Understanding Digital Twins in AI Applications

Standing on the factory floor of one of our manufacturing clients, I watched engineers troubleshoot a complex assembly line issue using a simulation. "We used to shut down for hours to test solutions," the manager told me. "Now we run scenarios in the digital twin while production continues." But this barely scratches the surface of what's coming. The conventional view of digital twins, virtual replicas of physical systems, misses their most transformative potential. Having implemented twins across hundreds of facilities, I see three non-obvious transformations unfolding by 2027: First, digital twins will evolve from "mirrors" to "memory systems." Today's twins reflect the current state. Tomorrow's will maintain continuous historical contexts of equipment behaviour. Imagine machines with perfect autobiographical memory, able to correlate maintenance events from years past with subtle performance variations today. I witnessed this emerging capability last quarter when a chemical processor's twin detected a correlation between valve performance and maintenance records from 14 months prior, something no human would have connected. Second, twins will transition from "observation tools" to "counterfactual engines." The true value isn't seeing what is happening but simulating what could happen under conditions never experienced. One manufacturer we work with now explores hundreds of production scenarios monthly that physical constraints would never allow them to test. They've discovered efficiency improvements that defied conventional wisdom. Third, twins will evolve from "digital replicas" to "operational consciousnesses", systems that understand not just how equipment functions but why it exists within broader production contexts. This represents what I call the "Contextual Integration Hierarchy": Level 1: Component awareness (what is happening) Level 2: System awareness (how components interact) Level 3: Purpose awareness (why systems exist) Level 4: Enterprise awareness (what outcomes matter) By 2027, leaders in manufacturing will use twins not just for monitoring but as the cognitive foundation for operations that continuously learn, adapt, and optimise toward business outcomes. What's your experience with digital twins? Are you seeing similar evolutions? #DigitalTwins #IndustrialIntelligence #FutureOfManufacturing #FaclonLabs #Industry40 #DigitalTransformation #IndustrialIoT #SmartFactory #ManufacturingTech #IndustrialAnalytics #TechnologyLeadership

𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐦𝐚𝐝𝐞 𝐰𝐢𝐭𝐡 𝐜𝐥𝐚𝐫𝐢𝐭𝐲, 𝐧𝐨𝐭 𝐠𝐮𝐞𝐬𝐬𝐰𝐨𝐫𝐤. Digital twins take the guesswork out of decision-making by creating a virtual model of your operations that reflects reality in stunning detail. From improving design to reducing downtime, they transform the unknown into actionable intelligence. To simplify the broad range of potential digital twin applications, a classification approach I like to use is called the “𝟓 𝐏𝐬“. This model is easy to remember and covers nearly all use cases of industrial digital twins: • 𝐏𝐚𝐫𝐭 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of individual components or parts typically to understand the physical, mechanical, and electrical characteristics of the part. This allows companies to monitor, analyze, and predict the performance and health of that particular part, optimizing maintenance schedules and extending its lifecycle. • 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of the interoperability of components or parts as they work together as part of a product. This enables companies to simulate and test product behavior under various conditions, improving design, ensuring quality, and speeding up the time to market. • 𝐏𝐥𝐚𝐧𝐭 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of a plant, facility, or system to understand how assets work together at an operational level. This allows businesses to enhance operational efficiency, reduce downtimes, and optimize production processes through real-time insights and predictive analytics. • 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of a specific process or workflow within a system or a facility. This helps companies refine and optimize processes, identify inefficiencies, and ensure smoother and more cost-effective operations. • 𝐏𝐞𝐫𝐬𝐨𝐧 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧: Digital representation of a person to capture their movements, habits, interactions, skills, knowledge, and preferences. This helps companies gain insights into workflow patterns, fatigue patterns, and safety concerns ensuring increased productivity and a reduction in workplace-related injuries. 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐡𝐫𝐞𝐚𝐝 𝐫𝐞𝐥𝐚𝐭𝐞 𝐭𝐨 𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧? A digital thread is a continuous flow of data and information that integrates processes, systems, and devices throughout the product lifecycle. It serves as the foundation for a digital twin, which is a virtual representation of a physical product or system, leveraging data from the digital thread to simulate, predict, and optimize its performance. For high-resolution image and to read full version: https://lnkd.in/ezmPkSag ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

