**6. Model of cost-benefit Impact to KMS investment**

In the KMS cost-benefit analysis of KMS investment, the costs calculation is structured by capital expenditures (*CXn*) and operating expenditures (*CYn*). On the other hand, the benefits of KMS (*B*) that calculated in Eq. (2) are derived from revenue increase (RI) and costs saving such as annual purchase material cost saving (*CSm*), cost saving on final product (*CSi*), and labor cost saving (*CSl*). Indeed, the variables of total benefit are calculated considering the increase rate in orders (s) which has been estimated by fuzzy rule based system as shown in Table 1. The increased orders (S') is calculated by Eq. (3).

$$B = (\text{CSm} + \text{CSi} + \text{CSl}) + RI \tag{2}$$

$$S' \triangleq \mathcal{S}(\mu, \sigma) \propto (1 + s) \tag{3}$$

where *S(µ,σ)* is the yearly orders with a mean *µ* and standard deviation *σ*. The cost savings are computed considering the increased orders (*S'*), cost unit (*c*), cost saving rates (*r*) as shown in Table 2. The cost saving is calculated by Eqs. (4)-(6).

$$CS\_m = S' + \varepsilon\_{material} + r\_{material} \tag{4}$$

$$\text{CSi} = \text{S}' + \text{c}\_{\text{final\\_product}} + r\_{\text{final\\_product}} \tag{5}$$

$$CSl = S' + \ c\_{labov} + r\_{labov} \tag{6}$$

The revenue increase is calculated considering yearly total orders (*S*), the increase rate in orders (*s*) and profit for each unit (*p*) in Eq. (7). Finally, the NPV of the total KMS investment is determined for *n* years in Eq. (8) where *i* indexed as discount rate.

$$R \!\!\!\!= \mathcal{S}(\mu, \sigma) \propto \text{s} \propto p \tag{7}$$

$$NPV = -\left(\mathbf{C}\_{X1} + \dots + \mathbf{C}\_{Xn}\right) + \sum\_{n=1}^{t} \frac{\left[B - \left(\mathbf{C}\_{Y1} + \dots + \mathbf{C}\_{Yn}\right)\right]}{\left(1 + i\right)^{n}} \tag{8}$$

In relation to investment analysis, the Monte Carlo simulation is the appropriate method for estimating the impact of KMS costs and benefits to the investment result by randomizing value from each of the uncertain variables and calculating the objective or target value of the investment model (Hacura et al., 2001). This method uses random numbers from probability distributions of increase rate in orders and cost saving rates to compute the probability distribution of NPV, which meant not only produce one value of NPV.

#### **7. Simulation, results, and discussion**

The simulation to calculate the NPV of KMS investment is carried out using software Crystal Ball Version 7.2.1. In addition, the simulations are run 500 times to minimize the possible errors arising from the random variables. A simulation generates the probability distribution for the total revenue increase, the total cost saving, and the total benefit which is the sum of total revenue increase and total cost saving as shown in Fig. 5, 6, and 7 respectively. Furthermore, the distribution of NPV in 3 years horizon is shown in Fig. 8 with the probability of a discount rate (i) of mean of 8% and standard deviation of logarithmic value of 0.22. The cost savings of material, labor and method on a final product are

Fuzzy-Monte Carlo Simulation for Cost Benefit

Fig. 7. The simulation results for the total benefit

Fig. 8. The simulation results for the NPV of KMS investment

for the KMS adoption.

As shown in Table 3, the KMS investment for three years horizon is more than 90% certainty that the NPVs will be positive. Therefore, the managers of the software house company ABC should decide to invest in KMS. However, there is small probability (<10%) that the KMS investment will be a loss with an amount of less than –USD48,705. This may be due to the uncertainty or risk of economic constrained, which can be represented by probability distribution of the discount rate. As a summary, although the managers should invest the KMS, they should also consider the small probability of loss by ensuring the effective performance of the KMS. In order to ensure that KMS performance is effective, the managers should assure both IT infrastructure support as well as the employee participants

Analysis of Knowledge Management System Investment 83

attractiveness of an investment is good or bad. If NPV is positive, then the investment

decision is acceptable. Otherwise, the investment should be rejected.

computed considering the increased orders, unit costs and cost saving rates using Eqs. (3)– (6). For calculating the total revenue increase, the estimated demand increase of the company is multiplied with the profit for each unit as in Equation (7). The total yearly benefit is calculated by Eq. (1), while the NPV by Eq. (8).

