In this episode of the Stay Sharp podcast, Juliann and Jonathan continue their digital product series with returning guest Oleg Shilovitsky, Co-Founder and CEO of OpenBOM. With over 25 years of experience in PLM and engineering software, Oleg unveils what truly sets OpenBOM apart—and why traditional PLM systems fail modern manufacturing organizations.

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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© 2024 Razorleaf Corp. All Rights Reserved.

What will digital engineering and manufacturing really look like in 2026?

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

Music is considered “royalty-free” and discovered on Story Blocks.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.

As 2025 comes to a close, Stay Sharp in Digital Engineering takes a look back at a year of standout conversations, memorable insights, and practical lessons from across the digital engineering landscape.

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.

Music is considered “royalty-free” and discovered on Story Blocks.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.

Episode Summary

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

Music is considered “royalty-free” and discovered on Story Blocks.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.

In this episode, Juliann and Jonathan sit down with John Salyers and Korey Greene of Sea Box, a company transforming standard shipping containers into high-engineering, mission-ready solutions.

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.

Music is considered “royalty-free” and discovered on Story Blocks.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.

In this episode of Stay Sharp in Digital Engineering, hosts Juliann Grant and Jonathan Scott sit down with globally recognized PLM pioneer Dr. Martin Eigner to unpack the evolving landscape of PLM, digital threads, configuration management, and cross-enterprise integration.

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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Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.

Why Data Models Matter in Digital Engineering (Now More Than Ever)

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.

Music is considered “royalty-free” and discovered on Story Blocks.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.

In this episode of Stay Sharp in Digital Engineering, hosts Jonathan Scott and Juliann Grant sit down with Oleg Shilovitsky, CEO and co-founder of OpenBOM, for a candid conversation about why so many engineering and manufacturing companies struggle with product data—and what they can do differently. Oleg breaks down the realities of managing product structure, BOMs, and cross-system data flow, and explains why connecting these elements early is essential for companies trying to scale.

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.

Music is considered “royalty-free” and discovered on Story Blocks.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.
© 2024 Razorleaf Corp. All Rights Reserved.