Key Takeaways:

• Discover how Razorleaf acts as the glue connecting products, processes, and data across the digital enterprise.
• Learn about Razorleaf's boutique, vendor-agnostic approach to solving complex technical problems.
• Understand the core services offered across PLM, MES, integration, and data migration.
• Explore Razorleaf's proprietary products, including the CLOVER integration platform, Aras Manufacturing Suite, and 3DEXPERIENCE Tools.
• Hear real-world customer success stories across aerospace, industrial equipment, and warehouse automation.
• Gain insight into Razorleaf's core values and company culture.
• Get a preview of future trends in digital thread, integration, and AI applications in manufacturing.

Main Points Covered:

• Defining Digital Engineering and Manufacturing: How Razorleaf fits into the technology landscape, managing product data across the entire lifecycle from design to disposal.
• Boutique and Vendor-Agnostic Approach: Why Razorleaf focuses on client technology needs rather than pushing specific software vendors.
• Consulting and PLM Services: Platform selection, roadmapping, implementation, customization, support, and de-customization across major platforms including Dassault 3DEXPERIENCE, PTC Windchill, Siemens Teamcenter, and Aras Innovator.
• MES, Integration, and Data Migration: Services spanning aerospace and discrete manufacturing platforms like iBase-t Solumina and First Resonance, along with integrations between ERP, PLM, MES, and CRM systems.
• Razorleaf Product Offerings: Overview of CLOVER, a low-code hub-and-spoke integration platform connecting dozens of systems, along with 3DEXPERIENCE Tools and the Aras Manufacturing Suite.
• Customer Success Stories: Examples of long-term client engagements, including a six-week integration deployment for an AI warehouse automation firm.
• Company History and Core Values: How Razorleaf grew from a CAD reseller spinoff into a global organization built on principles such as doing the right thing, being exceptional but humble, and taking the long view.
• The Future of Digital Engineering: How integration, digital thread maturity, and AI are shaping mid-market manufacturing solutions.

Guest Information

• Eric Doubell: CEO and Founder of Razorleaf. Eric founded Razorleaf in 2000 with a background spanning software development, engineering, database administration, and consulting management across multiple industries.

If you enjoyed this episode, please subscribe to Stay Sharp in Digital Engineering with Razorleaf on your favorite podcast platform, leave a review, and share this episode with colleagues in digital engineering and manufacturing. Stay tuned for Part 2, where we will dive deep into CLOVER.

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Key Takeaways

• The Complexity Gap: Products have evolved from purely mechanical parts to complex systems with electronics, wireless tech, and embedded software, but legacy PLM hasn't kept pace.
• Beyond MCAD: FPHE platforms are independent of mechanical CAD and focus on connecting data across the whole product life cycle—from system requirements to field service.
• The Reality on the Ground: Many engineering organizations rely on broken integrations, spreadsheets, and manual communications because traditional systems fail to manage cross-discipline workflows.
• An Emerging Market: Early FPHE pioneers like Cognyx, Daluss, EXP Software, Flow Engineering, Intercax, Makersite, SPREAD AI, and Violet Labs are tackling these modern challenges, though broad adoption faces hurdles against established vendors.

Guest Info

Doug Macdonald: Lifelong innovation enthusiast, former developer, consultant, and marketer across companies like PTC, SAP, Sherpa, and Aras.

Resources Mentioned

• Report: The Future of Product Lifecycle Management: After Forty Years, Where Are We and What's Next? by Doug Macdonald
• Email the show: podcast@razorleaf.com

Enjoying the show? Subscribe on Apple Podcasts or Spotify, leave us a review, and share this episode with an engineer or digital strategist who needs to hear it!

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Key Takeaways

• Traditional physics-based digital twins are built for design and back-office engineering, making them accurate but often too slow, stale, and siloed for live operations.
• Scientific AI embeds fundamental laws of physics directly into machine learning models, providing millisecond-level predictions while staying reliable under unseen operating conditions.
• Multimodal AI enables systems to process varied data sources together, including sensor time series, lab reports, simulator outputs, and engineering drawings.
• Self-calibrating digital twins reduce manual recalibration cycles from months to real time, preventing operational drift and enabling better decision-making.
• The frontier of operational digital twins includes multi-system connected models, self-maintaining architectures, probabilistic risk outputs, pre-trained foundation physics models, and LLMs acting as conversational interfaces to orchestrate physics engines.

Key Terminology:

• Scientific AI: Machine learning models with built-in physics equations (conservation of mass, energy, equations of state) rather than pure data inference.
• Multimodal AI: Absorbing disparate formats, frequencies, and data streams across complex engineering workflows.
• World Models: Pure observational learning versus physics-grounded models in live industrial environments.
• The AI-Enabled Digital Twin Stack: A layered breakdown covering the physics layer, data abstraction layer, physics-native models, continuous calibration, and decision optimization.
• The Future of Industrial AI: Connecting multi-unit constraints, models that self-detect drift, foundation models for physics, and using LLMs as orchestration interfaces.

Guest Info

Prithvi Singh Chauhan is a Machine Learning Engineer at Geminus AI, where he builds physics-native AI systems combining machine learning, high-fidelity simulation, and optimization for large-scale energy systems. His work spans assets across North America, Guyana, the Middle East, India, Malaysia, and Australia. He holds an M.Eng. in Petroleum Engineering from Texas A&M University and a B.Tech. from IIT (ISM) Dhanbad. Named SPE's 2026 Young Engineer of the Year and an inventor on four US patent applications, Prithvi has co-authored around 17 peer-reviewed papers and serves as Program Chair for SPE's Data Science and Engineering Analytics technical section.

If you enjoyed this episode, please subscribe, rate, and leave a review on Spotify, Apple Podcasts, or wherever you listen to podcasts. Share this episode with colleagues and friends working in digital engineering, energy, and industrial operations!

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Key takeaways from the episode:

• The Drivers Behind Upgrading: How addressing deferred maintenance prevents today's stable systems from becoming tomorrow's technical debt.
• Parallel Infrastructure Strategy: Standing up new Kubernetes application clusters in advance while leaving active production running for a seamless cutover.
• The Power Outage Test: How a sudden network connection drop during database migration proved the value of an immediate, executable rollback plan.
• Testing Beyond Release Notes: Why unit, integration, and user acceptance testing (UAT) must focus on end-to-end business processes, database triggers, and hidden code dependencies.
• Post-Go-Live Risk Mitigation: Preserving a legacy QA environment post-launch as an insurance policy to isolate new bugs versus pre-existing system issues.

Main Episode Points

Why Modernize MES Infrastructure? Honeywell originally implemented Solumina to replace two legacy, high-tech-debt MES solutions. To keep Solumina from becoming tech debt itself, Honeywell needed to move off aging Red Hat 7 operating systems to Red Hat 8, transition to version i110.2, and simplify its underlying landscape by dropping MongoDB dependencies.

 Architecting a Zero-Disruption Strategy To protect shop floor productivity, the team provisioned new application servers for the new Kubernetes cluster well before launch day. By maintaining existing ActiveMQ and Apache reverse proxy servers, they eliminated the need for IP or DNS changes across plant users and key integrations like SAP and Teamcenter. Cutting over—or rolling back—was as fast as switching a URL rewrite rule and restarting Apache.

 Managing Unexpected Hurdles During the migration weekend, a power outage in a neighboring building disrupted the database connection. Because SQL logs filled up during the blind run, the team relied on a pre-planned full database backup to restore and re-run upgrade scripts without delaying operations. Later, when a minor SAP bill of materials (BOM) integration issue arose during post-launch smoke testing, the technical team authored a pre-processing fix within hours.

Best Practices for Enterprise System Upgrades

• Leverage Non-Production Environments: Work through dev, test, and sandbox stages to refine custom IT security scripts before touching production.
• Set Go/No-Go Checkpoints: Establish strict time thresholds for rollback decisions using real-world data timing tests.
• Keep Old Environments Alive Temporarily: Maintain the previous version in a non-production QA state to verify whether post-launch issues are new or legacy.
• Audit Scheduled Tasks: Ensure legacy background tasks and cron jobs are disabled so old system nodes do not auto-start after server reboots.

Guest Information

• Ryan Sims - Senior IT Engineer at Honeywell in Muncie, Indiana. Ryan supports the Solumina environment for Honeywell Aerospace in Clearwater, Florida, having managed seven non-production and production instances.
• Brian Neff - MES Technical Team Lead at Razorleaf. Brian brings over 20 years of experience across software development, database architecture, and Agile technical leadership.

Resources Mentioned

●      Razorleaf Corporation

●      iBase-t Solumina MES

Enjoyed the episode? Subscribe to Stay Sharp with Razorleaf on Apple Podcasts, Spotify, or your favorite podcast app. 

Share this episode with fellow engineering and manufacturing IT leaders! 

Have a question or topic suggestion? Email us directly at podcast@razorleaf.com.

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Key Takeaways

●  The Product Memory Flywheel: Learn about the concept of a data context layer that collects information from disparate systems (PLM, ERP, CAD) and activities. It creates a structured foundation for AI to reason across your entire product development lifecycle.

●  Solving "Boring" Problems: Oleg argues that real innovation doesn't always come from flashy tech, but from solving the painful, mundane tasks engineers hate. We discuss how prioritizing these pain points creates the most immediate value.

●  Agentic Workflows:

• CAD File Agent: A new, chat-based interface that handles file management, versioning, and syncing, shielding engineers from the complexities of traditional PDM systems.
• BOM Review Agent: A tool that utilizes "Check Cards" to automate validation, highlight errors (like quantity mismatches), and facilitate team collaboration directly within the BOM.

●  The Future of Intelligence: How the "Flow" concept integrates data across enterprise systems, allowing companies to transition from manual, error-prone processes to a streamlined, automated digital thread.

Featured Guest

●  Oleg Shilovitsky: CEO and Co-founder of OpenBOM, and author of the widely-read industry blog Beyond PLM. With over 25 years in the data management and manufacturing space, Oleg brings a unique perspective on bridging the gap between engineering and the supply chain.

Resources & Links

●       OpenBOM: Explore the platform and its latest AI capabilities.

●       Beyond PLM: Oleg’s blog on PLM, digital transformation, and modern manufacturing strategies.

Support the Show

 If you enjoyed this episode, please subscribe, follow, and leave a review. Your feedback helps us bring you more content on the future of digital engineering. 

Share this episode with a colleague who is tired of fixing BOMs in Excel! 

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In Stay Sharp Episode 151, hosts Juliann Grant and Jonathan Scott sit down with Doug Macdonald, a longtime PLM industry veteran and self-described “lifelong innovation enthusiast and recovering advocate for PLM,” to explore the history of product lifecycle management—and how the technology evolved alongside the products and businesses it was designed to support.

Doug has been part of the industry since the early days of CAD/CAM and PDM, working across development, consulting, sales, advocacy, and marketing roles at companies including PTC, SAP, Sherpa, Aras, and CIMdata. His career provides a unique perspective on how the technology evolved and why some of the fundamental assumptions behind today's PLM platforms still remain.

From CAD Files to PDM

The conversation begins with the emergence of PDM as CAD moved into the mainstream. What started as a practical need to manage files, versions, and engineering data gradually became something much more significant.

As companies accumulated more product data in centralized repositories, managing versions and protecting that information became increasingly important. The repository itself also created an unexpected consequence: once a company's intellectual property and product history were deeply embedded in a system, moving to another CAD platform became considerably more difficult.

How PDM Evolved into PLM

From there, the discussion follows the evolution toward PLM.

As products became more complex, organizations needed to manage more than CAD files. Parts and assemblies led naturally to bills of material (BOMs), while increasing amounts of product data created a need for change management and configuration management.

The terminology evolved as well. The industry moved through concepts such as PDM, collaborative product commerce (CPC), and ultimately PLM—even though many of the underlying capabilities were continuing to evolve rather than being completely reinvented.

Has PLM Really Moved Beyond PDM?

One of the central questions of the episode is whether PLM ever truly became something separate from PDM—or whether much of PLM remains an extension of PDM.

Today's PLM platforms may include capabilities for quality, materials management, software, electronics, and other aspects of the product lifecycle. Yet, as Jonathan and Doug discuss, many organizations still primarily use these systems for managing CAD data, versions, structures, and related engineering information.

That creates an interesting gap between the vision of PLM and how organizations actually use it.

The MCAD Connection

The conversation also examines the continued influence of mechanical CAD on the PLM market.

Many of the industry's largest PLM platforms remain closely connected to CAD, and the hosts explore how that relationship may influence both product strategy and the architecture of today's systems. If MCAD remains a major source of revenue, there is an obvious incentive to continue building around that foundation.

But products and manufacturing organizations have changed.

Modern companies increasingly operate through complex global supply chains and collaborative networks rather than the highly vertically integrated models that characterized earlier generations of manufacturing. The question becomes whether today's PLM architectures have evolved enough to support that new way of working.

Why Aren't Companies Using More of Their PLM Capabilities?

If PLM platforms contain so many lifecycle capabilities, why aren't organizations using all of them?

The discussion considers whether some modules were developed around the requirements of individual customers or particular historical markets rather than broader industry needs. A capability can exist within a platform without necessarily being the right fit for the way organizations operate today.

This raises a larger question about the future of PLM: Are today's platforms designed for the manufacturing world we have—or the manufacturing world we used to have?

What You'll Take Away

In this episode, you'll hear:

• How CAD/CAM helped drive the emergence of PDM
• Why managing versions and product data became such a critical problem
• How PDM evolved toward BOM management, change management, and ultimately PLM
• The role of collaborative product commerce in the industry's evolution
• Why PLM may still be heavily rooted in PDM
• How the continued connection between MCAD and PLM influences the market
• Why some organizations aren't using the full range of capabilities available in PLM platforms
• How the shift from vertically integrated companies to complex supply-chain networks challenges traditional PLM architectures
• Why understanding PLM's history may be essential to understanding where it needs to go next

What's Next?

This conversation is only the beginning. Doug will return for a future episode to discuss where PLM and product platforms are headed next—and what the next generation of these systems may look like.

Listen to Episode 151 and then stay tuned for the next conversation.

Have thoughts about the evolution of PLM? Leave us a comment—we'd love to hear what you think.

Until next time, stay sharp!

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It’s a milestone worth celebrating: Stay Sharp in Digital Engineering has reached 150 episodes!

In this special episode, co-hosts Juliann Grant and Jonathan Scott take a break from the usual guest format to look back at nearly three years of Stay Sharp. Together, they revisit the topics, trends, guests, and conversations that have shaped the podcast—and consider what’s still left to explore.

From the beginning, the podcast has focused on the rapidly changing world of digital engineering, manufacturing, and product development. But as the hosts reflect on 150 episodes, one thing becomes clear: while the technology continues to change, many of the fundamental challenges remain remarkably consistent.

From PLM to AI—and Everything in Between

PLM has been a recurring theme since the earliest episodes. The hosts revisit the early What Is PLM? series, which remains one of the podcast's strongest-performing topics, and discuss why questions about PLM's role within the enterprise are still relevant today.

They also look at how the conversation has expanded into other areas, including MES, quality, integration, migration, and data management. These may not always be the most glamorous subjects, but they are often where digital transformation succeeds—or fails.