According to the results of the simulation, the cost saving varies between USD35,675 and USD64,772 while the revenue increase varies between USD38,128 and USD296,894. The simulation result for total benefit varies between USD75,307 and USD360,368. The distribution of the NPV of KMS investment has the mean value of USD355,492 and the standard deviation USD254,519, which varies between USD -48,705 and USD731,091.

Fig. 5. The simulation results for the total cost saving

Fig. 6. The simulation results for the total revenue increase

The NPV, which is defined as the difference between a present value of cash inflow and cash outflow by considering a discount rate is important for managers to know whether the

computed considering the increased orders, unit costs and cost saving rates using Eqs. (3)– (6). For calculating the total revenue increase, the estimated demand increase of the company is multiplied with the profit for each unit as in Equation (7). The total yearly

According to the results of the simulation, the cost saving varies between USD35,675 and USD64,772 while the revenue increase varies between USD38,128 and USD296,894. The simulation result for total benefit varies between USD75,307 and USD360,368. The distribution of the NPV of KMS investment has the mean value of USD355,492 and the standard deviation USD254,519, which varies between USD -48,705 and

benefit is calculated by Eq. (1), while the NPV by Eq. (8).

Fig. 5. The simulation results for the total cost saving

Fig. 6. The simulation results for the total revenue increase

The NPV, which is defined as the difference between a present value of cash inflow and cash outflow by considering a discount rate is important for managers to know whether the

USD731,091.

attractiveness of an investment is good or bad. If NPV is positive, then the investment decision is acceptable. Otherwise, the investment should be rejected.

Fig. 7. The simulation results for the total benefit

Fig. 8. The simulation results for the NPV of KMS investment

As shown in Table 3, the KMS investment for three years horizon is more than 90% certainty that the NPVs will be positive. Therefore, the managers of the software house company ABC should decide to invest in KMS. However, there is small probability (<10%) that the KMS investment will be a loss with an amount of less than –USD48,705. This may be due to the uncertainty or risk of economic constrained, which can be represented by probability distribution of the discount rate. As a summary, although the managers should invest the KMS, they should also consider the small probability of loss by ensuring the effective performance of the KMS. In order to ensure that KMS performance is effective, the managers should assure both IT infrastructure support as well as the employee participants for the KMS adoption.

Fuzzy-Monte Carlo Simulation for Cost Benefit

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Table 3. Percentile analysis of the NPV of KMS investment

### **8. Conclusion**

Knowledge management system (KMS) is developed to support and enhance the processes of knowledge creation, storage or retrieval, transfer, and application. 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. However, many enterprises fail in knowledge management activities, because they are unwilling to invest time and money in developing the knowledge when they do not know how to measure the benefits. For managers, it is very important to accurately measure the benefits of a KMS investment in the planning phase. Using the most proper attractiveness evaluation methods, the managers can take the accurate decisions on KMS investment.

In this study, attractiveness evaluation of a KMS investment within a company is investigated by cost-benefit analysis. The investment cost of the KMS is categorized in capital expenditures and operating expenditures. On the contrary, the cost saving, increasing the quality of products, and employee productivity and speed are considered as the benefits. The purpose of this paper is to propose an approach for bridging the tangible and intangible values of KMS investment into a model of cost-benefit analysis. Furthermore, an integrated model considering the expected revenue increase due to the KMS investment is determined. Therefore, the fuzzy rule based system is used to calculate the expected revenue increase, and the Monte-Carlo simulation method is applied to determine the expected NPV of KMS investment at different certainty levels. In the future study, the proposed model will be improved by considering risk and opportunity factors in KMS investment evaluation. In decision-making, there are criteria that are opposite in direction to other criteria, such as criteria in benefits (B) versus those in costs (C), and criteria in opportunities (O) versus those in risks (R). Thus, the BOCR should be involved into a quantitative financial model to assist the managers in KMS investment decision.

#### **9. References**

Alavi, M., & Leidner., D. E. (2001). Review: Knowledge management and knowledge management:Conceptual foundations and research issues. *MIS Quartely, 25*(1), 107- 136.

