Enhancing Design Efficiency Through Automation

This cabinet was engineered with Eplan Electric P8, and the result speaks for itself: clean wiring, clear structure, fast commissioning, and zero ambiguity for technicians. If you design control panels and want fewer errors, faster builds, and smoother handover to production, these practical EPLAN tips make a real difference: 1. Use proper device macros, not symbols Macros with mounting, connection points, and accessories ensure your design matches reality. This alone reduces wiring mistakes dramatically. 2. Keep structure identifiers consistent from day one Define your IEC structure (Function / Location / Installation) early. Changing it later costs hours and breaks reports. 3. Let EPLAN generate terminal strips and wiring lists Manual terminal planning is one of the biggest time-wasters. Auto-generated terminal diagrams speed up panel wiring and troubleshooting. 4. Standardize parts via Parts Management Link every symbol to a real manufacturer part number. This improves BOM accuracy and avoids procurement surprises. 5. Think like the panel builder, not only the designer Cable routing, spacing, and accessibility matter. A schematic that ignores physical reality always fails on the shop floor. 6. Use reports as engineering tools, not paperwork Connection lists, wire lengths, and PLC I/O reports are not “extras”—they are how you validate design quality. This is why modern electrical engineering is no longer just about drawing schematics. It is about data consistency, automation, and lifecycle efficiency. If you work with: • EPLAN • Industrial automation • Control panels • PLC systems • Electrical design standards this topic directly impacts your daily work. What is the one EPLAN feature that saved you the most time on a real project? #EPLAN #ElectricalEngineering #ControlPanel #IndustrialAutomation #PLC #PanelBuilding #ElectricalDesign #AutomationEngineering #DigitalEngineering #SmartManufacturing #Industry40

The more we automate, the more we learn. What starts as an experiment in efficiency always ends up teaching us more and more about the technology itself. This latest iteration of our SLA automation was no exception. We explored how the Form 4’s Flex Build platform behaves under repeatable robotic cycles, tested the navigation accuracy of the Unitree Go2, and pushed how far we could simplify the gripper design - in this case, making it completely passive. The result is less complexity, lower maintenance, and more reliability in long unattended runs. We also learned that the Flex Build platform introduces a bit of randomness when ejecting parts, so we integrated a small navigational add-on that corrects for positional offsets on the fly. Tiny improvements like this compound quickly when you run hundreds of cycles. Additive manufacturing gives mechanical engineering iteration speed close to that of software engineering. When automation enters the equation, the gap narrows further down. We believe we’ll see major advances in the hardware world in the near future thanks to these enablers. What do you think?

Most designers are using AI wrong. They use it to generate screens. But that’s not where the real leverage is. The real shift is this: AI should help you build systems, not just designs. Because screens change. Systems scale. And if you’re still designing everything manually… you’re already falling behind. Let me be honest with you. Creating a proper design system used to take weeks. Messy files. Inconsistent components. Endless revisions. And most teams never get it right. Not because they’re bad designers… …but because the process is broken. Now something has changed. With tools like Figma + Claude Code, you can completely rethink how components are created. You don’t start with screens anymore. You start with structure. Tokens. Systems. Reusable logic. Then you let AI handle the heavy lifting. Generating components. Applying consistency. Building scalable foundations. And you step in where it actually matters. Refinement. Decisions. Quality. That’s the role of a modern designer now. Not just creating… but directing. In this infographic, I’ve broken down the exact workflow: From setting up tokens connecting your design library prompting AI the right way generating clean, scalable components So instead of spending hours fixing inconsistencies… you build once, and reuse forever. If you’re serious about working faster, and designing at scale, this is something you need to understand. Because this is not a small improvement. It’s a complete shift in how design systems are built. I’ve simplified the whole process step by step in the infographic. If you learn this once, It will save you hundreds of hours.

𝗔𝗜 𝗙𝗼𝗿𝗺𝗼𝗿𝗽𝗵𝗶𝗻𝗴: 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗗𝗲𝘀𝗶𝗴𝗻 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗕𝗜𝗠 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀❗🏦 𝗨𝘀𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗕𝗜𝗠 𝗳𝗼𝗿 𝗳𝗼𝗿𝗺-𝗳𝗶𝗻𝗱𝗶𝗻𝗴, 𝗔𝗜 𝗙𝗼𝗿𝗺𝗼𝗿𝗽𝗵𝗶𝗻𝗴 refers to the process of exploring intermediate forms between initial designs. This involves leveraging AI algorithms to generate and visualize various design options, allowing for a more nuanced exploration of design possibilities. By employing AI Formorphing, designers can efficiently generate a wide range of design alternatives and evaluate them based on predefined criteria, helping to identify innovative and optimized solutions. 𝗧𝗵𝗲 𝘃𝗶𝗱𝗲𝗼 𝘀𝗵𝗼𝘄𝗰𝗮𝘀𝗲𝘀 𝗳𝗶𝘃𝗲 𝗱𝗲𝘀𝗶𝗴𝗻𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗯𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗡𝗲𝘁 that morph into each other using an AI workflow, resulting in 120 different design frames. This approach offers a unique workflow where AI is given some freedom to be creative while adhering to specific boundaries. This method allows designers to explore a vast design space, potentially discovering novel and unexpected solutions that may not have been considered through traditional design processes.