AI has become another major theme. While the hype around AI can sometimes feel repetitive, Juliann and Jonathan discuss why the conversation continues to evolve as companies move from experimentation toward practical implementation. A recurring lesson from many of their AI conversations is the importance of the underlying data foundation—and the fact that there is still no single answer for how organizations should approach it.

Digital Twins, Digital Threads and MBSE

The podcast's Digital Twin series gave the hosts an opportunity to explore a term that is often overused and misunderstood. Conversations with practitioners helped put digital twins into context and demonstrate how organizations are applying the concept in the real world.

Digital thread has been another important—and still evolving—theme. Rather than viewing digital thread as a single project, the hosts describe it as an incremental process of connecting data and information across systems and business processes.

Model-Based Systems Engineering (MBSE) is another topic they expect to remain relevant for years to come. Discussions with experts including Chris Finley and David Long helped demonstrate both the practical applications of MBSE and why it extends well beyond the traditional examples many people associate with it.

The Less Glamorous Side of Digital Transformation

Some of the most valuable conversations have focused on topics that don't always generate headlines: integration, migration, data quality, and unstructured data.

The hosts reflect on conversations about everything from the massive amounts of information associated with aircraft to CAD migration and the challenges of moving data between systems and formats.

These discussions reinforce an important reality: digital transformation isn't just about adopting new technology. It also means dealing with the existing data, systems, processes, and technical debt that organizations have accumulated over time.

Digital Engineering and the Defense Industrial Base

The podcast also spent considerable time exploring digital engineering and the defense industry, including cybersecurity, CMMC, and the Defense Industrial Base (DIB).

Juliann and Jonathan discuss the potential opportunities for small and midsize manufacturers to participate in the DIB, as well as the challenges organizations face as cybersecurity and compliance requirements evolve.

The People Behind the Technology

Technology may be at the center of digital transformation, but many Stay Sharp conversations have highlighted another critical factor: people.

The hosts discuss the importance of organizational culture, adoption, change management, partnerships, and the relationships between technology providers, customers, service providers, and systems integrators.

Their Digital Product series has also provided an opportunity to go beyond product features and specifications to understand the people and cultures behind different software companies. As Jonathan points out, conversations reveal things about a company that aren't always apparent from a website or product sheet.

A Few Favorite Guests

With 150 episodes and an impressive roster of guests, there are plenty of conversations worth revisiting. Juliann and Jonathan highlight several memorable guests, including Brion Carroll, Patrick Hilberg, Jos Voskuil, Michael “Fino” Finocchiaro, Martin Eigner, Chris Finlay, David Long, Dan Miklovic, and others.

They also give a special shout-out to Jen Ferello, who helped get the podcast off the ground and remains an important part of the Stay Sharp story.

And, of course, the hosts recognize the many guests who have shared their expertise throughout the first 150 episodes.

What's Next for Stay Sharp?

Reaching 150 episodes isn't the end of the conversation. If anything, the hosts discover that there are still more questions than ever.

They want to hear from listeners about the topics they should cover, companies and industries they should explore, and guests they should invite onto the show. They also plan to use LinkedIn surveys to make it easier for listeners to weigh in.

Most importantly, Juliann and Jonathan thank everyone who has listened, shared feedback, approached them at conferences, appeared as a guest, or helped make the podcast possible.

After 150 episodes, Stay Sharp in Digital Engineering is still doing what it set out to do: having thoughtful conversations about the technology, people, and ideas that help professionals stay sharp in digital engineering.

Here's to the next 150 episodes.

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Key Takeaways:

• What is Product Memory?  It’s a context layer that captures the "why" behind decisions, relationships, and conversations across a product's lifecycle—not just the static records found in PLM.
• The Three Parents of Product Memory: The concept is born from the need to capture decision-making context, the technological capability of context graphs, and the acceleration provided by AI agents.
• AI as a Forcing Function: Unlike previous tech trends (like cloud adoption), AI requires structured context to be effective. Without proper "memory," AI agents hallucinate, making the capture of this context an urgent priority rather than a "nice-to-have."
• Security & Ownership: While product memory raises questions about data privacy and intellectual property, Oleg argues it should be treated like any other process—with clear protocols and security standards.
• Future of Monetization: As we move beyond seat-based SaaS models, the industry is exploring new ways to monetize data and outcomes, though the exact business models for product memory are still emerging.

Resources Mentioned:

• Beyond PLM Blog: https://beyondplm.com
• Upcoming Book: From CAD Files to Product Memory by Oleg Shilovitsky (available soon via beyondplm.com).

If you found deep dive into the future of manufacturing valuable, please like, subscribe, and share it with a colleague or network peer who is tackling these same digital engineering challenges. 

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Key Takeaways

• The Power of Traceability: Discover how digital threads allow you to query designs in real-time, moving from manual, document-based matrices to instantaneous verification.
• Breaking the Silos: Learn how a digital thread integrates disparate engineering domains—from systems architecture to mechanical CAD and software—allowing teams to collaborate without needing to master every single tool.
• The Role of Configuration Management: Understand why CM is the backbone of a successful digital thread, especially when moving from document-based artifacts to an "Authoritative Source of Truth."
• Start with the End in Mind: Get actionable advice on how to begin your implementation journey by focusing on your specific business goals rather than trying to trace every single data point at once.

Episode Highlights

• Traceability on Steroids: Chris shares a real-world example from a Navy program where a complex engineering trace was completed in minutes, not days, saving hours of manual labor.
• The "Digital Cable" Concept: We discuss how the digital thread functions like a multi-strand cable, connecting various engineering disciplines and providing a single, consistent version of the truth.
• Impact Analysis: How a well-crafted digital thread enables you to perform impact analysis in minutes, revealing exactly which components are affected by a change—revealing complexity that was always there, but previously invisible.
• Overcoming Implementation Barriers: Advice for organizations of all sizes on how to start, when to automate, and why you should focus on "ad-libbing" and refining your processes rather than simply digitizing old workflows.

Connect with Us

• Guest: Chris Finlay, Digital Systems Engineering Director at Raytheon
• Podcast: Stay Sharp in Digital Engineering
• Join the Conversation: Have questions for Chris or thoughts on this episode? Leave us a comment or contact us at podcast@razorleaf.com

If you enjoyed this deep dive into digital engineering, please rate, review, and subscribe on Spotify or Apple Podcasts. Share this episode with a colleague who is looking to streamline their engineering workflows! 

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The digital engineering industry is experiencing one of its most significant periods of transformation in decades—but separating meaningful innovation from marketing hype has never been more challenging.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome industry analyst Christine Longwell, founder of PLM Insights. Drawing on a career that spans mechanical engineering, software implementation, competitive analysis, market research, and consulting, Christine shares a unique perspective on where product lifecycle management (PLM), AI, and digital engineering are really headed.

The conversation explores why understanding real customer challenges starts on the factory floor, why software vendors often struggle to understand small and medium-sized manufacturers, and how service providers are becoming trusted advisors in technology selection. Christine also discusses the rapid rise of AI, why organizations should be cautious of bold predictions, and why people—not technology—remain the biggest obstacle to digital transformation.

If you're trying to make sense of today's AI landscape while planning for tomorrow's digital engineering environment, this episode offers practical insights grounded in decades of industry experience. 

In this episode you'll learn:

• Why successful digital transformation starts with understanding business processes—not just technology 
• The importance of seeing customer challenges firsthand through factory visits and support teams 
• Why SMB manufacturers remain underserved by traditional market research 
• How AI is changing the PLM landscape—and where the hype ends 
• Why organizational silos may be the biggest barrier to AI success 
• The growing importance of trusted implementation partners and systems integrators 
• Why independent customer research and win/loss analysis are becoming increasingly valuable 
• How AI may accelerate convergence between PLM, ERP, and supply chain systems 

As AI reshapes the digital engineering landscape, one thing remains constant: successful transformation starts with understanding people, processes, and real business challenges—not just adopting the latest technology. Christine Longwell's unique perspective, spanning engineering, software vendors, market research, and consulting, reminds us that meaningful innovation comes from listening to customers, challenging assumptions, and making technology decisions based on business outcomes rather than hype.

Whether you're evaluating PLM strategies, navigating AI adoption, or planning your next digital transformation initiative, this conversation offers practical insights to help you make smarter, more informed decisions.

If you enjoyed this episode, subscribe to Stay Sharp in Digital Engineering on your favorite podcast platform so you never miss a conversation with industry experts shaping the future of product development and manufacturing.

Have a question for Christine Longwell or a suggestion for a future episode? We'd love to hear from you. Leave a comment, reach out to the Razorleaf team, or email us at podcast@razorleaf.com. Be sure to share this episode with colleagues who are navigating AI, PLM, and digital transformation.

Until next time, stay curious, stay connected—and Stay Sharp.

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The Department of Defense recently announced a pause on one of the most significant upcoming requirements of the Cybersecurity Maturity Model Certification (CMMC) program, creating confusion across the defense industrial base. Does this mean contractors can slow down their cybersecurity efforts—or does it simply change how compliance will be evaluated?

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome back Steve Nichols, Razorleaf's government solutions expert, to explain exactly what changed, what didn't, and what defense contractors should be doing during this temporary pause.

Steve breaks down the July announcement, explains why companies are not off the hook for CMMC compliance, and discusses why maintaining strong cybersecurity practices remains essential regardless of how future certification requirements evolve.

In this episode, you'll learn:

• Why the government paused third-party CMMC Level 2 assessments
• What requirements still remain in effect
• The difference between self-certification and third-party certification
• The real costs of preparing for a CMMC assessment
• Why good cybersecurity hygiene is still critical
• How organizations should prioritize the 110 security controls
• What the government may change after the public comment period
• How small businesses could be affected
• Why waiting for final guidance may be a risky strategy
• How AI could eventually play a role in cybersecurity assessments

Key Takeaways

The current pause affects only the third-party assessment requirement for CMMC Level 2 certification. Organizations are still responsible for meeting applicable cybersecurity controls and certifying compliance where required. Companies should continue improving their security posture rather than assuming requirements will disappear.

Steve also explains that cybersecurity compliance should be viewed as an ongoing business process—not a one-time audit. Organizations that continue improving their IT environment today will be in a much stronger position regardless of how the government ultimately adjusts the CMMC program.

Featured Guest

Steve Nichols leads Razorleaf Government Solutions practice, helping defense contractors navigate digital transformation, cybersecurity requirements, PLM strategy, and government compliance initiatives. His experience spans startups, commercial software companies, and federal programs, making him a trusted advisor for organizations operating within the defense industrial base.

If your organization works with the Department of Defense or plans to enter the defense supply chain, this conversation will help you understand what today's changes mean—and how to prepare for what's next.

🔔 Subscribe for more conversations about digital engineering, PLM, cybersecurity, manufacturing, AI, and digital transformation.

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In this episode of Stay Sharp in Digital Engineering, co-hosts Juliann Grant and Jonathan Scott welcome back Paul Empringham, VP of CAD, Simulation, and PLM at NYBS Consulting, to explore how software licensing is evolving—and why engineering leaders need to start thinking differently about budgeting, governance, and AI consumption before costs spiral out of control.

Building on their previous discussion about traditional licensing models, Paul explains why the industry's move to cloud platforms and tokenized licensing creates both opportunities and new financial risks. As AI becomes embedded directly into CAD, PLM, and engineering applications, organizations will need better visibility into software usage, stronger governance, and new strategies for managing unpredictable consumption-based costs.

In this episode you'll learn:

• Why cloud and SaaS platforms are fundamentally changing software licensing
• How token-based licensing differs from AI token consumption
• Why AI assistants may create unpredictable engineering software budgets
• The hidden cost of API calls, automation, and AI-powered workflows
• How vendors are moving toward usage-based licensing models
• Why engineering leaders need guardrails before deploying AI at scale
• The importance of measuring software usage before negotiating renewals
• Why visibility into licensing has never been more important

Key Discussion Topics

The shift to cloud-first engineering software

Software vendors are rapidly moving customers away from on-premises installations and toward cloud platforms. While this simplifies upgrades and accelerates feature delivery, it also reduces customer visibility into actual software usage.

Understanding tokenization

Paul explains the growing trend toward token pools, where engineering teams consume tokens for advanced capabilities across multiple software products instead of purchasing individual specialty licenses. This provides greater flexibility—but also introduces new complexity.

AI changes everything

As AI assistants become embedded inside CAD, PLM, and simulation tools, companies may soon be paying for both software licenses and AI consumption. Unlike traditional licensing, AI usage can fluctuate dramatically, making annual budgeting far less predictable.

Managing the unknown

Engineering organizations need better data than ever before. Understanding who uses which tools, how often they use them, and where AI is being adopted will become essential for controlling costs and preparing for future licensing changes.

Memorable Quote

"For the first time in our lives, I don't think we can predict what our software is going to cost us."

Featured Guest

Paul Empringham
Vice President of CAD, Simulation & PLM
NYBS Consulting

Paul specializes in engineering software licensing, optimization, and governance for global manufacturing organizations. He has helped companies maximize software investments while navigating increasingly complex licensing models across the CAD, PLM, and simulation landscape.

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Key Takeaways:

●       The Sustainment Trap: While initial implementations get all the budget, the real costs reside in ongoing maintenance, break-fix cycles, and the "interoperability spaghetti" of modern system architectures.

●       The Productivity Tax: Every update, patch, or system hiccup causes interruptions. Research shows that "deep work" recovery takes an average of 23 minutes after a distraction, making seemingly minor tech updates a massive hidden expense.

●       IT vs. Engineering: We break down where the line should be drawn. IT excels at commoditized tasks (security, networks, cloud infrastructure), but "Digital Environment Sustainment" requires niche domain knowledge that often falls into a gray area between departments.

●       The "Why Now?" Factor: The landscape has shifted from isolated systems to deeply integrated microservices, cloud-hybrid architectures, and heightened security requirements, making maintenance significantly more complex than it was a decade ago.

Main Points:

●       Defining Digital Environment Sustainment: Moving past "managed services" to a model that accounts for the specific, specialized needs of engineering and manufacturing software.

●       The Architecture Explosion: How the shift from a single server to containerized microservices has exponentially increased the complexity of maintaining even a single application.

●       The Human Cost: Discussing the importance of Change Management (OCM) and the high overhead of onboarding new engineers when systems are constantly evolving.

●       Specialized Expertise: Why traditional, fixed-team managed services may not be enough to handle the deep subject matter expertise required for complex CAD/PLM/MES environments.

Are you seeing these hidden costs in your organization? Let us know your "horror stories" or where you’ve seen the biggest drain on productivity. Drop us a comment or send us a note! If you found this episode helpful, please like, subscribe, and share it with your engineering team—it helps us keep the content coming. 

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In Episode 143 of Stay Sharp in Digital Engineering, co-hosts Juliann Grant and Jonathan Scott welcome back Jonathan Girroir, Technical Evangelist at Tech Soft 3D, for a forward-looking discussion about how AI-assisted coding is reshaping engineering software development. Together they explore the growing "build versus buy" dilemma, why software components are becoming more valuable than ever, and how organizations should think about protecting their intellectual property while embracing modern cloud architectures.

From AI-generated code and engineering toolkits to data hubs, APIs, and digital thread strategies, this episode examines the technology trends that will influence how manufacturers develop, deploy, and manage engineering software over the next decade.

