Stay Sharp in Digital Engineering

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.

Stay Sharp in Digital Engineering is a technology podcast hosted by Unknown Host, with 155 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Stay Sharp in Digital Engineering, a technology show with 155 episodes published.

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  Stay Sharp in Digital Engineering

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

  Sep 29, 202650 min

  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.

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  Stay Sharp in Digital Engineering

  #154: Operational Digital Twins: Closing the Speed Gap

  Sep 22, 202645 min

  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.

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  Stay Sharp in Digital Engineering

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

  Sep 15, 202641 min

  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.

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  Stay Sharp in Digital Engineering

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

  Sep 8, 202641 min

  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.

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  Stay Sharp in Digital Engineering

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

  Sep 1, 202650 min

  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.

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