#119: MBSE Isn’t Just for Rockets: Winning in Fast-Moving Consumer Markets

What happens when a company does everything you can do to a private jet - except build one from scratch?Aaron Lane has spent 25+ years at Duncan Aviation, navigating a PLM journey that went from literal paper boxes loaded onto departing aircraft, through a legacy ERP stretched beyond its limits, to SmarTeam, and finally to Aras Innovator. In this episode, he pulls back the curtain on what that transformation actually looked like - the messy parts included.Whether you're greenfield, brownfield, or somewhere in between, this conversation is packed with hard-won, practical wisdom.Key TakeawaysWhy Duncan Aviation's "product" is actually a service - and why that changes everything about PLM requirementsHow to manage uncontrolled vendor technical data from thousands of OEM suppliers at scaleThe power and the peril of a DIY customization cultureWhy you must clarify governance before go-live - not afterHow Duncan is now using Aras to automate tribal knowledge and eliminate paperwork gapsGuestAaron Lane - PLM Lead, Duncan Aviation A 25+ year Duncan veteran who started as an assistant finish tech and has held roles spanning production planner, project manager, certification coordinator, and engineering team leader. Aaron has been the driving force behind Duncan's PLM evolution from SmarTeam to Aras Innovator.Resources & Links MentionedDuncan Aviation - duncanaviation.aeroAras Innovator - PLM platform used by Duncan AviationDuncan Aviation Case Study - Available at razorleaf.com (published separately)SmarTeam - Legacy PLM system (Dassault Systèmes, now end-of-life)CATIA - CAD platform used by Duncan's European OEM partnerCMMC - Cybersecurity Maturity Model Certification, relevant to Duncan's manufacturing divisionHave a question for Aaron or want to connect with Duncan Aviation? Reach out via the podcast community.👉 Send topic ideas or guest suggestions: [email protected]🔔 Subscribe so you never miss an episode ⭐ Leave a review - it helps more engineers find the show 💬 Drop a comment - the hosts actually read themStay Sharp in Digital Engineering is the podcast about tools, trends, and tactics shaping digital transformation in product manufacturing. New episodes drop regularly.Music is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

Inside Solumina: The Digital Backbone for Aerospace & Defense ManufacturingWhat does it take to manage manufacturing where failure simply isn’t an option?In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott continue their digital product series with Sung Kim, CTO of iBase-t, for a deep dive into the Solumina platform—a manufacturing operations management system designed specifically for complex, highly regulated industries like aerospace and defense.Unlike traditional manufacturing systems that simply record what happened, Solumina is designed to actively guide production processes, enforce quality requirements, and maintain full traceability across the product lifecycle. Sung explains how the platform connects engineering intent to shop floor execution, integrating capabilities like MES, embedded quality management, supplier quality, MRO, and model-based manufacturing into a unified operational data model.The conversation explores the architecture behind the platform—from early two-tier systems to today’s cloud-native, microservices-based infrastructure—and how Solumina enables organizations to scale across global manufacturing sites while maintaining compliance, security, and operational continuity.If you’re curious about how modern manufacturing systems evolve from systems of record into systems that guide what happens next, this episode delivers a rare look under the hood.What You’ll Learn in This EpisodeWhy MES alone isn’t enough for complex aerospace and defense manufacturingHow Solumin integrates MES, quality management, MRO, and supplier quality into one platformWhy a unified operational data model is critical for AI in manufacturingHow manufacturers integrate Solumina with PLM, ERP, and shop floor systemsWhat model-based manufacturing looks like in real-world productionWhy cloud-native architecture and microservices matter for manufacturing softwareHow digital systems help preserve institutional knowledge as experienced workers retireKey Topics DiscussedManufacturing Operations Management (MOM) platformsAerospace and defense manufacturing systemsDigital thread and model based enterpriseEmbedded quality management systems (EQMS)Supplier quality integrationMaintenance, repair, and overhaul (MRO) workflowsAI-driven manufacturing operationsCloud-native industrial software architectureAbout the Guest:Sung Kim is the Chief Technology Officer at iBase-t, where he leads the company’s long-term technology vision and product architecture for the Lumina manufacturing operations platform. With more than 20 years of experience as a technology architect and computer scientist, Sung focuses on building scalable, secure systems for complex, highly regulated manufacturing environments.Before joining iBase-t, Sung worked in telecommunications and academia, teaching undergraduate and graduate students while publishing research in leading international journals and conferences, including ACM and IEEE. His work today centers on advancing modern, cloud-native manufacturing platforms that connect engineering, production, quality, and maintenance across the digital thread.📩 Subscribe for more conversations on digital engineering and product innovation💬 Share your experiences or questions in the commentsMusic is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

