Eliminating Knowledge Bottlenecks Using Fuzzy Logic

Abstract

In the formation of new processes, innovations generated by people possessing the right knowledge and talent play a crucial role. Our starting point was the fact that every new change in processes can alter the knowledge structure of a work position or work role. This means that a person can become a knowledge bottleneck in the process. If this person is found on a critical path, the process cannot produce the output in a desired form, extent or quality, unless the bottleneck is removed. For this reason, we developed a decision model founded on fuzzy logic. The result of the fuzzy model is knowledge estimation based on deviation between the required and actual knowledge. For faster decision making, we made a presentation of allocated people on desired roles using the heat map technique. Therefore, the employers make better decisions on actual knowledge allocation, acquiring missing knowledge, or defining knowledge required for the future, which makes them more competitive.