Using Network Simulation Heung - Suk Hwang, Gyu-Sung Cho Web-Based Project Risk Analysis Model Using Network Simulation 2005. 10. 11. Heung - Suk Hwang, Gyu-Sung Cho Department of Industrial Engineering , Engineering College, Dongeui University Gaya-dong, san-24 Pusanjin-ku, Pusan, 614-714 KOREA

Contents 2. Individual Project Alternative Evaluation Using 1. Introduction 2. Individual Project Alternative Evaluation Using AHP(Step 1) 3. Integrating the Results of Individual Evaluations 4. Project Risk Analysis Models - Project Risk Facets - Model Application 5. Summary and Conclusions

1. Introduction ☞ Developed a project risk analysis model based on simulation and multi-attribute structured decision support system ☞ Project Risk : Project schedule, Cost and Performance risk ☞ 1) Deterministic risk factor analysis model based or AHP (analytic hierarchy process) weighted value, and 2) Network simulation model based on venture evaluation and review technique. ☞ Also we developed computer program and demonstrated the pro posed methods, ☞ Then we carryout risk analysis

Decision Support System Web-based Decision Support System Internet/Intranet Project Management System Information Group-Joint Work

Figure 2 . Three-step Approach of Project Evaluation Model

Figure 4. Client and Server in Decision Support System

☞ Construct decision structure and Derive out the evaluation alternatives - the group decision ideas, the creative ideas ☞ we used a brainstorming method and developed a GUI-type program ☞ To create the ideas of project evaluation alternatives and methods for decision support system analysis, ☞ we construct decision structure using the brainstorming file in the internet/intranet–based environment

The GUI-type program of Solution Builder-2001

Figure 5. Main-program of Solution Builder 2001

2.1 Brainstorming ☞ We used a brainstorming method and developed a GUI-type program

☞ Sample output of alternative generation and construct the decision structure of an example for school selection

2.2 Alternative Evaluation Using AHP  

☞ A sample output pair-wise matrix of sample problem Table 1. Pair-wise Comparison Matrix

☞ the final result of school selection AHP which is given by School B(0.378) > School A(0.367) > School C(0.254).

The AHP Result of School Selection Problem Figure 9. The AHP Result of School Selection Problem The AHP Result of School Selection Problem

3. Integration of Individual Evaluation ☞ For the integration of the results of individual evaluations, prioritized sets, we used two Heuristic models 1, Model 2 and Fuzzy set priority method 1) Heuristic Model 1 : For example of the Heuristic Method 1, a sample result with N = 5 e valuators and M = 3 alternatives is given as : Evaluator 1 : B > A > C, Evaluator 2 : B > C > A, Evaluator 3 : C > A > B, Evaluator 4 : C > B > A, Evaluator 5 : C > B > A

☞ Heuristic Method 1 rank order is given by C(0.467) > B(0.400) > A(0.133).

2) Heuristic Model 2 : - The evaluator frequency matrices were added to form a summed frequency matrix - Then, the preference matrix was developed by a comparison of the scores in the component cells(A, B versus B, A). - If the A, B value equals B, A, then each component cell in the matrix is given by 1/2. On the other hand if the A, B value is greater than the B, A , then A, B is given by one and B, A cell of the preference matrix is given by 0. ☞ By applying the Heuristic Model 2 to the same example of Heuristic Method 1, the result is given by C(0.450) > A(0.392) > B(0.158) .  

3) Fuzzy Set Priority Method . The fuzzy matrix complement cell values sum to 1 and fuzzy set difference matrix is defined as follows : R-RT = U(A, B) - (B, A), if U(A, B) > U(B, A), = 0, otherwise To obtain fuzzy preferences, following five steps are considered : Step 1 : Find the summed frequency matrix (using heuristic method 2) Step 2 : Find the fuzzy set matrix R which is the summed frequency matrix divided by the total number of evaluators Step 3 : Find the difference matrix R - RT = U(A, B) - U(B, A), if U(A, B) > U(B, A), = 0, otherwise where, for U(A, B) quantifies, A is preferable to B. Step 4 : Determine the portion of each part Step 5 : The priority of the fuzzy set is then the rank order of values in decreasing. The sample problem result by fuzzy set priority method is given by C(0.492) > B(0.387) > A(0.121).

4. Project Risk Analysis 1) Project Risk Facets Figure 2. Three Steps of Risk Analysis

Figure 3. Project Risk in Life Cycle

2) PROJECT RISK ANALYSIS MODELS . Normally project risk can be assessed by following factors : ①  Contribution to project performance, ②  Technical validity, ③  Economic effect, ④  Systematic validity.

Figure 4. Project Risk Structure

Figure 5. Risk Identification

3) Risk Factor Analysis Method In this study, we proposed two practical risk analysis models : 1) risk factor analysis model, and 2) network simulation model[6] are given as following. A Deterministic model based on risk factor analysis method using a scoring method, such as AHP(Analytic Hierarchy Process)[4] weighted value. Four steps of this method is given by : Step 1 : construct the evaluation items and evaluate each items in the evaluating form using -2∼+2 scoring scale, Step 2 : compute the AHP weighted value of each evaluation items and compute the weighted score of each evaluation item, Step 3 : compute the total evaluation score of each major evaluating items considering following items(in this study, we used for items as following) - industrial improvement feasibility, - technical feasibility, - economical feasibility, - institutional feasibility Step 4 : compute the risk using probability scale

PF· PT · PE · PI=PE PF · PT · PE · PI=PE -2 -1 0 1 2 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0 Base Case Post-research PF· PT · PE · PI=PE PF · PT · PE · PI=PE 0.93×0.85×0.93×0.93=0.70 0.94×0.89×0.94×0.94=0.74

4) Stochastic Network Simulation Method Figure 6. Schematic Structure of Stochastic Network Simulation Model

Figure 7. Sample Output for Time/Cost.

Figure 8. Project Block Diagram 5) MODEL APPLICATION A new manufacturing system development : - In the advanced development step after successful completion of its 3 years basic research. - The system consisted of a main body and three sub-systems(A, B, C). - The main body is planned to develop in house, and three censers will be imported. The project block diagram is given as Figure 8. Figure 8. Project Block Diagram Four sub-systems ; new-CNC, Auto-assembler, main-body, and censers. - The detail network flow of this system is shown in Figure 9

Figure 9. The detail Network Flow Diagram of Sample System

Figure 10. Cost/Time Diagram

5. CONCLUSION - In this research, developed a risk analysis model, - To quantify the risks and to generate the choice of the actions to be taken to reduce the project uncertainties. - Two analysis models are proposed in this study; 1) risk factor analysis model and 2) network simulation model using VERT(venture evaluation and review technique). - The objective of proposed models are to estimate 1) the schedule, 2) cost and 3) performance risks. - The proposed models will be used in the area of R&D project evaluation to reduce project risks. - Also, developed computer programs and have shown the results of sample run for an acquisition project of manufacturing system. It was known that the proposed model was a very acceptable for R&D project evaluation.

Thank You Kainan University, Prof. Heung-Suk Hwnag