AI is everywhere right now—but in digital engineering and manufacturing, many leaders are still asking the same question: where does it actually help?In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with Dr. Diego Tamburini of CIMdata to separate hype from implementation reality. With decades of experience spanning Microsoft, Autodesk, and Siemens Digital Industries, Diego shares how organizations are applying AI today—not in theory, but inside real engineering and manufacturing environments.The discussion introduces five practical patterns of AI adoption, explains why many proof-of-concepts fail to scale, and explores emerging applications like AI-assisted workflows, predictive models, and natural-language interaction with engineering systems. Most importantly, the conversation reframes AI not as a technology trend, but as a decision framework leaders must apply carefully to solve the right problems.Key TakeawaysWhy today’s AI surge is driven largely by generative AI exposure—not a sudden invention of new intelligenceThe five patterns companies are using to implement AI, from vendor-embedded tools to fully custom modelsHow Retrieval-Augmented Generation (RAG) allows organizations to connect AI across enterprise systems without retraining modelsWhy many AI pilots fail due to unclear use cases, poor measurement, or data that cannot scaleHow AI is changing the way engineers interact with software—moving from menus to natural-language workflowsReal industrial applications already delivering value, including predictive maintenance, quality analysis, and simulation accelerationThe importance of aligning AI adoption with skills, governance, and measurable KPIs from the start👤 Guest InformationDr. Diego TamburiniExecutive Consultant, CIMdataLeads the company’s AI practice, helping industrial organizations apply AI to digital engineering and manufacturing transformation.Former Director of Engineering Agility at Microsoft, where he helped integrate AI into engineering workflows and marketplace platforms.Previously held leadership and strategy roles at Autodesk and Siemens Digital Industries Software, with more than 25 years of experience in CAD, CAM, CAE, and PLM innovation. 👉 Connect with Dr. Tamburini to learn more about practical AI adoption in industrial environments.If this episode helped clarify how AI fits into engineering and manufacturing:Subscribe to Stay Sharp in Digital Engineering on your favorite podcast platformShare this episode with a colleague evaluating AI initiatives Leave a review to help more listeners find the showMusic is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

In this episode of Stay Sharp in Digital Engineering, we break down the biggest themes and takeaways from 3DEXPERIENCE World (formerly SolidWorks World), held this year in Houston.The message was loud and clear: AI is here — and Dassault Systèmes is betting big on it.From the headline-making partnership with NVIDIA to live AI demos inside the 3DEXPERIENCE platform, the event showcased ambitious plans for AI-driven engineering workflows. But beyond the stage announcements, what are customers actually asking about? And what does this shift mean for data strategy, PLM adoption, and long-term system decisions?Juliann Grant discusses the highlights of the event with the attending Razorleaf team, Jonathan Scott, Francois Schlub, and Andrew Halley as they share what really stood out — including conversations happening off-stage.Key Themes from the EventAI Everywhere — But What Does It Really Mean?AI agents embedded in SolidWorks workflowsLive demos showing rapid simulation and design iterationPartnership announcement between Dassault Systèmes and NVIDIAThe broader message: AI as a force multiplier, not just a chatbotData Is the FoundationWhy clean, structured product data matters more than everThe push toward centralizing data in the 3DEXPERIENCE platformIntegration challenges across ERP, PDM, and PLM systems“Garbage in, garbage out” — especially with AIAdoption Still Matters More Than HypeReal-world customer questions about migration and workflowsPDM Pro vs. Manage vs. 3DEXPERIENCE decisionsCloud adoption realitiesLegacy systems vs. startup-native cloud environmentsEducation & the Next GenerationExpanded focus on 3DEXPERIENCE EDUHigh school and university programs teaching platform tools earlyThe long-term shift in engineering workforce readinessThe Big Question: Is AI Replacing Engineers?A recurring theme throughout the conference:If AI compresses hours of work into minutes… what changes for engineering teams?We explore the tension between productivity gains and workforce impact — and what it may mean for the future of digital engineering.Guests in This EpisodeJuliann Grant – HostJonathan Scott – HostAndrew Halley – Senior Director of PartnershipsFrançois Schlub– Sales ExecutiveThe Event TakeawaysAI dominated the headlines.But customers are still focused on:Getting PLM foundations rightManaging migration pathsStructuring and governing product dataEnsuring successful user adoptionThe shift is real — but the groundwork still matters.If you couldn’t make it to Houston, this recap gives you the highlights, insights, and honest takeaways from the floor.👉 Have questions about 3DEXPERIENCE, AI strategy, or PLM adoption?Reach out to the team at Razorleaf.Music is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

