Selection of Business Intelligence System Software as Decision Support: A Case Study

Year 2023, Volume: 03 Issue: 01, 19 - 28, 31.07.2023

Emir Hüseyin Özder Erman Erkan

Abstract

Abstract
This study focuses on Business Intelligence (BI) systems and investigates their benefits in the matter of better decision making for corporations. BI systems are computer-based decision support tools that analyse business data and generate meaningful information in order to incorporate decision making. BI systems work in parallel with existing information systems of the companies. As a matter of fact, BI systems are the next steps to be established on existing operational information systems. In this context, the purpose of this study is to examine the yield of BI systems for the companies tended to implement decision support systems aligned with their operational information systems. To investigate this phenomenon, an empirical study will be conducted among some companies in production sector in Turkey. The companies’ actual implementations will be highlighted by the help of the questionnaire to be applied to those companies. Moreover, in the case study, BI system alternatives will be compared as per specifically defined criteria. In this manner, Multi criteria decision making approach will be used and Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP) methods will be applied for the selection of the best alternative. Lastly, expected benefits and outcomes of proposed BI system will be estimated by taking into consideration of the facts and findings of the study.

Keywords

AHP, Fuzzy AHP, Decision Support Systems, Business Intelligence

Karar Desteği Olarak İş Zekası Sistem Yazılımının Seçimi: Bir Vaka Çalışması

Abstract

Bu çalışma, İş Zekası (İZ) sistemlerine odaklanmakta ve şirketler için daha iyi karar verme konusunda faydalarını araştırmaktadır. İZ sistemleri, iş verilerini analiz eden ve karar vermeyi dahil etmek için anlamlı bilgiler üreten bilgisayar tabanlı karar destek araçlarıdır. İZ sistemleri, şirketlerin mevcut bilgi sistemleri ile paralel çalışır. Nitekim İZ sistemleri, mevcut operasyonel bilgi sistemleri üzerine kurulacak sonraki adımlardır. Bu bağlamda bu çalışmanın amacı, operasyonel bilgi sistemleri ile uyumlu karar destek sistemlerini uygulama eğiliminde olan şirketler için İZ sistemlerinin getirisini incelemektir. Bu olguyu araştırmak için Türkiye'de üretim sektöründe faaliyet gösteren bazı şirketler arasında ampirik bir çalışma yapılmıştır. Firmalara uygulanacak çalışma ile firmaların fiili uygulamaları ortaya konulacaktır. Ayrıca vaka çalışmasında İZ sistem alternatifleri, özel olarak tanımlanmış kriterlere göre karşılaştırılacaktır. Bu doğrultuda Çok kriterli karar verme yaklaşımı kullanılacak ve en iyi alternatifin seçimi için Analitik Hiyerarşi Prosesi (AHP) ve Bulanık AHP (BAHP) yöntemleri uygulanacaktır. Son olarak, önerilen İZ sisteminin beklenen faydaları ve sonuçları, çalışmanın gerçekleri ve bulguları dikkate alınarak tahmin edilecektir.

