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Technology Brokering in Silicon Valley

01/06/2016 01:39 am ET | Updated Jan 06, 2016

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How do engineers contribute to concepts through integration of new technologies? How do they turn inventions into innovative applications supporting new or existing user needs?

Interviewing five engineers at an automotive company's technology search organization, we learned how they search, evaluate, validate and implement technologies into new innovative concepts. Special characteristics are critical in the areas of access, learning, linking, implementation and organizational structure, for realizing successful innovation.

The 'technology search' company is located in the Silicon Valley in the midst of the high-tech world. This brings the organization within reach of the latest technological developments. By sponsoring projects and working together with Stanford and Berkeley and through short development projects (three to six months) with outside companies, the office has been able to create contacts outside of its traditional automotive field.

The company has left the development of technology to its high-tech partners and focused on linking these with user needs as defined by the company's headquarters, thus, creating new and unique applications.

What they effectively do is transferring of innovation, focusing on less traditional aspects for the company and on knowledge transfer rather than in- house development.

By having short development projects of three to six months, the company focuses on accelerated learning. They do this by utilizing and working closely with experts in various areas to learn the tacit knowledge. They then benchmark the various technologies and use a "prototype often" approach.

Most of this prototyping is farmed out, thus focusing their internal resources on analysis and management. The company cycles through technology search, design, prototyping and testing.

The engineers are mostly mechanical engineers with a wide variety of backgrounds. Conducting brainstorming sessions aids them in facilitating cross-functional learning and creation of unique concepts.

The company is successful in bringing itself up to speed in new areas. However, it seems to rely on a mechanical engineering problem-solving approach when it comes to reformulating the problem and seeing it from various angles.

The difficulty with problem definition is a function of presumed problem solution possibilities one is left with. One can never define a new problem if one starts off in the traditional way. What makes up for this is the collaboration with an industrial design company. By a joint kick-off of projects, the likelihood of multiple perspectives on problems is greatly increased.

The company has an immense flexibility and freedom to choose, prioritize and run projects. However a relatively large portion of the engineer's workday consists of communicating with HQ and their vendors, which may detract from the creation of innovation.

Unusual problems and their solutions result from analogical thinking. This highlights the non- obvious solutions between two things, which seem to be dissimilar. Thus, removing the problem and solution from the familiar context and placing it in another allows unexpected links.

One needs internal communication for this to occur and problem-solving activities focus on intense activity between individuals. Analog thinking can take place during informal conversations in the hallway, by tapping into one's personal network and during brainstorming sessions. The team utilizes all three approaches, which may increase the likelihood of analogical thinking.

According to research in transfer of innovation, in searching for technological solutions the creation of databases traditionally fails because they choke the process of analogical thinking. The contradiction is that if one knows what one is looking for there is little chance of innovation since one is unlikely to find something significantly different.

Brokering entails finding non-obvious connections; a search needs to take place in ways, which encourage unexpected analogical connections to happen. Seemingly random search paths maintain the potential for creating unexpected connections.

What finally turns innovative ideas into real products has to do with turning the tacit knowledge about an idea into an explicit artifact, which can be shared with the rest of the organization. The engineers do a first class job of "learning by doing" and turning ideas into prototypes or simulations.

Another important aspect is to make sure the ideas are accepted. The team ensures this by getting a commitment up front and continually updating the team at headquarters on their progress.

Within the company, decisions are primarily based on gut feelings and the engineer's personal interests. Periodically a matrix approach, weighting the various criteria and assigning values to each technology, is used.

In theory, using a weighted approach is the most attractive, although it does not account for a cross category synergy/influence. The challenge is also that it is notoriously difficult to determine the individual weights and values. However, increasing the number of judges with various competencies could improve upon this approach, thus allowing a variety of perspectives.

Decision-making based on gut feelings has the advantage of facilitating action. However, since the feedback loop is weak, the gut feeling might have little to do with reality.

The Stanford approach, "prototype early and often," which was adopted by the teams, does a good job of tuning the technology and its implementation. However, it does not solve the issue of the weak feedback loop, related to user factors. The rapid turnaround in projects at the company may resolve some of the weak "user feedback" issues, if feedback is sufficiently frequent.

The consistent but loose process is a strong approach to technology search and data management and is very similar to the one used by industrial design companies.

Checking assumptions and using "prototype often" along with reviewing technical and interface aspects leaves the process continually validated. The main opportunities for improvement seem to lie in exploring how problems are being framed and evaluated. Here, personal bias may tend to subtly color the decision-making process. It is difficult to escape who you are.

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