{"id":3764,"date":"2026-04-28T10:13:52","date_gmt":"2026-04-28T14:13:52","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/news\/?p=3764"},"modified":"2026-04-28T10:20:14","modified_gmt":"2026-04-28T14:20:14","slug":"a-new-manufacturing-playbook-digital-thread-data-integrity-and-ai","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/news\/a-new-manufacturing-playbook-digital-thread-data-integrity-and-ai\/","title":{"rendered":"A New Manufacturing Playbook: Digital Thread, Data Integrity and AI"},"content":{"rendered":"\n
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\"Tony
Tony Hemmelgarn, Realize Live 2025<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

Summary<\/h2>\n\n\n\n

Big idea:<\/strong> Complexity in modern manufacturing isn’t a burden to manage \u2014 it’s a competitive advantage to seize. <\/p>\n\n\n\n

Why it matters:<\/strong> As AI reshapes product development, companies that lack clean, integrated data foundations will find their AI initiatives stalled before they start \u2014 and those that invest now are already pulling ahead. <\/p>\n\n\n\n

Real-world examples:<\/strong> Three customers illustrate how Siemens’ comprehensive digital twin and connected software stack are turning complexity into results: <\/p>\n\n\n\n

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  1. GM uses Teamcenter Configurator to validate hundreds of thousands of vehicle build combinations \u2014 covering all product variations based on defined rules, replacing costly custom tools with out-of-the-box software that handles between 100,000 and one million nightly production orders. <\/li>\n<\/ol>\n\n\n\n
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    1. Rolls-Royce built a full digital thread connecting engineering, manufacturing and operational data across Teamcenter, Opcenter, Insights Hub, Simcenter and Mendix \u2014 enabling a production AI copilot that identifies quality deviations in turbine blade manufacturing, suggests fixes and auto-generates maintenance tickets. <\/li>\n<\/ol>\n\n\n\n
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      1. BAE Systems used Mendix to build 40 enterprise applications \u2014 including part approval and task management \u2014 cutting development timelines from years to as little as four weeks and saving \u00a340 million on a single application. <\/li>\n<\/ol>\n\n\n\n

        What’s changed with digital twins <\/h2>\n\n\n\n

        Heraclitus\u2019 metaphor \u2014 no man steps into the same river twice \u2014 applies to technology as it does to life in general. After nearly 40 years in industrial software, we\u2019ve seen companies either endure change or embrace it to gain a competitive advantage. <\/p>\n\n\n\n

        Product development is only getting more complex. New features, regulations, sustainability, supply chain issues, and AI continually reshape design, manufacturing, and maintenance. The challenge is not to avoid complexity but to leverage it. <\/p>\n\n\n\n

        A few years ago, we introduced Siemens Xcelerator, an open digital business platform designed to help customers move faster through the manufacturing and engineering gauntlet. At its core is the comprehensive digital twin. <\/p>\n\n\n\n

        Digital twins have existed for years. What’s transformative now is their ability to integrate manufacturing, design, electrical, software, and automation into a synchronized model. The closer the virtual version mirrors reality, the more confidently you can decide amid rising complexity. <\/p>\n\n\n\n

        This philosophy drove our $10.3 billion acquisition<\/a> of Altair Engineering, completed in March 2025 \u2014 Siemens’ largest ever. Altair uniquely integrates nonlinear structural analysis and advanced electromagnetic simulation for electrified products, setting it apart from competitors. Its integrated data science and AI workflows via RapidMiner further strengthen its value. Most distinctively, Altair\u2019s high-performance computing solutions dynamically balance workloads across GPU and CPU infrastructure, both on-premises and in the cloud, enabling efficient scaling as simulation and AI demands accelerate. <\/p>\n\n\n\n

        But technology alone doesn’t solve current challenges. We have also focused on what we call ‘adaptive’ solutions \u2014 software that scales up and down without data loss or disruption. Our Designcenter suite enables engineers to start with entry-level 3D modeling and seamlessly migrate to the full power of NX, our flagship CAD\/CAE system. This happens without any data translation. Whether you’re working in the cloud, on a desktop, or moving between versions, the data flows consistently. This might sound mundane, but for engineers who’ve lost weeks to data translation errors, it’s liberating. <\/p>\n\n\n\n

