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Stay Sharp in Digital Engineering

Stay Sharp in Digital Engineering

Hosted by Razorleaf Corp.

Episodes

158

Latest episode

Oct 2026

Language

EN-US

About the show

Welcome to 'Stay Sharp in Digital Engineering,' the ultimate podcast for all things digital in the manufacturing industry by Razorleaf. Join us as we take a deep dive into the multifaceted world of digital transformation, exploring topics such as the digital thread, digital twins, IDEs, model-based strategies and delving into the frontiers of cutting-edge technologies like PLM, MES, Integration, and more. Our expert hosts, Jonathan Scott, Jen Ferello, Juliann Grant, and Eric Doubell, will be your guides, providing valuable insights, captivating interviews, and the latest industry updates to ensure you remain at the forefront of the ever-evolving digital landscape. Whether you're a technology enthusiast, a business leader, or simply curious about the digital realm in manufacturing, this podcast is your essential resource for staying sharp and well-informed.

Listen to episodes

60 recent
October 20, 202643 min

#158: AI Broke Out of the Sandbox. Now What?

Every new engineer who starts at a company receives limited access, supervision, and earns trust over time, but AI agents often receive none of that. In this episode of Stay Sharp in Digital Engineering with Razorleaf , hosts Juliann Grant and Jonathan Scott are joined by Nick Link, co-founder of Bitoda to discuss what happens when AI agents break out of their test environments. They unpack recent high-profile incidents involving OpenAI and Anthropic, define key concepts like safety, security, and deterministic guardrails, and explain why treating AI agents like new hires is the safest way to bring AI into engineering and manufacturing organizations. Key Takeaways: The Hugging Face & Anthropic Incidents : What really happened when AI agents breached testing environments and reached external systems. Security vs. Safety : Security protects systems from external threats, while safety protects organizations from internal missteps. Guardrails vs. Sandboxes : Sandboxes provide virtual test environments, while guardrails enforce boundaries around what agents can do. Deterministic vs. Probabilistic Controls : Why prompt instructions are non-deterministic and weak guardrails, and why deterministic code controls (like custom shells and command whitelists) are necessary. Treat AI Agents Like New Hires : Implement onboarding, grant least-privilege access, and expand permissions gradually as capabilities are proven. Atomic Outcomes : Work backward from simple, atomic goals to keep agents focused on single areas of expertise. Auditing and Vendor Accountability : Maintain logs to trace agent actions and hold software vendors accountable when system guardrails fail. Guest Information: Nick Link : US Marine Corps veteran and co-founder of Bitoda (a managed application services firm supporting defense and aerospace MBSE programs) and Fawdy (an AI-driven incident response platform for IT operations). Resources & Links Mentioned: Become a Member of the Stay Sharp Community: https//staysharpindigitalengineering.com/become-a-member Podcast Feedback & Contact : Email podcast@razorleaf.com Enjoyed this episode? Please subscribe to Stay Sharp in Digital Engineering with Razorleaf , leave us a rating or review , and share this episode with your engineering and IT colleagues! Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

October 13, 202642 min

#157: Digital Product Series: Geminus AI

In Episode 157 of Stay Sharp in Digital Engineering with Razorleaf , co-hosts Juliann Grant and Jonathan Scott welcome back Prithvi Singh Chauhan, Machine Learning Engineer at Geminus AI , for a closer look at the company, its computational autonomy platform, and the future of AI-driven engineering decisions. Geminus AI takes a different approach to industrial AI. Rather than replacing the physics-based simulators engineering teams already trust, its platform adds a decision layer that helps turn complex simulations into faster, actionable operational recommendations. By connecting existing models, operational data, and multiple physical assets, Geminus aims to help organizations make informed decisions in real time—with physics-based accuracy and an understanding of uncertainty. What You'll Learn in This Episode: Where Geminus AI fits in digital engineering: How its computational autonomy platform bridges the gap between traditional simulation and real-time operational decision-making. Why physics-based AI matters: Why high-consequence systems require more than conventional pattern recognition, particularly when decisions affect safety, production, cost, and sustainability. How faster decisions deliver business value: Hear how Geminus helped an energy operator develop pipeline and production diversion recommendations in just a few days after regional conflict damaged part of its network—a process that traditionally could have taken weeks. How the platform works: Explore its four core capabilities: reasoning, model building, model calibration and drift detection, and system-of-systems optimization. How Geminus builds trust: Learn how operators can validate recommendations against the high-fidelity simulators they already use. What it takes to deploy the technology: Discover how the platform works with existing simulators and data, supports cloud and on-premises environments, and accommodates different engineering and operational roles. Where computational autonomy is headed: Explore the vision for connecting assets across entire enterprises and enabling faster decisions around production, energy resilience, and sustainability. From Simulation to Operational Decisions: Many industrial organizations already have sophisticated simulation tools and physics-based models. The challenge is making those models fast, current, and connected enough to support decisions as operating conditions change. Geminus AI's computational autonomy platform is designed to address that challenge. It builds models using trusted simulators, monitors their accuracy against live operational data, incorporates uncertainty into decisions, and connects individual assets into larger systems. Prithvi shares a real-world example involving a damaged energy pipeline network, where the team developed recommendations to redirect production while maintaining operational constraints and customer commitments. The discussion also explores how model drift can reveal potential physical problems in an asset, making AI useful for maintenance as well as optimization. Meet the Guest Prithvi Singh Chauhan is a Machine Learning Engineer at Geminus AI, where he combines machine learning, high-fidelity simulation, and optimization to improve efficiency and sustainability across complex energy systems. His experience spans international energy projects and other high-consequence industries. He holds a master's degree in petroleum engineering from Texas A&M University, is a named inventor on four U.S. patents, and was named the Society of Petroleum Engineers' Young Engineer of the Year in 2026. Continue the Conversation For more context, listen to Prithvi's previous Stay Sharp in Digital Engineering with Razorleaf, Episode 154 appearance on operational digital twins , where he discusses the technology behind physics-based AI and its role in complex industrial systems. Subscribe to Stay Sharp in Digital Engineering with Razorleaf, for more conversations about digital engineering, product development, manufacturing, AI, and the technologies connecting them. Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

