Embedia.io’s Post

🚀 Software-Defined Manufacturing (SDM) Explained Manufacturing is becoming software-driven, but most factories are still built on tightly coupled stacks where change is slow, risky, and expensive. We just published a new article introducing a practical, engineering-grade Software-Defined Manufacturing (SDM) reference architecture, designed for real factories, including brownfield environments. 👉 In this article, we explain: What Software-Defined Manufacturing really means (beyond buzzwords) Why Digital Twins & reference models are the core of SDM How to safely combine AI, optimization, and real-time control How to decouple applications from machines without breaking determinism or safety 🔗 Read the article here: https://lnkd.in/ePXfx-5b 📌 This is the first article of a series. Over the coming weeks, we’ll publish weekly deep dives, one per layer, covering design principles, pitfalls, and real-world implementation patterns. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering #ThinkSoftware Embedia.io

Software-Defined Manufacturing is moving from theory to practice, and this series does a solid job grounding SDM in real factory constraints and engineering realities. Worth reading and worth following as the deeper technical layers roll out: https://lnkd.in/ePXfx-5b Embedia.io Safouen SELMI Joseph Wehbe

I recorded this short video (simplistic demo at the end) to show how Retrieval Augmented Generation (RAG) can be applied in real PLM environments. The focus is on architecture — how generative AI can stay grounded in authoritative enterprise data across systems, revisions, and lifecycle states, and how that supports more reliable, agent-based approaches over time. This builds on the blog and reflects what I’m seeing across engineering and manufacturing organizations today. https://lnkd.in/g-cmQw-f #PLM #RAG #AI

🔎 Layer 2 of Software-Defined Manufacturing: The Digital Twin as a Reference Model If there is one layer that makes Software-Defined Manufacturing possible, it is not AI. It is not the cloud. It is not even the edge. It is the Digital Twin acting as a semantic reference layer. Most “Digital Twin” initiatives in manufacturing focus on dashboards, 3D visualization, or analytics. Valuable — yes. Transformational — rarely. In Software-Defined Manufacturing (SDM), the Digital Twin has a much more structural role: ✔️ It decouples applications from machines ✔️ It standardizes capabilities across heterogeneous assets ✔️ It enables safe, bidirectional interaction ✔️ It makes closed-loop optimization possible ✔️ It reduces long-term integration debt Without a proper reference model layer, SDM collapses into fragile integrations and AI experiments that cannot scale. In our new article, we go deep into: • Why abstraction is the real objective • How Asset Administration Shell (AAS) supports SDM • The business value of capability-based modeling • Common failure modes in Digital Twin programs • A pragmatic implementation roadmap This is the second article in our SDM architecture series — and probably the most critical one. 👉 The link to the full article is in the first comment. Next week, we will deep dive into Layer 3 — Analytics, AI & Optimization Services, and explain how to build scalable closed-loop optimization without breaking industrial constraints. If you are working on industrial digital transformation, IT/OT architecture, or AI in manufacturing, follow along. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering Embedia.io #ThinkSoftware

🔎 Layer 2 of Software-Defined Manufacturing: The Digital Twin as a Reference Model If there is one layer that makes Software-Defined Manufacturing possible, it is not AI. It is not the cloud. It is not even the edge. It is the Digital Twin acting as a semantic reference layer. Most “Digital Twin” initiatives in manufacturing focus on dashboards, 3D visualization, or analytics. Valuable — yes. Transformational — rarely. In Software-Defined Manufacturing (SDM), the Digital Twin has a much more structural role: ✔️ It decouples applications from machines ✔️ It standardizes capabilities across heterogeneous assets ✔️ It enables safe, bidirectional interaction ✔️ It makes closed-loop optimization possible ✔️ It reduces long-term integration debt Without a proper reference model layer, SDM collapses into fragile integrations and AI experiments that cannot scale. In our new article, we go deep into: • Why abstraction is the real objective • How Asset Administration Shell (AAS) supports SDM • The business value of capability-based modeling • Common failure modes in Digital Twin programs • A pragmatic implementation roadmap This is the second article in our SDM architecture series — and probably the most critical one. 👉 The link to the full article is in the first comment. Next week, we will deep dive into Layer 3 — Analytics, AI & Optimization Services, and explain how to build scalable closed-loop optimization without breaking industrial constraints. If you are working on industrial digital transformation, IT/OT architecture, or AI in manufacturing, follow along. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. hashtag #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering Embedia.io #ThinkSoftware