In this episode, you'll learn:

• Why AI-assisted coding is fueling an explosion of engineering software startups
• How manufacturers are building custom applications faster than ever
• When it makes sense to build software—and when buying is the better strategy
• How reusable software components accelerate product development
• Why data sovereignty has become a critical consideration
• The hidden costs of maintaining custom software
• How cloud delivery models and hybrid architectures are changing engineering workflows
• Why APIs and data hubs may become more important than traditional file formats
• The growing role of standards like STEP AP242, QIF, and Universal Scene Description (USD)
• Why connected engineering data is becoming one of manufacturing's greatest competitive advantages
• How digital thread strategies create richer AI training data for future innovation

Key Takeaways

AI isn't replacing engineering software developers—it's making software creation dramatically more accessible. Organizations now have the opportunity to prototype and deploy specialized engineering applications much faster than in the past.

However, successful organizations won't simply build everything themselves. They'll carefully evaluate where commercial software provides mature capabilities and where custom development creates true competitive differentiation.

The conversation also highlights an important shift occurring across manufacturing: engineering data is evolving from isolated files into connected, API-driven data ecosystems. Companies that invest in clean, connected digital thread data today will be better positioned to leverage AI tomorrow.

Whether you're evaluating PLM modernization, developing engineering applications, or planning your organization's AI strategy, this episode offers practical guidance for making smarter technology decisions.

Be sure to subscribe for more conversations on digital engineering, PLM, AI, MBE, digital thread, manufacturing, and engineering technology.

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In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott are joined by Paul Empringham, VP of CAD, Simulation & PLM at NYBS Consulting, to unpack the increasingly complex world of engineering software licensing. Drawing on decades of experience working with organizations including Rolls-Royce, BAE Systems, Red Bull Racing, JCB, and Sunseeker, Paul explains why many companies are unknowingly overspending on CAD, PLM, simulation, and engineering software—and how better data can dramatically improve licensing decisions.

The discussion covers the evolution from perpetual licensing to subscriptions, named-user models, and cloud licensing, along with practical strategies for optimizing license usage, avoiding costly compliance mistakes, and negotiating smarter renewals. Whether you're an engineering leader, IT professional, procurement specialist, or PLM administrator, this episode offers actionable advice that can save your organization significant time and money.

Key Topics Discussed:

• How engineering software licensing has evolved from perpetual licenses to subscription and cloud-based models
• The differences between concurrent, named user, and subscription licensing
• Why engineering, IT, and procurement all play critical roles in software purchasing
• What software license optimization actually means—and why usage data matters
• Common licensing mistakes that lead to unnecessary costs and compliance risks
• How organizations can negotiate license "remixes" instead of purchasing additional software
• The future of engineering software licensing, including token-based consumption models

Key Takeaways:

Know How Your Software Is Really Being Used

Most organizations don't actually know how many licenses they need. Monitoring real-world usage reveals idle licenses, underutilized features, training gaps, and opportunities to reduce costs without impacting productivity.

Don't Buy Bigger Bundles Than You Need

Vendors often encourage customers to purchase larger software bundles with additional features. Without usage data, companies frequently pay for capabilities they rarely—or never—use.

Compliance Is More Complicated Than You Think

Many licensing violations happen accidentally. Geographic restrictions, shared logins, outdated contract terms, and improper license usage can all create compliance issues that organizations don't discover until an audit.

License Data Creates Better Negotiations

Understanding actual license utilization gives organizations leverage during renewals. Instead of automatically renewing everything, companies can negotiate license remixes, exchange unused products, or right-size their environment.

The Licensing Landscape Is Still Changing

Engineering software continues to evolve toward subscription, cloud delivery, and token-based consumption. Organizations should understand where their software vendors are headed before making long-term purchasing decisions.

Memorable Quotes:

"Most companies simply don't know who's using which software—and where."

"Knowledge is power when you're negotiating software licenses."

"Don't be afraid to ask for a license remix. Everything is negotiable."

"Start small, understand your usage, and build from there."

Connect With Us:

Have questions about software licensing or want to continue the conversation? Leave a comment or reach out to the Stay Sharp team.

If you enjoyed this episode, be sure to subscribe, leave a review, and share it with a colleague who manages engineering software.

Stay Sharp in Digital Engineering is the podcast exploring the people, technologies, and ideas shaping the future of product development, manufacturing, and digital transformation.

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© 2026 Razorleaf Corp. All Rights Reserved.

Implementing a new engineering methodology sounds straightforward on paper. In reality, it often means navigating organizational resistance, changing long-standing habits, proving value quickly, and building momentum one success at a time.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with Chris Finlay, Vice President of Engineering Innovation at SAIC, to discuss his journey helping organizations adopt Model-Based Systems Engineering (MBSE), digital engineering, digital thread strategies, and AI-enabled engineering practices across the defense industry.

Chris shares candid stories from the front lines of transformation, including early MBSE projects, lessons learned overcoming resistance, and the practical strategies that helped move digital engineering from experimentation to enterprise adoption.

Key Topics Discussed

The Accidental Start of Digital Engineering

Chris recounts how one of his earliest experiences with what would later be called MBSE started simply as a way to manage overwhelming engineering complexity. By leveraging modeling tools and connecting data across engineering artifacts, his team unknowingly created one of their first digital threads long before the term became commonplace.

Why Resistance is Normal

New methodologies often challenge established workflows and expose inefficiencies. Chris explains why resistance from program leaders and engineers isn't necessarily opposition—it’s often a rational response to perceived risk. Successful transformation requires empathy, patience, and meeting people where they are.

The Power of Quick Wins

One of Chris's strongest recommendations is to avoid trying to "boil the ocean." Instead, organizations should identify a manageable use case, demonstrate measurable value quickly, and build momentum through visible successes.

The Story of the Skeptical Product Owner

Chris shares a memorable example of an engineering leader who entered a model review determined to prove MBSE didn't work—only to immediately identify critical issues that the model exposed. The experience became a powerful demonstration of how digital engineering can reveal problems earlier and at lower cost.

Finlay's Five Firsts of Digital Engineering

Chris outlines the principles that have guided his transformation efforts for over a decade:

1. Digital engineering is not a substitute for good systems engineering.
2. Digital engineering exposes bad engineering practices faster.
3. Connected data enables the digital thread—but avoid overconnecting everything.
4. You cannot hire your way out of transformation; upskilling is essential.
5. Organizations that haven't started their digital engineering journey are already behind.

The Gray Beard Phenomenon

One of the most interesting insights from the discussion is Chris's "Gray Beard Phenomenon." Engineers working closely with digital models rapidly become subject matter experts because they gain access to the system's "crystal ball"—the connected knowledge captured within the model.

Measuring and Celebrating Success

Transformation efforts gain traction when organizations track meaningful metrics and celebrate progress. Chris emphasizes that cultural change requires both measurable outcomes and visible recognition of teams that deliver value.

Key Takeaways

• Digital engineering succeeds when it solves real problems, not when it is adopted for its own sake.
• Cultural transformation is often more challenging than technology implementation.
• Early wins build credibility and momentum.
• Metrics help drive behavioral change and secure organizational support.
• Upskilling existing teams is often more effective than hiring new specialists.
• Celebrating success is critical for sustaining transformation efforts.

About the Guest

Chris Finlay is Vice President of Engineering Innovation at SAIC, where he leads enterprise digital engineering transformation initiatives spanning MBSE, digital thread, digital twin, and AI-enabled engineering. With more than 20 years of experience in defense systems engineering, Chris has helped scale digital engineering capabilities across major organizations and mission-critical programs.

Connect With Stay Sharp in Digital Engineering

Have questions about digital engineering transformation, MBSE adoption, or digital thread strategies? Leave a comment, connect with the Razorleaf team, or reach out to continue the conversation.

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What happens when you take a traditional PLM conference out of a corporate convention center and place it inside a Spanish bodega with a dirt floor? According to the attendees of the Share PLM Summit 2026, you create an environment where people—not technology—become the focus of the conversation.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome Razorleaf International team members Ashish Kulkarni and Luc Van Helmerijck to share their firsthand experiences from the Share PLM Summit in Jerez de la Frontera, Spain. The discussion explores standout presentations, emerging AI use cases, organizational change management, and why Share PLM continues to redefine what a modern industry conference can be.

Key Topics Discussed:

A Different Kind of PLM Conference

Unlike traditional industry events, Share PLM intentionally creates a more human-centered experience. The unique venue, informal atmosphere, and collaborative discussions encourage meaningful conversations about the people side of digital transformation.

The Mood Barometer: Measuring Change Management Success

One of the most memorable presentations came from Andreas Wank of Pepperl+Fuchs, who shared his company's PLM implementation journey through a "mood barometer." Instead of focusing solely on project milestones, the presentation tracked employee sentiment throughout the project lifecycle, highlighting the critical role organizational change management plays in successful PLM deployments.

Practical AI Use Cases in PLM

Antonio Casaschi from ASSA ABLOY presented eight real-world AI applications being used across the organization. Rather than discussing AI as a future possibility, the presentation demonstrated practical ways AI can help companies better capture, organize, and leverage institutional knowledge.

Key takeaways included:

• AI should augment people, not replace them.
• The greatest value comes from solving business problems, not simply deploying technology.
• Organizations should focus on current-generation AI capabilities rather than outdated perceptions of AI tools.

AI Happens With People, Not To People

A presentation from Share PLM co-founder Helena Gutierrez emphasized a powerful concept:

"Make AI something that happens with people and not to people."

Her framework highlighted how organizations can automate commodity tasks while elevating uniquely human contributions such as creativity, decision-making, and strategic thinking.

PLM's Role in the AI Era

While AI dominated many conversations, attendees agreed that PLM remains foundational. Rather than replacing PLM, AI is creating new opportunities to improve adoption, simplify implementation, and enhance productivity across product development organizations.

The Concept of "Cognitive Surrender"

Industry thought leader Jos Voskuil explored both the promise and risks of AI, introducing the concept of "cognitive surrender"—the growing tendency for people to outsource thinking to AI systems. The discussion balanced optimism about innovation with caution about maintaining critical thinking skills in an AI-driven world.

Networking That Actually Matters

Beyond the presentations, attendees highlighted the value of the event's networking opportunities. The informal setting fostered meaningful conversations among consultants, software providers, customers, and industry leaders, leading to potential partnerships, customer discussions, and collaboration opportunities.

Episode Highlights:

• Why Share PLM's unconventional format continues to resonate with attendees
• How Pepperl+Fuchs used employee sentiment to guide a PLM transformation
• Eight practical AI use cases being deployed at ASSA ABLOY
• Real-world examples of AI augmenting product lifecycle management
• The growing debate: Is AI evolutionary or disruptive for PLM?
• Lessons learned from industry leaders navigating digital transformation
• How Razorleaf's Clover integration platform sparked conversations across the event

Featured Guests:

Ashish Kulkarni
Director of Growth, Razorleaf International

Luc Van Helmerijck
Business Development and Strategic Alliances Leader, Razorleaf International

Listen and Subscribe

If you enjoyed this episode, subscribe to Stay Sharp in Digital Engineering and join us as we explore the technologies, processes, and people shaping the future of product development and manufacturing.

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© 2026 Razorleaf Corp. All Rights Reserved.

If you've worked with CAD, PLM, simulation, or digital engineering software, chances are you've relied on Tech Soft 3D technology—even if you've never heard of the company. In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with Jonathan Girroir, Technical Evangelist at Tech Soft 3D, to explore the foundational technologies that power hundreds of engineering applications across the industry. 

Jonathan Girroir explains how Tech Soft 3D provides the "building blocks" behind many of the world's leading engineering software platforms, including data translation, visualization, simulation, and graphics technologies used by more than 750 companies. From major software vendors to aerospace manufacturers and emerging startups, Tech Soft 3D enables organizations to access, visualize, and exchange complex engineering data more efficiently. 

The conversation also explores the company's unique position within the digital engineering ecosystem, where customers, competitors, and partners often overlap. Jonathan Girroir shares how Tech Soft 3D works with industry leaders such as Autodesk, Siemens, and Dassault Systèmes while simultaneously helping software vendors and manufacturers build next-generation engineering solutions. 

Finally, the discussion turns to artificial intelligence and the future of engineering data. Rather than focusing on chatbots and assistants, Tech Soft 3D is investing in ways to prepare, structure, and leverage engineering data for machine learning applications, helping organizations unlock insights from CAD models, metadata, manufacturing information, and product structures. 

Key Topics Discussed: 

• What Tech Soft 3D does and why it's embedded in so many engineering applications 
• The role of data translation, visualization, and interoperability in digital engineering 
• Supporting CAD, PLM, simulation, and manufacturing software ecosystems 
• How manufacturers use software components to build custom internal applications 
• The importance of engineering data access and data quality 
• Tech Soft 3D's relationships with major software vendors and technology partners 
• Cloud, desktop, mobile, and hybrid deployment strategies 
• The future of AI and machine learning in engineering environments 
• Why organizations should begin experimenting with AI tools today 

Memorable Quote:

"We're the company that everybody uses but nobody knows." 

Key Takeaways:

• Tech Soft 3D Powers the Industry Behind the Scenes 
• Many of the engineering applications professionals use every day rely on Tech Soft 3D technology for data translation, graphics, simulation, and visualization capabilities. 
• Engineering Data Is the Foundation for AI 
• As AI adoption grows, the ability to access, clean, structure, and understand engineering data becomes increasingly important. Tech Soft 3D is focused on enabling machine learning applications that work directly with engineering models and product data. 
• Manufacturers Are Building More Custom Software 
• Beyond software vendors, many large manufacturers are creating internal applications tailored to their unique workflows, leveraging software components rather than building everything from scratch. 
• AI Literacy Matters 
• Organizations don't need all the answers today, but they do need to start experimenting. Understanding how AI tools work and where they can provide value will be essential for future competitiveness.  

About Our Guest: 

Jonathan Girroir is Technical Evangelist at Tech Soft 3D. With more than two decades of experience in 3D data translation, simulation, and computer graphics, he helps organizations accelerate innovation through engineering software technologies and digital engineering solutions. 

 Listen, Subscribe, and Stay Sharp 

Enjoyed this episode? Subscribe to Stay Sharp in Digital Engineering for conversations with industry leaders, technology innovators, and practitioners shaping the future of product development, manufacturing, and digital transformation. 

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In this episode of Stay Sharp: Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with two returning experts to break down one of the most misunderstood opportunities in manufacturing: the Defense Industrial Base. Whether you're running a five-person shop or a 500-person operation, there is a lane for you in the DIB, and today's guests explain exactly how to find it.

Guests:

Steve Nichols heads the public sector and defense group at Razorleaf, bringing decades of experience across technology, operations, strategy, and business development spanning organizations from startups to large enterprises including Silicon Graphics, CACI, MRI Software, and Razorleaf Government Solutions.

John Biagioni is the President of Lampin Corporation and brings deep manufacturing expertise shaped by a career that started on the shop floor as a machinist. He has nearly a decade of leadership experience as president of Dynisco, Viatran, and DJ Instruments, holds four patents in sensing and rheological instruments, and is a published authority on operational strategy.

