How to Turn Tribal Knowledge into Institutional Knowledge in Manufacturing

In the context of manufacturing, tribal knowledge is an understanding of information and processes that is not recorded in any formal or shared resource. It is only known to certain employees in an organization.

Tribal knowledge in manufacturing usually consists of valuable information on products, processes, and customers that were acquired from decades of hands-on experience. This information is unwritten: it resides only in the minds of certain workers. That means if this employee leaves or retires, that knowledge is gone forever.

Workers who have been at the same organization for 10, 20, 30 years have definitely accumulated tribal knowledge.

For example, a hydraulic assembly press started overheating in a factory fifteen years ago. At the time, a team of engineers figured out, through trial and error, that bringing the oil temperature below 120 degrees Fahrenheit could solve the problem. However, since the informal problem-solving procedure was not recorded, these engineers remain the only people in the company to know how to solve this specific problem.

Our post on the silver tsunami in manufacturing also details another example of tribal knowledge, related to an engineer who worked with the same packaging machines for 20 years and calculated the settings from the algorithm in his head every time something needed an update.

1. Aging workforce

The silver tsunami in manufacturing is coming: in the next few years, more than a quarter of workers will retire.

Research shows that 27% of manufacturing workers are over the age of 55. What this means is that a lot of factory workers will be retiring in the next few years. If these workers’ tribal knowledge is not captured, it will disappear with them.

2. Gaps in training

The aging manufacturing workforce, combined with younger generations’ lack of interest in the industry, are expected to lead to 2.4 millions of unfilled manufacturing jobs by 2028.

Training programs will be crucial to ensure that every new hire performs to their full potential. If some information on products and processes is not recorded, it will be impossible to include it in training programs — which will lead to gaps in training.

3. Unfair to other employees

In order to compare operator performance in a fair way, it is essential that all operators have access to the same information. Data will not represent operators accurately if senior employees keep their valuable know-how to themselves.

Institutional knowledge is straightforward: it is information that is known by everyone (or almost everyone) in an institution. It is recorded in shared resources or embodied in company culture.

Every single employee in a manufacturing company might not know how to handle a particular machine. Machine operation is considered institutional knowledge if all information on handling machines is recorded, and everyone knows where to access this information.

When it comes to institutionalizing tribal knowledge, there is a hard way, and an easy way.

The hard way consists of going through all of your workers and identifying the “tribal knowledge gurus”, namely the workers who have been around for years and have extensive experience with their tasks.

Once the gurus are identified, you should try to extract their tribal knowledge from them through discussion and observation. (Of course, write down every single thing they say!)

Let’s look at the easy way now.

The easy way relies on smart tools. By connecting your machines and devices to the Internet, you can start automatically collecting data on operator and machine performance.

Analytics software can then analyze this data and reveal production insights. If an operator is performing certain tasks much faster than other operators, there is certainly some tribal knowledge to be uncovered there. The same can be said if the cycle time for a process is much shorter when a certain process engineer is monitoring it.

Connected devices and manufacturing apps can monitor step time by operator. Operators with significantly lower step times probably hold tribal knowledge.

The two examples of tribal knowledge given earlier could also be solved easily with the right manufacturing software. For example, if the hydraulic assembly press was equipped with IoT connectivity and sensors, information on oil temperature would be recorded constantly and automatically.

IoT-connected sensors and operations apps give visibility into how ambient conditions impact quality and operations.

Once the tribal knowledge has been identified and recorded, solutions can be designed quickly and easily with operations apps. For instance, tribal knowledge can be added to digital work instructions and training modules with a few clicks (no need to edit and print hundreds of pages of paper-based instructions).

When manufacturers start collecting real-time data on their operations, they gain production visibility and ensure that from that point on, all knowledge will be institutional knowledge.

Tulip, the frontline operations app platform, helps organizations uncover opportunities to increase efficiency, turn tribal knowledge into institutional knowledge, and shed light on the invisible factory. Read our case study on Formlabs to see how companies are concretely capturing key data in their operations.