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        Moving from custom BOM<\/h2>\n\n\n\n

        We’ve long provided both design and manufacturing BOMs in Teamcenter. Recently, customers asked for an enterprise BOM: a ready-made system for all materials and variants, supporting forecasts for capacity and scheduling. <\/p>\n\n\n\n

        Many custom BOM systems still rely on decades-old IBM mainframes with hard-to-replace customizations. According to ABI Research<\/a>, only 5% of industrial data is used because it’s fragmented and siloed. Legacy BOMs are major obstacles for AI. <\/p>\n\n\n\n

        Customers must<\/em> modernize. We’ve invested in new Teamcenter features, replacing legacy enterprise BOMs with out-of-the-box solutions. Teamcenter BOM performance is now 20 times faster for part organization and production planning. <\/p>\n\n\n\n

        Automotive OEMs handle hundreds of thousands of vehicle configurations and need instant BOM resolution. They process up to a million orders nightly, solving for 40 weeks of production. <\/p>\n\n\n\n

        GM once used 10-15 control models to validate builds. Now, Teamcenter Configurator covers all variations, validating only buildable vehicles and avoiding impossible combinations. This replaces costly custom tools with standard Teamcenter features. <\/p>\n\n\n\n

        This complexity isn’t limited to the automotive industry. Leading phone manufacturers face similar challenges. Maintaining separate BOMs for each variant creates change management headaches. Having a single massive BOM leads to million-line confusion. Teamcenter\u2019s variant-option approach addresses this smoothly with standard tools. <\/p>\n\n\n\n

        What about AI as a solution to this growing complexity? That question was central in our decision to acquire life sciences R&D company Dotmatics<\/strong><\/a> last year. <\/p>\n\n\n\n

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        Data management and AI<\/h2>\n\n\n\n

        You\u2019ve seen headlines about the AI bubble and hype cycle. It\u2019s hard to place where we are, but a core truth about AI comes from USA Today\u2019s Chris Gallagher, in a piece sponsored by Ataccama and Dotmatics<\/strong><\/a>, which we acquired<\/strong><\/a> last year: <\/p>\n\n\n\n

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        Without robust data management practices to ensure data integrity, accuracy, and availability, AI’s capabilities will remain stunted. Data quality and governance play a pivotal role in unlocking AI’s true potential by providing the essential infrastructure for its success. In essence, the future of AI is inextricably linked to advancements in data management, making it an indispensable enabler of the AI revolution.<\/p>\n<\/blockquote>\n\n\n\n

        In 2024, we released an AI copilot for Teamcenter, the most proven and scalable product data management system in the world. Teamcenter is precisely where you go to ensure data integrity, accuracy, and availability. Now we’re applying AI to that lifecycle intelligence. For example, ask Teamcenter Copilot, \u201cWhat are the top five warranty hotspots?\u201d It uses RapidMiner to analyze data, project issue severity, and pinpoint the top risk. When you request a replacement, AI locates the part, creates a work set, tracks change impacts, and starts a change request. It also analyzes costs and risks and recommends a replacement. The BOM updates automatically. Everything is tracked. Stakeholders see the changes.  <\/p>\n\n\n\n

        This automated process ensures speed and accuracy. Teamcenter has always been where engineers handle change, configuration, and digital mock-ups. Now it\u2019s an AI foundation. Using it just for CAD check-in\/check-out wastes its potential. Companies that overlook these features risk falling behind as AI transforms product development. <\/p>\n\n\n\n

        Dotmatics<\/h2>\n\n\n\n

        When we looked at the pharmaceutical and life sciences industries, we saw something familiar. It was reminiscent of PLM 25 years ago \u2014 disparate applications with no APIs and no enterprise-level data integration. This opportunity is part of what spurred our $5.1 billion acquisition<\/a> of Dotmatics, a leader in life sciences R&D software.  <\/p>\n\n\n\n