This is design and make intelligence in practice. Customer testimonials from our early Autodesk Forma Building Design users say the same thing: the tool is intuitive, it reduces rinse-and-repeat work, and it preserves design intent into Revit. What that translates to in real projects: • Faster schematic exploration so teams test more ideas earlier • Fewer repetitive tasks thanks to purpose-built automations for massing and layouts • Built-in analysis so daylight, sun hours, and carbon inform decisions before a direction is locked in • Native, geolocated handoff into Revit so design intent carries forward and rework drops Forma Building Design is not a novelty. It is a practical step toward the Forma industry cloud and toward workflows where knowledge and decisions travel with the project. That continuity makes AI and automation useful in context and helps teams deliver better outcomes with less friction. Proud of the teams who built this and grateful to the customers who tested it with honesty. The video below captures their reactions and the practical value they are already realizing. #Forma #DesignAndMakeIntelligence #Revit #AEC #Design

Weekend Insights | #72 A typical screenshot from a Computational Designer's screen ! One of many advantages of a CD approach At Ramboll, we are committed to delivering the best outcomes for our clients and projects. To achieve this, we embed computational design into our workflows, ensuring efficiency, precision, and innovation. Below is an example I took from my screen, of a live workflow in action, showcasing how we integrate multiple tools seamlessly. What You See: - Grasshopper (GH): The main driver for inputs and parametric modeling. - Rhino: Complements GH by allowing manual adjustments and real-time visualization of results. - Revit + Rhino.Inside.Revit: Eliminates the need for remodeling by pushing geometry directly into Revit, complete with families. - One Click LCA: Connected to GH (gh plugin) to perform carbon assessments and Life Cycle Analysis (LCA). - [Missed Screenshot]: Grasshopper links to ETABS for structural analysis, ensuring geometry is analyzed effectively. Results of This Workflow: - Parametric Modeling: Facilitates flexible and efficient design iterations. - Reduced Redundancy: Eliminates the need to remodel geometry across multiple platforms. - Rapid Iterations: Enables quick optioneering to optimize designs structurally and sustainably. - Streamlined Visualization: Produces clear results in a short time. - Centralized Control: Inputs and changes are managed in one place (Grasshopper), ensuring interoperability between tools for seamless updates. - Consistent Quality: Maintains high-quality outcomes throughout project phases, enabling efficient updates ahead of deadlines. - Scalability: This workflow can integrate automated tools for reports, detailed drawings, optimization algorithms, and more. Why It Matters: If you’re not working at this level, you’re missing out on valuable time savings with higher quality. By automating repetitive tasks, engineers can shift their focus to crafting the best designs, leaving the tedious work to the machines !

In today’s rapidly evolving digital era, Generative AI (GenAI) is transforming how project professionals plan, execute, and deliver successful outcomes. No longer limited to automation, AI has become a strategic partner helping project managers design smarter plans, predict challenges, and lead with data-driven confidence. CoachPro Consulting presents a curated collection of 14 essential Generative AI tools every project manager should know. These tools span the entire project lifecycle from planning and prototyping to risk control, workflow automation, and performance optimization, enabling you to make intelligent, informed decisions with speed and precision. 1. Planning Excellence Generative AI tools streamline project planning by automating Gantt charts, network diagrams, and progress visualizations, allowing managers to focus on strategy rather than manual coordination. Tools like Show Me Diagrams (ChatGPT Plugin) instantly generate visual workflows and dependencies, while GenAI-based design platforms propose multiple plan variations to enhance creativity and innovation. 2. Intelligent Prototyping AI-driven design tools such as Autodesk Fusion 360, Catia, and Ansys Discovery revolutionize how prototypes are built and tested. They enable 3D modeling, simulation-driven design, and interactive product analysis, empowering teams to visualize outcomes early and reduce time-to-market. 3. Time and Cost Optimization AI-powered platforms like Smartsheet enhance project accuracy through predictive forecasting, intelligent scheduling, and automated cost estimations. By leveraging data analytics, project managers can ensure better budget control, optimized resources, and timely delivery. 4. Control and Risk Management In the control phase, tools like WebPilot (ChatGPT Plugin) and AI Assistants for Jira provide real-time monitoring, predictive analysis, and risk identification. They help identify potential issues early, minimize uncertainties, and maintain consistent alignment between goals and progress. 5. Workflow Automation and Efficiency Modern AI productivity tools like ClickUp AI automate repetitive workflows, generate intelligent recommendations, and streamline dependency tracking. This allows project teams to shift focus from administration to innovation, ensuring smooth and efficient project execution. The Future of Project Leadership with AI Adopting Generative AI is no longer an option but a necessity. By integrating these tools, you can move beyond traditional methods and embrace a new era of intelligent project leadership. From automating tasks to anticipating risks, AI empowers you to lead strategically, decide confidently, and deliver successfully. Which AI tool or platform have you personally used in your projects, and how has it improved your workflow or decision-making? #GenerativeAI #ProjectManagement #CoachProConsulting #AIforPMs #FutureOfWork #ProjectLeaders #InnovationInProjectManagement 

