Investment Cost Model in Business Process Intelligence in Banking And Electricity Company

Authors

  • Arta Moro Sundjaja Bina Nusantara University,

DOI:

https://doi.org/10.21512/comtech.v7i2.2248

Keywords:

Business process intelligence, business process management, investment cost model

Abstract

Higher demand from the top management in measuring business process performance causes the incremental implementation of BPM and BI in the enterprise. The problem faced by top managements is how to integrate their data from all system used to support the business and process the data become information that able to support the decision-making processes. Our literature review elaborates several implementations of BPI on companies in Australia and Germany, challenges faced by organizations in developing BPI solution in their organizations and some cost model to calculate the investment of BPI solutions. This paper shows the success in BPI application of banks and assurance companies in German and electricity work in Australia aims to give a vision about the importance of BPI application. Many challenges in BPI application of companies in German and Australia, BPI solution, and data warehouse design development have been discussed to add insight in future BPI development. And the last is an explanation about how to analyze cost associated with BPI solution investment.

Dimensions

Plum Analytics

References

Andersen, H., Cobbold, I., & Lawrie, G. (2003). Balanced Scorecard Implementation in SMEs: Reflection in Literature and Practice. In 2GC Conference (pp. 1–9). Copenhagen: 2GC Limited. Retrieved from http://www.csudh.edu/dearhabermas/smebal01.pdf

Davis, S., & Albright, T. (2004). An investigation of the effect of Balanced Scorecard implementation on financial performance. Management Accounting Research, 15(2), 135–153. http://doi.org/10.1016/j.mar.2003.11.001

Genrich, M., Kokkonen, A., Moormann, J., Muehlen, M. Zur, Tregear, R., Mendling, J., & Weber, B. (2008). Challenges for business process intelligence: Discussions at the BPI workshop 2007. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial

Intelligence and Lecture Notes in Bioinformatics), 4928 LNCS, 5–10.

http://doi.org/10.1007/978-3-540-78238-4_2

Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., & Shan, M.-C. (2004). Business process intelligence. Computers in Industry, 53(3), 321–343.

Hawking, P., & Sellitto, C. (2015). Business intelligence strategy: a utilities company case study. Business Intelligence: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, 305-315.

Mansmann, S., Neumuth, T., & Scholl, M. H. (2007). OLAP Technology for Business Process Intelligence: Challenges and Solutions. Data Warehousing and Knowledge Discovery, 4654 LNCS, 111–122. http://doi.org/10.1007/978-3-540-74553-2_11

Mutschler, B., Bumiller, J., & Reichert, M. (2005). An Approach to quantify the Costs of Business Process Intelligence. Int’l. Workshop on Enterprise Modelling and Information Systems Architecture (EMISA), 152–163. Retrieved from http://dbis.eprints.uni-ulm.de/270/

Wade, D., & Recardo, R. J. (2001). Corporate performance management how to build a better organization through measurement-driven strategic alignment. Boston: Butterworth-

Heinemann. Retrieved from http://public.eblib.com/choice/publicfullrecord.aspx?p=535001

Watson, H. J. (2009). Tutorial: Business intelligence - Past, present, and future. Communications of the Association for Information Systems, 25(1), 487–510.

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Published

2016-06-01

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Articles
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