Manufacturing has always depended on experience. The operator who knows when a machine “sounds wrong.” The technician who understands which adjustment prevents a quality issue. The supervisor who knows which sequence of steps keeps production moving.
The problem
Much of this knowledge is rarely captured in a structured, reusable way.
When experienced employees retire, change roles, or leave the organization, manufacturers do not simply lose headcount. They can lose years of accumulated knowledge about equipment, processes, quality, troubleshooting, and the small decisions that keep production running smoothly.
Research from LNS Research highlights the scale of the issue: 84% of manufacturers report that the loss of experienced personnel has negatively affected operational performance, while 44% are still identifying best practices or piloting a knowledge library.
This makes knowledge management an operational resilience issue.
The hidden cost of undocumented knowledge
When operational knowledge lives primarily in the heads of a few experienced people, the consequences appear across everyday manufacturing:
- new employees take longer to become productive,
- different shifts perform the same task differently,
- recurring mistakes are harder to eliminate,
- supervisors spend time answering the same questions,
- quality can depend too heavily on who happens to be working that day.
LNS Research defines Industrial Knowledge Management as the processes and technologies used to generate, capture, evaluate, organize, transfer, and reuse information and knowledge across the organization.
The goal is to make critical operational knowledge repeatable, transferable, and available at the point of work.
Why training alone is not enough
Traditional training often follows a familiar model:
employee attends training → learns the process → returns to the shop floor.
But production is rarely that simple.
An operator may need to know:
- which sequence to follow,
- which tool to use,
- which tolerance applies,
- what changes for a specific product,
- what to do when a deviation occurs.
That is where digital process control changes the model.
Epicor’s guidance on digital process control describes practices such as granular work instructions, skill-based guidance, digital operator support, videos and GIFs, IoT-enabled checks, and in-system time tracking.
Instead of asking employees to remember every detail, manufacturers can embed knowledge directly into execution; hence, the operator sees the right instruction at the right moment.
Epicor also explains how Connected Process Control can help bridge the gap between knowledge and execution by bringing process guidance and best practices into shop-floor workflows.
Where ERP, MES, and digital workflows meet
This is where knowledge management becomes an operational system rather than a document repository.
| ERP provides the business context | MES captures production reality |
| Broader operational framework – customer orders, – BOMs and routings, – materials and inventory, – jobs and work orders, – planning, – costing. |
The reality of the shop floor – actual start and completion times, – downtime, – quality checks, – process data, – reject and scrap codes, – machine or IoT data. |
| What needs to be produced, with which resources, and under which constraints? | Epicor’s digital process control guidance specifically highlights capturing process data, quality checkpoints, traceability, genealogy, and digital records that can support root-cause analysis. |
Digital workflows deliver the knowledge
Digital workflows connect that context with human execution. For the specific job, process, or operator, they can provide:
- the appropriate SOP,
- visual instructions,
- the correct sequence,
- required tolerances,
- quality checkpoints,
- deviation and repair guidance,
- skill-appropriate instructions.
Knowledge therefore moves from being something an employee must remember to something the production system can deliver in context.
The flow is also bottom-up
The integration does not stop with instructions moving from system to operator.
Execution creates new knowledge. When production data, deviations, rejects, quality results, and execution times are captured digitally, manufacturers gain a clearer picture of how the process actually performs.
They can begin to identify:
- recurring quality issues,
- skill gaps,
- delayed tasks,
- bottlenecks,
- processes that require improvement.
Digital records can then support root-cause analysis and continuous improvement.
From a connected workforce to an augmented workforce
Once this foundation exists, manufacturers can extend the model further.
LNS Research notes that Connected Frontline Workforce applications can support skills matrices, assessments, micro and in-context training, SOPs, work instructions, collaboration, and even 3D or AR/VR guidance.
The practical meaning of the augmented workforce is to give operators better access to knowledge, context, and guidance when they need it.
AI can eventually strengthen this model further by helping surface relevant knowledge, detect anomalies, or make complex information easier to access. LNS specifically identifies technologies such as NLP, vision systems, anomaly detection, and generative AI as potential tools for capturing and cataloguing frontline knowledge.
Yet, before AI can enhance operational knowledge, manufacturers need a structured foundation of:
documented processes → connected execution data → reliable ERP/MES context.
What changes operationally?
| Area | Before systematization | With connected digital workflows |
| Onboarding | Heavy dependence on job shadowing | In-context digital guidance |
| Execution | Different methods across shifts | Standardized SOPs and checkpoints |
| Error handling | Depends on individual experience | Documented deviation and repair guidance |
| Knowledge retention | Knowledge may leave with the employee | Knowledge remains available to the organization |
| Improvement | Recurring issues difficult to analyze | Digital records support root-cause analysis |
From individual experience to operational capital
Manufacturers should try to preserve experience, structure it, and make it reusable. That means turning:
- experience → into a process
- process → into digital guidance
- guidance → into consistent execution
- execution → into data for continuous improvement
What’s the point?
The question is no longer only: “How do we train the next employee?”
It is: “How do we make sure critical knowledge remains inside the production system, even when people change?”
That is the shift from tribal knowledge to operational capital.
