A Complimentary CIMdata Educational Webinar with Sandeepak Natu, Co-Director, Simulation-Driven Systems Development Practice, CIMdata
8 August 2024
11:00 EDT | 08:00 PDT | 17:00 CEST
A digital thread is an integrated, connected framework of data that provides a comprehensive view of a product’s lifecycle, from initial design through manufacturing, operation, and end-of-life. It connects data from various stages and systems, enabling seamless information flow and real-time visibility into all aspects of the product's life. A significant portion of an enterprise's intellectual property and possibly its unique value proposition lies hidden in its digital threads. As product complexity rises and product development cycles shrink, the insights gained from an enterprise’s digital threads become critical to an organization’s success.
AI and ML technologies are compelling in identifying patterns and developing predictive models from large amounts of data. When exposed to AI and ML techniques, product and process data from an enterprise’s digital thread can transform the product lifecycle. Many potential use cases are becoming visible across industries including:
At CIMdata, we have been studying and researching the evolution of enterprise digital threads for several years. We are looking at it from the perspective of applying AI and ML technologies to generate additional enterprise value. Join us for this webinar where we will share CIMdata’s view on the current state of the application of AI and ML technologies across enterprise digital threads and how we see it evolving in the near term.
Executives responsible for PLM and digital thread initiatives, those responsible for digital transformation, PLM team leaders, PLM team members, PLM users, product managers, IT leadership, PLM solution providers, or anyone else who wants to understand AI trends and status within the global PLM industry.
During the webinar, you’ll also have the opportunity to ask questions about the topics discussed.
The Guide is designed to help potential PLM users evaluate the applicability and payoffs of PLM in their enterprise, and to help existing users of PLM monitor the impact it is having on their product programs.
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