How Digital Twins Change Industry Operations

Your manufacturing plant is already talking. The question is—are you listening? Every second, your production line sends invisible signals: Where it's slowing down. Where energy is being wasted. Where a future bottleneck is quietly forming. When something breaks, you fix it. When output dips, you analyze it. When quality drops, you investigate it. But what if… You could see it coming before it ever happened? That’s exactly what the world’s smartest factories are doing. And no—it’s not luck. It’s Digital Twins. Here’s how they’re quietly winning: ✅ They simulate everything—before touching the floor. Using Discrete Event Simulation, they model thousands of “what-if” scenarios ahead of time. ✅ They test scalability virtually. No downtime. No wasted effort. Just pure clarity on what works at 10 units—or 10,000. ✅ They build feedback loops that self-correct. Production issues don’t surprise them—they notify them. ✅ They optimize resource flow in advance. Material, machine, and manpower aligned like clockwork—before the day begins. ✅ They plan for “what if” scenarios—before they happen. What if a supplier delays shipment? What if demand spikes overnight? What if a station fails? Digital Twins let you test it all—before it hits the floor. ✅ They validate line changes without stopping production. Need to rearrange stations or introduce a new variant? It’s simulated, validated, and tweaked—all before operators touch it. ✅ They make daily operations visual and data-driven. From shift supervisors to plant managers—everyone sees the same digital reality. No guesswork. No misalignment. Just clarity. This isn’t a pipe dream. This isn’t reserved for billion-dollar tech companies. This is now. This is Digital Twin Technology. It’s like giving your factory a second brain: • One that never sleeps • One that learns faster than humans • One that speaks in data, not guesses And the outcome? - Less waste - More throughput - Smarter decisions at every level I broke this approach down in a visual you can show your CEO, ops team, or even your board. One page. Clear. Actionable. - Digital Twins are your factory’s second brain ♻️ Repost if you're scaling smart.

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

a missing package story with a happy ending. imagine it's December 23rd... your company awaits a shipment of holiday gifts for your valued clients. the packages are 3 stops away. then suddenly – package status: "exception." we've all been there. the dreaded delivery limbo that puts client relationships at risk. but UPS is rewriting this story with digital twin technology. what's happening behind the scenes? → UPS created virtual replicas of their entire logistics network → every package now has an RFID-enabled smart label → sensors track packages within three feet of accuracy → AI predicts delays before they happen → workers see the entire system in real-time is this just another tech or AI upgrade? I don't think so. it's changing the entire customer experience, too. ✅ live tracking without blind spots ✅ hands-free tech for workers ✅ faster, more reliable deliveries digital twins help UPS adapt to real-time demand, reroute shipments instantly, and keep deliveries on track. UPS digital twins upgraded the way package moves. it's like a continuous GPS signal that never sleeps. and the impact ripples through the entire system: → workers see potential bottlenecks before they form → facility managers optimize sorting paths instantly → drivers receive route adjustments in real-time → customers track packages with unprecedented accuracy and your company waiting on those critical client gift shipments? with digital twins, you receive an instant notification: "packages rerouted due to congestion. New delivery time: 4:30 PM." relief replaces anxiety. transparency builds confidence. your client relationships remain uncompromised. this transformation goes beyond tracking. digital twins predict the future by simulating what-if scenarios: → "what if this storm hits our Memphis hub?" → "what if holiday volume spikes 20% in Chicago?" → "what if we adjust our sorting algorithm?" the system answers before problems materialize. every innovation in logistics eventually touches human lives. what digital twin would transform your business operations? share your own ideas in the comments 👇 want more break down on how digital transformation is reshaping industries? subscribe to our Lighthouse newsletter for weekly innovation deep dives that keep you ahead of the curve. #digitaltwins #supplychain #Innovation #Transformation

🚀 Shaping the Future of Supply Chains with AI, Computer Vision & Digital Twins Excited to be featured in FreightWaves, where I shared why the intersection of AI computer vision, digital twins, and real-world operations is so transformative. From my experience at A.P. Moller - Maersk to today at Interwoven Ventures, I’ve seen how these technologies can revolutionize supply chain resilience and efficiency: Unloading containers: Deploying smart cameras and AI raised prediction accuracy to ~82%—enabling minute-by-minute insights into workforce throughput and driving new performance incentives. Streamlining drayage: By consolidating data from 13 systems into a digital twin, we unlocked visibility, operational clarity, and multi-million‑dollar savings. What‑if scenario planning: Digital twins allow us to model disruptions—be it tariffs, wars, pandemics—and proactively engineer the resilient supply chains of tomorrow. My core message: “You have to declare what your problems are first, so then you can actually measure them.” Digital twins and AI shouldn’t be deployed for hype—they should be precision tools built to solve defined challenges . As co‑founder and GP at Interwoven Ventures, I’m keen to support #founders and execs driving these real-world solutions. If you’re developing tools that merge data, AI, vision, and operational benchmarks to streamline logistics—let’s connect! 🔗 Read the full article here: https://lnkd.in/grZVx443 Thanks to Noi Mahoney and the team at FreightWaves for the thoughtful coverage. #SupplyChainTech #DigitalTwins #AI

Imagine a patient walks into a hospital, needing a complex procedure. In the past, doctors relied on their expertise, general statistics, and maybe a few similar cases to predict outcomes. Now, picture this: with a digital twin—a virtual model of that patient built from their unique medical data—we can tap into thousands of anonymized patient records. Each record is a data point, a story of symptoms, treatments, and results. Using advanced analytics and AI, we compare the patient’s digital twin to this vast pool of outcomes. We’re not just guessing anymore—we’re seeing patterns. How did someone with similar vitals, genetics, or conditions respond to this procedure? What complications arose? What worked best? Suddenly, we’re not treating a single case in isolation; we’re leveraging a collective knowledge base to personalize care. The digital twin becomes a predictive tool, helping doctors optimize the procedure, reduce risks, and improve recovery odds—all before the patient even enters the operating room. This is the future of healthcare: precision medicine powered by digital twins. It’s not just about replicating a patient digitally—it’s about connecting their story to thousands of others, finding the best path forward. What do you think—how else could digital twins transform industries like healthcare?

From mapping all of Singapore’s roads to digitizing a 200,000 m² museum, Daniel Kruimel of Woolpert walks through real-world digital twin case studies across Australia, Singapore, and the Middle East. The projects are diverse, but the mission is consistent.. create scalable digital ecosystems that streamline planning, inspection, and decision-making. Whether you're building digital twins for infrastructure, government, or facilities, this presentation offers a front-row look at what’s working. Key Takeaways 🔵 Singapore’s National 3D Map now supports solar planning, mobility analysis, and crash reconstructions using MLS point clouds 🔵 Port of Mackay saved thousands by enabling remote asset inspection, reducing physical site visits from hours to minutes 🔵 NSW’s Department of Education is using digital twins to accelerate preschool construction and enable virtual site simulations 🔵 The Louvre Abu Dhabi now has an LOD 500 verified as-built model integrated with an asset management system 🔵 Digital twins unlock value when more stakeholders are involved early, and when the procurement model supports agility 🔵 “Start with the foundation. The use cases will come.” 🎯 Watch the full presentation here: https://lnkd.in/gvuaPf4T

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!

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