Digital engineering gets a lot of attention. But what happens after the design is complete?In this episode of Stay Sharp in Digital Engineering, we sit down with Naveen Poonian, CEO of iBase-t, to explore why execution — not just engineering — is the real battleground of digital transformation in aerospace and defense.From Manufacturing Execution Systems (MES) and Manufacturing Operations Management (MOM) to Supplier Quality Management (SQM) and Maintenance, Repair & Overhaul (MRO), we unpack how digital tools connect engineering, production, service, and compliance into a true lifecycle ecosystem.We also explore: Why complex discrete manufacturing requires a different approachThe shift toward model-based enterpriseHow AI is being applied to real execution problemsWhat makes digital transformation succeed (and fail)The culture behind iBase-t’s family-owned legacyIf you're navigating digital manufacturing, aerospace innovation, or operational transformation, this conversation delivers practical insight grounded in real-world execution.Key TakeawaysExecution is the missing link between digital engineering and real-world manufacturing outcomes. Aerospace and defense demand precision, traceability, and compliance at every stage.MES and MOM platforms must connect deeply with supply chain and MRO processes.AI must solve real operational problems — not just exist as a feature.Digital transformation requires partnership, focus, and strong organizational alignment.The future is model-based, interoperable, and data-driven. Guest -Naveen PoonianCEO, iBase-tAerospace and defense technology leader focused on manufacturing execution, lifecycle transformation, and operational excellence.Resources & MentionsiBase-tiBase-t Excelerate ConferenceModel-Based Enterprise (MBE)Manufacturing Execution Systems (MES)Manufacturing Operations Management (MOM)Supplier Quality Management (SQM)Maintenance, Repair & Overhaul (MRO)Connect With Us📩 Have questions or topic ideas? Email: [email protected]💬 Leave a comment — we read them all⭐ Subscribe for future conversations on digital engineering and manufacturingIf you found value in this episode, please share it with a colleague in aerospace, defense, or advanced manufacturing.Music is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

AI may dominate headlines, but the real transformation in product development is happening elsewhere—in how teams manage complexity across mechanics, electronics, and software without slowing time to market.In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott continue their conversation on Model-Based Systems Engineering (MBSE) with Eran Reinshmidt and Safae El Abkari of Dassault Systèmes. This discussion moves beyond aerospace and defense to explore how MBSE is reshaping consumer product development—from small appliances and power tools to connected products and manufacturing systems.The conversation dives into virtual prototyping, multidisciplinary collaboration, faster trade studies, platform thinking, product line engineering, and why MBSE is becoming essential for consumer companies that need speed without chaos.🔑 Key TakeawaysWhy consumer products now face the same complexity challenges as aerospace systemsHow MBSE enables parallel work across mechanical, electrical, and software teamsUsing virtual prototypes to test software months earlier than physical buildsHow parametric trade studies unlock better product decisions without slowing timelinesThe role of MBSE in product line engineering, modularity, and upgradeabilityWhy verification and validation must start earlier—and happen continuouslyHow MBSE principles are extending into manufacturing and factory variability👤 Guest Information Eran Reinshmidt Solution Leader, Consumer Industries – Dassault Systèmes Over 20 years of experience spanning furniture, home goods, appliances, sporting goods, apparel, and connected products.Safae El Abkari PhD, Engineering Sciences Leads the Perfectly Connected Product Solution at Dassault Systèmes, specializing in multidisciplinary product development and MBSE adoption.👉 Follow both guests on LinkedIn for insights on MBSE and consumer product innovation.If this episode helped clarify how MBSE applies beyond aerospace:Subscribe to Stay Sharp in Digital Engineering on your favorite podcast platformShare this episode with a colleague working in product development or manufacturingLeave a review to help more listeners find the showMusic is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

