Riverside Event Title
Product Memory: The Missing Layer Between PLM, Digital Thread, and AI Agents

Riverside Event Description
Everyone talks about the single source of truth.

Then the real product decision happens in a meeting, spreadsheet, email, Teams chat, supplier exchange, or inside someone’s head.

In this episode of The Future of PLM, I’m joined by Oleg Shilovitsky of OpenBOM, Rob McAveney CTO of Aras, Brion Carroll of Digital Solution Group, David Segal of TCS, and Jonathan Scott of Razorleaf for a sharp discussion on one of the most important emerging ideas in PLM and enterprise AI: Product Memory.

The core question:
If digital thread connects the data, what captures the reasoning?

PLM manages parts, BOMs, changes, documents, requirements, and workflows. But it often misses the “why” behind decisions: assumptions, rejected options, supplier constraints, manufacturing context, cost tradeoffs, effectivity logic, and informal reasoning.

This discussion explores whether Product Memory becomes the next layer above PLM, ERP, MES, QMS, ALM, supplier systems, documents, and collaboration tools: a contextual, semantic, AI-ready memory of how product decisions are made across the enterprise.

We cover:

Can Product Memory avoid becoming another inconsistent data layer?
What should be captured, and what should be filtered out?
Why does eBOM-to-mBOM still break so many digital threads?
How do semantics and ontology determine whether AI can trust product context?
Can AI agents safely recommend or execute PLM changes?
How do we capture human decision-making without scaring the humans?

Timeline
00:16 — Introduction: single source of truth, broken digital threads, and Product Memory
03:02 — Oleg defines Product Memory beyond single source of truth and digital thread
06:28 — Rob on dependency graphs and hidden context in unstructured documents
08:36 — Brion on Product Memory as an “orb” fed by siloed enterprise systems
11:39 — Jonathan on semantics: why “part” means different things across functions
13:46 — David on Product Memory from an enterprise architecture perspective
18:21 — Avoiding inconsistent data across PLM, ERP, PIM, e-commerce, and supply chain
22:09 — Why engineering-to-manufacturing translation is so hard
25:00 — Why engineering release is not the finish line
30:05 — Missing memory: decisions in people’s heads, spreadsheets, and informal actions
33:57 — Why skipping change steps can slow the enterprise down
35:57 — AI agents, requirements ingestion, and asking “why” like a three-year-old
39:48 — Why AI agents must document their own reasoning
42:49 — Product Memory flywheel: capture, review, flow, and distribution
45:35 — Industrial AI, physical AI, agentic AI, and real-time product memory
48:21 — Semantic consistency, meta layers, and vetting data before Product Memory
52:15 — Dependency graphs, imperfect data, and improving ontology over time
55:12 — Human maturity: is the organization ready?
56:56 — Where companies should start looking for missing Product Memory
1:03:58 — Rob’s call to action: start capturing decision traces now
1:05:03 — Closing: eBOM, mBOM, ISA-95, and semantic translation

This is not a theoretical PLM buzzword session. It is a practical debate about architecture, governance, trust, and human maturity before AI agents can operate safely inside the product lifecycle.

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