AI supercharges all the best benefits of digital twins, but you’ll need a stable digital foundation to get started properly.
Artificial intelligence (AI) is supercharging digital twins. The two technologies are a perfect complement to one another, as each uses data to provide real-world insight. And while combining them requires a certain level of digital sophistication, engineering companies that meet the bar can achieve accurate, reliable and streamlined digital twins that unlock even greater business benefits.
Here’s what to expect from the marriage of AI and digital twins—and how to make sure your organization is ready for the union.
How AI adds capability to digital twins
There are many ways that AI supercharges digital twin capabilities. For example, AI can:
- Offer insights that go beyond real-world data from the sensors that typically drive digital twins.
- Introduce iterative machine learning (ML) that independently decides which scenarios the digital twin needs to run based on the sensor data plus results from prior simulation runs.
- Run more digital twin simulation scenarios that provide previously unavailable insights.
- Reduce the elapsed time to analyze vast amounts of sensor data collected from physical assets.
- Identify patterns, anomalies and potential issues in the data that lead to valuable insights.
- Automate the analysis and interpretation of data produced by digital twins.
- Streamline complex digital twin creation and rapid deployment.
- Enhance efficiency and scalability of digital twins across multiple assets.
- Label ML real-world and synthetic data to create high-quality training datasets for AI models.
Wait a minute, you may be thinking. My current digital twin has most of these capabilities already. What’s new? What’s better? Where’s the beef? Adding AI to digital twins removes many of the constraints you’re experiencing currently. These include insufficient data resolution, long elapsed times to run scenarios, missed insights and crude forecasts.
How to use AI-enhanced digital twins
Digital twins receive considerable attention because they are a universally applicable theoretical and technical system. Digital twins model complex systems in real-time, providing valuable insights into system behavior and performance. Their ability to reduce cycle time and avoid physical prototypes delivers huge cost, schedule and quality benefits.
Adding AI offers advanced analytics and decision-making capabilities, enabling models to self-optimize and self-configure. This improved situational awareness of cause and effect supports more agile and sustainable decision-making along the entire value chain for use cases in many industries, including:
- Basic science.
- Product development.
- Material science.
- Supply chain.
- Manufacturing.
- Energy consumption.
- Emissions.
- Environmental, social and governance (ESG) analysis.
- Immersive virtual staff training.
Business benefits of digital twins with AI
The combination of AI and digital twins enhances the benefits that digital twins have previously delivered without AI. These benefits include:
- Improving how engineers design, manufacture and maintain products.
- Providing actionable insights to improve the increasingly complex and interconnected business world.
- Achieving cost savings, efficiency gains and sustainability efforts.
- Detecting asset performance issues and better predicting future ones to minimize the risk of failure of critical assets and maximize operational performance.
- Improving product quality through reduced defect rates and scrap.
- Accelerating resolution of customer issues or preventing them in the first place throughout the product lifecycle.
- Responding to changing business conditions in near real-time for greater safety, profitability and sustainability.
- Sparking innovation in product development.
Applications of digital twins with AI
Example simulations where AI-based digital twins add value for engineers include:
- Modeling, simulation and optimization of products and processes.
- Planning and design for products and production facilities.
- Evaluating and optimizing production facilities and product performance.
- Predicting maintenance, fault diagnosis and decision-making for computer networks.
- Managing resources.
- Analyzing Industrial Internet of Things (IIoT) data.
- Enabling industrial and vertical applications such as health, transportation, manufacturing, smart cities, smart homes and energy production.
- Evaluating cross-disciplinary perspectives.
- Monitoring the state of security defenses and privacy compliance.
- Advancing standardization, interoperability and testing.
- Supporting testbeds, proof of concepts, field trials and commercial deployments.
The future of digital twins with AI
AI will play an even more significant role in future digital twin simulations as AI algorithms:
- Continue to evolve to become more accurate and adaptive.
- Become capable of handling more complex scenarios.
- Become smarter, meaning more context-aware.
- Become more autonomous.
- Can process the additional data of higher resolution models at an acceptable elapsed time.
The major tech companies, venture capitalists and many startups are investing vast sums of money in AI software and hardware development. These investments will advance future digital twins with the following:
- Enhanced functionality for better simulations.
- Improved reliability and consistency for increased confidence in results.
- Reduced resource consumption leading to faster responses.
Risks of digital twins with AI
All exciting new technologies come with risks. The risks associated with AI-enabled digital twins include:
- Inaccurate or conceptual models.
- Inadequate AI algorithms.
- Insufficient or not well-suited data.
- Inadequate data management.
- Loss of proprietary data.
- Violation of intellectual property rights.
- Cybersecurity infiltration.
Impediments to adoption of digital twins with AI
As new AI tools, platforms and techniques surface every day, adoption impediments include:
- Scarcity of AI talent, leading to higher costs and elapsed time for training.
- Knowledge gaps among staff.
- Absence of standardized AI model interfaces and platforms.
- Difficulty keeping up with AI developments.
- Insufficient compute capacity as AI model processing consumes enormous resources.
AI doesn’t replace human intelligence and creativity but augments it.
When not to implement digital twins with AI
Sometimes, ambition exceeds a company’s internal ability. As appealing as AI technology and its benefits are, a significant level of organizational readiness is required to achieve the benefits. The readiness elements include:
- Digital transformation of most business processes to provide the needed internal data.
- Evidence of at least a partial data-driven culture to appreciate the results.
- Operation of widespread data management and stewardship to maintain a high level of data accuracy and completeness.
- Correction of recent historical data to a high level of data accuracy and completeness.
- Successful operation of digital twins without AI to build experience and trust in the technology, especially the AI algorithms.
- A defined strategy that describes goals for the use of digital twins with AI.
- Sufficient computing and software infrastructure to run the AI models.
- A corporate policy for the use of AI.
If some of these elements are not in place, an investment in digital twins with AI will most likely disappoint. Work on this list first.
Engineers can expect to realize additional business benefits from advanced digital twins with AI capabilities once they have advanced their digital transformation work.
—
Yogi Schulz has over 40 years of Information Technology experience in various industries. He writes for IT World Canada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.
