Moving Beyond Tribal Knowledge in Component Selection for Engineering Designs

In technology and manufacturing, tribal knowledge—informal, unwritten information passed down within a team—often influences decision-making. While valuable, it can hinder component selection in engineering designs. Over-reliance on this knowledge can lead to inefficiencies, missed opportunities, and costly mistakes for both sales teams and engineers.

By embracing new tools and methods, component sales teams and OEM engineers can ensure they make data-driven decisions that lead to better outcomes.

The Blessing and Curse of Tribal Knowledge

Tribal knowledge is often seen as a double-edged sword in engineering and sales. On one hand, it provides valuable insights accumulated through years of experience. It can guide engineers in making quick decisions based on what has worked in the past and help sales teams understand what their customers typically look for in a component.

However, the downside of tribal knowledge is that it is inherently limited. It is based on past experiences and may not account for new developments, technologies, or materials that could be better suited for the task at hand. Moreover, tribal knowledge is often confined to a few key individuals within an organization. If these individuals leave or retire, their knowledge goes with them, leaving a gap that can be difficult to fill.

The Challenges for Engineers

For OEM engineers, selecting the right components for a new design is critical. The chosen components will determine the final product's performance, reliability, and cost-effectiveness. When decisions are based solely on tribal knowledge, engineers may overlook newer, more advanced components that could offer significant advantages. Additionally, tribal knowledge can lead to a “we’ve always done it this way” mentality, which stifles innovation and prevents exploring alternative solutions.

Another challenge is that tribal knowledge is often undocumented. Engineers may have to rely on verbal instructions or colleague notes, which can lead to misunderstandings or incomplete information. This lack of documentation can also make it difficult for new team members to get up to speed, leading to delays in the design process.

The Challenges for Sales Teams

For sales teams at component manufacturers and distributors, the reliance on tribal knowledge can also be problematic. Sales representatives who rely solely on their personal experience or the shared knowledge of their team may take advantage of opportunities to offer newer, more innovative products to their customers. They may also struggle to communicate the benefits of these newer components if they lack a deep understanding of their capabilities.

Furthermore, tribal knowledge can create inconsistencies in the sales process. Sales representatives may have different interpretations of what customers want or need, leading to a need for more cohesion in the sales strategy. This inconsistency can confuse customers and reduce their confidence in the manufacturer or distributor.

Leveraging Emerging Technologies to Overcome Tribal Knowledge

The good news is that emerging technologies are making it easier for engineers and sales teams to move beyond the limitations of tribal knowledge. By embracing these tools, organizations can make more informed decisions, streamline their processes, and achieve better results.

1. AI-Powered Component Selection

Artificial intelligence (AI) is revolutionizing how engineers select components for new designs. AI-powered tools can analyze vast amounts of data, including historical performance data, material properties, and industry trends, to recommend the most suitable components for a specific application. These tools can also identify potential issues that may not be apparent based on tribal knowledge, such as compatibility problems or supply chain risks.

For example, AI can help engineers quickly compare components based on various criteria, such as cost, performance, and availability. This data-driven approach ensures that engineers make decisions based on the most up-to-date information rather than relying on past experiences that may no longer be relevant.

2. Digital Twins and Simulation

Digital twins and simulation technologies allow engineers to create virtual models of their designs and test different components in a simulated environment. This approach provides a more accurate understanding of how a component will perform in the real world without physical prototypes.

Engineers can use digital twins to explore a broader range of component options and optimize their designs for performance, cost, and reliability. This reduces the risk of relying on tribal knowledge, which may not account for all the variables involved in a complex engineering project.

3. Knowledge Sharing Platforms

To combat the fragmentation of tribal knowledge, organizations can implement knowledge-sharing platforms that allow team members to document and share their insights in a centralized location. These platforms can be integrated with AI tools to ensure up-to-date and relevant information.

By making knowledge accessible to everyone in the organization, these platforms ensure that critical information is not lost when key individuals leave or retire. They also promote collaboration and continuous learning, as team members can learn from each other’s experiences and contribute to a shared knowledge base.

Looking Ahead

Tribal knowledge will always be in the engineering and sales worlds, but it should not be the sole basis for decision-making. By leveraging emerging technologies such as AI, digital twins, and knowledge-sharing platforms, engineers and sales teams can move beyond tribal knowledge's limitations and make more informed, effective decisions.