Predictive Analytics

Smart recommendations for proactive actions


  1. 1. Alerts and warnings are available in real-time from resources.
  2. 2. Reactive maintenance after fatal failures is costly as a result of penalties and customer perception.
  3. 3. Proactive maintenance without any data is also very resource intensive and expensive.


  1. 1. Recommendations based on the alerts and warnings from machines on what maintenance is needed for a particular resource.
  2. 2. Continuous feedback on the recommendations based on the maintenance work carried out using workflow automation within the DataTwin platform.
  3. 3. Easy integration with other systems and ability for the users to easily collaborate and work.


  • – Maintenance cost reduced by 35%.
  • – Machine uptime increased from 95% to 97%.

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