Title: A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach

Authors: Saeed Rouhani; Ahad Zare Ravasan

Addresses: Faculty of Management, University of Tehran, P.O. Box: 1651849661, Golbarg Complex, East Golbarg Ave, Narmak, Tehran, Iran ' Department of Industrial Management, Allameh Tabataba'i University, P.O. Box: 1651849661, Golbarg Complex, East Golbarg Ave, Narmak, Tehran, Iran

Abstract: As traditional concept in management, decision support had a remarkable role in competitiveness or survival of organisations and following, as modern impression, nowadays business intelligence (BI) has various applications in achieving desirable decision supports. Consequently, assessing BI competencies of enterprise systems can enable decision support in firms. This paper presents a practical framework for assessing the business intelligence capabilities of enterprise systems based on a set of novel factors and utilising fuzzy analytic network process (FANP). Through this, the construct of BI competency is decomposed into three main competency parts including 'managerial', 'technical' and 'system enabler' sub-goals, five main factors and 26 criteria. Using this framework, the BI competency level of enterprise systems can be determined which can help the decision makers to select the enterprise system that best suits organisations' intelligence decision support needs. In order to validate the proposed model, it is applied to a real Iranian international offshore engineering and construction company in the oil industry to select and acquire ERP system. This research provides a complete frame (factors, criteria and procedures) for firms to assess their proposed software and systems in the field of BI competencies and functions.

Keywords: business intelligence capabilities; assessment models; fuzzy ANP; analytical network process; FANP; enterprise systems; enterprise resource planning; ERP selection; fuzzy logic; Iran; offshore engineering; offshore construction; oil industry.

DOI: 10.1504/IJADS.2015.066559

International Journal of Applied Decision Sciences, 2015 Vol.8 No.1, pp.52 - 82

Received: 18 Dec 2013
Accepted: 19 Jul 2014

Published online: 24 Dec 2014 *

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