Unlocking Next-Gen Potential – Key Capabilities of Agile Digital Twins for Labs

While the digital twin concept shows immense promise for revolutionizing lab operations, traditional digital twin solutions come saddled with burdensome drawbacks. Typically requiring over a year for custom coding and debugging, these systems demand hefty investments often tallying hundreds of thousands to even millions of dollars for a single-use case implementation.
Seeking to overcome these barriers to entry for broader adoption, pioneering Professor Mike Stonebraker conceptualized “Agile Digital Twins” – a streamlined alternative combining virtual modeling with real-time data analytics. By emphasizing minimized requirements, flexible configurability and optimized user experience, agile digital twins unveil a vision of virtual replications finally accessible for organizations of all sizes.

1. Minimized Requirements
   Departing from traditional twins, agile digital twin platforms eliminate unnecessary data processing and 3D modeling components that commonly delay deployment and inflate costs. Businesses can first launch 2D blueprints to quickly build functional prototypes in just hours, incorporating 3D models over time as needed. With accessible templates and thousands of icon libraries for visualization, most labs can take their first steps into virtual mirroring without intensive technical skills or coding.
2. Configurable Architecture
   The agile digital twin environment enables versatile low-code customization for adapting to evolving needs. Lab technicians and managers can easily construct, update and share detailed analytical dashboards for equipment monitoring, maintenance tracking and more without extensive development skills. Should priorities shift in the future, modifying embedded ML models or connecting new IoT feeds proves straightforward without needing to re-engineer entire solutions.
3. Infinite Adaptability
   The agile twin platform allows seamlessly incorporating new sensors and expanding use cases without requiring entire rebuilds, ensuring flexibility. Labs can readily augment existing equipment blueprints with higher precision tracking or scale up mirrored processes across additional sites. Unlike unwieldy traditional digital twin coding, agile solutions enable painless growth.
4. While collecting billions of granular measurements, agile digital twin solutions allow users to smoothly navigate between forest and tree-level visibility. Intuitive navigation pans across operational dimensions – from multi-site trends to individual device vitals for rapid insights. Augmented 3D environments also allow “stepping into” equipment models to investigate sensor data in context.
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By minimizing setup needs, providing versatile architecture and democratizing access to operational data science, agile digital twins promise to make virtual modeling a viable innovation driver for labs both large and small at last. The next article reviews pioneering examples already paying dividends.

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