What You'll Take Away:

• What the Defense Industrial Base actually is, including the NIB, the hidden DIB, the Marine Industrial Base, and sub-categories most SMBs never hear about
• Why the DLA currently has 22,000 non-bidded parts and what that gap means for small manufacturers
• How CMMC certification works, what it costs ($50K to $200K+), and why you can actually include that cost in your first bid
• The difference between CUI and classified information, and why the safe move is to protect everything
• Why poor data quality on aging weapon systems creates both friction and opportunity for SMBs
• What it means to be the "easy button" for a prime contractor, and why that's a smarter goal than chasing prime status yourself
• How the variable capacity model, or "Uberization" of the DIB, could give smaller shops a real entry point into defense contracts
• State-level grants (including $30,000 programs in Massachusetts and Connecticut) that can offset CMMC compliance costs
• First steps any SMB can take today: SAMs registration, getting a CAGE code, connecting with prime supplier portals, and joining DIB-focused industry groups

Key Insight:

The government's goal of building a Civil Reserve Manufacturing Network means the Department of Defense is actively looking for shops with verified process capabilities, not just finished products. If your shop can demonstrate capacity, you could be "put in stasis" and called on when demand spikes.

Resources Mentioned:

DLA (Defense Logistics Agency) non-bidded parts spreadsheet available for public download at dla.mil

SAM.gov: System for Award Management, the starting point for any DIB entry

PIEE: Procurement Integrated Enterprise Environment for vendor registration

CMMC (Cybersecurity Maturity Model Certification): cybercoe.osd.mil

Razorleaf Government Solutions

Lampin Corporation

Contact the Stay Sharp team: podcast@razorleaf.com

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Subscribe to Stay Sharp: Digital Engineering on your favorite podcast platform and leave us a review. It helps us reach more engineers, operators, and manufacturing leaders doing the hard work of keeping industry sharp. Got a topic you want us to cover? Email us at podcast@razorleaf.com or drop a comment wherever you're listening. 

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Forget the polished vendor slides and standard generative AI hype—this is a look into the "messy data" reality that engineering teams face behind closed doors. The team breaks down real-world case studies from industry giants, handles the core questions of data governance, and discusses why AI won't magically fix a broken digital thread—but why you must start using it anyway.

Key Takeaways

• The "20-Year-Old" Analogy: AI in the engineering space right now behaves like a brilliant 20-year-old: it has an incredible memory but zero real-world intent or contextual experience. It requires guardrails and continuous guidance.
• Progress Over Perfection: Waiting for 100% perfect data before implementing AI leads to "analysis paralysis". Industry frontrunners are building out specific use cases, failing fast, and correcting workflows on the fly.
• Use-Case Driven ROI: High-impact applications must focus on process shrinkage. If an AI project cannot clearly map to saving manual time, it likely won't survive past the proof-of-concept phase.
• Monolithic vs. Best of Breed: While legacy PLM vendors continue pushing all-in-one monolithic architectures, end-users are loudly demanding interoperability and seamless integrations.

Main Points & Deep Dives

• Real-World Case Studies:  
  • Cummins: Implemented an AI-driven search capability across legacy engineering documentation to radically accelerate internal data retrieval.
  • Eaton: Used intelligent automation to shrink a complex RFP response process for custom turbochargers from four experts and six months down to one person and one week—without removing engineering review from the loop.
  • MIT Lincoln Lab: Reimagined their historical paper-based processes into a digital format, emphasizing that half of their engineering center resources go strictly toward driving user adoption, not just building the tech.
• The Interoperability Battle: A review of the exhibition floor, tracking how traditional PLM providers (Aras, Contact PLM, Dassault, PTC) match up against modern digital thread and integration specialists.
• The Governance Hurdle: How to safely address data exposure, restrict private customer data, and define explicit system access when introducing AI into enterprise architectures.

Guests & Resources Mentioned

●      Guest: Andrew Halley, Global Partnership and Alliances Leader, Razorleaf

●      Event Host:CIMdata (PLM/PDT Roadmap North America 2026)

●      Speakers Highlighted: Dr. Martin Eigner, Denise Fitzgerald (MIT Lincoln Lab), Diego Tamburini (CIMdata), Vishwajeet Uddanwadiker (Boeing)

Clean data in, clean insights out! If this episode got you thinking about cleaning up your own digital house, we want to hear from you.

📩 Connect with us: Drop a comment or reach out directly at podcast@razorleaf.com. 

⭐ Support the show: If you found value in this deep dive, please give us a review or follow the podcast on your favorite platform.

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Data fuels every digital initiative — but who’s actually managing it?

In this episode of Stay Sharp in Digital Engineering, Jonathan Scott and Juliann Grant sit down with Razorleaf’s Ann Garcia to unpack the fundamentals of Master Data Management (MDM) and why it plays a critical role in modern digital engineering environments.

With more than 30 years of experience across medical device, consumer packaged goods, and manufacturing industries, Ann brings a practical perspective to one of the most misunderstood topics in digital transformation: data ownership, governance, and lifecycle management.

The conversation explores what master data actually is, how it differs from transactional and reference data, and why organizations struggle to define and manage it consistently across systems like PLM, ERP, and manufacturing platforms.

The team also dives into:

• The difference between master data, transactional data, and reference data 
• Why data governance often becomes more complex as organizations grow 
• The role of master data during PLM and ERP implementations 
• How mergers, acquisitions, and global operations impact data quality 
• Why defining data standards early matters for digital transformation 
• The relationship between clean master data, digital thread initiatives, and AI readiness 
• How organizations balance speed with data accuracy during system rollouts 

Ann also shares real-world examples of how poor data definitions and inconsistent ownership can create downstream challenges — and why many companies underestimate the effort required to maintain clean, trusted data across the enterprise.

As AI and analytics become increasingly important in manufacturing and engineering environments, this episode highlights a foundational truth: clean insights require clean data.

If your organization is investing in PLM, ERP, digital thread, AI, or enterprise modernization, this conversation offers an important look at the often-overlooked infrastructure behind successful digital transformation.

Featured Guest

Ann Garcia – Data management and governance professional with 30+ years of experience supporting manufacturing, medical device, CPG, and enterprise transformation initiatives.

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In this special on-site episode of Stay Sharp in Digital Engineering, the Razorleaf team recaps their experience at the iBase-t Excelerate 2026 conference in National Harbor.

Jonathan Scott and Juliann Grant share insights from customer presentations, emerging AI strategies, MES modernization efforts, and the growing push toward model-based manufacturing. From practical upgrade strategies to global deployment challenges, this episode explores how manufacturers are balancing innovation with operational realities.

The event highlighted a clear industry shift: manufacturers are under increasing pressure to modernize quickly while maintaining quality, compliance, and production continuity. Discussions throughout the conference focused on pragmatic digital transformation, data integration across the “golden triangle” of ERP, PLM, and MES, and how AI can be securely and effectively embedded into enterprise systems.

The team also discusses:

• GE Aerospace’s large-scale Solumina deployment for MRO and sustainment 
• iBase-t’s practical AI strategy and security-first approach 
• Real-world MES upgrade lessons from Honeywell and Razorleaf 
• Global rollout strategies across multiple manufacturing sites 
• Model-based work instructions and AI-powered data ingestion 
• Integration architecture challenges and the role of connected data 
• Why manufacturers are moving faster than ever to eliminate paper processes 

Beyond the technology, the episode captures the collaborative spirit of the iBase-t community — where even competitors openly share lessons learned to improve manufacturing outcomes across the industry.

If you’re involved in MES, manufacturing operations, aerospace and defense, or digital transformation initiatives, this episode offers a grounded look at where the industry is heading next.

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In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with returning guest Brion Carroll to explore how PLM evolved beyond engineering-heavy industries into fashion, footwear, and retail.

Brion shares firsthand insights from building one of the first retail-focused PLM solutions—revealing why apparel required a completely different mindset, how FlexPLM was born, and what lessons every industry can take from retail’s speed, complexity, and creativity.

From colorways and line planning to supplier collaboration and real-time change, this conversation uncovers how retail pushed PLM to become more flexible, visual, and business-centric.

Key Topics Covered

• Why traditional PLM didn’t work for apparel and retail
• The shift from “parts and assemblies” to materials and styles
• Understanding style, colorways, and seasonal product variation
• Why costing comes before design in footwear
• The role of suppliers as active collaborators in product development
• How line planning and merchandising changed PLM
• The need for visual, intuitive user experiences
• Managing rapid, continuous change vs. formal engineering change processes
• The importance of multi-team collaboration across the business
• Building FlexPLM and lessons from early customers like Timberland and Reebok
• Why a single, shared bill of materials (BOM) matters
• Connecting PLM with upstream and downstream systems (ERP, supply chain, etc.)

Key Takeaways

1. PLM Must Serve the Entire Business

Retail proved that PLM isn’t just for engineering—it must support design, merchandising, sourcing, supply chain, and more.

2. One Product, Many Views

A unified BOM with multiple views enables different teams to work from the same data without duplication or inconsistency.

3. Speed Changes Everything

Retail operates in rapid, iterative cycles—requiring PLM systems to be flexible, responsive, and user-friendly.

4. User Experience Matters

Creative teams need visual, intuitive interfaces—not engineering-style data structures.

5. Collaboration is Critical

From internal teams to global suppliers, successful PLM depends on seamless data sharing and connectivity.

Notable Quote

“There’s really no difference in the core of PLM across industries—the difference is how you apply it and who you bring into the process.” 

About the Guest

Brion Carroll is CEO and Principal Consultant at Digital Solution Group, LLC and a pioneer in PLM, including early development of retail-focused solutions like FlexPLM. He has decades of experience helping organizations connect product data across the digital thread—from concept to market.

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Stay tuned for more conversations on digital engineering, PLM, and the technologies shaping product innovation.

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© 2026 Razorleaf Corp. All Rights Reserved.

In this episode of Stay Sharp in Digital Engineering, the Razorleaf team recaps insights from ACE 2026—Aras Community Event (ACE) 2026—the annual gathering for users of Aras Innovator.

Featured Hosts and Guests:

• Jonathan Scott – Co-host, Razorleaf 
• Juliann Grant – Co-host, Razorleaf 
• Milan Obradovic – Aras Pre-Sales Architect, Razorleaf 
• Steven Binstock – Senior Solution Architect, Razorleaf 

From bold AI vision to the growing impact of Innovator Edge, the conversation explores how Aras is evolving beyond traditional PLM—focusing on user-centric experiences, flexible architectures, and empowering customers to build their own solutions.

Whether you attended ACE or missed it, this episode delivers key takeaways, standout moments, and what it all means for the future of digital engineering. 

Key Takeaways:

• AI is the headline—but vision leads the story
  Rather than flashy product launches, ACE 2026 emphasized direction: how AI will be embedded into workflows, decision-making, and the broader digital thread. 
• “Meet users where they are” is the new PLM mantra
  Aras is shifting toward task-based, accessible experiences that extend beyond traditional interfaces—bringing PLM to more users across the enterprise. 
• InnovatorEdge is a major catalyst
  The platform enables lightweight, targeted apps and micro-experiences that connect users to PLM data without requiring full system access. 
• The digital thread is becoming more actionable
  With AI and new architecture in place, organizations are closer than ever to truly connecting data across engineering, manufacturing, and service. 
• Community remains a defining strength
  From open Q&A sessions to collaborative demos, ACE continues to stand out for its transparency and user-driven innovation. 

From AI-driven experiences to flexible app ecosystems, ACE 2026 made one thing clear: PLM is evolving—and Aras Corporation is building the foundation for what comes next.

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
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Stay Sharp.

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This week on Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott are joined by special guest Ashish Kulkarni, Senior PLM Leader at Razorleaf International (Netherlands), for a dual-continent recap of CIMdata's Annual Market and Industry Conference — held simultaneously in Ann Arbor, Michigan, and Paris, France.

Between them, the three cover the global PLM market data, the AI conversation dominating the industry, vendor vs. buyer disconnects, and what it all means if you work in digital engineering, PLM, or manufacturing technology.

Key Takeaways:

• The global PLM market grew 8.7% to $87.3 billion — strong, but below the 9.3% forecast
• Top PLM investment priorities have shifted: knowledge management, global collaboration, and configurability are now leading over traditional part and BOM management
• The percentage of respondents saying PLM has "run its course" jumped from 22% in 2025 to 55% in 2026 — a striking shift in market sentiment
• AI is not replacing PLM — yet. But the question is now being asked openly, especially in Europe
• Architecture/Engineering/Construction (AEC) and Electronic Design Automation (EDA) were the surprise growth leaders, with AEC growth well above forecast
• CIMdata recategorized how it reports the market: moving from CPDM to "Product Innovation Platforms," with new scrutiny on how tools vs. platforms are classified
• SI/VAR growth significantly missed forecast (7.5% vs. 12.6% predicted) — reasons remain unclear
• A "fluency-deployment divide" is emerging end users are experimenting with AI tools like ChatGPT, but in-house enterprise AI deployments in PLM are still very early
• Vendors are racing to embed AI capabilities; buyers are moving slower and prioritizing data readiness first
• Fear factors around AI — "your data has to be perfect before you can use it" — are being actively addressed by presenters like Diego Tamburini

About Our Guest

Ashish Kulkarni is a Senior PLM Leader at Razorleaf International, based in the Netherlands. With over 23 years of experience driving enterprise-scale digital transformation across Europe and 15+ years in PLM (including ENOVIA), Ashish has led strategic growth across Europe and the Mediterranean region. He was instrumental in building and growing the Razorleaf India office.

Resources & References

• CIMdata Annual PLM Market and Industry Conference (Ann Arbor, MI and Paris, France)
• CIMdata PLM Market Report — official publication expected May 2025
• CIMdata PLM Roadmap and PDT Conference — May (North America) and Fall (Sweden)
•  Diego Tamburini — previous Stay Sharp episodes on Strategic Patterns for Implementing AI in Manufacturing and The AI Reality Gap – What You Need to Know Before Building at Scale.
• Topics mentioned: RAG (Retrieval-Augmented Generation), model-based systems engineering (MBSE), digital thread

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 Is your company ready to build its own AI solution? 

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott welcome back Dr. Diego Tamburini — executive consultant and AI practice lead at CIMdata — for a deep dive into what it actually takes to move from AI experimentation to production-ready deployment in manufacturing and engineering organizations.

Diego brings 25+ years of experience across PLM, CAD/CAM, digital manufacturing, and AI strategy, with past roles at Microsoft, Autodesk, and Siemens Digital Industry Software. In this episode, he cuts through the noise to give you a practical, honest look at the AI build-vs-buy landscape.

What You'll Learn:

• The difference between AI workflows and true agentic AI — and why conflating the two leads to bad decisions
• The full spectrum of AI implementation options: from fully embedded vendor tools to building from scratch in Python
• When it makes sense to use low-code/no-code platforms (like Microsoft Copilot Studio or Amazon Bedrock) vs. full custom development
• Why subject matter experts — not just developers — are becoming the key builders of AI agents
• How the digital thread concept is getting new life in the AI era and why it matters for cross-system reasoning
• The "make, buy, or wait" framework for prioritizing AI initiatives wisely
• The traceability and privilege elevation challenges that regulated industries must address in agentic AI
• What skill sets organizations actually need at each level of AI implementation

Guest: Dr. Diego Tamburini Executive Consultant, AI Practice Lead at CIMdata, Former Director of Engineering Agility at Microsoft, Former Design & Manufacturing Strategist at Autodesk 

Connect with Diego for AI strategy consulting through CIMdata

Related Episode - Give it a Listen!