Keywords

İş Zekası, AHP, Bulanık AHP, Karar Destek Sistemleri

References

• [1] O’Brien,J. and Marakas,G., Management Information Systems, (2009). p.384.
• [2] Simpchi-Levi,D., Kaminsky, P. And Simpchy-Levi, E.: Designing and Managing the Supply Chain, (2008).p.421.
• [3] Chopra, S., Meindl, P., Supply Chain Management, (2010). p.470-471.
• [4] O’Brien,J. and Marakas,G., Management Information Systems, (2009). p.383.
• [5] Ohson, D., Managerial Issues of Enterprise Resource Planning Systems, (2010). p.144.
• [6] SAS Institute., How to Select a Business Intelligence Vendor. p.2.
• [7] Riabacke, A., Larsson, A. and Danielson, M.: Business Intelligence as Decision Support in Business Processes: An Emprical Investigation. (2011). p. 384-386.
• [8] Zhang, L., Comparison Of Classical Analytic Hierarchy Process (AHP) Approach and Fuzzy AHP Approach In Multiple-Criteria Decision Making For Commercial Vehicle Information Systems And Networks (Cvisn) Project, (2010). p.5.
• [9] Ozdagoglu, A. and Ozdagoglu, G. Comparison Of Ahp And Fuzzy Ahp For The Multicriteria Decision Making Processes With Linguistic Evaluations. (2007). p. 65-75.
• [10] Ballı, S. and Korukoğlu, S.: Operating System Selection Using Fuzzy Ahp And Topsis Methods. Mathematical and Computational Applications, Vol. 14, No. 2. (2009). p. 119-130.
• [11] Cebeci, U.: Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Systems with Applications, No. 36 (2009). p. 8900-8909.
• [12] Kong, F. and LIU, H.: Applying Fuzzy Analytic Hierarchy Process To Evaluate Success Factors Of E-Commerce. International Journal of Information and Systems Sciences, Vol 1.(2005). p. 406-412.
• [13] Riabacke, A., Larsson, A. and Danielson, M.: Business Intelligence as Decision Support in Business Processes: An Emprical Investigation. (2011). p. 384-391.
• [14] Rasoul, D. G., and Mohammad, H. (2016). A model of measuring the direct and impact of business intelligence on organizational agility with partial Mediatory role of Empowerment: Tehran construction Engineering Organization (TCEO) and EKTA organization industries.co. Social and Behavioral Sciences, Vol. 230, pp. 413-421.
• [15] Solberg Søilen, K. (2015). A place for Intelligence studies as a Scientific Discipline, Halmstad, Sweden. Journal of Intelligence Studies in Business, Vol. 5(3), pp. 35-46.
• [16] Singh, H., & Samalia, H. (2014). A Business Intelligence Perspective for Churn Management. Procedia – Social and Behavioral Sciences, 109, 51-56.
• [17] Nofal, M., & Yusof, Z. (2013). Integration of Business Intelligence and Enterprise Resource Planning within Organizations. Technology, Vol. 11, pp. 658-665.
• [18] Den Hamer, P. (2005). The organization of Business Intelligence. The Hague: SDU Publishers.
• [19] Hannula, M. and Pirttimäki, V. (2003) ‘Business intelligence: empirical study on the top 50 Finnish companies’, Journal of American Academy of Business, Vol. 2, No. 2, pp.593–599.
• [20] Cheng, C. H., Yang, K. L., and Hwang, C. L., (1999), “Evaluating Attack Helicopters by AHP Based on Linguistic Variable Weight”, European Journal of Operational Research, 116, 423-435.
• [21] Taha, H.A. (2003) Operations Research. Pearson Education, Inc., Fayetteville.
• [22] Sarkis, J., and Talluri, S., (2004), “Evaluating and Selecting e-Commerce Software and Communication Systems for a Supply Chain”, European Journal of Operational Research, 159, 318-329.
• [23] Kulak, O., and Kahraman, C., (2005), “Fuzzy Multi-Criterion Selection Among Transportation Companies Using Axiomatic Design and Analytic Hierarchy Process”, Information Sciences, 170, 191-210.
• [24] Leung, L. C., and Chao, D., (2000), “On Consistency and Ranking of Alternatives in Fuzzy AHP”, European Journal of Operational Research, 124, 102-113.
• [25] Bouyssou, D., Marchant, T., Pirlot, M., Perny, P., Tsoukias, A., and Vincke, P., (2000), Evaluation Models: A Critical Perspective, Kluwer, Boston.
• [26] Boender, C. G. E., De Graan, J. G., and Lootsma, F. A., (1989), “Multicriteria Decision Analysis with Fuzzy Pairwise Comparisons”, Fuzzy Sets and Systems, 29, 133-143.
• [27] Buckley, J. J., (1985/a), “Ranking Alternatives Using Fuzzy Members”, Fuzzy Sets and Systems, 15, 21-31.
• [28] Buckley, J. J., (1985/b), “Fuzzy Hierarchical Analysis”, Fuzzy Sets and Systems, 17, 233-247.
• [29] Chang, D. Y., (1996), “Applications of The Extent Analysis Method on Fuzzy- AHP”, European Journal of Operational Research, 95, 649-655.
• [30] Laarhoven, P. J. M., and Pedrycz, W., (1983), “A Fuzzy Extension of Saaty’s Priority Theory”, Fuzzy Sets and Systems, 11, 229-241.
• [31] Lootsma, F., (1997), Fuzzy Logic for Planning and Decision-Making, Kluwer, Dordrecht.
• [32] Ribeiro, R. A., (1996), “Fuzzy Multiple Criterion Decision Making: A Review and New Preference Elicitation Techniques”, Fuzzy Sets and Systems, 78, 155-181.
• [33] Chang, D. Y., (1992), “Extent Analysis and Synthetic Decision”, Optimization Techniques and Applications, World Scientific, Singapore, 1, 352.
• [34] Kahraman, C., Cebeci, U., and Da, R., (2004), “Multi-Criterion Comparison of Catering Service Companies Using Fuzzy AHP: The Case of Turkey”, International Journal of Production Economics, 87, 171-184.