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Driving the future of engineering using Digital Twins

The use of real-time data volumes captured from machine sensors, and control systems are able to
present insights that previously were not possible. The application of such insights is far and wide in
business operations. For example, predictive failure detection through the use of machine learning
models can be implemented to help companies reduce operational downtime, improve efficiency, and
improve safety. Predictive failure detection is also a primary platform for showcasing the use of digital
A digital twin is a virtual model of a machine/structure/system that collects real-world information via
sensors, connectors, video, drones and other technology. It continuously learns from multiple sources,
including advanced analytics, machine-learning algorithms and artificial intelligence (AI) to gain valuable
insights about the performance, operation or profitability of a machine or an asset.
Does Digital Twin make sense for your company? Try thinking about it through these steps:

  • Identify a dilemma in your process. For example, does a machine continue to fail? Does it lead
    to down time? Is there the possibility that something in your process is not optimized and
    causing delays?
  • Understand business need and develop value targets.
  • Outline the entire process, steps, and outcomes.
  • Map Existing Data Sources.
  • Create Solution Architecture.
  • Develop a proof of concept.



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