Business Intelligence System Selection with Hesitant Fuzzy Linguistic MCDM Methods

Abstract

Increasing amount of data and the need for analysis of this data have made the concept of Business Intelligence System (BIS) more important to plan the future of businesses. BIS is a set of technologies, processes, methodologies, and architectures that enable the processing of large amounts of data and their transformation into high-quality information. Companies implement BIS for monitoring business processes, receiving reports on systems operation, distributing the right information in the right way at the right time and analyzing business indicators. BIS has a mixed structure with many different and conflicting criteria. Nevertheless, it is difficult to assess and decide on alternatives if information is not clear. In this study, the hesitant fuzzy linguistic term set (HFLTS) methodology overcomes the uncertainty related difficulties of this multi-criteria decision-making (MCDM) problem. This methodology facilitates decision-making processes of experts in hesitate situations. The integrated hesitant fuzzy linguistic (HFL) MCDM methodology is presented to determine the most appropriate BIS. HFL Analytic Hierarchy Process (AHP) method is implemented to find the criteria weights. Then, the most important BIS alternative is determined with HFL Complex Proportional Assessment (COPRAS) method. Lastly, an application is given to demonstrate the potential of this methodology.