When it comes to selecting the right DXP, things can go off the rails. But don’t lose your caboose. Dan Drapeau has a two-track evaluation model that can keep you rolling – full steam ahead. In today's Guest Critic article, Dan proves why he’s a masterful conductor on the digital experience train. As he explains, the DXP competitive landscape has converged across architectural models, and evaluation approaches haven’t evolved at the same pace. Dan’s observation is that single-track processes are still chugging along – focusing on feature breadth and demo cohesion and favoring pre-composed suites. In his experience, what we really need is a more balanced approach that separates platform evaluation from ecosystem architecture. How do you leave the station? By running two structured tracks, which he outlines in detail. This helps vet not only current capabilities but also long-term durability relative to core considerations like vendor lock-in, CDP and DAM strategies, and the ever-evolving target of AI. In his post, Dan details the operational requirements, where the complexities exist, and how to synthesize results. He even digs into the critical topics of validation and stress testing, and distills it all into a handy checklist to help guide your DXP evaluation. As he says, composable and pre-composed DXPs represent architectural strategies along a spectrum, and these pointers can help keep you on track. “A structured two-track evaluation approach ensures that whichever model is selected, it is chosen for structural alignment and long-term outcomes rather than surface cohesion.” If you’re currently evaluating DXPs – or considering it in the future – this is a fantastic read from a sharp practitioner. Check it out on CMS Critic: 🔶 https://lnkd.in/e-XB3Pig #dxp #digitalexperience #digitalexperienceplatform #softwareevaluation #dam #digitalassetmanagement #cdp #customerdata #customerdataplatform #cmp #contentmarketingplatform #ai #artificialintelligence

Platforms are not just tools. They are operating systems. Swipe for what AI control towers mean for MSP service design. #ITSM #WorkflowAutomation #EnterpriseAI #ServiceManagement

Changing things like the color of money is part of the unsexy, behind the scenes work that requires a common understanding of the problem by exec and leg branch and the willingness to make difficult structural changes. A very tall order. But the payoff will be worth it, if we can rally disparate actors through great storytelling and evidence. cc Lauren Lombardo Ann Lewis Ben B. Solitaire Carroll Loren DeJonge Schulman Kevin Hawickhorst Soren Dayton Robert Gordon Anne Healy Bryon Kroger Dan Lips

AI in PLM : The first study closes on March 8 - Take your chance to participate and receive a preview of the findings. !

In recent discussions with strategic stakeholders and enterprise decision-makers, I’ve noticed a recurring architectural bottleneck: "Organizations are attempting to 'teach' LLMs factual data through expensive fine-tuning." From an ROI perspective, this is a strategic error. Fine-tuning for knowledge retrieval is the equivalent of rebuilding a machine’s engine every time you want to update the operator’s manual. It is invasive, permanent, and creates massive technical debt. In my latest analysis, I break down the Specialist Pattern a multi-model architecture that separates Reasoning (#Claude) from Tactical Execution (#Llama3). We discuss: ✅ Why RAG is your library, but Fine-Tuning is your muscle memory. ✅ Eliminating the "Prompt Tax" to reduce OpEx by 60%. ✅ Achieving sub-second latency via Edge-based specialist models. Manufacturing leaders aren't looking for "chatty" AI they are looking for deterministic industrial functions. The full breakdown of the framework is available below. I’m curious to hear from my network: Are you prioritizing model "intelligence" or model "execution" in your 2026 roadmap? #AIStrategy #IndustrialAI #CTO #ManufacturingInnovation #ROI #EnterpriseArchitecture #DecodeAIWithNiraj