Business Intelligence and Strategic Decision Simulation

Authors: S. Sabbour, H. Lasi, P. von Tessin

Abstract:

The purpose of this study is two-fold. First, it attempts to explore potential opportunities for utilizing visual interactive simulations along with Business Intelligence (BI) as a decision support tool for strategic decision making. Second, it tries to figure out the essential top-level managerial requirements that would transform strategic decision simulation into an integral component of BI systems. The domain of particular interest was the application of visual interactive simulation capabilities in the field of supply chains. A qualitative exploratory method was applied, through the use of interviews with two leading companies. The collected data was then analysed to demonstrate the difference between the literature perspective and the practical managerial perspective on the issue. The results of the study suggest that although the use of simulation particularly in managing supply chains is very evident in literature, yet, in practice such utilization is still in its infancy, particularly regarding strategic decisions. Based on the insights a prototype of a simulation based BI-solution-extension was developed and evaluated.

Keywords: Business Intelligence, decision support, strategic decisions, simulation, SCM.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073567

References:

[1] Balaceanu, D. (2007). Components of a Business Intelligence Software solution, Informatica Economicˇa, vol. 42, no. 2, pp. 69-73.
[2] Becker, J. (2010). Prozess der gestaltungsorientierten Wirtschaftsinformatik, in: H. Österle, R. Winter, W. Brenner (Ed.), gestaltungsorientierte Wirtschaftsinformatik, pp. 13-18.
[3] Bohanec, M. (2001). What is Decision Support, in Proceedings of 4th International Multi-conference Information Society (IS 2001), Ljubljana, Slovenia, pp. 86-89.
[4] Bonabeau, E. (2001). Don-t Trust Your Gut, Harvard Business Review, vol. 81, no. 5, pp. 116-123.
[5] Chang, Y. and Makatsoris, H. (2001). Supply Chain Modelling using Simulation, International Journal of Simulation, vol. 2, no. 1, pp. 24-30.
[6] Crawford, I. (1997). Marketing Research and Information Systems, Rome: FAO Regional Office.
[7] Fazlollahi, B. and Vahidov, R. (2001). Extending the Effectiveness of Simulation-Based DSS through Genetic Algorithms, Information and Management, vol. 39, no. 1, pp. 53-65.
[8] Ingalls, R. (2008). Introduction to Simulation, in Proceedings of the 2008 Winter Simulation Conference, The United States of America, p. 1726.
[9] Kellner, M., Madachy, R. and Raffo, D. (1999). Software Process Simulation Modeling: Why? What? How? Journal of Systems and Software, vol. 46, no. 2/3, pp. 1-18.
[10] Kemper, H.-G. and Baars, H. (2009). From Data Warehouses to Transformation Hubs - A Conceptual Architecture, In: Proceedings of The 17th European Conference on Information Systems (ECIS 2009), Verona.
[11] Kopackova, H. and Skrobaekova, M. (2006). Decision Support Systems or Business Intelligence: What Can Help in Decision Making? A Scientific Paper of the University of Pardubice.
[Online]. Available: http://dspace.upce.cz/bitstream/10195/32436/1/CL585.pdf.
[12] Kossik, R. (2007). White paper on dynamic simulation and supply chain management. A White Paper by GoldSim.
[Online]. Available: http://www.goldsim.com/downloads/whitepapers/scmpaper.pdf.
[13] Li, W., Li, B. and Zhang, Y. (2008). Container Terminal Scheduling and Decision- Making Using Simulation Based Optimization and Business Intelligence, in Proceedings of 2008 IEEE International Conference on Mechatronics and Automation, Takamatsu, Japan, pp. 1053-1058.
[14] Mintzberg, H., Raisinghani, D. and Theoret, A. (1976). The Structure of Unstructured Decision Processes, Administrative Science Quarterly, vol. 21, no. 2, pp. 246-275.
[15] Mitchell M. and Jolley J. (2010). Research Design Explained. The United States of America: Cengage Learning.
[16] Power, D. (2001). Supporting Decision-Makers: An Expanded Framework, in Proceedings of Informing Science Conference, The United States of America, pp. 431-436.
[17] Power, D. (2004). Specifying an Expanded Framework for Classifying and Describing Decision Support Systems, Communications of the Association for Information Systems, vol. 13, no. 1, pp. 158-166.
[18] Power, D. and Sharda, M. (2007). Model-Driven Decision Support Systems: Concepts and Research Directions, Decision Support Systems, vol. 43, no. 3, pp. 1044-1061.
[19] Sein, M. K., Henfridsson, O., Purao, S., Rossi, M. and Lindgren, R. (2011). Action Design Research, appears in MIS Quarterly, vol. 35, no. 2.
[20] Silva, A. D. and Botter, R. (2009). Method for Assessing and Selecting Discrete Event Simulation Software Applied to the Analysis of Logistic Systems, Journal of Simulation, vol. 3, no. 2, pp. 95-106.
[21] Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision. United Kingdom: John Wiley and Sons.
[22] Watson, H. (2009). Tutorial: Business Intelligence Past, Present, and Future," Communications of the Association for Information Systems, vol. 25, no. 1, pp. 487-510.
[23] der Zee, D. V. and der Vorst, J. V. (2005). A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making, Decision Sciences, vol. 36, no. 1, pp. 65- 95.
[24] Zhang, J., Creighton, D. and Nahavandi, S. (2008).Towards a Synergy Between Simulation and Knowledge Management, Cybernetics and Systems, vol. 39, no. 7, pp. 768-784.