AI is everywhere right now—but in digital engineering and manufacturing, many leaders are still asking the same question: where does it actually help? In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with Dr. Diego Tamburini of CIMdata to separate hype from implementation reality. With decades of experience spanning Microsoft, Autodesk, and Siemens Digital Industries, Diego shares how organizations are applying AI today—not in theory, but inside real engineering and manufacturing environments.The discussion introduces five practical patterns of AI adoption, explains why many proof-of-concepts fail to scale, and explores emerging applications like AI-assisted workflows, predictive models, and natural-language interaction with engineering systems. Most importantly, the conversation reframes AI not as a technology trend, but as a decision framework leaders must apply carefully to solve the right problems.Key TakeawaysWhy today’s AI surge is driven largely by generative AI exposure—not a sudden invention of new intelligenceThe five patterns companies are using to implement AI, from vendor-embedded tools to fully custom modelsHow Retrieval-Augmented Generation (RAG) allows organizations to connect AI across enterprise systems without retraining modelsWhy many AI pilots fail due to unclear use cases, poor measurement, or data that cannot scaleHow AI is changing the way engineers interact with software—moving from menus to natural-language workflowsReal industrial applications already delivering value, including predictive maintenance, quality analysis, and simulation accelerationThe importance of aligning AI adoption with skills, governance, and measurable KPIs from the start👤 Guest InformationDr. Diego TamburiniExecutive Consultant, CIMdataLeads the company’s AI practice, helping industrial organizations apply AI to digital engineering and manufacturing transformation.Former Director of Engineering Agility at Microsoft, where he helped integrate AI into engineering workflows and marketplace platforms.Previously held leadership and strategy roles at Autodesk and Siemens Digital Industries Software, with more than 25 years of experience in CAD, CAM, CAE, and PLM innovation. 👉 Connect with Dr. Tamburini to learn more about practical AI adoption in industrial environments.If this episode helped clarify how AI fits into engineering and manufacturing:Subscribe to Stay Sharp in Digital Engineering on your favorite podcast platformShare this episode with a colleague evaluating AI initiativesLeave a review to help more listeners find the showMusic is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott continue the digital product series with a deep dive into mechanical variation management and tolerance analysis. Joined by James Stoddard, co-founder and research fellow at Sigmetrix, the conversation explores how tolerance analysis acts as a critical bridge between ideal CAD models and real-world manufactured parts.The discussion unpacks why variation can never be eliminated, how poor tolerance decisions quietly drive cost and quality issues, and why managing variation across the entire product lifecycle is essential for modern, model-based enterprises.Key TakeawaysWhy tolerance analysis is more than a final design check-it's a lifecycle disciplineHow variation breaks the digital thread between design and manufacturingThe tradeoff between tighter tolerances, higher quality, and rising costWhy GD&T is a language—and how misunderstanding it drives manufacturing riskHow tolerance analysis supports better decisions in design, manufacturing, and inspectionThe role of tolerance analysis in the transition from virtual models to real-world productionGuest:James StoddardCo-Founder & Research Fellow, SigmetrixJames has spent over 25 years advancing tolerance analysis and mechanical variation management technologies, helping organizations improve quality while controlling cost.Resources Mentioned●      Mechanical Variation Management●      Tolerance Analysis●      Model-Based Definition (MBD)●      Geometric Dimensioning & Tolerancing (GD&T)●      Digital Thread & Model-Based Enterprise (MBE) If you found this episode valuable, subscribe to Stay Sharp in Digital Engineering, leave a review, and share this episode with a colleague working in design, manufacturing, or quality.Music is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

