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

Ferdinand Murni Hamundu1,

Ahmad Suhaimi Baharudin2 and Rahmat Budiarto3 *1,2School of Computer Sciences-Universiti Sains Malaysia,3InterNetWorks Research Group College of Arts and Sciences-Universiti Utara Malaysia* 

#### **1. Introduction**

70 New Research on Knowledge Management Technology

Uden, Lorna Department of Computing, Engineering and Technology, Staffordshire

Activity theory for designing mobile learning

University, Beaconside, Stafford, ST18 0AD, UK E-mail: l.uden@staffs.ac.uk

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

Fuzzy-Monte Carlo Simulation for Cost Benefit

also have to be counted in the costs.

software release when it is launched).

even if more user friendly than competing products.

**2.1.1 Software** 

the task.

**2.1.2 Hardware** 

necessary.

**2.1 Costs** 

Analysis of Knowledge Management System Investment 73

expert systems so that the jumbled business data is well-organized and more integrated (Khandelwal, 2003). During the development of KMS, attention should be paid to various issues and challenges related to using IT to support KM (Jungpil & Mani, 2000). This issue is considered by the managers to evaluate whether the KMS investment is feasible or not. Thus, the accurately calculating the cost and the benefit of KMS investment are

The first step of cost-benefit analysis for a KMS investment is to determine the costs. On the surface, this may seem deceptively simple, but there are costs involved in a knowledge management investment that may not be readily obvious to the manager. In fact, investment cost of EIS likes KMS is a common factor influencing the purchaser to choose the EIS (Davis & Williams, 1994). Obviously, the project will incur the cost of whatever EIS to be used. This can range from free, to nearly free, to several thousand dollars for an EIS. In addition, any technical infrastructure for the EIS that is needed will

Investment costs of KMS include, but are not limited to the costs of software, hardware, incentive programs, implementing and maintaining. Technically, these costs can be grouped under two major criteria, namely, capital expenditures and operating expenditures (Ngai & Chan, 2005). Capital expenditures are the non-recurring costs involved in setting up the KMS such as product costs (the basic cost of the KM tool), license costs (the cost of the KM tools in terms of number of users) and training costs. Operating expenditures are the recurring costs involved in operating the KMS, which include maintenance costs and software subscription costs (the annual, pre-paid cost of upgrading the product to a major

The standard software such as e-mail, web servers, corporate intranets, newsgroups, shared file systems, or centralized databases in an enterprise is commonly already existed. Hence, there is no software cost even only transfer knowledge such as the exchange of e-mail, the use of instant messaging tools, or the use of internet search engines. However, if the enterprise wishes to establish a level of knowledge integration and wishes to manage, encourage, and shepherd the transfer of knowledge, these tools are probably inadequate for

In this case, the enterprise will want to invest in a commercially available product designed specifically for the tasks that the company wishes to be able to accomplish with the KMS. Costs for this may be quite high, but this KMS will be more likely to be utilized by the users,

Along with the cost of software, the enterprise must also consider the costs of the infrastructure or hardware that will be needed to support the KMS. The application that is chosen may need its own application server on which to run or it may co-locate with existing applications on a server that the company already owns. If the system is placed on a server with applications already running, the company will have to consider the cost of any performance degradations that the other applications may occurrence. A server will need

ability to decide which one is correct, difficulty in exploiting past evaluations, and the risk of producing meaningless or highly faulty results.

In this paper, a fuzzy rule based system is proposed to bridging the tangibles and intangible benefits of KMS investment. The fuzzy component addresses the vagueness associated with human judgement, especially of intangible parameters. Furthermore, a Monte-Carlo simulation method is used to consider the uncertainty of economic in calculating an expected net present value (NPV). The Monte Carlo simulation is a method that appropriate for estimating the impact of KMS critical factors to the financial result by randomizing value from each of the uncertain variables and calculating the objective or target value of an investment model.

This paper starts with an introduction about problem of KMS investment decision in section 1, and then followed by discussion about cost and benefit of KMS investment, related works in cost-benefit analysis of KMS investment, and including the Fuzzy-Monte Carlo simulation as the proposed approach for this paper in section 2. Section 3 provides a framework for cost benefit analysis of KMS investment. The real life problem that the authors dealt with and intangible benefit analysis due to this problem are introduced in Sections 4 and 5, respectively. Section 6 provides a mathematical model of cost-benefit impact to KMS investment. In Section 7, the results of the Monte-Carlo simulation are analyzed and discussed. Finally, Section 8 presents the conclusions and outlines for further research.

### **2. Literature review**

As managers became aware that the power of knowledge is the most valuable strategic resource, knowledge management (KM) became widely recognized as essential for the success or failure of enterprises. Consequently, over the past 20 years, KM has progressed from an emergent concept to an important factor in sustainable competitive advantage of business (Wagner et al., 2011). According to one estimate, 81% of the leading organizations in Europe and the U.S. are utilizing some form of KM (Grossman, 2006). Knowledge is based on data and information. Data represents the raw facts without meaning; information symbolized to what is obtained when data is organized in a meaningful context, while knowledge is characterized as the meaningfully organized accumulation of information (Zack, 1999). Nonaka (1994) points out that there are two different types of knowledge in an organization: explicit and tacit knowledge. Explicit knowledge is formal and systemic, while tacit knowledge is highly personal and difficult to formalize. These two types of knowledge are both essential to the organization and must be captured and shared for others to benefit. Thus, knowledge in the organization should be managed properly and carefully.

The KMS refers to the set of processes or practice to develop the ability of an employee in creating, acquiring, capturing, storing, maintaining and disseminating the enterprise's knowledge (Hamundu & Budiarto, 2010). Many companies are building KMS to manage their organizational learning and business "know-how". For instance, a software engineer is able to know immediately the algorithm of a security system in prior software development. Sharing this information organization widely can lead to more effective security design, and it could also lead to ideas for new or improved software. Indeed, the ability to perform all functions of KMS depends on the information technology (IT) role. Facing a tremendous amount of data on a daily basis, enterprises only use IT to integrate each division of various tools, such as intranet, data warehouse, electronic whiteboard, artificial intelligence and expert systems so that the jumbled business data is well-organized and more integrated (Khandelwal, 2003). During the development of KMS, attention should be paid to various issues and challenges related to using IT to support KM (Jungpil & Mani, 2000). This issue is considered by the managers to evaluate whether the KMS investment is feasible or not. Thus, the accurately calculating the cost and the benefit of KMS investment are necessary.
