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Michael Grieves


Michael Grieves

Michael Grieves
Professor, Florida Institute of Technology (FIT), USA


Dr. Michael Grieves splits his time between the business and academic worlds. He is the author of the seminal books on PLM: “Product Lifecycle Management: Driving the Next Generation of Lean Thinking” (McGraw-Hill, 2006) and Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management” (SCP, 2010). Dr. Grieves is an acknowledged world expert in PLM and lectures world-wide on engineering, manufacturing, and PLM in both industry and academia conferences. In addition to his books, Dr. Grieves has numerous publications and articles. Dr. Grieves consults with a number of leading international manufacturers and governmental organizations such as NASA.

Presenting: IoT, the ‘Digital Twin’ and Monitoring After-Market Product Performance

9 Mar 2016, 09:25

Allowing revenue streams to stop at point of sale is, for many, a thing of the past. Providing a product and an attached service contract however, maximizes the bottom line whilst maintaining client/consumer engagement and loyalty. After all, if you made the product and have all of its related data, there is no one else better placed for the job of servicing it. But this needs to go beyond a warranty and the real-time and continuous connectivity opportunity offered by IoT provides a method of constant information generation, collection and analysis so a full prognostic operation is underway at all times. By leveraging on existing product data and the data being generated in the after-market, a Digital Product Twin can be created that allows for the continuous monitoring of its physical partner and allows you to always be one step ahead of failure.

  • What does the Digital Twin mean in terms of product information from design through to manufacturing?
  • Front running simulation of after-market performance in real-time to predict and therefore avoid downtime and failure
  • What is the role of IoT in realizing this model?
  • Exploring example use cases
  • What part does PLM and it’s associated product data play in enabling a prognostic strategy?
  • How do companies need to be thinking, what understanding is needed and what information is required to make this a business reality?