When we talk about digital twins with clients, I find this model to be tremendously helpful. Successful digital twin initiatives tend to follow this progression: 0️⃣ Foundational Twin - “what is there.” We want to gather an inventory of building components and basic data - major electrical, plumping, etc. is a great place to start. 1️⃣ Descriptive Twin - “what is happening.” We’re taking the data and representing it visually, in a way all key stakeholders can access, to understand what’s going on in real time. 2️⃣ Integrated Twin - “why it is happening.” You connect the digital twin with your other systems. Now the twin can communicate back and forth with your other systems, helping you identify the root cause of issues. 3️⃣ Predictive Twin - “what will happen.” With sufficient data and the appropriate feedback loops in place, your digital twin can start to forecast tasks and plan for future maintenance. 4️⃣ Prescriptive Twin - “what should happen.” The digital twin can now learn, can anticipate issues before they happen, and can automate certain tasks. Obviously we believe Level 4 is ideal. But you need to walk before you run. Be wary of any providers trying to get you to jump ahead prematurely. 👉 You likely don’t have the data structures in place to make something like that useful. 👉 You likely don’t have the institutional knowledge necessary to decipher its meaning. 👉 You likely lack the insight to know which systems are worth integrating and when. If you’re just starting out, VIATechnik suggests going for level 0. If you think about your building, a scenario where you are preventing a leak from happening in the first place is where you ultimately want to be. But simply being able to pull up your twin and identify the likely source is orders of magnitude better than what you have now. Get comfortable embedding the digital twin into your workflows. Then layer on capabilities over time. #digitaltwin VIATechnik

Demystifying Tech: Digital Twins 🌍🔍 Think of a fitness tracker. It monitors your heart rate, sleep patterns, and activity levels in real-time. Using this real data along with scientific models of human physiology, it tells you when to rest, how fit you are, and when something might be off. In many ways, a fitness tracker creates a simple digital twin of you — but on a basic level. Now, imagine that same concept applied to machinery, buildings, or entire cities. This is what a Digital Twin does. It’s a virtual model that mirrors a physical object or system, collecting data in real-time and allowing businesses to make informed decisions before issues arise. But unlike static digital models, Digital Twins maintain a continuous connection with their physical counterparts through IoT sensors and advanced data analytics. Just like a fitness tracker helps you optimize your health by identifying patterns and predicting potential issues, Digital Twins can help businesses optimize performance and predict failures before they happen. The promise of Digital Twins is huge. In theory, they can revolutionize industries by enabling predictive maintenance, improving operational efficiency, and driving cost savings. But in practice, the implementation capabilities often fall short, with the investment being too high and the real-world applications not quite living up to the hype. That said, some industries are making strides. Digital Twins are being used in: ➡️ Facility management: Monitoring everything from HVAC systems to energy usage, enabling proactive maintenance and reducing energy consumption. ➡️ Manufacturing: Simulating production lines, monitoring equipment health, and providing predictive insights that increase efficiency and reduce downtime. ➡️ Supply chains: Tracking goods in real-time, predicting bottlenecks, and suggesting routing alternatives. As this technology evolves, it’s exciting to imagine what the future holds. But I’m curious: What part of your operations could benefit most from real-time monitoring and predictive insights? #DigitalTwins #Innovation #IoT #BusinessOptimization #Industry4.0 #FacilityManagement