Probability (%) Net present value of KMS investment (USD)

Knowledge management system (KMS) is developed to support and enhance the processes of knowledge creation, storage or retrieval, transfer, and application. 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. However, many enterprises fail in knowledge management activities, because they are unwilling to invest time and money in developing the knowledge when they do not know how to measure the benefits. For managers, it is very important to accurately measure the benefits of a KMS investment in the planning phase. Using the most proper attractiveness evaluation methods, the managers can take the accurate decisions on KMS investment. In this study, attractiveness evaluation of a KMS investment within a company is investigated by cost-benefit analysis. The investment cost of the KMS is categorized in capital expenditures and operating expenditures. On the contrary, the cost saving, increasing the quality of products, and employee productivity and speed are considered as the benefits. The purpose of this paper is to propose an approach for bridging the tangible and intangible values of KMS investment into a model of cost-benefit analysis. Furthermore, an integrated model considering the expected revenue increase due to the KMS investment is determined. Therefore, the fuzzy rule based system is used to calculate the expected revenue increase, and the Monte-Carlo simulation method is applied to determine the expected NPV of KMS investment at different certainty levels. In the future study, the proposed model will be improved by considering risk and opportunity factors in KMS investment evaluation. In decision-making, there are criteria that are opposite in direction to other criteria, such as criteria in benefits (B) versus those in costs (C), and criteria in opportunities (O) versus those in risks (R). Thus, the BOCR should be involved into a

quantitative financial model to assist the managers in KMS investment decision.

Alavi, M., & Leidner., D. E. (2001). Review: Knowledge management and knowledge

management:Conceptual foundations and research issues. *MIS Quartely, 25*(1), 107-

0% (48,705) 10% 7,401 20% 57,059 30% 151,300 40% 251,613 50% 377,746 60% 473,571 70% 573,595 80% 642,519 90% 681,202 100% 731,092

Table 3. Percentile analysis of the NPV of KMS investment

**8. Conclusion** 

**9. References**

136.


**6** 

*P. R. China* 

**The Semantic Web-Based** 

**Collaborative Knowledge Management** 

*2Research Institute of Information Technology, Tsinghua University, Beijing* 

The current knowledge processing models can be classified into two categories-Man's Knowledge Processing Model and Machine's Knowledge Processing Model-according to literature reviews of knowledge processing studies in knowledge management and Artificial Intelligence. Man's Knowledge Processing Model is based on knowledge management theory, especially the Second Generation Knowledge Management (SGKM), and focuses on processing tacit knowledge by human brains. Machine's Knowledge Processing Model is based on Artificial Intelligence or First Generation Knowledge Management (FGKM), and engages in processing explicit knowledge by computers. Furthermore, there are two challenges faced by current research of knowledge processing. One of these challenges is how to break through bottlenecks in the two knowledge processing model by lowering the cost of knowledge sharing and innovation and adopting machine-readable knowledge reorientation technology; the other one is how to make full use the complementary advantages of human and computer through combining the two

In this chapter, we carry out in-depth study of knowledge life cycle on the semantic web and propose the model for collaborative knowledge processing and its implementation framework. The remainder of this paper is organized as follows. In Section 2, we review the development of semantic web technologies and discuss machine readability of semantic web knowledge representation. In the next part, section 3, we describe the knowledge life cycle on the semantic web. Then, section 4 proposes a model for collaborative knowledge management on the semantic web and section 5 discusses how to implement the model. Section 6 provides a case study by analyzing the FOAF project. In the conclusion (section 7)

The term "Semantic Web" was coined by Tim Berners-Lee in 1998 and defined as not a separate Web but an extension of the current one, in which information is given welldefined meaning, better enabling computers and people to work in cooperation [2]. The layer cake framework of the semantic web implicates that the development of Semantic Web technologies proceeds in steps and each step building a layer on the top of another. It

some topics that should be further studied are proposed.

**1. Introduction** 

models [1].

**2. Related work** 

Lemen Chao1,2, Yong Zhang2 and Chunxiao Xing2 *1Beijing Institute of Petrol-Chemical Technology, Beijing* 

http://www.tbssct.gc.ca/fin/sigs/revolving\_funds/bcag/bca2\_e.asp


http://www.kmadvantage.com/docs/km\_articles/Measuring\_km.pdf