#125 : Strategic Patterns for Implementing AI in Manufacturing

Connect & Subscribe Have a question, topic idea, or guest suggestion? Reach out anytime at podcast@razorleaf.com.

🎧 Subscribe on your favorite podcast platform so you never miss an episode — and if this was valuable, leave us a review or share it with a colleague navigating the AI landscape in their organization.

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About This Episode

Brion Carroll, CEO/Principal Consultant at Digital Solution Group, is not just a PLM practitioner — he's one of the people who built the very first PDM system and coined the category name. In this episode, Brion joins Juliann Grant and Jonathan Scott for a rare firsthand account of how product data management came to be, how it evolved into PLM, and why decades later, the industry is still wrestling with the same fundamental challenge: getting data to flow across systems.

Whether you're deep in a PLM implementation or just starting to think about your AI strategy, this episode is a grounding, practical reality check you won't want to miss.

Key Takeaways

• PDM was born as a defensive move — Computer Vision built it to protect its CAD customer base from competitors, not to transform the industry
• The first PDM system sold for $1 million per seat and handled only four functions: backup/recovery, revision control, access/security, and archive/restore
• Portability was a watershed moment — Brion famously declared from a conference stage that their product would run equivalently across three competing operating systems, and delivered on it in six months
• The evolution from PDM to PLM wasn't a grand vision — it was vendors needing new features to sell, starting with workflow engines to manage product lifecycle stages
• Web technologies (Java, JavaScript, TCP/IP) democratized access and pushed the shift toward client-server and eventually true cloud architectures
• Today's AI problem is yesterday's integration problem — AI applied inside a silo gives narrow insight; real business intelligence requires harmonized data flowing across all systems
• ~4% of AI initiatives have yielded measurable value (per BCG research cited in the episode), and Brion argues dirty, siloed data is the reason why

What We Discuss

Brion takes us back to the mid-1980s and Computer Vision's "Project David" — the small team tasked with building a data management system to protect the company's installed CAD base. From there, the conversation moves through the birth of the PDM category name, the wild competitive landscape of early vendors (Sherpa, Workgroup Technology, CDC), and the technical leap of making software portable across VMS, Unix, and IBM VM/CMS environments.

We then trace the shift to PLM — why workflow management changed everything, how web technology opened the door to broader connectivity, and how the retail industry became an unlikely early adopter of enterprise PLM. The conversation closes with a clear-eyed look at today's AI landscape and why Brion believes companies that skip the hard work of data integration will find AI disappointing at best and damaging at worst.

Guest Resources

• Brion Carroll— CEO & Principal Consultant, Digital Solution Group
• Website:digitalsolutiongroup.net

Enjoyed This Episode?

Leave a review, drop a comment, or share this episode.

Reach out anytime at podcast@razorleaf.com.

👉 Subscribe and never miss an episode — and don't forget to Stay Sharp

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What’s really happening with PLM, AI, and cloud adoption right now?

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott, with their Razorleaf colleagues, François Schlub and Andrew Halley, break down the biggest takeaways from the ENOVIA User Conference 2026, hosted by Dassault Systèmes in Boston.

From AI-powered virtual twins to cloud migration strategies and digital thread integration, this conversation goes beyond the keynote hype to share what real users, engineers, and manufacturers are actually experiencing today.

👉 If you're working in product development, engineering, manufacturing, or PLM—this is what you need to know.

🔑 What You’ll Learn

• Why cloud migration is now a “how,” not “if” decision 
• How AI is shaping the future of PLM (and what’s still missing) 
• Why integration—not tools—is the biggest challenge 
• What companies like Ford Motor Company are doing to move faster 
• The growing gap between PLM beginners and advanced users 
• Why organizational change management is critical to success 

🧠 Key Topics Covered

• 3DEXPERIENCE platform strategy and roadmap 
• AI scalability and compute requirements 
• Digital thread and system integration (ERP, MES, more) 
• Supply chain collaboration and cloud adoption 
• Real-world user stories from global manufacturers 

If you found this helpful:

• 👍 Like the video 
• 💬 Share your perspective in the comments 
• 🔔 Subscribe for more insights on digital engineering, PLM, and AI 

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• Why You Need a Business Digital Twin
• Beyond Survivability: The model is not just for business continuity; it's also a tool for understanding how your business can thrive by layering financial modeling (leverage, bull/bear cases) on top of operational inputs and outputs.
• The Power of Iteration: The difference between a static "virtual twin" and an active "digital twin" is continuous iteration and calibration with live data (like IoT sensing) to reflect real-world performance.
• Stakeholder Trust: The digital twin is a relationship tool, building trust with your employees, board, and customers by providing confident, data-backed answers to complex questions about capacity, lead times, and financial performance.
• Resilience Over Efficiency: In a post-pandemic supply chain world, the core value of a business digital twin is not just efficiency but the resilience it provides against unforeseen disruptions.

Thank you for joining us on Stay Sharp in Digital Engineering! If you enjoyed this conversation, please take a moment to like, share, and subscribe to our podcast.

Got a question for John or the hosts? Send us a note at podcast@razorleaf.com or leave a comment on our post!

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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 Takeaways

• Why Duncan Aviation's "product" is actually a service - and why that changes everything about PLM requirements
• How to manage uncontrolled vendor technical data from thousands of OEM suppliers at scale
• The power and the peril of a DIY customization culture
• Why you must clarify governance before go-live - not after
• How Duncan is now using Aras to automate tribal knowledge and eliminate paperwork gaps

Guest
Aaron 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 Mentioned

• Duncan Aviation - duncanaviation.aero
• Aras Innovator - PLM platform used by Duncan Aviation
• Duncan 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 partner
• CMMC - Cybersecurity Maturity Model Certification, relevant to Duncan's manufacturing division

Have a question for Aaron or want to connect with Duncan Aviation? Reach out via the podcast community.
👉 Send topic ideas or guest suggestions: podcast@razorleaf.com
🔔 Subscribe so you never miss an episode 
⭐ Leave a review - it helps more engineers find the show 
💬 Drop a comment - the hosts actually read them

Stay Sharp in Digital Engineering is the podcast about tools, trends, and tactics shaping digital transformation in product manufacturing. New episodes drop regularly.

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What 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 Episode

• Why MES alone isn’t enough for complex aerospace and defense manufacturing
• How Solumin integrates MES, quality management, MRO, and supplier quality into one platform
• Why a unified operational data model is critical for AI in manufacturing
• How manufacturers integrate Solumina with PLM, ERP, and shop floor systems
• What model-based manufacturing looks like in real-world production
• Why cloud-native architecture and microservices matter for manufacturing software
• How digital systems help preserve institutional knowledge as experienced workers retire

Key Topics Discussed

• Manufacturing Operations Management (MOM) platforms
• Aerospace and defense manufacturing systems
• Digital thread and model based enterprise
• Embedded quality management systems (EQMS)
• Supplier quality integration
• Maintenance, repair, and overhaul (MRO) workflows
• AI-driven manufacturing operations
• Cloud-native industrial software architecture

About 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 comments

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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 Takeaways

• Why today’s AI surge is driven largely by generative AI exposure—not a sudden invention of new intelligence
• The five patterns companies are using to implement AI, from vendor-embedded tools to fully custom models
• How Retrieval-Augmented Generation (RAG) allows organizations to connect AI across enterprise systems without retraining models
• Why many AI pilots fail due to unclear use cases, poor measurement, or data that cannot scale
• How AI is changing the way engineers interact with software—moving from menus to natural-language workflows
• Real industrial applications already delivering value, including predictive maintenance, quality analysis, and simulation acceleration
• The importance of aligning AI adoption with skills, governance, and measurable KPIs from the start

👤 Guest Information

Dr. Diego Tamburini
Executive Consultant, CIMdata
Leads 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 platform
• Share this episode with a colleague evaluating AI initiatives
•  Leave a review to help more listeners find the show

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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 Event

AI Everywhere — But What Does It Really Mean?

• AI agents embedded in SolidWorks workflows
• Live demos showing rapid simulation and design iteration
• Partnership announcement between Dassault Systèmes and NVIDIA
• The broader message: AI as a force multiplier, not just a chatbot

Data Is the Foundation

• Why clean, structured product data matters more than ever
• The push toward centralizing data in the 3DEXPERIENCE platform
• Integration challenges across ERP, PDM, and PLM systems
• “Garbage in, garbage out” — especially with AI

Adoption Still Matters More Than Hype

• Real-world customer questions about migration and workflows
• PDM Pro vs. Manage vs. 3DEXPERIENCE decisions
• Cloud adoption realities
• Legacy systems vs. startup-native cloud environments

Education & the Next Generation

• Expanded focus on 3DEXPERIENCE EDU
• High school and university programs teaching platform tools early
• The long-term shift in engineering workforce readiness

The 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 Episode

• Juliann Grant – Host
• Jonathan Scott – Host
• Andrew Halley – Senior Director of Partnerships
• François Schlub– Sales Executive

The Event Takeaways

AI dominated the headlines.
But customers are still focused on:

• Getting PLM foundations right
• Managing migration paths
• Structuring and governing product data
• Ensuring successful user adoption

The 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.

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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 approach
• The shift toward model-based enterprise
• How AI is being applied to real execution problems
• What makes digital transformation succeed (and fail)
• The culture behind iBase-t’s family-owned legacy

If you're navigating digital manufacturing, aerospace innovation, or operational transformation, this conversation delivers practical insight grounded in real-world execution.

Key Takeaways

• Execution 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 Poonian
CEO, iBase-t
Aerospace and defense technology leader focused on manufacturing execution, lifecycle transformation, and operational excellence.

Resources & Mentions

• iBase-t
• iBase-t Excelerate Conference
• Model-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: podcast@razorleaf.com
💬 Leave a comment — we read them all
⭐ Subscribe for future conversations on digital engineering and manufacturing

If you found value in this episode, please share it with a colleague in aerospace, defense, or advanced manufacturing.

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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 Takeaways

• Why consumer products now face the same complexity challenges as aerospace systems
• How MBSE enables parallel work across mechanical, electrical, and software teams
• Using virtual prototypes to test software months earlier than physical builds
• How parametric trade studies unlock better product decisions without slowing timelines
• The role of MBSE in product line engineering, modularity, and upgradeability
• Why verification and validation must start earlier—and happen continuously
• How 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 platform
• Share this episode with a colleague working in product development or manufacturing
• Leave a review to help more listeners find the show

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 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 Takeaways

• Why today’s AI surge is driven largely by generative AI exposure—not a sudden invention of new intelligence
• The five patterns companies are using to implement AI, from vendor-embedded tools to fully custom models
• How Retrieval-Augmented Generation (RAG) allows organizations to connect AI across enterprise systems without retraining models
• Why many AI pilots fail due to unclear use cases, poor measurement, or data that cannot scale
• How AI is changing the way engineers interact with software—moving from menus to natural-language workflows
• Real industrial applications already delivering value, including predictive maintenance, quality analysis, and simulation acceleration
• The importance of aligning AI adoption with skills, governance, and measurable KPIs from the start

👤 Guest Information

Dr. Diego Tamburini
Executive Consultant, CIMdata
Leads 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 platform
• Share this episode with a colleague evaluating AI initiatives
• Leave a review to help more listeners find the show

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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 Takeaways

• Why tolerance analysis is more than a final design check-it's a lifecycle discipline
• How variation breaks the digital thread between design and manufacturing
• The tradeoff between tighter tolerances, higher quality, and rising cost
• Why GD&T is a language—and how misunderstanding it drives manufacturing risk
• How tolerance analysis supports better decisions in design, manufacturing, and inspection
• The role of tolerance analysis in the transition from virtual models to real-world production

Guest:
James Stoddard
Co-Founder & Research Fellow, Sigmetrix

James 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.

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How 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 Takeaways

• Why private equity ownership is often less risky than buyers assume
• What really determines software risk: usability, fit, vendor trust, and longevity
• How SaaS and cloud delivery are reshaping buying expectations—and support gaps
• Why reseller and channel consolidation is accelerating across the industry
• The difference between AI hype and practical, usable innovation
• How digital twins, UX improvements, and hybrid cloud models are quietly advancing
• Practical advice for evaluating software purchases without chasing shiny objects

About the Guest

Monica 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 software

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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 Takeaways

• MBSE is not about product size or industry. It is about rate of change and system complexity
• Consumer products face continuous requirement churn from sustainability, regulation, and competition
• Document-based requirements and behavior models break down as products become multidisciplinary
• A shared system model improves traceability, alignment, and decision-making across teams
• Behavioral modeling is critical for integrating mechanical, electronic, and software systems
• Product Line Engineering (PLE) enables scalable variant management without restarting from scratch

Guests

Eran Reinshmidt
Solution Leader, Consumer Industries, Dassault Systèmes
Expert in PLM and systems engineering for consumer products across home goods, appliances, lifestyle, and wearable industries

Safae El Abkari, PhD
Solution Engineering Consultant, Dassault Systèmes
Specialist in connected products, multidisciplinary systems, and embedded systems engineering

Resources Mentioned

• Model-Based Systems Engineering (MBSE)
• Product Line Engineering (PLE)
• Connected and smart consumer products
• Digital engineering for consumer industries

If 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 podcasts@razorleaf.com.

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From the shift from single-company databases to interconnected digital networks, to how product data must support real-time collaboration across engineering, procurement, and manufacturing, Oleg explains how OpenBOM tackles an industry-wide pain point: sharing the right data with the right people at the right time.

He also introduces the concept of product memory—a foundational element for enabling AI and next-generation decision-making across engineering lifecycles.

Key Discussion Topics

• Why traditional PLM systems were built for single-company control, not interconnected supply chains
• The “Google Sheets paradigm” and why customers value OpenBOM’s ease of adoption
• How OpenBOM enables secure data sharing across multiple companies without compromising independence
• The technology driving it: graph-based data models, polyglot persistence, and cloud-native microservices
• Signals that a company is ready for OpenBOM: complex products, limited resources, and fast-paced innovation cultures
• Using OpenBOM to bridge engineering and procurement (and eradicate Excel chaos)
• Introducing Product Memory—the data layer that enables AI-driven design and lifecycle intelligence

Memorable Quote

“AI starts with the right data. If you can’t feed the AI with clean product memory, you’ll just get noisy answers. That’s why OpenBOM builds the data foundation first—everything else comes from that.”

— Oleg Shilovitsky

If this episode helped clarify what modern PLM tools must deliver—and how product data can become a strategic advantage—make sure to:

Follow the podcast so you don’t miss upcoming episodes in our digital product series. Leave a review sharing your biggest insight from today’s conversation

Send us your topic ideas at podcast@razorleaf.com

—We’d love to hear what you want us to dissect next.

Stay tuned and stay sharp.

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In this forward-looking episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott step back from day-to-day execution and examine the trends, technologies, and curveballs shaping the next phase of the industry. 

From AI-driven data enrichment and digital twins moving beyond PLM, to ERP vendors entering new territory, humanoid robotics, additive manufacturing at scale, and even quantum computing, this conversation blends grounded experience with thoughtful speculation. 

The result is a candid, wide-ranging discussion about what feels inevitable, what feels risky, and what may arrive faster than expected. 

