Configuration Management is still one of the hardest problems in PLM—and this panel doesn’t sugarcoat it.

In this episode of the Future of PLM Podcast, Michael Finocchiaro is joined by Rob Ferrone, Brion Carroll, Jim Brown, Oleg Shilovitsky, and Eric Schrader (Propel) to break down why BOMs still don’t match, why “single source of truth” is mostly fiction, and where AI might actually help.

Key themes:

• Why CM ≠ BOM management
• The myth of a single version of truth
• Variant chaos and effectivity complexity
• Why most companies still fail at adoption
• AI, product memory, and the future of CM

If you work in PLM, engineering, manufacturing, or digital thread—this is a must-watch.

👇 Drop your thoughts in the comments:
Where is configuration management breaking down in your org?

⏱️ TIMELINE

00:00 – Intro + panel lineup
00:09 – What is configuration management (5 definitions)

03:12 – Biggest false beliefs about CM

• “We have a single source of truth” (we don’t)
• CM seen as bureaucracy vs performance lever
• Methodology ≠ success (adoption is the issue)

06:47 – Minimum data model for CM

• Identity, effectivity, baseline, traceability
• Why data governance matters more than tools

10:22 – Where CM actually lives (PLM, ERP, MES, everywhere)

• The “octopus problem” across systems

15:12 – Hardest real-world CM problems

• Variant management = BOM chaos
• Effectivity vs configuration confusion
• Software + firmware breaking traditional models

21:53 – Debate: Effectivity (date vs serial vs lot)

• Why “it depends” is unavoidable
• Safety vs cost trade-offs

24:09 – Configuration rules debate

• 150% BOM vs model-based approaches
• Why rules drift over time

26:10 – Digital thread reality check

• Why duplication is inevitable
• Importance of product identity

30:09 – As-designed vs as-built vs as-maintained

• Where control breaks down (hint: service)
• Why “as maintained” is the weakest link

39:38 – AI in configuration management

• Change impact analysis
• Data structure vs AI hype
• “AI is useless without governed data”

48:55 – When is the ChatGPT moment for PLM?

• Simplicity vs complexity
• People problem vs technology problem
• Product-as-agent concept

59:10 – Final thoughts

• Data governance as the core issue
• Why we’re still having the same debates after 20 years

🎯 Key Takeaways

• There is no single source of truth—only closest approximations
• Variant + effectivity = core chaos engine
• CM failure is mostly organizational, not technical
• AI will help—but only if data is structured and governed
• The real frontier: making CM consumable across the enterprise

📢 Follow / Connect

• LinkedIn: Michael Finocchiaro
• More content: DemystifyingPLM.com
• Events: Threaded! Conference Series