        This isn’t entirely new territory for Siemens. We’ve long designed medical devices with our tools. Our manufacturing execution software runs pharmaceutical production facilities globally, and our process automation systems operate many pharma factories worldwide. In Dotmatics, we saw an opportunity to create enterprise-level data management that ensures data integrity, accuracy, and availability. This is the foundation AI requires to accelerate drug discovery and life sciences R&D.  <\/p>\n\n\n\n

        Digital twins have existed for years. What’s transformative now is their ability to integrate manufacturing, design, electrical, software, and automation into a synchronized model. <\/p><\/blockquote><\/figure>\n\n\n\n

        We’re starting with pharma, but the biochemistry capabilities extend to oil and gas, personal care products, agriculture, and beyond. As Dotmatics integrates into our portfolio, we’re applying the same data backbone philosophy that transformed discrete manufacturing. Now, we\u2019re bringing it to the molecular sciences. <\/p>\n\n\n\n

        Across industry domains, our approach is to help customers move through complexity faster \u2014 critical as AI evolves. Realizing this requires data integrity, accuracy, and availability, which have been central to our strategy and the foundation of our investments. <\/p>\n\n\n\n

        Next, we’ll explore how these principles are applied across specific industries. This ranges from photorealistic digital collaboration that brings non-technical stakeholders into engineering conversations to comprehensive digital threads that connect design through quality inspection. We will also look at data centers as integrated products, from chip to power grid. The technology is maturing faster than most realize. The companies positioned to capitalize on it are those investing in robust foundations today. <\/p>\n\n\n\n

        Democratizing engineering intelligence<\/h2>\n\n\n\n

        One persistent challenge in product development is that engineering data remains locked in formats only specialists can interpret. CAD models, simulation results, and manufacturing instructions are rich in insights but inaccessible to teams such as marketing, sales, service, and executives. These groups need to make critical product decisions. <\/p>\n\n\n\n

        Last year, we released<\/a> Teamcenter Digital Reality Viewer \u2014 a BOM-driven, photorealistic visualization tool embedded directly in Teamcenter. It’s cloud-based and uses real-time ray tracing powered by Nvidia GPUs. It also leverages our partnership with Nvidia Omniverse. Instead of relying on local hardware, which creates visualization inequality across the organization, our ‘rendering-as-a-service’ architecture is hosted on the secure Siemens Xcelerator cloud. This provides a consistent, powerful visualization for all users regardless of their devices. <\/p>\n\n\n\n

        Here’s what’s changed: you can now chat with the system in natural language. Say, “Show me a cross-section and slide through the vehicle. Show it in a realistic environment and let me walk around it. Add the latest version with white trim so I can compare them side by side. Change to a sunset view gradually as I move.” <\/p>\n\n\n\n

        The amount of manual setup work this used to require was staggering. Specialists spent hours or days configuring visualization environments. We now do it conversationally, which democratizes access to engineering intelligence. When a CFO asks, “Why are we making this design change?” they can see it, understand it, and make informed tradeoffs. That’s transformative for organizational alignment. <\/p>\n\n\n\n

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        Rolls-Royce<\/h2>\n\n\n\n

        Rolls-Royce has been a Siemens customer for many years, using our software to manufacture the sophisticated engines they design. Their challenge was typical of heritage industrial companies: document-based systems, some over 50 years old, generating, transforming, and consuming data in silos not easily accessible and certainly not integrated. <\/p>\n\n\n\n

        With Siemens Xcelerator, they’re now implementing a comprehensive digital thread. They collect engine test and performance data from engineering, compare it to the design intent (“as-simulated”) and the actual production (“as-built”), identify deviations, and feed those insights back into product development. They’re using Insights Hub, our industrial IoT platform, as a functional data store to unify manufacturing and engineering data. <\/p>\n\n\n\n

        The results are compelling: Rolls-Royce is using Teamcenter, Opcenter (our manufacturing execution system), Insights Hub, Simcenter (simulation), and Mendix (low-code development) in a single digital thread that connects engineering, functional, and manufacturing data. Performance engineers use this integrated data foundation to make continuous product improvements, turning operational reality into design intelligence. <\/p>\n\n\n\n

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