Focus Technology - Acceleration of Adaptivity, Improvement of Products

A Method for Improving the Adaptivity of Technology Development in Science and Engineering is the title of the second so-called Presidential Project of the Fraunhofer Society. Within this project research groups of altogether eight FhG-institutes are dealing with the subject Management and Acceleration of Innovation Processes. Main topics herein are:

• dentification of methods for the early detection of emerging innovative technologies or trends.
• Combination of different methods for the management of innovation processes.
• Development of an integrated method for the representation and acceleration of innovation processes and its realisation as a software prototype.
• Evaluation of the approach using scenarios from polymer-, bio-, and communications-engineering.

The evolution of technologies can be described approximately using qualitative models like for example the Hype-Cycle model developed at the consulting company Gartner.

The vague qualitative terms (maturity, visibility) in this model can be made concrete through analysis of the development of the values of specific indicators. These could for example be the number of patent declarations or scientific publications in fields relevant for the technology under consideration. Having fixed a set of indicators, that altogether yield sufficiently powerful descriptions, the status of an emerging technology can be defined abstractly as the pattern visible in the developing of the indicator's values until the current date.

Utilizing methods from time series analysis and pattern recognition the developing of the indicators' values of two different technologies A and B may then be compared. In the project, Fuzzy Logic is used to combine the results of the comparisons performed for each of the indicators.

The subsequent graphic depicts the result of a comparison of an observed evolution of the number of patents created in a certain technology (red curve) with the evolution observed in the field of Robotics (blue curve). According to the two positions at which the red curve is shown, two time intervals have been identified in which the evolution represented by the red curve is similar to the evolution that took place in Robotics. Here similarity is measured using a mathematical approach.

In what way does such a comparison of technologies help in determining the status of an emerging technology? Here two approaches are to be mentioned:

• The comparison of technology A with a fully developed technology B serving as a reference yields hints, which phase the technology A has currently reached in relation to B. Assuming comparable evolutions of the two technologies, one may use the result of the comparison to give a prognosis for the further evolution of A.
• The reference B can also be chosen to be an artificial standard evolution, created from observational data by mathematical means. Herein, analytic models reproducing for example the shape of Gartner's Hype-Cycle may be applied.

The data needed to perform the approaches just described are taken from different resources, in particular from pertinent data sources accessible via the internet. The necessary search algorithms are provided by the Fraunhofer IITB, while the Fraunhofer institutes IGB, IAP and HHI provide a suitable set of indicators. Over the course of an innovation process an enormous number of methods are applied, working on various innovation levels (markets, products, functionalities, technologies, competences) and at various innovation phases (discovery, euphoria, disillusion, reorientation, ascendancy, diffusion). These so-called local methods access miscellaneous information sources (e.g. general conditions, perspectives of technologies, and of course results from other local methods), generate knowledge, and in this way provide the basis for decisions that are needed by subsequent local methods.

These methods differ in the way of their execution (discussion, interview, questionnaire, workshop, check list, flowchart, software, …), in their type of application (economical, technical, mathematical, …), or in their degree of automation (manual, interactive, automatic). The method applied for a given situation depends on the innovation object itself, on the course of the current project, on the history of similar processes, on the know-how of the decision maker etc.

 In this way an innovation process evolves as a concatenation of local methods. But this chain is not determined exactly in advance, rather it depends on dynamical framework conditions (e.g. new promotion programs), on updated technology perspectives (e.g. new discoveries), and on current criteria of evaluations (e.g. spirit of the age).

Global methods, like time series analysis and pattern recognition mentioned above, assist in positioning the current process status (finding of location), in forecasting of possible future developments (scenarios), and by that, in supporting the navigation along the path of innovation.

All methods, local and global ones, require and generate multi criteria data. By that, graphical tools are inevitable for mapping the management of such complex tasks, and for presenting to the user various navigation possibilities through the complete innovation process.

The Method for Improving the Adaptivity of Technology Development in Science and Engineering consequently serves as a tool for the identification, adaptation, and navigation of globally controlled chains of local methods, to optimize the management of complex innovation processes and to accelerate their progress of development