How Digital Twins Improve Decision-Making

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

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.

“The day we turn the reactor on—with a thousand sensors—AI will be learning from the reactor as it goes online.” This line from TerraPower’s Christopher Levesque points to a fundamental shift in how we approach nuclear reactor startups. The Natrium plant is designed with detailed multiphysics models—essentially a full digital twin of the plant. So when the reactor starts for the first time, the control system won’t just be logging data. AI will be processing real-time streams from thousands of sensors, learning the plant’s unique behavior. It’s as if the reactor comes with an embedded AI operations co-pilot, getting smarter by the second. Consider what an AI-sensor fusion architecture like this involves. On day one, sensors across the plant—temperature probes, flow meters, neutron detectors, strain gauges—stream high-fidelity data into an analytics core. Instead of just flagging values out of range, the AI cross-analyzes patterns, refining a live model of plant behavior. This creates a tight feedback loop. As the reactor heats up and settles into operation, the AI compares predictions to actual performance, continuously calibrating the plant’s digital twin. Expected outcomes: • Enhanced safety: With AI watching a thousand inputs, anomalies surface early, giving operators time to intervene before small issues escalate. • Operational efficiency: A learning reactor is an optimizing reactor. Real-time insights enable smoother startups, lower transients, and reduced wear. • Scalable learning: Insights from one startup can improve the next. Over time, the fleet grows smarter with every iteration. This is nuclear engineering meeting intelligent systems design. TerraPower’s Wyoming project will show us what happens when real reactors start learning. And the faster those insights enter industry practice, the more transformative this becomes. Dive deeper into TerraPower’s vision and the role of AI in nuclear energy: https://lnkd.in/e4aF9Xu9 #NuclearEnergy #AdvancedReactor #ArtificialIntelligence #DigitalTwin #EnergyInnovation

Disruption - it's the biggest risk to managing a business in today's fast changing world. How do business leaders factor in the disruptions of natural disasters, tariffs and trade wars, ongoing geopolitical conflict and changing environmental regulations into supply chain decisions, all while technological innovation runs at break-neck speed? A few years ago, it was COVID and then the electronics shortages. Today, it's new topics such as tariffs or innovations such as the recent release of DeepSeek. Change is inevitable, and next year it will be something new. It is essential for companies to be agile and adaptable to stay successful and thrive. Now, more than ever, digital transformation is of prime importance to ensure resilience in the face of new and ever challenging business disruptions. You need the comprehensive digital twin to understand your business operations and have the transparency in your supply chain to quickly assess and respond to the impacts of these disruptions. When you picture a digital twin, the mental image that most likely appears is a group of engineers looking at a CAD model of a factory infrastructure or prototypes of different products. This is, however, just one way to leverage the digital twin. This technology can be applied to more than just physical assets. It also can be applied to manufacturing processes and systems and business operations.   By applying the comprehensive digital twin to business models, processes, and strategies, companies can more easily navigate through current as well as future challenges. For example, during my time at a previous company, there is one particular instance when it would have made a massive difference to have a true digital twin of our business processes. In an effort to understand prolonged hiring times, my team mapped the entire hiring and onboarding process using Visio diagrams. It was a time-consuming process, and the system relied on manually updating different scenarios in Vizio. This made the whole process to optimize more difficult and less efficient. Had this process been attached to a comprehensive and real-time digital model, several “what if?” scenarios could easily be tested to facilitate effective, data-informed decision making.   Imagine if you had a comprehensive digital twin of your supply chain, where you could easily understand the tradeoffs of cost and carbon footprints, and then factor in risks of conflicts or transport disruptions? With this understanding of your business processes you could quickly make informed decisions about tradeoffs between cost, schedule and sustainability impacts. Digital transformation fosters greater productivity, innovation and flexibility in design, manufacturing, and business operations, making it possible to be ready to quickly react and adapt to the next crisis that the tides of time splash at us all.

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

Last week, I asked the LinkedIn community what will reshape supply chain operations the most in the next 5 years. Most chose #AI and #robotics—and for good reason. But there’s one answer I believe is still flying under the radar: #DigitalTwins. Why? Because they’re not just dashboards. They’re becoming the real-time control centers of logistics—linking visibility, optimazation, simulation, and intelligent action. Here’s what makes digital twins powerful: 1. Live visibility across operations, assets, and partners 2. Data consolidation that enables automated decision-making and optimization 3. Predictive modeling + what-if simulations before you commit resources In a A.P. Moller - Maersk project, we used a digital twin to increase trucking utilization time by 17%. The tech is here. What’s missing is widespread adoption and integration. What’s your experience with digital twins in supply chain?