, predictive analytics and the ability to automate the next best action for equipment repair.

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