There is a very powerful consumer behavior simulation tool emerging that will replace traditional A/B and multi-variate testing. Ever heard of a "digital twin?" A digital twin is a virtual replica of your customer that mirrors real-world conditions. Major enterprises create these digital twins to test into new pricing strategies, products, processes, or even marketing campaigns to see how they will affect real-world conditions. By creating these virtual environments, you can simulate different pricing models, merchandising strategies, and marketing tactics without the risk of negatively impacting revenue or customer trust in the real world. Here are a few-ways that leading consumer-facing businesses are using digital twins today: - Test pricing strategies by simulating how different customer segments would react to various price points. - When merchandising, digital twins make it possible to optimize product assortments and see which combinations drive the most engagement across different personas, all before implementing changes across an entire customer base. - Marketers can test various campaigns in these virtual environments, identifying the messaging that resonates best without the delays and costs associated with traditional A/B testing. According to a recent study by Gartner, organizations that invest in digital twins can improve decision-making processes by up to 30%. In a market where the margin for error is small, this kind of precision is a significant competitive advantage. Right now, for most consumer-facing brands, access to creating your own digital twin is cost-prohibitive, but I suspect this will change in the next 1-2 years as existing solutions which enable creation/adoption of a digital twin (such as IBM's Maximo) continue to become more powerful and accessible beyond the enterprise. Definitely worth keeping an eye on this concept as it will become readily available to the mid-market and SMB audience sooner than you might think.

𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬, where AI plays a crucial role in creating and enhancing these virtual replicas, is one of the most exciting combos for the future of business and technology. Example one: Predictive Maintenance. Predictive maintenance is one of the most essential uses of artificial intelligence in engineering. AI systems can detect equipment breakdowns by evaluating real-time sensor data and optimizing maintenance plans, resulting in reduced downtime and operational expenses. Combining Digital Twins with AI enhances these advantages. AI improves the capability of Digital Twins by offering predictive analytics for real-time simulations and scenario modeling. This combination dramatically increases operational insights and decision-making capabilities. Example 2: Industry 4.0 (Cars) Consider the development of self-driving autos as an example. Training an AI-empowered Digital Twins model to mimic virtually billions of kilometers of driving scenarios is significantly faster, safer, and less expensive than physical testing. The AI model may predict behavior that contradicts physical laws, such as a car speeding suddenly or cornering impossibly. However, physics-based digital twin simulations provide the required safeguards, guaranteeing these virtual tests generate valid and actionable results and reassuring us of the safety and cost-effectiveness of this technology. Example 3: Healthcare/Medicines It is a computer-generated heart, or digital twin, used to test implantable cardiovascular devices such as stents and prosthetic valves, which, once proven safe, will be placed on actual patients. Using artificial intelligence and massive amounts of data, they constructed a variety of hearts. These AI-generated synthetic hearts may be customized to match not just biological characteristics such as weight, age, gender, and blood pressure but also health conditions and ethnicities. Because these disparities are frequently not represented in clinical data, Digital Twin Hearts can assist device manufacturers in conducting trials over a broader range of populations than human trials or trials utilizing only digital twins and no AI. Example 4: Education. The potential of AI and digital Twins has particularly piqued the interest of many in the EdTech industry. Creating accurate digital clones to support human educators is more than just a faddish trend. These AI-powered counterparts are highly trained productivity and support boosters who can free educators from demanding work schedules. Their outputs go beyond simple automated responses; they are crafted & capable of engaging the client in meaningful conversations, all while making well-informed decisions and capturing the intricate nuances of an individual's personality. The examples here can go on and on. It's fascinating (at least in my eyes) to see the combination of #IoT, #AI, #DigitalTwins, and #SaaS intertwined in such an innovative and productive means in the future.

