The most expensive sentence in a factory is often: “Ask Mike. He knows how that build really works.”
That sentence sounds harmless, it is usually said by someone trying to keep production moving. But inside it is a serious operational risk. If one person knows the real setup, the real sequence, the common failure mode, the workaround, or the reason a step is done a certain way, then the factory does not own that knowledge. Mike does.
Capturing tribal knowledge means taking the experience-based know-how that lives inside operators, technicians, inspectors, and manufacturing engineers, then converting it into structured production knowledge the organization can reuse. In manufacturing, that knowledge is rarely abstract. It is tied to a part, a fixture, a station, a sequence, a defect pattern, a tool, a supplier variation, or a build condition.
For manufacturers, this is no longer a soft workforce initiative. It now functions as production infrastructure.
The Knowledge That Never Makes It Into the Process
Most manufacturers already have formal documentation: drawings, SOPs, work instructions, inspection plans, routings, nonconformance records, and training material. The problem is that the formal process often lags behind the working process.
The working process includes everything experienced people know because they have lived through the build:
- Which fastener is easy to cross-thread
- Which orientation looks correct but causes fit issues later
- Which inspection point catches the defect before it becomes scrap
- Which tool setting works only when the fixture is slightly worn
- Which supplier lot needs extra attention
- Which sequence prevents rework downstream
- Which step always confuses new operators
That knowledge usually spreads through side conversations, shadowing, handwritten notes, and “watch out for this” warnings. It is valuable precisely because it is specific. It is also fragile because it is rarely captured at the point where it matters.
APQC’s research on the “Great Retirement” notes that organizations face growing risk from the loss of critical institutional knowledge as experienced workers leave, and that many companies still lack consistent strategies to capture and transfer expertise. APQC
Manufacturing feels that problem acutely because process knowledge is physical. It is not just what someone knows. It is what they know while holding a part, watching a machine, inspecting a feature, or seeing a build go wrong.
Why the Old Capture Methods Fail
Most tribal knowledge capture programs start with good intent and weak mechanics.
A company interviews senior workers. Someone creates a training deck. A supervisor asks people to update the SOP. A process engineer adds a few comments to a document. A video gets recorded and stored in a shared folder.
Those efforts can help, but they usually fail for three reasons.
First, they capture knowledge away from the work. A senior technician sitting in a conference room will remember some lessons, but the best knowledge often appears while doing the job. The cue is visual, tactile, sequential, or situational. If the capture method is disconnected from the workstation, much of the context is lost.
Second, they create passive knowledge. A PDF, video, or wiki page may exist, but that does not mean the next operator sees it at the moment of need. Knowledge that sits outside the workflow becomes optional. In production, optional knowledge gets skipped under schedule pressure.
Third, they do not close the loop. If an operator discovers a better sequence or flags a recurring issue, that feedback needs to be reviewed, approved, and incorporated into the production process. If it stays in a comment field, shift note, or hallway conversation, the factory keeps relearning the same lesson.
This is why capturing tribal knowledge cannot be treated as a documentation cleanup project. It has to be part of how production knowledge is created, maintained, and delivered.
The Labor Shift Makes This Urgent
The workforce transition gives this problem a deadline.
Deloitte and The Manufacturing Institute project that U.S. manufacturers may need as many as 3.8 million additional employees between 2024 and 2033, and that 1.9 million of those jobs could go unfilled if workforce challenges are not addressed. The same study found that 65% of surveyed manufacturers named attracting and retaining talent as their primary business challenge. Deloitte
That changes the economics of knowledge transfer. Manufacturers cannot assume every new hire will learn slowly through years of proximity to senior people. They need ways to compress learning without lowering quality standards.
The retirement risk is also concentrated in roles that matter. Manufacturing Momentum cites Bureau of Labor Statistics data showing that 24% of U.S. production workers are age 55 or older, with certain roles much higher, including nearly 45% of tool and die makers. Manufacturing Momentum
When those workers leave, manufacturers lose more than labor capacity. They lose pattern recognition.
Pattern recognition is what lets an experienced operator know that a step is about to go wrong before the defect appears. It is what lets an inspector distinguish noise from a real problem. It is what lets a manufacturing engineer understand why the official build plan and the actual build behavior are drifting apart.
Capturing tribal knowledge is how manufacturers preserve that pattern recognition in a form the next team can use.
