For years, manufacturers have had to navigate in relative blindness, implementing improvements on an as-needed, reactive basis.
This approach, although functional, has been markedly inefficient and reactive, particularly in terms of process optimisation and asset reliability, two vital aspects of industrial operations that can profoundly impact efficiency and profitability.
Digital Twins represent a transformative shift from this reactive approach to a proactive, predictive one. They facilitate a deeper understanding of how systems behave, providing industrial operators with actionable insights that were previously unavailable.
To learn more about the application of digital twins in manufacturing, I had a podcast conversation with Erik Udstuen, who is the CEO and co-founder of TwinThread, a company that provides a digital twin platform that combines Industrial Data with Industrial AI in an integrated development environment for engineers and data scientists.
Here's the outline of our conversation
✅ Challenges driving Digital Twins adoption in modern manufacturing
✅ Key Functions of Digital Twins in Manufacturing
✅ Industrial AI Ops
✅ Use Cases for Asset and Process Digital Twins
✅ Connectivity standards for physical assets to digital twins
✅ Best practices for modelling assets and processes for digital twins
✅ Effective infrastructure abstraction techniques for Digital Twin Implementation
✅ ISA 88 / 95, and Data Modeling standards for Digital twins
✅ Principles-based vs Machine Learning-based modelling for advanced analytics
✅ Practical examples of successful digital twin applications in Manufacturing
✅ Selecting a digital twin platform and evaluating capabilities
✅ TwinThread Digital Twin Integrated development environment
Ep 23 : Digital Transformation Through Digital Twins - Dr. PG Madhavan (IoT Digital Twin, Causality, Data Science) )
Ep 22 : Building Scalable and Secure IIoT Solutions Using Open Source - Jeremy Theocharis (Co-Founder & CTO - United Manufactruing Hub) )
Ep 21: Edge AI: Artificial Intelligence and Distributed Computing at The Industrial Edge - Angelo Corsaro (CEO & CTO - ZettaScale Technology )
Ep 20: OPC UA and Time-Sensitive Networking for Field Level Communication - Bhagath Singh Karunakaran (CEO, Founder - Kalycito )
Ep 19: Application of Web 3 in Industrial IoT & Edge Computing - Rex St. John ( Jetson Software Ecosystem - NVIDIA )
Ep 18: Edge Computing for Industrial IoT - Dominik Pilat & John Kalfayan( Hivecell)
Ep 17: AutomationML, OPC UA & Asset Administration Shell for Industry4.0 - Dr. Miriam Schleipen ( Chief Research Officer, EKS InTec GmbH )
Ep 16: Application of Artificial Intelligence in Process Industries - Simon Rogers ( Digital Transformation Consultant, Yokogawa )
Ep 15: Edge Analytics and Intelligent Automation for Industrial IoT - Martin Thunman ( CEO & Co-Founder, Crosser )
Ep 14: IIoT Semantic interoperability with MTConnect - Russell Waddell ( Managing Director, MTConnect Institute )
Ep 13: Time-Series Databases for IIoT [ InfluxDB ] - Brian Gilmore ( Product Manager for IoT, InfluxData )
Ep 12: Embedded Machine Learning for IIoT - Zin Kyaw ( Senior User Success Engineer, Edge Impulse )
Ep 11: Global Industry Standards for Industrial IoT - Claude Baudoin ( cébé, OMG, IIC, Cutter )
Ep 10: Open Source Software for Industrial IoT - Frédéric Desbiens ( Program Manager - IoT and Edge Computing, Eclipse )
Ep 09: Industrial Internet of Things (IoT) 101 - Benson Hougland ( VP of Product Strategy, Opto22 )
Ep 08: Technical Foundations of IoT - Dominik Obermaier ( Co-Founder & CTO, HiveMQ )
Ep 07: Kafka for Industrial IoT Streaming - Kai Waehner ( Field CTO & Global Technology Advisor, Confluent )
Ep 06: Foundations of Industrial IoT Data Architecture - John Harrington ( Co-Founder, HighByte )
Ep 05: Advanced Plant-Floor Data Analytics - Marcos Taccolini ( Founder and CTO, Tatsoft )
Ep 04: A Practical Guide to IIoT Connectivity - Stan Schneider ( CEO, Real-Time Innovations )
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