How can we accelerate product development and match “China Speed” in engineering? A central lever is clear: AI-driven automation. But the real question is where along the V-model AI integration generates the greatest impact and where the strongest time savings can be realized. In our recent white paper (https://lnkd.in/ez6eQCM9), we argue that horizontally integrated use cases deliver the biggest acceleration effects. Connecting architectures, design artifacts, testing steps, and eliminating long handover times across toolchains and teams are the true speed multipliers. While fully automated end-to-end workflows for entire systems are still out of reach today, these approaches are already emerging at the component level: in mechanics, E/E, and embedded software. Mechanical Engineering CAD models are increasingly coupled with structural and CFD simulations. Multi-agent systems parameterize components, transfer models across simulation environments, and feed results back into topology optimization. The loop between design, simulation and optimization is closing and getting faster. E/E Development Multi-agent systems are now designing PCB layouts and validating them in simulation tools regarding energy efficiency, thermal performance, and electromagnetic robustness. Closed design loops enable agents to reshape PCB designs directly in E-CAD environments based on simulation feedback. Embedded Software Agents are connecting source code development with compiler-based code generation and automated test pipelines. Test outputs (from MiL, SiL or even PiL) flow back into the code generation process, enabling short-cycle quality loops that dramatically speed up iteration. The trend is clear: tightly coupling design, simulation, and testing unlocks massive acceleration on the component level and gradually shifts engineering from classical V-model thinking toward CI/CD-inspired workflows in both hardware and software development. And throughout all of this, one principle remains essential: humans stay in the loop, ensuring transparency, oversight and final approval. Christian Erb | Vlad Larichev | Daniel Spiess | Dr.-Ing. Tobias Guggenberger | Rajesh Katyal #AIEngineering #ChinaSpeed #ProductDevelopment #DigitalEngineering

The market pressure is real. By 2027, about $130 trillion is expected to flow into capital projects, even as productivity has only ticked up around 1% compared to 3.6% in manufacturing. Typical overruns reach roughly $1.2 billion with delays from six months to two years, and margins hover near 5%. Research shows top performers lean into platform, modular, and rules-based design. They’re more likely to automate quotes and use design automation, which helps them move faster while controlling risk.     If your teams are stuck translating bespoke requirements through siloed tools and manual steps, you feel the strain fast. Long lead times, margin-eroding errors, and penalties for late delivery stack up. I’ve seen the same pattern across capital assets. When engineering is the bottleneck, quoting and ordering slow to a crawl.     There’s a way to change the shape of the work. Industrialize the design process. Build modular platforms that are standardized yet configurable. Then layer rules-driven design automation on top. Capture the design rules once, reuse them across orders, and automatically generate the outputs your downstream teams need. Think BOMs, 3D models, and drawings produced with the same speed and precision you expect from standardized products. That shift reduces unique upfront engineering, protects quality, and frees specialists to focus on the hard problems.     Want to cut through complexity? Do this, pick one asset family. Map the core design rules that drive 80% of variation. Connect those rules to CAD so the system auto-generates BOMs and drawings for your two most common configurations. Run it for 30 days and track cycle time, rework, and the number of manual handoffs removed. If the signal is positive, expand.     If this is your world, what’s the first rule you’d automate to remove a bottleneck? 

AI i n UX Design: Friend or Foe? 🤔 AI is rapidly transforming the UX landscape, but what does it mean for designers? Here are three ways AI can enhance your design process and three pitfalls to watch out for: 1. Automation: AI can automate repetitive tasks like data analysis and user research synthesis, freeing up time for more creative work. This allows designers to focus on higher-level design challenges and strategic thinking. 2. Personalization: AI allows for more personalized user experiences. By analyzing user behavior and preferences, we can create tailored interactions that better meet individual needs. 3. Efficiency: AI can speed up the design process by generating design suggestions based on data patterns and previous successful designs, helping to iterate quickly and effectively. However, there are pitfalls: 1. Over-reliance: Depending too much on AI can stifle creativity and human-centered design. It’s essential to balance AI-driven insights with human intuition and empathy. 2. Bias: AI systems can perpetuate existing biases in data. Designers must be vigilant in ensuring that AI outputs are fair and inclusive. 3. Complexity: Integrating AI into the design process can be complex and requires a new set of skills. Continuous learning and adaptation are crucial. Let’s discuss how we can leverage AI to create better user experiences while staying true to the core principles of UX design. How are you integrating AI into your design process?