Fuzzy-Monte Carlo Simulation for Cost Benefit Analysis of Knowledge Management System Investment

Nowadays, knowledge management system is not doubtful as an important tool in an enterprise business process by reason of the effective knowledge management system can give a competitive advantage. Knowledge management system (KMS) is an information technology (IT) based system, which is developed to support and enhance the processes of knowledge creation, storage or retrieval, transfer, and application (Alavi & Leidner., 2001;Tseng, 2008). There are some benefits that can be achieved by implementing KMS such as increased employee productivity, better quality of a finished product, production and labor cost saving (M.-Y. Chen et. al, 2009; Wickhorst, 2002). Many managers know these benefits, but they are still vacillating to decide for investing KMS in their structure. This vacillation comes from consideration of budget and uncertainties or risk of economic constrained. In addition, the managers do not know how to analyze cost and benefit of KMS investment correctly. Without being able to make the analysis, managers cannot determine whether investing a KMS is worthwhile or a waste for the enterprise. Therefore, the costbenefit analysis of KMS investment is necessary in order to evaluate its attractiveness. The traditional cost-benefit analysis that always used in KMS and other enterprise information system (EIS) investment evaluation such as net present value (NPV), internal rate of return (IRR), and payback period (PB) seek to adopt a monetary unit as a basis of analysis, in which all non-monetary parameters are given monetary values (TBC, 1998; Tang and Beynon, 2005). However, it is observed in (Phillips-Wren et al., 2004) that most benefits associated with EIS like KMS are mostly intangible, which makes the use of traditional quantitative financial models heavily biased towards tangible costs and benefits. In an attempt to bridging the intangible towards tangible in the benefits related decision-making process, some enterprises analyze based on subjective judgement. This approach constantly in linguistic term contains ambiguity data that has a number of weaknesses (Uzoka, 2009) such as: inaccurate representation of the uncertainty lack of historical data, inability to understand completely and reproduce the results, poor explanation of a decision process and associated reasoning, a possibility of missing out important problem details for the evaluation, high probability of different experts producing different results without the