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Heung - Suk Hwang, Gyu-Sung Cho

Department of Industrial Engineering ,Engineering College, Dongeui University

Gaya-dong, san-24 Pusanjin-ku, Pusan, 614-714 KOREA

Web-Based Project Risk Analysis ModelUsing Network Simulation

2005. 10. 11.

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Contents 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

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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

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Web-based Decision Support System

Internet/Intranet

Project ManagementSystem

Project ManagementSystem

InformationSystem

Group-JointWork

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Step 1 : Individual Evaluation of Alternatives- Brainstorming to Generate Alternatives and

to Define the Performance Factors- Evaluation of Alternatives Using AHP and

Fuzzy set ranking methodologies

Step 2 : Integrate the Individual Analysis- Heuristic Model- Fuzzy Set Priority Method

Step 3 : Risk Analysis Model - Stochastic Network Simulation Model- Fuzzy Set Priority Method

- Web-based Internet/Intranet Solution Builder

- GUI-type Program

- Integrated decision supportsystem

Step 1 : Individual Evaluation of Alternatives- Brainstorming to Generate Alternatives and

to Define the Performance Factors- Evaluation of Alternatives Using AHP and

Fuzzy set ranking methodologies

Step 2 : Integrate the Individual Analysis- Heuristic Model- Fuzzy Set Priority Method

Step 3 : Risk Analysis Model - Stochastic Network Simulation Model- Fuzzy Set Priority Method

- Web-based Internet/Intranet Solution Builder

- GUI-type Program

- Integrated decision supportsystem

Figure 2 . Three-step Approach of Project Evaluation Model

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NetworkInternet/Intranet

Server

Protocol Encoding

Protocol Decoding

Client

NetworkInternet/Intranet

Server

Protocol Encoding

Protocol Decoding

Client

Figure 4. Client and Server in Decision Support System

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☞ 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

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The GUI-type program of Solution Builder-2001

New File

Open

Close

Save

Save in Other Name

Encoding

Project Information

Printer Setup

Pre-Show

Print

E-Mail Sending

Brainstorming

AHP

Integrate

Convert Brainstorming File into AHP File

AHP

Convert AHP File into Integrating Ranking File

Aggregating Ranking Method

Exit

Edit

Edit

Add

Delete

Move

Copy

Insert

Select All

Line Up

Sort

Change

Edit Levels

Add Level

Delete Level

Left - line

Upper - line

Right - line

Bottom - line

Centering

Centering Vertical

Show

Initial Size

Enlargement

Reduce

Tool Bar

Search Node

Status Bar

Standard

Item

Character

Line up

AHP

Integrating Ranking

File

New File

Open

Close

Save

Save in Other Name

Encoding

Project Information

Printer Setup

Pre-Show

Print

E-Mail Sending

Brainstorming

AHP

Integrate

Convert Brainstorming File into AHP File

AHP

Convert AHP File into Integrating Ranking File

Aggregating Ranking Method

Exit

Edit

Edit

Add

Delete

Move

Copy

Insert

Select All

Line Up

Sort

Change

Edit Levels

Add Level

Delete Level

Left - line

Upper - line

Right - line

Bottom - line

Centering

Centering Vertical

Show

Initial Size

Enlargement

Reduce

Tool Bar

Search Node

Status Bar

Standard

Item

Character

Line up

AHP

Integrating Ranking

File

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Help

Index

Homepage

Solution Builder?

AHP

Compute

Partial Mode

Ideal Mode

Perf. Sensitivity

Dynamic Sensitivity

Trend Sensitivity

Node Information

Aggregating Priority

Basic Information

Heuristic 1

Heuristic 2

Fuzzy Set Priority

Tool

Copy in Clipboard

Save in Image File

Save in HTML

Option

Network

Server Setup

Client Setup

Help

Index

Homepage

Solution Builder?

AHP

Compute

Partial Mode

Ideal Mode

Perf. Sensitivity

Dynamic Sensitivity

Trend Sensitivity

Node Information

Aggregating Priority

Basic Information

Heuristic 1

Heuristic 2

Fuzzy Set Priority

Tool

Copy in Clipboard

Save in Image File

Save in HTML

Option

Network

Server Setup

Client Setup

Figure 5. Main-program of Solution Builder 2001

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

File Edit Show

New File

Open

Close

Save

Save in Other Name

Encoding

Project Information

Printer Setup

Pre-Show

Print

E-Mail Sending

Exit

Edit

Add

Delete

Move

Copy

Insert

Select All

Line Up

Sort

Change

Initial Size

Enlargement

Reduce

Tool Bar

Search Node

Status Bar

Standard

Item

Character

Line up

Left - line

Upper - line

Right - line

Bottom - line

Centering

Centering Vertical

File Edit Show

New File

Open

Close

Save

Save in Other Name

Encoding

Project Information

Printer Setup

Pre-Show

Print

E-Mail Sending

Exit

Edit

Add

Delete

Move

Copy

Insert

Select All

Line Up

Sort

Change

Initial Size

Enlargement

Reduce

Tool Bar

Search Node

Status Bar

Standard

Item

Character

Line up

Left - line

Upper - line

Right - line

Bottom - line

Centering

Centering Vertical

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

Education

Friendship

School Fee

Campus Life

Course

Transportation

School Selection

Solution Builder 2001 Brainstorming

File(F) Edit(E) Show(S) Tool(T) Client/Server Window(W) Help(H)