Episode SummaryHow risky is it to buy engineering software from a company owned by private equity? Does SaaS actually reduce risk—or introduce new problems? And what should technology buyers really be evaluating in today’s rapidly consolidating digital engineering market?In this episode of Stay Sharp, hosts Juliann Grant and Jonathan Scott sit down with industry analyst Monica Schnitger, President and Principal Analyst at Schnitger Corporation. With nearly two decades of experience advising software vendors, investors, and technology buyers, Monica offers a grounded, behind-the-scenes look at private equity, SaaS adoption, cloud realities, and how buyers can make smarter, lower-risk decisions.This conversation cuts through common myths and focuses on what actually matters when selecting engineering, PLM, CAD, and simulation software in 2026 and beyond.Key TakeawaysWhy private equity ownership is often less risky than buyers assumeWhat really determines software risk: usability, fit, vendor trust, and longevityHow SaaS and cloud delivery are reshaping buying expectations—and support gapsWhy reseller and channel consolidation is accelerating across the industryThe difference between AI hype and practical, usable innovationHow digital twins, UX improvements, and hybrid cloud models are quietly advancingPractical advice for evaluating software purchases without chasing shiny objectsAbout the GuestMonica Schnitger is President and Principal Analyst at Schnitger Corporation, where she advises engineering software vendors, investors, and enterprise buyers. She brings deep expertise across CAD, CAE, PLM, and digital engineering ecosystems, with a background in Naval Architecture and Marine Engineering from MIT and an MBA from Babson College.Resources Mentioned●      Engineering software risk evaluation●      SaaS and cloud adoption in digital engineering●      Private equity trends in software●      Digital twins and PLM evolution If you found this episode valuable:●      ✅ Follow or subscribe to Stay Sharp on your favorite podcast platform●      ⭐ Leave a review to help more listeners find the show●      📤 Share this episode with a colleague evaluating engineering softwareMusic is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

Model-Based Systems Engineering (MBSE) is often viewed as a heavyweight discipline reserved for aerospace, defense, or highly regulated industries. In this episode of Stay Sharp, hosts Juliann Grant and Jonathan Scott challenge that assumption.Joined by Eran Reinshmidt and Safae El Abkari from Dassault Systèmes, the conversation explores how MBSE principles apply directly to fast-moving consumer markets. From coffee machines and vacuum cleaners to furniture and connected appliances, the guests unpack why changing consumer trends, regulatory pressure, and competitive intensity demand a more connected, model-driven approach to product development.Through clear examples and memorable analogies, this episode shows how MBSE helps consumer product teams manage requirements, behavior, variants, and cross-disciplinary complexity without slowing innovation.Key TakeawaysMBSE is not about product size or industry. It is about rate of change and system complexityConsumer products face continuous requirement churn from sustainability, regulation, and competitionDocument-based requirements and behavior models break down as products become multidisciplinaryA shared system model improves traceability, alignment, and decision-making across teamsBehavioral modeling is critical for integrating mechanical, electronic, and software systemsProduct Line Engineering (PLE) enables scalable variant management without restarting from scratchGuestsEran ReinshmidtSolution Leader, Consumer Industries, Dassault SystèmesExpert in PLM and systems engineering for consumer products across home goods, appliances, lifestyle, and wearable industriesSafae El Abkari, PhDSolution Engineering Consultant, Dassault SystèmesSpecialist in connected products, multidisciplinary systems, and embedded systems engineeringResources MentionedModel-Based Systems Engineering (MBSE)Product Line Engineering (PLE)Connected and smart consumer productsDigital engineering for consumer industriesIf this episode challenged how you think about product development, subscribe to Stay Sharp on your favorite podcast platform.Leave a review or share this episode with a colleague navigating complex product systems.Have a topic you want us to cover next? Email [email protected] is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.