Where Product Lifecycle Knowledge Gets Lost (And Why It Matters)

Product lifecycle knowledge is not created in a single system or at a single moment. It emerges across discussions, iterations, and trade-offs. It takes shape in design and engineering conversations, in support and maintenance calls, and in production and procurement planning, evolving through decisions made under uncertainty, engineering changes, BOM edits, and the flow of change orders moving through the organization.

And yet almost none of it is actually preserved.

The Lifecycle of a Product Decision

Every product decision has its own lifecycle. It begins as a question. It becomes a discussion. It evolves through constraints and trade-offs. And eventually, it turns into a structured outcome. We like to think our systems capture this process. But they don’t. They capture the result. Not the journey that created it.

Think about the last time a significant design choice was made on a product your team worked on. Someone proposed something. Others pushed back. A supplier constraint changed the picture. A cost target forced a compromise. Maybe a previous failure was in the room, even if nobody said it out loud. All of that shaped the final decision, and almost none of it was written down anywhere a system could find it.

From Discussions to Systems: Where Knowledge Collapses

At the top of the process, knowledge is rich, messy, and deeply human. Discussions. Slack threads. Meetings. Whiteboards. Supplier conversations. Field reports. Experiments that didn’t quite work. At the bottom, everything becomes structured: CAD files, PDM vaults, PLM systems, ERP records, MES data.

Somewhere in between, something critical happens.

Data lives in Excel spreadsheets, is transferred via emails, discussed in forums and web meetings. It moves across tools, formats, and conversations, without ever being fully captured. The transition from all these discussions to a system of record is not a transformation. It is a collapse.

By the time information reaches structured systems, most of the context is gone. Even the simplest structured tool, like a spreadsheet, freezes information in time. It preserves what was decided, not why, not what alternatives were considered, not what was rejected and for what reason. The richness of the process gets compressed into a row of cells.

What remains is clean. Structured. Traceable. But incomplete.

Where Product Lifecycle Knowledge Disappears

If you follow the lifecycle of a product carefully, you can see exactly where knowledge begins to slip away, and how the pattern repeats at every stage.

Before a design is committed to CAD, ideas are explored and alternatives are debated. Engineers sketch on whiteboards, trade messages, pull up supplier datasheets. But almost nothing is formally captured. The early reasoning, often the most important reasoning, because it establishes direction, remains informal and transient. By the time a geometry exists in a CAD file, the backstory is already gone.

During review, comments exist. But decisions are rarely explicit. Someone in a meeting agrees to a change, and it gets made. The conversation that led there lives in someone’s memory, or in a chat thread that will scroll off into irrelevance within a week. The reasoning is scattered across tools, buried in conversations, or implied rather than documented.

During change, the process becomes more structured. Change orders track what changed, sometimes very precisely. Serial numbers, revision levels, effective dates, the mechanics are there. But they rarely capture why it changed. A critical tolerance gets updated after a field failure, and the change order records the new value. It does not record the failure mode, the customer call that surfaced the problem, or the three alternatives that were discussed before this one was chosen.

After release, the structure is frozen into BOMs and systems of record. What remains is a clean, consistent representation of the product, but without the story behind it.

And the process doesn’t stop at release. During maintenance and support, products continue to evolve. BOMs are updated, components are replaced, manufacturing adjustments are made, corrections are introduced. These changes are often critical, sometimes urgent, but again, the reasoning is fragmented across service calls, emails, supplier feedback, and ad-hoc fixes. Over time, what accumulates is not a coherent body of knowledge, but a series of disconnected updates. The product continues to change. The knowledge behind those changes continues to disappear.

The Cost of Not Remembering

At first, nothing seems broken. The data is there. The BOM is correct. The system is working. Audits pass. Products ship.

But the cracks show over time, and they show in specific, recognizable ways.

Teams repeat decisions because they cannot see the reasoning behind previous ones. A material is specified, and three product cycles later, a different team asks whether it should be specified differently, and discovers that nobody can explain why it was chosen in the first place. The discussion happens again from scratch.

New engineers spend months reconstructing context that was never captured. They read BOMs and PDM histories, they look at revision logs, they eventually find someone who was there and ask them directly. Onboarding is slow not because the systems are hard to learn, but because the systems don’t hold the knowledge that actually matters for making good decisions.

Knowledge becomes tribal. It lives in the heads of individuals rather than in systems. The engineer who designed a particular subsystem knows things about it that nobody else knows, not because that information is proprietary, but because it was never recorded anywhere. The knowledge exists, but only in one place, and that place is fragile.

Organizations become dependent on specific people to explain why things are the way they are. These people become bottlenecks. Their expertise is real and valuable, but it isn’t transferable through normal channels, because the channels were never built to carry it.

And when those people leave, which they do, the knowledge leaves with them. The system shows what the product is. It cannot explain why it became that way.

Companies Don’t Lose Data, They Lose the Story Behind It

This is the real problem, and it’s worth saying plainly.

Data is stored. Structure is maintained. Revision control is working. The system of record is doing exactly what it was designed to do. And yet something essential is missing, the reasoning that connects decisions to outcomes, the context that makes data interpretable, the continuity that allows a product to be understood not just as a current state but as a history of deliberate choices.

Without that reasoning, data becomes harder to trust, harder to reuse, and harder to evolve. A BOM is technically accurate but practically opaque. A change order is traceable but not explainable. An assembly drawing shows how parts fit together but not why they were designed that way.

The information is there. The knowledge is not.

The Missing Layer: Product Lifecycle Memory

The problem is not the absence of systems. We already have systems of record, BOM management, PLM, ERP, MES.

These systems are mature, often sophisticated, and they do what they were designed to do: store results, maintain structure, enforce process.

But they were designed for a different purpose than preserving reasoning. A PLM system is not trying to remember why a decision was made. An ERP system is not trying to capture the conversation that led to a supplier choice. These systems weren’t built for that, and it’s not a failure on their part. It’s simply a gap in how we’ve thought about product data.

What’s missing is something fundamentally different from a system of record. Call it a memory layer, a way to connect decisions to outcomes, to preserve context alongside structure, to link discussions, changes, and formal records into a continuous flow of knowledge rather than a series of disconnected snapshots.

Some of what that layer needs to capture is explicit: the reasoning behind a design choice, the trade-offs that were evaluated, the constraints that drove a decision. Some of it is implicit: the timing of a change, the pattern of revisions, the connection between a field problem and a specification update that happened six months later.

None of this is easy. The information is often informal, scattered, and transient by nature. The challenge isn’t storing it, it’s capturing it at all, in a form that remains useful across time, across teams, and across the full arc of a product’s life.

But the cost of not solving this problem is already visible. It shows up every time a team has to reconstruct context that should have been preserved. Every time institutional knowledge walks out the door. Every time a product change creates a mystery instead of a record.

A New Way to Think About Product Knowledge

Building systems that remember decisions, not just store results, may be one of the most important unsolved problems in how engineering and manufacturing organizations actually work. The question is whether we’re ready to treat it as one.

Interested to talk more about the concept of memory layer in product lifecycle?
Contact OpenBOM, we would be happy to talk.

Best, Oleg