Key Topics & Takeaways 

• Why AI’s real value may be cleaning up decades of messy engineering data 
• How ERP vendors could become unlikely leaders in digital twin platforms 
• The growing importance of semantic data and context, not just volume 
• Why multi-modal AI inputs (speech, sketches, visuals) could change engineering workflows 
• The return of additive manufacturing headlines through mass customization 
• How vertically integrated digital manufacturing companies could disrupt today’s ecosystems 
• What humanoid and collaborative robots mean for factory integration 
• The unresolved risks of AI autonomy, liability, and regulation 
• Why quantum computing remains a wild card worth watching 

If you enjoyed this episode: 

• Subscribe to Stay Sharp in Digital Engineering 
• Leave a rating or review on your favorite podcast platform 
• Share this episode with a colleague navigating digital transformation 

 Have a take on 2026 or want to join the conversation? 

📩 Email: podcast@razorleaf.com 

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In this special year-end episode, hosts Juliann Grant and Jonathan Scott revisit powerful soundbites from guests who helped shape the show’s most meaningful discussions. 

Spanning AI, digital thread and digital twin, model-based systems engineering (MBSE), PLM lessons learned, and emerging industry trends, this mashup captures the ideas that resonated most with listeners and continue to influence how organizations design, build, and manage products.

This episode runs a bit longer than the usual format, but it’s packed with clarity, perspective, and a few laughs along the way.

Episode 116 also marks a milestone moment for Stay Sharp in Digital Engineering, celebrating over 100 episodes and a growing, engaged listener community. 

Juliann and Jonathan extend heartfelt thanks to their guests, production team, and listeners for making 2025 such a meaningful year for the podcast.

Have ideas for future topics or guests? The team would love to hear from you.

📩 Send suggestions to: podcasts@razorleaf.com

🌐 Visit the new podcast hub: staysharpindigitalengineering.com 

If you enjoyed this episode, please consider leaving a review or sharing it with a colleague, and as always, thanks for listening.

 Stay sharp.

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AI is everywhere, but in engineering and manufacturing, hype often outpaces reality. In this episode of Stay Sharp in Digital Engineering, we unpack what it actually means to be “AI-ready” and why most organizations are struggling to get there.

Hosts Juliann Grant and Jonathan Scott are joined by Graham Law, PLM Solution Architect at Razorleaf, to explore the unglamorous but essential foundation of successful AI initiatives: clean, governed, connected engineering data. With over 16 years in PLM and a deep IT background, Graham offers a grounded perspective on what works, what fails, and where organizations should realistically start.

This conversation cuts through buzzwords to focus on practical steps, real risks, and how PLM enables AI to deliver meaningful insight rather than confident misinformation.

🔑 Key Takeaways

• AI success in engineering is 80% data hygiene and 20% technology
• Messy, duplicated, or poorly linked data leads to confidently wrong AI answers
• PLM provides the structure, context, and governance AI depends on
• Organizations should start small, not attempt to “boil the ocean”
• AI can surface insights humans miss, but only if the data foundation is solid
• Governance is just as important as cleanup—stop creating bad data going forward

👤 Guest

Graham Law

• PLM Solution Architect, Razorleaf
• 16+ years in Product Lifecycle Management Experience with Windchill, Teamcenter, Aras, SOLIDWORKS PLM                                                                   
• Former IT Manager with deep systems expertise

📣 Call to Action

If this episode helped sharpen your thinking about AI and engineering data:

●      Subscribe to Stay Sharp in Digital Engineering

●      Leave a review on Apple Podcasts or Spotify

●      Share this episode with a colleague working on AI or PLM initiatives

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This is a refreshingly honest look at a PLM journey filled with false starts, side-hustle implementation work, CAD file chaos, revision control challenges, and the realities of deploying PLM in a fast-moving engineering organization.
John and Korey share how Sea Box went from a home-grown Microsoft Access database that crashed “every other day” to a modern PLM system tied directly to CAD, BOMs, and ERP. They walk through what they would do differently, what worked, and how the engineering + project-management partnership has been key to moving everything forward.

If you’re on your own PLM or ERP journey, you’ll hear something familiar in their story—and you’ll walk away with practical lessons to avoid the same pitfalls.

Key Takeaways

• False starts happen—and they’re recoverable.
Sea Box had to abandon their first PLM integrator and restart the project, losing time and budget in the process.

• “Just stand it up” creates long-term debt.
Rushing to replicate old systems (like their EPN database) created unnecessary customization and downstream headaches.

• CAD chaos is a universal pain point.
Without revision control and file governance, something as simple as quoting a replacement part becomes a scavenger hunt.

• CAD Connector changed everything.
Push/pull of CAD models, drawings, and revisions now ties engineering design directly to BOMs and ERP.

• ERP forced better discipline.
Migrating to Business Central required rigor around BOM structure, revision control, and part history.

• Multi-perspective leadership matters.
A PM + engineer co-admin partnership allowed them to weigh impact on both engineering workflows and business processes.

• Community accelerates progress.
Sea Box credits the Razorleaf community and user conference for solving problems they thought were unique.

• Always use a dev environment.
You will break things. You should. But do it safely.

If you enjoyed this conversation, please follow the podcast, leave us a rating, or share your biggest takeaway in the comments.

Have a topic you'd like us to explore next? Email us at podcast@razorleaf.com.

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Dr. Eigner brings decades of research, academic leadership, and industry experience to dissect topics many engineering leaders struggle with:
  • What configuration items really are
  • Why extended digital threads matter
  • Why companies are starting to value integration more than best-of-breed functionality
  • How PLM, ERP, MES and requirements tools must work together
  • Why AI and graph databases could shape the next phase of engineering digitization

This is a dense, high-value conversation for anyone looking to modernize engineering systems, understand digital transformation beyond buzzwords, or align organizational leadership around PLM strategy. 

Key Takeaways

● Configuration items span far beyond CAD models; there are typically 200–300 essential objects across the product lifecycle—from requirements to manufacturing process plans.

● A PLM system is only one piece of product lifecycle management; the full lifecycle includes concept, requirements, engineering, production, service, and recycling.

● Many companies now see integration as more valuable than functional specialization.

● The extended digital thread connects configuration items across systems, enabling true change impact analysis.

● Legacy systems create siloed data; extended threads aim to mask these silos through better connectivity.

● Graph databases may be essential to future PLM and digital-thread performance.

● Organizational challenges—not technology—are the biggest roadblocks to transformation.

● Change requires leadership ownership, better alignment between vertical and horizontal teams, and sometimes a rethink of organizational structure.

Memorable Quote

“There’s a trend that companies are evaluating integration higher than best-in-function—and that is new, but extremely important.” — Dr. Martin Eigner 

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In this episode, Juliann Grant and Jonathan Scott dive deep into the growing conversation around data models in digital engineering. With increasing pressure to enable the digital thread, digital twins, and emerging AI capabilities, understanding how data is structured and why it varies across systems is more critical than ever.

Together, they unpack:

• What a data model really is and why “model” is the key word
• Why every engineering and business system represents data differently
• The mounting challenges created by siloed, mismatched data structures
• How digital twin initiatives have heightened the urgency for clean, connected data
• Real-world examples showing why context, meaning, and structure matter
• The risks and limitations of approaches like data lakes
• How manufacturers can begin evaluating, modeling, and aligning their data for desired business outcomes
• Why there will never be a universal data model — and why that’s okay
• Best practices for getting started, staying adaptable, and keeping data meaningful as technology evolves

This episode is especially relevant for anyone interested in:

• Digital Transformation
• PLM / PDM Modernization
• Digital Thread Initiatives
• Digital Twin Strategy
• AI Readiness in Engineering and Manufacturing

Notable Quote:

"If the AI doesn't understand the data, and it's just doing statistical prediction, the predictions can be junk. In a safety-critical situation, that's not cool." – Jonathan Scott 

Have questions or thoughts on this episode? Leave a comment or email podcast@razorleaf.com.

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The team explores the cultural, technical, and process-driven challenges organizations face when they attempt to modernize their digital infrastructure—especially when legacy tools, disconnected databases, and informal workflows get in the way.

Discussed In This Episode

The Product Data Gap:

Why companies often underestimate the complexity of product information—and how this gap causes friction between engineering, manufacturing, supply chain, and procurement.

Document-Centric vs. Data-Centric Thinking:

Oleg explains why traditional document-driven approaches fall short and how a shift to connected, granular data changes everything. 

BOM Management Reality Check:

Why spreadsheets still dominate BOM management, when they break down, and how to transition to structured, traceable product data.

Connecting Systems, Not Just Software:

The pitfalls of building tools in isolation and why establishing a shared “system of product truth” prevents downstream chaos.

Cultural and Organizational Resistance:

Why digital transformation isn’t just a tooling problem—it’s a mindset challenge across teams who have “always done it this way.”

Scalability and Supply Chain Insight:

How connected product data improves visibility, reduces surprises, and helps companies navigate supply chain complexity.

Key Takeaways

•   Product information isn’t just CAD files—it’s a living structure that must stay connected across the entire company.

•   Moving from documents to data creates clarity, consistency, and the ability to scale.

•   Spreadsheets may feel convenient, but they break under pressure as products grow more complex.

•   A unified view of product structure strengthens communication between engineering, manufacturing, and supply chain.

•   True digital transformation only sticks when the organization aligns culturally—not just technologically.

Notable Quote

“You can build the best software in the world, but if the data is disconnected, nothing works. The magic happens when all the pieces come together.” — Oleg Shilovitsky 

Listen & Subscribe

Stay up to date on all things digital engineering—from PLM and BOM management to digital thread strategy.

Subscribe on Apple Podcasts, Spotify, or watch the conversation on YouTube. 

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In This Episode

• Getting Started Right: Why the “just start using the software” approach often backfires and how to balance speed with strategic vision.
• Building a Roadmap: The importance of aligning short-term goals with long-term growth so today’s choices don’t limit tomorrow’s innovation.
• Proof of Concept vs. Pilot vs. Quick Start: Derek breaks down what each approach actually means, when to use them, and how they fit together.
• Executive Buy-In and Business Cases: How to educate leadership, define success metrics, and prevent misalignment between tactical and strategic goals.
• Process Improvement and Adoption: Why optimizing processes first matters—and how education accelerates adoption rather than slowing it down.
• Consultants, Culture, and Customization: When to bring in outside expertise, how to balance it with internal knowledge, and how company culture shapes implementation success.

Key Takeaways

• Success isn’t about the software—it’s about alignment, education, and adaptability.
• A roadmap helps avoid costly restarts and ensures scalability as business needs evolve.
• Quick wins are important, but they must connect to a larger strategic vision.
• Every organization’s culture and maturity level should guide its implementation path.
• Adoption hinges on people understanding why changes are happening, not just what to click.
  
  

Notable Quote

“Education isn’t a delay—it’s an accelerator. At some point, you’ll need to know how these systems connect, so you can either learn it when everything’s on the floor or before you start.” — Derek Nieding

Listen & Subscribe

Stay up to date on all things digital engineering—from PLM and MES to digital thread strategy.

Subscribe on Apple Podcasts, Spotify, or watch on YouTube.

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Eric, a serial entrepreneur and product leader with experience at Agile Software, Coupa, and BetterWorks, shares how Propel’s SaaS solution helps high-tech, medtech, and consumer goods companies connect engineering, quality, and go-to-market teams around a single digital thread of product information.

💡 Key Topics Covered

• What is Product Value Management (PVM)?
  •  Eric explains how PVM connects every stage of a product’s journey—engineering, manufacturing, quality, marketing, and customer support—creating a unified “product thread.” Unlike traditional PLM, PVM bridges the gap between technical and commercial data, democratizing access to product information across departments.
• Technology Behind Propel
  •  Built on the Salesforce Platform, Propel leverages the scalability, data security, and AI capabilities of Salesforce while maintaining its independence as an OEM provider. Eric shares how the team uses Flow for low-code automation, Lightning Web Components for custom UIs, and Agentforce AI to deliver context-aware assistance—transforming PLM from a static database into a dynamic, guided experience.
• AI & the Future of Product Management
  •  Propel is pushing PLM into the AI era by using agent-based automation and large language model (LLM) integrations to extract value from unstructured data—like reading product documents and generating training quizzes or summarizing change requests in seconds.

If you’re curious how modern PLM platforms are evolving into connected, intelligent ecosystems that empower both engineers and marketers, this episode offers a clear, practical look at how PVM redefines collaboration, compliance, and customer value.

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Our guest, Devin Patel, Head of Solutions at First Resonance, shares how their flagship platform ION—a modular, data-driven factory operating system—helps manufacturers move fast without losing control. From aerospace and defense to climate tech, robotics, and energy, First Resonance empowers teams to prototype quickly, scale complex hardware production, and stay agile in an ever-changing market.

What You’ll Learn in This Episode:

• How First Resonance differs from traditional MES and ERP systems
  
  
• Why speed with control is the key to modern manufacturing
  
  
• The kinds of companies best suited for ION (hint: advanced, complex hardware)
  
  
• Real-world examples, including how Epirus went from prototype to field-ready product in under a year
  
  
• How First Resonance’s culture, rooted in the “SpaceX Mafia” spirit, fuels rapid iteration and innovation
  
  
• Where the company is heading with agentic AI, automation, and adaptive manufacturing
  
  

Devin also shares insights into the company’s origins, customer-first approach, and how they’re working to give a 10-person startup the same visibility and control as an enterprise manufacturer—all while maintaining startup speed.

If you’ve ever wondered how digital manufacturing is evolving beyond rigid, legacy systems, this conversation will open your eyes to what’s possible.

👉 Subscribe to Stay Sharp in Digital Engineering on your favorite podcast platform or YouTube, and stay tuned for more episodes in the Digital Product Series.

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Listeners will gain a data-driven look at where agility, investment, and artificial intelligence are fueling the next wave of disruption in digital twin, simulation, MES, and smart manufacturing ecosystems.

Key Takeaways

• Innovation in digital engineering is no longer centralized. Emerging players from North America, Europe, and Asia are building faster, more agile systems that challenge industry giants.
  
  
• AI isn’t just a feature—it’s a foundation. From design automation to code refactoring and predictive analytics, startups are embedding intelligence across every phase of the product lifecycle.
  
  
• The economic model for engineering software is changing: token-based consumption, multi-agent systems, and new interoperability standards could disrupt traditional licensing structures.
  
  
• The future of PLM is modular, AI-assisted, and built by an ecosystem—not a single vendor.
  
  

About the Guest

Michael Finocchiaro is a Franco-American technology evangelist, consultant, and recognized voice in PLM, digital twin, and engineering software. With over 30 years of experience at HP, IBM, PTC, and Dassault Systèmes, he now advises startups, VCs, and enterprise clients on competitive strategy and digital transformation.

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Tom shares how Propel bridges the gap between product development and commercialization, enabling manufacturers to bring products to market faster through connected data, collaboration, and AI.

💡 Key Topics Discussed

• The Propel Vision
Propel helps manufacturers manage everything required to make, market, sell, and service products successfully—not just engineering data. By combining PLM, QMS, and PIM in one platform, Propel helps unify the product journey from concept to customer.

• Built on Salesforce
Propel was founded in 2015 to modernize PLM using the Salesforce platform. By leveraging Salesforce’s proven infrastructure, Propel focuses on business logic, collaboration, and innovation—allowing customers to scale faster with open, configurable systems.