Node Data

School Selection

Education

Friendship

Campus Life

School Fee

Transportation

Course

Education

Friendship

School Fee

Campus Life

Course

Transportation

School Selection

Solution Builder 2001 Brainstorming

File(F) Edit(E) Show(S) Tool(T) Client/Server Window(W) Help(H)

Node Data

School Selection

Education

Friendship

Campus Life

School Fee

Transportation

Course

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2.2 Alternative Evaluation Using AHPSolution Builder 2001 BrainstormingFile(F) Edit(E) Show(S) Tool(T) Client/Server Window(W) Help(H)

Level 1Level 2Level 3Level 4Level 5Level 6Level 7Level 8

Lev

e l 1

Lev

e l 2

Lev

e l 3

Lev

e l 4

Lev

e l 5

School Selection

FriendshipEducation School Life School Fee Course Transportation

School A School CSchool B

Tree-View

Solution Builder 2001 BrainstormingFile(F) Edit(E) Show(S) Tool(T) Client/Server Window(W) Help(H)

Level 1Level 2Level 3Level 4Level 5Level 6Level 7Level 8

Lev

e l 1

Lev

e l 2

Lev

e l 3

Lev

e l 4

Lev

e l 5

School Selection

FriendshipEducation School Life School Fee Course Transportation

School A School CSchool B

Tree-View

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☞ A sample output pair-wise matrix of sample problem

Table 1. Pair-wise Comparison Matrix

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

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Solution Builder 2001 BrainstormingFile(F) Edit(E) Show(S) Tool(T) Client/Server Window(W) Help(H)

School A

School C

Conf HelpPrintDel

School C

School B

School A

School B

Solution Builder 2001 BrainstormingFile(F) Edit(E) Show(S) Tool(T) Client/Server Window(W) Help(H)

School A

School C

Conf HelpPrintDel

School C

School B

School A

School B

Figure 9. The AHP Result of School Selection Problem

The AHP Result of School Selection Problem

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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

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

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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) . 

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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, otherwiseTo 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).

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Figure 2. Three Steps of Risk Analysis

Identification · That is the risk ?· How can it be categorized?

Estimation

Identification

· What is the size of the risk ?· What is exposed to risk ?· What is the the risk?s likelyhood

of occurring ?· What is considered an acceptable risk ?· What is the exposure to risk ?· What is other choices exist to avert the risk?

Identification · That is the risk ?· How can it be categorized?

Estimation

Identification

· What is the size of the risk ?· What is exposed to risk ?· What is the the risk?s likelyhood

of occurring ?· What is considered an acceptable risk ?· What is the exposure to risk ?· What is other choices exist to avert the risk?

1) Project Risk Facets

4. Project Risk Analysis

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Researsh anddevelopmentuncertainty

R/D Risk

Economic productionand acquisition

uncertainty

Acquisition Risk

Operational Risk

Technical andoperationaluncertainty

Figure 3. Project Risk in Life Cycle

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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.

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Technical Capabiliy/Approach MethodTechnical Capabiliy/Approach Method

Cost Cost Schedule TimeSchedule Time Performance Performance

Uncertainty Uncertainty

RiskRisk

Figure 4. Project Risk Structure

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Risk Risk

AcquisitionAcquisition Decision Decision

Performance/Tech.Schedule.

Cost.

Operational

Support.

Figure 5. Risk Identification

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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

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-2 -1 0 1 2

.1 .2 .3 .4 .5 .6 .7 .8 .9 1.0Base 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

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4) Stochastic Network Simulation Method

SIMULATION

(Stochastic Network

SIMULATION)

NetworkFormulation of

Project

Parameter Estimate ofNetwork Activities : T, C, P

- Input Data- Criteria for project Risk (Success or fail Prob.)

Random No. Generationfrom the Distributions ofT, C, P

Simulation Spec andOptions

Output

Figure 6. Schematic Structure of Stochastic Network Simulation Model

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Timet

30.65

c274.34

0.50.6

0.7

Cost

Success probability

Figure 7. Sample Output for Time/Cost.

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5) MODEL APPLICATIONA 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.

S.I.DT2OT2

DT1OT1

Stop

Advanced Developmentof Main Body

Senser Acquisition

System IntegrationMain Body, Sample

- Four sub-systems ; new-CNC, Auto-assembler, main-body, and censers. - The detail network flow of this system is shown in Figure 9

Figure 8. Project Block Diagram

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INIT

ALL

OR

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Auto-assembler

New-CNC

MBODY

Senser

CNC

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SUCWIN

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AA-COK

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APLDF

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Auto-assembler

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Figure 9. The detail Network Flow Diagram of Sample System

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Figure 10. Cost/Time Diagram

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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.

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Thank You

Kainan University,Prof. Heung-Suk Hwnag