Agnostic Solutions for Total Integration
With digital twin software, industries can build virtual replicas that help drive real-world performance improvements.
With digital twin software, industries can build virtual replicas that help drive real-world performance improvements.
Turn a Plant’s Potential Into Performance With Process Digital Twins
Industrial digital twin software is more than a visualization tool. By creating virtual replicas of industrial assets and processes, digital twins can provide insights into past performance, present operations and future scenarios.
Digital twin technology can deliver particularly strong ROI wherever precision, compliance and uptime are essential: in critical manufacturing sectors including pharmaceuticals, semiconductors, gigafactories and chemical manufacturing, as well as in oil and gas, LNG, refining, petrochemicals, renewable fuels and mining operations.1
By testing changes in a secure virtual environment before implementation, organizations across industries can reduce operational risk—and unlock their plants’ full potential.
Source:
1 https://process.honeywell.com/us/en/initiative/digital-prime-twin
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Up to $37.9 billion in annual benefits
National Institutes of Standards and Technology (NIST) research approximates the potential aggregated manufacturing industry benefits of digital twins to be $37.9 billion annually if adopted throughout the U.S. manufacturing industry.¹
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$245 billion per year in potentially preventable downtime losses
Production downtime costs U.S. discrete manufacturers $245 billion annually, with defects adding another $32-$58.6 billion in losses.¹ NIST research indicates these losses are significant and many are preventable, with costs that can potentially be reduced by digital twins.²
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$16-$39B validated impact range
NIST analysis of digital twin adoption across U.S. manufacturing establishes a 90% confidence interval between $16.1 billion and $38.6 billion in annual economic impact, with a median of $27.2 billion.¹
Digital Twins, Real-World Results
Frequently Asked Questions About Digital Twin Technology
Traditional simulation models are often static representations used during design or specific studies. Digital twins are dynamic, continuously updated virtual replicas that can synchronize with actual plant operations. They're systematically tuned based on current operating conditions and connected to synchronous data streams. While teams may use traditional simulations periodically for broader analysis, digital twins provide ongoing insights into past performance, present operations and future scenarios. This continuous connection can make them valuable for day-to-day decision-making (as opposed to one-time studies).
Honeywell Process Digital Twin is purpose-built for complex process industries. Teams can create digital replicas at multiple levels: individual assets like compressors or heat exchangers, entire process units like distillation columns or reactors, or complete plant systems. The technology can work across critical manufacturing industries—including pharmaceutical production, semiconductor fabrication, gigafactories and chemical manufacturing—as well as oil and gas operations, liquefied natural gas facilities, refineries, petrochemical plants, renewable fuel production and mining, minerals and metals processing. Using advanced process simulation capabilities to model steady-state and dynamic behaviors, the platform is suitable for most process equipment and systems within these industries.
Our industrial digital twin software is source-agnostic and comes with out-of-the-box connectors for multiple operational technology applications. To create more complete simulations, teams can integrate data from distributed control systems, historians, laboratory information management systems and other plant data sources. Pre-packaged data preprocessing capabilities help turn raw data into meaningful KPIs and calculations, using operational data to systematically tune itself based on actual plant conditions. While specific data requirements may vary by application, the platform is built to work with the data sources plants already have, which can help avoid major infrastructure changes.