AI in surgery is entering the era of real-time digital twins… I came across an interesting article in Nature on Digital Twin-Assisted Surgery (DTAS), a concept that integrates real-time virtual models with surgical workflows to enhance precision, planning, and decision-making (link in the comments). The article outlines how DTAS merges AI, extended reality (XR), and real-time physiological data to create patient-specific digital twins, allowing surgeons to simulate and adjust procedures dynamically. Some takeways: 👉 Unlike current Computer-Assisted Surgery (CAS) tools, DTAS continuously updates based on intraoperative data, predicting tissue behavior, blood flow changes, and surgical outcomes in real-time. 👉 One of the biggest challenges in robotics is the lack of haptic feedback. DTAS integrates sensor data and AI-driven modeling to replicate force and tactile sensations, improving surgeon control. 👉 DTAS allows for preoperative virtual simulations, intraoperative navigation, and even remote-assisted procedures, making complex surgeries more accessible worldwide. It prompted me to highlight the work of one of our portfolio companies in this space, Medivis. We backed them some years ago because they are advancing spatial computing in surgery with its SurgicalAR platform, which translates CT and MRI scans into interactive 3D holographic models for real-time surgical navigation. Check them out! 👇 Image credit Medivis, from a live hospital implementation

First and foremost, a #digitaltwin is a #data structure that mirrors the properties and state of its physical twin counterpart. Continuous #telemetry flowing from the physical twin via #IoT plumbing provides the digital twin with its current and historical state. This process results in the dynamic creation of #AI models for training and inference where the properties of the twin map to the features of the model. A flow of commands from the digital twin are used to control, update, and improve the physical twin. 2D and 3D visualizations of the data structure, state, and data flow of the digital twin can optionally be rendered and displayed for eyes to see.

🌟 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬—𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬. 🌟 Unlocking the Future: The Synergy of AI and Digital Twin Technology In today’s rapidly evolving digital landscape, the fusion of 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈) with 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 is not just a trend—it’s a transformative force reshaping industries. 🔎 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧? A Digital Twin is a virtual replica of a physical asset, process, or system, continuously updated with real-time data. This digital counterpart allows organizations to monitor, analyze, and simulate scenarios, offering a 360-degree view of their operations. 𝐃𝐓𝐓 𝐌𝐚𝐫𝐤𝐞𝐭 𝐒𝐢𝐳𝐞 𝐚𝐧𝐝 𝐆𝐫𝐨𝐰𝐭𝐡: The global digital twin market size was estimated at USD 16.75 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 𝟑𝟓.𝟕% from 2024 to 2030. 𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐆𝐫𝐨𝐰𝐭𝐡 🔷 The Asia Pacific Digital Twin market is expected to grow at a CAGR of 36.7% from 2024 to 2030. 🔷India's market is forecast to grow at a 45.8% CAGR from 2024 to 2030. 💪 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈: When AI enters the equation, the potential of Digital Twins is amplified. AI enhances these digital models by enabling them to learn from vast amounts of data, predict outcomes, and even optimize processes autonomously. This marriage of technologies opens doors to new possibilities that were once unimaginable. 🟢 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬: 1. Operational Excellence: ◾ Predict and prevent failures with predictive maintenance. ◾ Reduce downtime and extend the life of assets. ◾ Optimize resource usage, leading to significant cost savings. 2. Data-Driven Decision-Making: ◾ Make informed decisions with real-time insights. ◾ Simulate various scenarios to assess risks and opportunities. ◾ Accelerate response times to changing conditions. 3. Innovation and Agility: ◾ Test and validate new designs in a risk-free virtual environment. ◾ Rapidly iterate and refine processes without disrupting operations. ◾ Foster a culture of continuous improvement and innovation. 🟢 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: ◾ Manufacturing: Streamline production lines, enhance quality control, and reduce waste. ◾ Healthcare: Personalize patient care, optimize hospital operations, and improve medical device performance. ◾ Smart Cities: Manage infrastructure efficiently, reduce energy consumption, and improve citizen services. 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐢𝐬 𝐍𝐨𝐰: The integration of AI with Digital Twins is not just a concept for the future—it’s happening now. Companies that embrace this synergy are gaining a competitive edge by unlocking new levels of efficiency, innovation, and resilience. How do you see AI and DTT shaping your industry? Let’s connect and explore how we can leverage these powerful tools to drive transformation together. 👇