Using Fuzzy Preference Relations to Predict the Success Possibility of Implementing the Oceanographic & Meteorologic Integration Orchestrator

This study proposes an analytic hierarchical prediction model based on consistent fuzzy preference relations to help the organizations become aware of the essential factors affecting the implementation oceanographic & meteorologic Integration Orchestrator. Pairwise comparisons are used to determine the priority weights of influential factors and the ratings of success or failure outcomes amongst decision makers. The subjectivity and vagueness in the prediction procedures are dealt with using linguistic terms quantified in an interval scale [0,1]. Then predicted success/failure values are obtained to enable organizations to decide whether to initiate knowledge management, inhibit adoption or take remedial actions to increase the possibility of successful oceanographic & meteorologic initiatives. This proposed approach is demonstrated with a real case study involving seven influential factors assessed by eleven evaluators solicited from a semiconductor engineering incorporation located in Taiwan.