• Industry Focus & Use Cases
Propel is designed for discrete manufacturers that rely on contract manufacturing and global supply chains, including:

• MedTech and medical device manufacturers
• High-tech and consumer electronics
• Industrial equipment and smart home technologies
• Consumer products with configurable or long-life products

• Success Story: Savant & GE Lighting
Savant Industries, owner of the GE Lighting brand, adopted Propel to streamline product management and accelerate commercialization. Results include:

• 📈 35% revenue growth in 2 years
• ⚡ 74% faster product introduction cycles
• 💼 50% reduction in order entry labor

 Notably, after acquiring a new company, Savant ingested all product data and commercialized it the same day using Propel’s unified Salesforce-powered system.

• Product Value Management (PVM)
Tom introduces the concept of Product Value Management (PVM)—a unified approach that combines PLM, QMS, and PIM to drive both cost reduction and revenue growth, while mitigating risk across the product lifecycle.

• The Future: AI + Interoperability
Propel’s next phase centers on AI-driven automation (Propel One) and multi-CAD interoperability.

• AI is embedded with guardrails for data trust and human oversight.
• New tools will simplify CAD-to-PLM integration across mechanical, electrical, and PDM systems—reducing complexity and cost for engineering teams.

Learn more about Propel Software at propelsoftware.com.

Got questions for Tom or thoughts on this episode? Leave a comment or email podcast@razorleaf.com.

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Devin shares insights on moving away from traditional monolithic systems like ERP and MES toward a more modular, data-driven approach that prioritizes flexibility, scalability, and real-world results. Together, they dive into:

●      Why a strong data footprint matters more than rigid systems.

●      How cloud computing, open APIs, and AI are enabling faster, more nimble manufacturing strategies.

●      Real-world examples from aerospace, energy, and medical device companies that are embracing modular approaches.

●      The unique challenges (and advantages) startups face compared to large, established manufacturers.

●      The role of digital thread and digital twins in creating visibility, traceability, and agility across teams.
 
From building satellites and space thrusters to streamlining complex assembly, Devin explains how manufacturers can transform operations by solving one problem at a time—without years-long IT projects.

If you’ve ever wondered how to build a future-proof digital backbone for manufacturing, this episode offers a blueprint for rethinking the status quo.

🔗 Resources & Links

●      Learn more about First Resonance

●      Connect with Devin Patel on LinkedIn

●      Subscribe to Stay Sharp in Digital Engineering for more conversations on digital transformation in manufacturing
 
 

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In this episode, the Razorleaf team unpacks insights from the Global Product Data Interoperability Summit (GPDIS) in Phoenix — one of the industry’s most collaborative events where aerospace and manufacturing leaders come together to solve shared challenges around data, digital engineering, and interoperability.

From AI skepticism to digital thread strategy, Juliann, Jonathan, Tony, and François discuss what’s changing fast, what’s still a struggle, and why cooperation between competitors like Boeing, Airbus, and Lockheed Martin is key to advancing the industry.

💡 Key Topics Discussed

• Event Overview & Collaboration
GPDIS brings together major OEMs, suppliers, and vendors from across aerospace, defense, and manufacturing to discuss digital transformation challenges. Despite being competitors, attendees openly shared lessons and standards to advance interoperability across systems and companies.

• AI in Aerospace
Artificial intelligence dominated conversations—but with healthy skepticism. Attendees debated AI’s limits, data accuracy, and the future role of AI in engineering workflows.

• Digital Thread Maturity
The “digital thread” remained the central theme. While most companies are still building point-to-point integrations, speakers emphasized the need for hub-and-spoke models and long-term data strategies to achieve true interoperability.

• MBSE on the Factory Floor
A strong focus on model-based systems engineering (MBSE) emerged, highlighting how manufacturing itself should be viewed as a system that benefits from model-based practices.

• Emerging Standards: DDP, TDP & CASCADE
Jonathan shared updates on evolving data standards like Digital Data Packages (DDP) and Technical Data Packages (TDP)—and how new frameworks such as CASCADE aim to improve how organizations link and share digital data.

• Legacy Systems & Integration Challenges
Many manufacturers are still running decade-old PLM and ERP systems, which complicates integration and modernization efforts. François noted that upgrading legacy systems is often the biggest barrier to achieving a seamless digital thread.

• Governance & Co-Creation
Tony and François emphasized that successful digital transformation requires joint ownership between engineering and IT—not siloed efforts. Strong governance and “co-creation” across teams are critical to managing PLM evolution and integration projects.

🧭 Key Takeaways

• Collaboration > Competition: True interoperability demands industry-wide cooperation and shared standards.
  
  
• AI Is Not a Silver Bullet: It’s a powerful tool, but human oversight remains essential—especially in safety-critical industries.
  
  
• Legacy Systems Hold Back Progress: Companies must modernize infrastructure before pursuing advanced digital thread initiatives.
  
  
• Governance Matters: Structured decision frameworks and cross-functional collaboration are essential for scaling PLM and MBSE efforts.
  
  
• Digital Thread Is a Journey: Most organizations are still early in building connected, intelligent ecosystems across engineering, manufacturing, and supply chains.
  
  

🗣️ Memorable Quote

“Everybody raise your hand—who wants to fly in a plane built entirely by AI?”

🔗 Connect with Razorleaf

Visit Razorleaf.com for insights, solutions, and resources on PLM, digital thread, and systems integration.
Have thoughts or takeaways from GPDIS? Leave a comment or email podcast@razorleaf.com.

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From DOD digital engineering requirements to data rights and risk management, they unpack what contractors—especially small businesses—need to know to stay competitive. 

The conversation covers:

• How digital engineering requirements impact primes, subcontractors, and suppliers.
• Why awareness is as important as readiness when pursuing federal contracts.
• The role of digital twin, digital thread, and model-based systems engineering (MBSE) in government programs.
• Strategies for small business teaming and winning set-aside work.
• Navigating data rights, intellectual property protection, and information sharing.
• Factoring the cost of digital deliverables—from CAD models to lifecycle data—into bids.
• Building strong government–industry relationships through collaboration, industry days, and broad agency announcements (BAAs).

Whether you’re a prime contractor, a subcontractor, or a supplier in the defense and aerospace sector, this episode offers practical insights into meeting digital engineering compliance and shaping better outcomes for government customers.

🎧 Listen now and learn how to integrate digital engineering best practices into your government program strategy.

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Mark brings nearly two decades of experience in engineering, system integration, and enterprise transformation, and he shares what really goes into shutting down old PLM, ERP, CRM, and supply chain systems—and moving organizations onto modern platforms.

From outdated DOS-based tools to massive migrations involving hundreds of systems, Mark explains the technical, organizational, and people-related obstacles that can derail a system retirement—and how to overcome them.

What You’ll Learn in This Episode:

• When it’s time to retire a system — from end-of-life support to functionality gaps and security risks.
• How to plan a smooth transition — data archiving, cleaning up “junk” data, and mapping functionality to modern tools.
• The biggest roadblocks organizations face — user resistance, licensing challenges, security concerns, and integration hurdles.
• Why people are the hardest (and most important) part — strategies for training, managing change, and turning skeptics into champions.
• Mark’s best advice — focus on data cleanliness, open communication, and embracing change as an opportunity, not a burden.

Whether you’re sunsetting a small PDM tool or migrating hundreds of interconnected legacy systems, this conversation will help you understand the hidden challenges—and how to get ahead of them.

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Frank shares how CONTACT Software—best known in Germany but lesser-known in North America—has evolved from its early days as "CIM Database" into the highly modular and extensible platform now known as CONTACT Elements.

We cover:

• The origins and evolution of the CONTACT Elements platform
• What makes their modular “elements” approach unique—like a periodic table for PLM, MES, and IoT
• The powerful integration between PLM, MES (Manufacturing Execution Systems), and IIoT
• Use cases where customers seamlessly connect engineering and shop floor data
• Why many global clients are choosing CONTACT’s cloud deployment options (SaaS, PaaS, and hybrid)
• How CONTACT's long-standing use of Python and open APIs empowers extensibility
• The rise of energy-aware manufacturing and AI-enabled analytics
  
  
  

Key takeaway:
 CONTACT Software might just be one of the most powerful, composable digital engineering platforms you’ve never heard of—until now.

🔗 Learn more: contact-software.com

📩 Questions or feedback? Email us at podcast@raiseleaf.com or leave a comment wherever you listen.

🎧 Don’t forget to subscribe, rate, and review—every click helps spread the word.

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Expect laughter, a few stumpers, plenty of partial credit, and who will be ultimately crowned the “King/Queen of PLM.” The crew reflects on the fun (and occasional frustration) of decoding acronyms while celebrating the milestone with listeners who made it all possible.

👑 Acronyms, banter, and bingo—what better way to mark 100 episodes?

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Whether you’re curious about AI-driven data enrichment, smarter migration projects, or bridging the gap between engineering documentation and manufacturing, this conversation is packed with insights and actionable examples.

What You’ll Learn in This Episode:

• Why AI’s current capabilities are less about “intelligence” and more about prediction
  
  
  
• How the “language of engineering” can benefit from large language models
  
  
  
• Why training data quality is critical — and how to keep a human in the loop
  
  
  
• Real-world AI use cases in digital engineering:
  
  
  
  • Data enrichment for cleaner, more searchable part databases
    
    
    
  • Accelerating and improving PLM/CAD data migrations
    
    
    
  • Automating PDF-to-digital conversions for technical publications
    
    
    
• The difference between generative AI and specialized solvers — and how they work together
  
  
  

Links & Resources:

• Follow Alex Bruskin on LinkedIn
  
  
  
• Contact Razorleaf: podcast@razorleaf.com
  
  
  
• Watch this episode on YouTube
  
  
  
• Subscribe to Stay Sharp in Digital Engineering for more conversations on digital transformation, PLM, and manufacturing innovation
  
  
  

Enjoyed this episode? Follow Razorleaf’s Stay Sharp in Digital Engineering, rate the show in your favorite podcast app, and share it with a colleague who’s exploring AI in engineering.

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Our guest, Dimitri Baumtrok, Head of International Sales at CONTACT Software, joins Juliann Grant and Jonathan Scott to share the company’s story, its global expansion, and its unique definition of digital engineering.

What You’ll Learn in This Episode:

• What is “Top Floor to Shop Floor”? How CONTACT Software connects innovation at the design stage with execution in manufacturing and beyond.
  
  
• Who They Serve: Why mid-sized and upper mid-sized companies are their sweet spot, and how industries from automotive to industrial machinery—and even fashion—benefit from their platform.
  
  
• The Secret’s Out: How a “best-kept secret” in Europe is expanding globally through partnerships, OEM collaborations, and customer-driven innovation.
  
  
• Company Culture & History: From its founding in the 1990s to today’s family-owned, privately held structure focused on “energizing great minds.”
  
  
• The Future of Digital Engineering: CONTACT’s vision for AI-driven product lifecycle management, mobile-first solutions, and international growth.
  
  

Dimitri also shares how CONTACT works closely with customers to co-develop solutions, ensuring their platform evolves alongside real-world needs.

If you’ve been curious about lesser-known players in the digital engineering space—or want to understand how CONTACT Elements stacks up against big-name PLM and MES providers—this is a must-listen.

🎧 Tune in to discover why CONTACT Software might just be digital engineering’s best-kept secret.

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What You’ll Learn in This Episode:

• How AI is reshaping PLM across major platforms like Dassault, Siemens, and PTC

• The rise of agent-based AI orchestration and cross-platform integration

• The role of startups—like Propel, Duro, and Aras—in pushing PLM forward

• Why quantum computing and advanced manufacturing could redefine the industry’s capabilities

• Emerging technologies beyond AI that could impact the future of PLM

• Insights into evolving business models and economic considerations for PLM adoption

If this episode was helpful, follow Razorleaf’s Stay Sharp in Digital Engineering, share it with your PLM team, and rate/review in your podcast app.

Tell us what you’re testing next: agents, RAG search, or cloud PDM? Drop a comment and we may feature your question in a future episode.

Want more like this? Subscribe and get new episodes the moment they drop.

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We explore:

• The historical context and drivers behind MBSE
  
  
• The critical role of SysML v2 and why its release is a game-changer
  
  
• How MBSE enables cross-domain collaboration and digital thread continuity
  
  
• Key software tools and vendors shaping the MBSE ecosystem
  
  
• The convergence of PLM and MBSE in the age of digital transformation
  
  
• What MBSE has in common with the evolution from drafting to CAD
  
  

Whether you're a seasoned systems engineer or just learning about digital engineering, this episode offers an insightful look into the tools, standards, and thought leaders shaping the future.

🛠️ MBSE Tools & Platforms:

• Cameo Systems Modeler by Dassault Systèmes (formerly MagicDraw)
  
  
• Enterprise Architect by Sparx Systems
  
  
• Capella by Thales
  
  
• GENESYS by Vitech
  
  
• Innoslate by SPEC Innovations
  
  
• MBSE tools from Ansys and MathWorks
  
  

📚 Thought Leaders & Trainers:

• Sanford Friedenthal (SysML / MBSE thought leader)
  
  
• Ed Seidewitz (UML & SysML standards contributor)
  
  
• Lenny Delligatti – Author of SysML Distilled
  
  
• Tim Weilkiens – MBSE Trainer and Consultant
  
  

🧪 Standards Bodies:

• INCOSE (International Council on Systems Engineering)
  
  
• OMG (Object Management Group) – Developers of UML, SysML, UAF
  
  
  

🔗 Learn More About SysML v2:

• SysML v2 Initiative at OMG
  
  
• SysML v2 Release Updates & Resources
  
  

📩 Contact & Subscribe:

Have questions for David or our hosts? Leave a comment or email us at podcast@razorleaf.com.

👉 Be sure to subscribe to Stay Sharp in Digital Engineering on your favorite podcast platform or watch full episodes on YouTube.

🔗 YouTube: Razorleaf YouTube Channel

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With decades of experience in requirements management, Fernando offers deep insight into how PLM (Product Lifecycle Management), ALM (Application Lifecycle Management), and MBSE (Model-Based Systems Engineering) can come together to create smarter, faster, and safer product development cycles. The conversation also explores how AI is transforming requirements processes and what the future holds for digital engineering.

🔍 Topics Covered:

• What PLM and ALM really mean—and where requirements engineering fits in
  
  
• The cultural divide between hardware and software teams—and how to bridge it
  
  
• How systems engineering and MBSE provide a common language across domains
  
  
• The role of digital twins in syncing product cadences
  
  
• How AI is already improving requirements validation, traceability, and compliance
  
  
• Why system engineers are key players in modern product integration
  
  
• What organizations must consider when blending ALM and PLM in real-world use
  
  

🧠 Key Quotes:

“AI can now do 80% of what a requirements engineer does—but it’s the boring 80%.”
 “Digital twins are speeding up hardware development the same way DevOps transformed software.”
 “MBSE isn’t just about models. It’s about giving people better ways to see and simulate complexity.”

👤 Guest Bio:
Fernando Valera is a Certified Professional in Requirements Engineering and CTO at Visure Solutions. With over 20 years of experience helping organizations streamline complex product delivery, Fernando brings a global perspective and deep technical expertise to digital transformation initiatives in safety-critical industries.

📩 Got Questions?
 Drop us a comment on our LinkedIn post or email us at podcast@raise.com.

