Information management
AN EVALUATION MODEL OF BUSINESS INTELLIGENCE FOR ENTERPRISE SYSTEMS WITH NEW EXTENSION OF CODAS (CODAS-IVIF)
Name and surname of author:
Jalil Heidary Dahooei, Edmundas Kazimieras Zavadskas, Amir Salar Vanaki, Hamid Reza Firoozfar, Mehdi Keshavarz-Ghorabaee
Keywords:
Business Intelligence, CODAS, Enterprise System, CODAS- IVIF, MADM
DOI (& full text):
Anotation:
Due to today’s dynamic and changing environment and the organization need to decide in emergencies and accurate analysis of the internal and external environment from different aspects, creating a decision support environment is considered as a vital factor for the success of organizations that is achieved using business intelligence. Hence, it is necessary to have enterprise systems at a reasonable level of business intelligence to provide an environment suitable for supporting decision makers through aggregation and analysis of data in their database. Therefore, this study provides a novel assessment framework of BI for enterprise systems, by extending of CODAS method with interval-valued intuitive fuzzy sets. The CODAS is a new method for multiple attribute decision making (MADM) problems. In the proposed model, a number of 34 criteria from the most important BI indexes are identified and, accordingly, five enterprise systems are evaluated through expert discussions. The results reveal that the most important assessment criteria defined by expert panels include visual graph display, dashboard design, capable of data storage, meeting stakeholder needs, and the possibility for detailed realistic analysis. Then, one alternative is defined as the final selection which provides an outstanding performance on the criteria of groupware programs, group decision-making tools, training techniques, data transfer capability, knowledge inference, supporting fuzzy concepts under ambiguity and uncertainty, realtime analytical processing, managing email channels, and achieving stakeholder satisfaction. The results obtained from the extended method are compared with three different ranking techniques. And, the analysis of correlation coefficients confirms similarity between this solution and such methods as COPRAS-IVIF and MABAC-IVIF.
Due to today’s dynamic and changing environment and the organization need to decide in emergencies and accurate analysis of the internal and external environment from different aspects, creating a decision support environment is considered as a vital factor for the success of organizations that is achieved using business intelligence. Hence, it is necessary to have enterprise systems at a reasonable level of business intelligence to provide an environment suitable for supporting decision makers through aggregation and analysis of data in their database. Therefore, this study provides a novel assessment framework of BI for enterprise systems, by extending of CODAS method with interval-valued intuitive fuzzy sets. The CODAS is a new method for multiple attribute decision making (MADM) problems. In the proposed model, a number of 34 criteria from the most important BI indexes are identified and, accordingly, five enterprise systems are evaluated through expert discussions. The results reveal that the most important assessment criteria defined by expert panels include visual graph display, dashboard design, capable of data storage, meeting stakeholder needs, and the possibility for detailed realistic analysis. Then, one alternative is defined as the final selection which provides an outstanding performance on the criteria of groupware programs, group decision-making tools, training techniques, data transfer capability, knowledge inference, supporting fuzzy concepts under ambiguity and uncertainty, realtime analytical processing, managing email channels, and achieving stakeholder satisfaction. The results obtained from the extended method are compared with three different ranking techniques. And, the analysis of correlation coefficients confirms similarity between this solution and such methods as COPRAS-IVIF and MABAC-IVIF.
Section:
Information management