A Look at GenAI and Digital Twins, with Data Presented by Info-Tech
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A Look at GenAI and Digital Twins, with Data Presented by Info-Tech

By Alex Passett

Here on GenAI Today (and on Industrial IoT News, one of our affiliated news hubs), we’re no strangers to the nuts and bolts, so to speak, of generative artificial intelligence (GenAI) and digital twins. In essence, GenAI is like a creative problem-solver and digital twins are digitized counterparts to physical machines (from which experts can learn, update, and iterate upon for real-world use cases).

Together, these two are proving to be a powerful duo; imagine GenAI, for example, easily creating in-depth sketches of different engine designs for a vehicle; when based on data derived from a real-time readout provided by an engine’s fully mapped digital twin, the design achieves more than mundane iteration – it’s palpable innovation, and industrial ecosystems benefit more from it, as a result.

Case in point: Info-Tech Research Group recently delivered results-driven research on GenAI and digital twins, offering a comprehensive analysis for manufacturing leaders seriously considering best practices for harnessing such technologies.

We won’t spoil the full Info-Tech report (or “blueprint,” as it’s being referred to), but here are some high-level details for readers interested:

  • In the blueprint, Info-Tech places great emphasis on AI’s more-than-significant role in manufacturing, specifically. Suggesting that organizations prioritize AI investments, a smart AI-lead approach can expand capabilities, increase market presence and revenue growth, and improve overall customer satisfaction. The transformations must be handled strategically, however, lest a.) user error or b.) bad actors take center stage and cause undue complications.
  • The research identifies several obstacles facing IT and OT systems in the manufacturing sector; these include compatibility challenges, a lack of skilled professionals versed in both enhanced intelligence and digital twin implementations, and a pressing need for ethical GenAI regulations and general assurances to further ensure the integrity of training models and subsequent decision-making processes across operations.
  • The blueprint also names other novel advantages achievable by responsibly utilizing GenAI and digital twins; they include deeper analytics, system optimizations, improved resilience and supply chain visibility, simulated risk management scenarios for proactivity, and sustainability-centric practices for measurable and impactful waste reduction.

Per Shreyas Shukla, Principal Research Director at Info-Tech:

“Digital twins have long been recognized for their potential to revolutionize predictive maintenance and streamline processes. When combined with GenAI, the possibilities for the manufacturing industry expand exponentially. However, many manufacturers worldwide continue to grapple with the challenges of Industry 4.0 and the remnants of pandemic-era supply chain disruptions. The fusion of GenAI and digital twins promises not only to optimize current manufacturing processes but also to usher in a new era of design, simulation, and real-time predictive analysis.”




Edited by Greg Tavarez
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