✅ Don’t forget to like and subscribe on your favorite podcast platform or watch the full episode on YouTube.

🎧 Stay sharp, and see you in the next episode!

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Steve outlines six key components for digital engineering success:

1. Integrated Tools and Data Interoperability
2. Collaborative Digital Environments
3. Authoritative Sources of Truth (ASOTs)
4. Digital Thread Planning and Maintenance
5. Workforce Readiness and Culture Change
6. Governance and Policy Alignment

The conversation dives into practical approaches for getting started, even if you're not at the beginning of an acquisition cycle. From digitizing artifacts and improving basic workflows to developing a “digital first” mindset, Steve explains why success starts with clear strategy, small wins, and a vision for long-term sustainability.

💡 Key takeaways:
 – Don’t try to “boil the ocean”—start small and build momentum
 – Digital transformation requires cultural and organizational shifts, not just new tools
 – Programs should consider lifecycle interoperability and future workforce expectations from day one
 – External guidance and internal advocacy are both critical for success

📍 Whether you’re supporting legacy programs or launching new initiatives, this episode offers a clear roadmap for navigating digital engineering in government.

👉 Got questions or want to join the conversation? Drop us a comment or email us at podcast@razorleaf.com.

 🔔 Don’t forget to subscribe and stay sharp!

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They explore the difference between digital models and true digital twins, the importance of having a physical counterpart, and why configuration management is crucial for maintaining a digital thread. Dave emphasizes that a digital twin isn’t just a model—it’s a living representation that evolves alongside its physical asset, incorporating real-time data and changes over time.

The conversation also covers:

• The nuances between terms like digital twin, digital shadow, surrogate, and virtual twin
• Why CAD models alone don’t qualify as digital twins
• How deviations in manufacturing and maintenance create one-of-a-kind digital twin instances
• Why robust configuration management and a traceable digital thread are essential
• Real-world applications in aerospace and defense, including mission-specific modeling and sensor feedback
• The growing role of AI in connecting systems, automating documentation, writing test cases, and refining models—and why human oversight is still essential

David also shares a few laughs with the hosts, poking fun at the overuse of buzzwords and “digital trophies,” and stressing the importance of context, scalability, and ethics in digital engineering.

Listeners will walk away with a clearer understanding of what a digital twin is (and isn’t), where AI fits in, and how to start building toward a truly connected, model-based future.

If you enjoyed this conversation, be sure to like this episode and subscribe to Stay Sharp on your favorite podcast platform or on YouTube. Got questions or feedback for the hosts? Drop a comment or email us at podcast@razorleaf.com.

Until next time—stay sharp.

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We cover:

• What MBSE is and isn’t (hint: it’s not its own silo)
  
  
• Where MBSE fits into the Digital Twin journey
  
  
• How MBSE supports architecture across the product, factory, and enterprise
  
  
• The importance of first-order (0D/1D) models in early systems thinking
  
  
• MBSE’s relationship to simulation, sustainability, and organizational design
  
  
• Why the middle management layer is the key to successful adoption
  
  

David also introduces the idea of MBSE acting as the “connective tissue” that unifies disciplines—not a high-fidelity replacement tool, but a crucial framework for clarity, collaboration, and continuous evolution.

🧰 Resources & Links Mentioned:

📘 MBSE & Digital Twin Concepts:

• MBSE Primer (co-authored by David Long)
  
  
• INCOSE – International Council on Systems Engineering
  
  
• Digital Twin Types: Surrogate, Shadow, and Full Twin
  
  
• Conway’s Law – How Organizational Structure Impacts System Design
  
  
  

🧠 Simulation, Modeling & Architecture:

• Functional Mock-up Interface (FMI)
  
  
• Virtual Twin vs. Digital Twin (Dassault Systèmes)
  
  

📩 Contact & Subscribe:

🎧 Subscribe to Stay Sharp in Digital Engineering on your favorite podcast platform
▶️ Watch full episodes on Razorleaf’s YouTube Channel
📬 Got questions for David Long? Send us an email at podcast@razorleaf.com

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In this episode, hosts Juliann Grant and Jonathan Scott continue the Digital Twin Series with a deep dive into simulation, featuring guest expert Shawn Freeman from Vias3D. With over 30 years of experience in CAE and simulation-driven product development, Shawn helps demystify how traditional CAE, surrogate modeling, and emerging technologies like AI are transforming how we design, manufacture, and operate products.

🔍 Topics covered:

• Why traditional simulations alone aren’t enough for real-time digital twins
  
  
  
• How surrogate modeling helps speed up decision-making
  
  
  
• The four key components of simulation: inputs, outputs, model, and solver
  
  
  
• How simulation results are stored, trusted, and integrated into PLM systems
  
  
  
• Operational use cases, from vehicle design to in-flight aircraft assessments
  
  
  
• The rising impact of AI in generating richer, faster simulation models
  
  
  

🎧 Whether you're designing consumer products or managing aircraft performance, this episode gives you a comprehensive look at how simulation fits into the digital twin ecosystem.

👉 Got questions or comments? Email us at podcast@razorleaf.com or comment on our latest post.
 Until next time—stay sharp.

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While most conversations in digital engineering focus on structured data—PLM, ERP, and CAD—this episode dives deep into the massive (and often untapped) world of unstructured data: paper documents, legacy drawings, tribal knowledge, and more.

💡 Topics Covered:

• What unstructured data actually means in a manufacturing context
  
  
• Why up to 80% of valuable data remains unused—and how AI is changing that
  
  
• The difference between systems of record and systems of insight
  
  
• How AI is accelerating the conversion of historical documents and tribal knowledge into searchable, useful intelligence
  
  
• Real-world applications for AI-powered insights—from reducing engineering inefficiencies to improving quality control
  
  
• What makes manufacturing data uniquely challenging for general AI tools—and how purpose-built platforms are adapting
  
  
• The future of connected insights across structured and unstructured data domains
  
  
• Why “agentic AI” and human-in-the-loop workflows matter now more than ever
  
  

🔧 Key Insight:

AI isn’t just a buzzword—it’s a powerful tool for surfacing decades of buried knowledge and connecting data silos across the digital thread. Whether it’s improving design reuse, surfacing legacy quality fixes, or simply saving engineers from being “librarians,” the potential is vast.

🎧 Listen now on your favorite podcast platform or watch on YouTube.
 📩 Questions or feedback? Email us at podcast@razorleaf.com

Stay Sharp—and stay curious.

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💡 In this episode, we cover:

• The founding story of Razorleaf — how an unexpected turn led to its creation
• Behind-the-scenes tales, including the surprising role 9/11 played in team formation
• Lessons learned from growing a business with heart, integrity, and adaptability
• Why being “people first” has driven Razorleaf’s culture and long-term succes
• The evolution of the digital engineering landscape — and how Razorleaf has stayed ahead
• How global growth has challenged and enriched the company’s perspective
• The “hard things” Razorleaf chooses to take on — and why that matters for innovation
• What’s next for Razorleaf in an age of AI, cloud PLM, and multi-tenant systems
  
  

🔥 Whether you’re an entrepreneur, PLM expert, digital engineering leader, or longtime Razorleaf partner, this episode offers insight into building a resilient, values-driven company in a rapidly changing tech world.

📩 Have a question for Eric or the team?
 Email us at: podcast@razorleaf.com

 👍 Like this episode? Subscribe and leave a review on your favorite platform, and follow Razorleaf on social media.

🔗 Learn more at razorleaf.com

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You’ll learn:

  ✅ The difference between digital twins and virtual twins

  ✅ How process twins drive efficiency, lean manufacturing, and pull-based production

  ✅ Real-world use cases from bottling lines to automotive to greenfield factory builds

  ✅ Why simulation matters more than ever in high-speed, high-volume production

  ✅ How virtual twins support sustainability, supply chain resilience, and even human ergonomics

Whether you’re designing a new facility or trying to modernize an existing one, this episode will give you practical insights on how to use virtual twins to cut costs, reduce risk, and increase adaptability.

Topics Discussed:

• What sets a “virtual twin” apart from a standard digital twin
• Examples of product vs. process value in different industries
• Planning new factory builds with simulation-first strategies
• Creating human and robot twins for safety and efficiency
• Integrating sustainability into manufacturing process design
• A roadmap for getting started and scaling virtual twin technology

Resources  Contact:
Have questions or want to connect with our guest? Drop us a line at 📧 podcast@razorleaf.com

📌 Don’t forget to like, subscribe, and leave a review on your favorite podcast platform!

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From Hollywood film sets and football fields to harbor cranes, commercial airliners, and collapsed buildings, this episode dives into how digital twins—and more importantly, digital threads—are being used to simulate, diagnose, and even prevent catastrophic events.

🔍 Highlights from the Episode:

• How Apollo 13's analog twin scene inspired a new way to understand digital twin value
  
  
  
• Real-world failure analysis: a Texas harbor crane collapse and the missed opportunity for a front-running simulation
  
  
  
• The tragic 737 door plug and MAX incidents—and what could have been prevented with a robust digital thread
  
  
  
• Digital twins in consumer tech: how your car app may already be a sparse but effective digital twin
  
  
  
• Why insurance companies might lead the charge in demanding front-running simulations for risk mitigation
  
  
  
• A strong case for designing your digital thread before you build your product
  
  
  

Dr. Hillberg also walks us through the six foundational pillars of product lifecycle management, originally laid out by Michael Grieves, and reframes them as essential elements of a functioning digital twin ecosystem.

Whether you're an engineer, innovator, or executive, this episode challenges you to rethink how you capture, use, and respond to real-time data—and how digital twins can be your most valuable asset in designing for safety, efficiency, and accountability.

🎧 Tune in to hear:

• The critical difference between a digital twin and a digital thread
  
  
  
• Why sparse twins can still deliver massive value
  
  
  
• How front-running simulations could prevent billion-dollar mistakes—and save lives
  
  
  

👉 Got a question for Patrick? Want to suggest a future topic? Leave us a comment or send an email to podcast@razorleaf.com.

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In this episode, Juliann and Jonathan welcome industry veteran Dan Miklovic to unpack how digital twins are evolving to encompass not just the product, but the process that creates it. With over 50 years in manufacturing—from the plant floor to analyst firms like Gartner—Dan brings unparalleled insight into what it takes to build accurate, actionable digital twins in today’s fast-moving industrial landscape.

You’ll learn:

• The difference between MES, MOM, and IIoT—and why they matter
  
  
• How digital twins connect to digital threads (and why it’s more of a tapestry)
  
  
• Why capturing how something was built is as important as what was built
  
  
• The critical role of process data in enabling agility, quality, and sustainability
  
  
• How digital twins empower smarter decision-making in a world of AI, tariffs, and supply chain disruption
  
  
  

Whether you work with engineered-to-order products or high-volume manufacturing, this conversation will change how you think about digital twins—from static snapshots to living, breathing models of your operation.

🔧 Topics Covered:

• MES vs MOM vs IIoT: Definitions and real-world relevance
  
  
• The evolution from hardwired sensors to cloud-connected IIoT
  
  
• Capturing the “as-built” truth for compliance and quality
  
  
• Using process twins for optimization and adaptability
  
  
• How Industry 4.0 and 5.0 shape digital manufacturing strategies
  
  
• Why accurate, timely data is foundational for AI and ML applications

📨 Have a question for Dan or the Razorleaf team?
 Email: podcast@razorleaf.com

📺 Watch on YouTube or subscribe wherever you get your podcasts.

Until next time—work hard, and stay sharp.

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Juliann and Jonathan break down what software digital twins actually are (hint: it’s not always straightforward), where they live in the tech ecosystem, and how they power everything from simulation and testing to configuration management and DevOps pipelines. They also explore how these twins are used differently depending on whether software is the product or part of a larger physical system—like a smart refrigerator or a modern aircraft.

You’ll learn about:

• The difference between digital twins for software-only products vs. cyber-physical systems
• How DevOps and Application Lifecycle Management (ALM) tools support digital twin environments
• The role of emulators and virtual machines in simulating real-world software performance
• Why configuration management is critical to successful digital twin implementation
• How industries like aerospace and automotive use software digital twins for reliability and safety
  
  
  

Key Quote:
 "Software digital twins may not always be visible—but they're absolutely vital when lives, safety, and product performance are on the line."

💡 Want to join the conversation or request a follow-up episode? Email us at podcast@razorleaf.com or leave a comment wherever you listen.

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💡 What you'll learn in this episode:

• Key trends shaping PLM in 2025, from digital threads to AI readiness
• Why clear business cases and executive sponsorship are essential for PLM success
• The critical (but often overlooked) role of data in transformation strategies
• Real-world insights from companies like Airbus, Boeing, Hollister, and Hitachi Energy
• A behind-the-scenes look at the Aerospace & Defense PLM Action Group’s findings on digital thread use cases
• Highlights from Dr. Martin Eigner’s 40-year retrospective on PLM evolution

Whether you're an engineering leader, IT professional, or digital transformation strategist, this recap offers practical frameworks and honest reflections to help your PLM strategy drive real business outcomes.

🔗 Resources & Mentions:

• CIMdata & Eurostep PLM Roadmap/PDT Conference
• A&D PLM Action Group Digital Thread Research
• Featured Speakers: Dr. Martin Eigner, Caroline Gustafson (Hitachi Energy), Stacy Burgart (Hollister), Kenny Swope (Boeing)
  
  
  

📍Next stop: PLM Roadmap Europe – November in Paris – Learn more at cimdata.com

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🔍 Topics Covered:

• What sustainability really means in the context of PLM
• Regulatory drivers like RoHS, REACH, and the EU Digital Product Passport
• The shift from hazardous material tracking to full carbon reporting
• Why “right to repair” and circular economy models are gaining traction
• How the product-as-a-service model transforms business sustainability
• Practical steps for manufacturers to begin measuring environmental impact
• Real-world examples of data-driven PLM and the use of digital twins
• Tools and databases supporting sustainability analysis (e.g., EcoInvent, LCA, AI-driven platforms)
• The importance of model-based systems engineering and connected data

🌍 Key Insight: 

Sustainability is no longer a side concern—it’s becoming a central factor in product innovation and compliance. And while regulations are often the spark, digital transformation is the fuel that helps companies prepare, respond, and lead.

📩 Got questions or feedback? Email us at podcast@razorleaf.com or drop a comment wherever you listen.

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💡 Highlights

• Real-world challenges of enterprise PLM at scale
  
  
• Balancing customization with vendor capabilities
  
  
• Strategies for digital engineering: start small, stay agile, engage stakeholders early
  
  
• Advice for vendors on openness and smoother upgrades
  
  
  

📩 Get in Touch

Have a question or want to connect with Bob? Email us at podcast@razorleaf.com

🎧 Follow Us

Subscribe on your favorite podcast platform or YouTube—and as always, stay sharp!

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Takeaways:

• AI and AR are no longer futuristic—they’re embedded in everyday workflows.
• Secure, cloud-based design environments are becoming viable for even the most regulated industries.
• COExperience remains a crucial event for innovation sharing, collaboration, and community.

👉 Get Involved:

Interested in volunteering with COExperience? Visit www.coe.org or email coe@coe.org.

🎧 Listen & Subscribe:

Subscribe on your favorite podcast app or watch on YouTube. Don’t forget to like and share this episode if you enjoyed it!

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