| In information systems development, there have been consistent efforts to reduce human involvement which causes time, cost, and error in many cases. However, this is difficult and often impossible to do. With the increasing importance of collaboration, we proposed the concept of collaboration intelligence to measure the level of capacity required to achieve a given task, while collaborating with each other. We defined four kinds of intelligence to represent the cooperative impact of human and machine elements in your system. These four kinds of intelligence are human, machine, interface, and system. To that end, we developed a mathematical model and analyzed the characteristics of collaboration intelligence. We also performed a simulation analysis for various situations. In the above noted mathematical model and comparison, we addressed design guidelines for enterprise information systems.; After we studied collaboration intelligence both by mathematical and empirical methods we increased the descriptive power of our model by focusing on the quality of information. It can be addressed in semantic interoperability and Bayesian network. After we identified critical features, we provided an ontology model for interfacing car stylist and engineers. This model can be readily applicable to solve real world problems. Then we applied Bayesian network to our ontology model, taking advantage of conditional independence. We expanded our model by applying it to GPDS feasibility assessment items at Ford Motor Company. By applying the ontology model and other Bayesian network approaches, we can expect many benefits, including better communication between engineers and designers and the opportunity for earlier design freezes. |
