I’m heading to CIMdata Industry and Market Vendor Forum 2024 in Ann Arbor later this week. The annual forum is usually a great place to catch up with PLM professionals. CIMdata usually gathers PLM software and service vendors for this annual event to share information about industry performance and discuss topics that on the top of PLM industry vendors and professionals. Check the notes from CIMdata 2023 Forum here. Last year, the forum was about digital thread and digital transformation. The topic of this year forum is Digital Twin.
As anticipation builds for the CIMdata 2024 PLM Market & Industry Forum themed “Digital Twins: Enhancing Lifecycle Performance,” set to convene in Ann Arbor this week, it’s an opportune moment to reflect on the future trajectory of PLM technologies and concepts.
CIMdata Forum Agenda Highlights
I captured a few sessions from the agenda that caught my special interest. 2023 Market Analysis Results presented by Christine Longwell, Director of Research, CIMdata [bio], Stan Przybylinski, Vice President, CIMdata [bio]. [CIMdata]
Recognized as the best source of comprehensive and accurate insight into the PLM industry, CIMdata’s annual global market analysis is first revealed in this setting. Get ready to dive into the market analysis results for 2023.
PLM Twin in the context of Lifecycle Performance is the topic that caught my attention as it presents not only a way to simulate the product performance, but most probably look at the performance in the perspective of timeline and product lifecycle. I’m curious to learn more about it.
Digital Twins: Enhancing Lifecycle Performance. Initially rooted in electro-mechanical product design and development, ‘digital twin’ has expanded beyond its origins to cover comprehensive multi-attribute modeling and analysis from beyond engineering, manufacturing, and support. Multi-attribute modeling and simulation is core to CIMdata’s definition of the Product Innovation Platform, which underlies most industrial PLM strategies and implementations. The upcoming presentations will consider CIMdata’s expertise on digital twins, define the digital twin and related topics, and provide perspectives from both industry and software/service providers.
Deeply connected to the lifecycle and digital twins is a topic of analytics and machine learning. AI is clearly on the top of everyone’s mind these days, so the topic is driving lot of interest.
Analytics & AI/Machine Learning. Sandeepak Natu, Executive Consultant, Simulation-Driven System Development Practice, CIMdata [bio] In 2019, the theme for our forum was “Augmented Intelligence: Applications Across the Lifecycle.” At that event we discussed the application of analytics and Augmented Intelligence/Machine Learning (AI/ML) to the product lifecycle. Fast forward to today, the landscape has evolved, and the rise of groundbreaking technologies, such as ChatGPT, have led to renewed interest. These innovations, along with their interconnectedness to digital twins, have paved the way for a new era in intelligent solutions and make it time to revisit this topic.
Questions ahead of the CIMdata Forum
I have three questions to ask ahead of the event: 1/ How to track product lifecycle in a digital world? 2/ Who is supporting Digital Twin today? 3/ How does it intertwined with generative AI initiatives.
Product Lifecycle In a Digital World
In the dawn of the digital era, tracking the lifecycle of products demands a paradigm shift towards dynamic, real-time approaches. The traditional siloed models preventing companies to support interconnected systems that offer holistic insights across the entire product journey. Embracing the digital thread, companies can seamlessly weave together design, manufacturing, and service data, enabling predictive analytics and proactive decision-making. It is not clear how data can be connected to provide a holistic data perspective.
Who Supports Digital Twin Initiatives Today
As industries embrace this technology, the focus shifts towards fostering ecosystems that nurture and sustain Digital Twin initiatives. Who does it today, how current systems is capable to support it and, most importantly, how data is shared across multiple systems to support this digital models. By fostering a culture of innovation and knowledge sharing, organizations can accelerate the adoption of Digital Twins, unlocking new avenues for optimization and performance enhancement.
Synergy of Generative AI and Digital Twins
As Artificial Intelligence (AI) continues to revolutionize product design and optimization. The last two years, we’ve seen a lot of very interesting progress is generative AI initiatives. Adoptions of ChatGPT was skyrocketing. By leveraging generative AI algorithms, companies can explore vast design spaces, uncovering optimal solutions that were previously unattainable. When integrated with Digital Twins, these AI-driven insights pave the way for agile, data-driven decision-making throughout the product lifecycle.
What is my conclusion?
Looking forward to the event to gather more information about the state of PLM economy as well asbout such a hot topic that digital twin. I’m very curious about how the concept of digital twin can be developed in the context of lifecycle. Which means modeling and simulating product performance over time. How data can be collected across multiple silos and how performance of the product can be measured over time. The topic is paving the way for a new era of digital innovation and excellence where modern data management and data modeling technologies will be playing the key role. Every digital twin starts from data. Just my thoughts…
Best, Oleg
Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital-thread platform with cloud-native PDM & PLM capabilities to manage product data lifecycle and connect manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.
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