What Should Actually Be Captured
A useful tribal knowledge program should focus on production decisions, not general wisdom. The most valuable knowledge usually falls into five categories.
- Build sequence knowledge: why one step must happen before another, which operations create downstream risk, and which sequence changes reduce rework.
- Defect prevention knowledge: early warning signs, recurring failure modes, inspection cues, and conditions that increase scrap risk.
- Tooling and fixture knowledge: setup details, wear patterns, calibration sensitivities, and practical checks that experienced people perform automatically.
- Configuration knowledge: differences between variants, options, revisions, and customer-specific builds that create operator confusion.
- Rationale: the reason behind a step. This is often the most important piece because people follow instructions better when they understand the risk the instruction is controlling.
The goal is not to record every opinion from the floor. The goal is to capture knowledge that changes outcomes: fewer mistakes, faster onboarding, better first-pass quality, faster ramp, and less dependence on a handful of experts.
Cost makes this worth taking seriously. Quality Digest cites expert estimates that cost of poor quality typically amounts to 5% to 30% of gross sales across manufacturing and service companies. Quality Digest ASQ defines cost of poor quality as the cost associated with poor products or services, including internal failures found before delivery and external failures found afterward. ASQ
If a piece of expert knowledge prevents a defect, reduces rework, or helps a new operator avoid a common mistake, it has measurable operational value.
The Better Model: Capture Knowledge Inside the Build
The strongest approach is to capture tribal knowledge inside the production workflow itself.
That means knowledge should be attached to the specific part, step, station, tool, inspection, and revision where it matters. If an operator is installing a component, the relevant tribal knowledge should appear in that context. If a technician flags an issue, the feedback should be tied to the exact build condition. If engineering approves a better method, the update should become part of the controlled production process.
This is where modern manufacturing systems are heading. Deloitte’s 2025 Smart Manufacturing and Operations Survey found that 57% of manufacturers use cloud computing at the facility or network level, 57% use data analytics, 46% use industrial IoT, and 42% use 5G. Deloitte
Manufacturers are digitizing the factory. The next question is whether they are digitizing the knowledge that actually determines how the factory performs.
A strong tribal knowledge system should do four things:
- Capture floor feedback at the point of work
- Tie that knowledge to the product and process context
- Give manufacturing engineers a way to review and standardize it
- Redeploy the approved knowledge into future builds and onboarding
Without that loop, knowledge capture becomes an archive. With that loop, it becomes a production advantage.
Where BuildOS Fits
Dirac’s BuildOS is built around the idea that manufacturing knowledge should live inside the production system, not outside it.
BuildOS uses AI-driven authoring from the 3D model to generate interactive work instructions directly from CAD. Manufacturing engineers can review, refine, and release production-ready build sequences instead of manually assembling screenshots, callouts, and step text. That matters for tribal knowledge because the instruction is no longer just a document. It becomes the structured place where product geometry, process logic, operator guidance, and floor learning come together.
Most knowledge capture tools create a repository. BuildOS creates production context.
That distinction matters. A repository can store what an expert said. BuildOS can help attach that expertise to the build sequence, the visual model, the station, and the revision where it becomes useful. As operators and engineers add knowledge, the factory can preserve it in the flow of work rather than hoping someone finds it later.
BuildOS is also stronger than a typical digital work instruction platform because it starts from the 3D model. Traditional systems often digitize instructions after humans have already translated engineering data into production steps. BuildOS uses AI to reason over geometry, assembly structure, dependencies, and sequence logic, then helps turn that context into interactive work instructions. That gives manufacturers a better foundation for preserving expert process knowledge in a structured, reusable form.
Dirac has a production proof point with Anduril. Anduril selected Dirac as its core partner for AI-driven work instruction authoring across its factories. In initial deployments, Anduril reported an average 87.5% reduction in work instruction authoring time, reducing a process that previously took 12 business hours to 90 minutes. PR Newswire
Capturing tribal knowledge is not about preserving stories from experienced workers after the fact. It is about turning the knowledge that makes builds succeed into a live production asset.
Manufacturers that do this well will onboard faster, reduce repeated mistakes, and keep process knowledge from disappearing every time an expert leaves. Manufacturers that do not will keep depending on the sentence that should make every operations leader nervous:
“Ask Mike. He knows.”