October 6, 202642 min

#156: Digital Product Series: Razorleaf

In this episode of Stay Sharp in Digital Engineering with Razorleaf , hosts Juliann Grant and Jonathan Scott turn the spotlight on Razorleaf. Joined by CEO and founder Eric Doubell, the discussion provides an overview of Razorleaf, exploring its history, core values, services, products, and role in the digital engineering and manufacturing landscape. 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 3D EXPERIENCE 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 3D EXPERIENCE, 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 3D EXPERIENCE 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 . Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

September 29, 202649 min

#155: The Future Platform for Hardware Engineering: The Report Explained

Is PLM dying, or has the engineering world simply outgrown it? Juliann Grant and Jonathan Scott welcome back industry veteran Doug Macdonald for round two to unpack his report, The Future of Product Lifecycle Management: After Forty Years, Where Are We and What's Next? Doug makes the case for FPHE (Future Platform of Hardware Engineering) , a new category designed to replace legacy PLM systems that no longer fit the complex, multi-disciplinary nature of modern hardware development. 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! Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

September 22, 202644 min

#154: Operational Digital Twins: Closing the Speed Gap

In this episode of Stay Sharp in Digital Engineering , hosts Juliann Grant and Jonathan Scott are joined by Prithvi Singh Chauhan , Machine Learning Engineer at Geminus AI . They explore the practical application of AI and machine learning to operational digital twins across oil and gas, manufacturing, and energy systems. The discussion covers why traditional physics models fall short in real-time operations and how combining physics-native engineering with fast machine learning solves speed, staleness, and domain silos. 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! Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

September 15, 202640 min

#153: Zero Downtime, No Disruptions: Honeywell's MES Upgrade Story

Halfway through a live database migration, Honeywell’s team lost their server connection. With no way to tell whether the scripts were running or failing, all they could do was wait. In this episode of Stay Sharp with Razorleaf , hosts Juliann Grant and Jonathan Scott sit down with Ryan Sims (Senior IT Engineer at Honeywell) and Brian Neff (MES Technical Team Lead at Razorleaf) to discuss how Honeywell Aerospace successfully upgraded its Solumina MES with zero shop floor disruptions. 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 Razorlea f 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 . Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

September 8, 202640 min

#152: OpenBOM's New AI Agents Solving the Problems Everyone Ignores

Digital engineering is moving fast, but the fundamental challenge remains: how do you get your mechanical, electrical, and procurement data to "talk" to each other? In this episode, we dive deep into the "Product Memory Flywheel" with Oleg Shilovitsky, co-founder and CEO of OpenBOM. We explore how OpenBOM is using AI agents to solve the industry's most persistent, "boring" problems—like CAD file management and Bill of Materials (BOM) errors—and why fixing these unglamorous issues is the key to true digital transformation. 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! Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

September 1, 202650 min

#151: The Untold Origins of PLM, with Doug Macdonald

How did we get from CAD and PDM to the PLM systems we use today? And have we really moved beyond PDM? 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! Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

August 25, 202634 min

#150: 150 Episodes In - What We Got Right (and Wrong) in Digital Engineering

150 Episodes of Digital Engineering Conversations—and Counting 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. Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

August 18, 202645 min

#149: Product Memory: The Context Layer Behind Every Product Decision

In this episode, we sit down with Oleg Shilovitsky, industry expert, author of Beyond PLM , and CEO of OpenBOM, to explore a transformative concept he calls "product memory." If you’ve ever looked back at a product design decision two years later and wondered, "Why did we make that choice?", you aren't alone. Oleg explains why product memory is the missing context layer that connects decisions to data, filling the gaps that traditional PLM, PDM, and ERP systems often miss. We discuss the "three parents" of product memory—including the critical role AI plays as a forcing function—and dive into the complex questions surrounding data ownership, security, and the future of business models in a post-SaaS world. 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. Music is considered “royalty-free” and discovered on Story Blocks. Technical Podcast Support by Jon Keur at Wayfare Recording Co. © 2026 Razorleaf Corp. All Rights Reserved.

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