WEB-BASED MULTI-ATTRIBUTES
ANALYSIS MODEL FOR MAKE-OR-BUY
DECISION
8th ISAHP 2005, Hawaii,
University of Hawaii Honolulu,
July 8-10, 2005
Prof. Heung Suk Hwang,
Department of Business Management ,
Kainan University, Taiwan
Tel : +886-3-341-2500 ext. 6088
e-mail : hshwang@mail.knu.edu.tw
Kainan University
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Contents
1. Introduction
2. Properties of Make-or-Buy Decision Problem
3. Three-step Approach of Decision Alternative Analysis,
Project Risk Analysis Models
4. Model Application to Cellular Manufacturing System
5. Summary and Conclusions
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1. Introduction
☞ Developed an internet/intranet-based solution builder
(Solution Builder2004) for a three-step multi-attribute decision
support system for School Food Service System
☞ Web-based Three-step Decision Support System Using Multiattribute Analysis Method
1) brainstorming for the idea generation,
2) fuzzy-AHP( fuzzy analytic hierarchy process) as a multi-attribute
structured an analysis method ,
3) aggregation logic model to integrate the results of individual
analysis
☞ We applied this decision support system to the make-or-buy
decision problem for school foodservice system considering the
multi-attributes in the decision making.
☞ Web-based GUI-Type Program is developed and demonstrated in
internet/intranet-based decision problem.
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Web-based
Decision Support System
Group-Joint
Work
Web-based Integrated
Decision Support System
Information
System
Internet/Intranet
Web-based Integrated Decision Support System
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Step 1 : Individual Evaluation of Alternatives
1) Brainstorming to Generate Alternatives and
to Define the Performance Factors
2) Evaluation of Alternatives Using AHP and
Fuzzy AHP methodologies
Step 2 : Integrate the Individual Analysis
- Heuristic Model 1, 2
- Fuzzy Set Priority Method
Step 3 : Application, Resource Allocation Model
- LP formulation using AHP weighted value
- Developed Computer Program
- Web-based
Internet/Intranet
Solution Builder
- GUI-type
Program
- Integrated
decision
support
system
Figure 2 . Three-step Approach of the Evaluation Model
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2. Properties of Make-or-Buy Decision
Problem
☞ Properties and issues responsible for differentiating one
type of make-or-buy decision problem .
- What backgrounds trigger a make-or-buy decision problem?
- What factors could be considered in make-or-buy decision
problem ?
- Along which dimensions should make-or-buy decision
problem be categorized ?
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Table 1. Major factors Influencing make-or-buy decision problem
(by literatures)
Performance Measure
Criteria
• Cost
• Quality
• Delivery speed
• Delivery reliability
• Volume flexibility
• Product flexibility
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Examples of measurement
Parameters
- Total unit cost
- Internal failure cost-scrap,
rework, rejected
- Delivery lead time
- Percentage of on-time delivery
- Average volume fluctuation
- Number of component substitutions
made over a given time period.
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Make
Low
High
Flexibility
Control
High
Low
Full Ownership
Partial Ownership
Retainer
Short Term Contract
Spot Market
Buy
Figure 3. Range of Source Structure of make-or-buy
decision problem
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Table 2. Example of performance Evaluation of make-or-buy
decision problem
Item
Manufacturing
Technology
Out Source Risk
Managerial Issues
Financial Issues
Operational Issues
Major Factors
- Importance of technology for
competitive advantage
- Maturity of technology
- Technology uncertainty
- Probability of future improvements
- Appropriation risk
- Technology diffusion
- End-product degradation
- Benchmarking
- Workforce stability
- Complexity level in planning, control, or supervision
- Assurance and reliability of supply
- Benchmarking
- Cost
- Investment
- Return on investment
- Manufacturing capability
- Quality
- lead time
- Volume uncertainty
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3. Three-step Approach of Decision
Alternative Analysis
Model
Step
11 :: Brainstorming
Stochastic Set
단계
- Generate
Alternatives and
?
적정보급센터의
소요Factors
- Define
the Performance
보급센터의
위치결정
- Relationship
Between
Factors
적정 보급지원수준결정
Hierarchy
Step
Secter-Clustering
Model Process
단계22 :: AHP,Analytic
- Construction Evaluation Structure
- Evaluation of Alternatives Using AHP
영역활당
and보급지원
Fuzzy AHP
methodologies
Zone-Based
-- Visual
시각화Program
• GYI-Type
GUI-Type
?•프로그램개발
SW
Developed
프로그램개발
?•사용자
사용자위주의
위주의
Customer
Responsive
프로그램개발
프로그램개발
Web
-- Web-based
기반의
통합화
Network
Netork
• System
System
System
System
• 확장성
Flexibility
,
확장성
• Usability
활용성
활용성
Model
Step 33 :: Aggregating
GA-VRP Model
-Evaluation
- 개별 of Alternatives Using AHP
and Fuzzy set ranking methodologies
- 종합우선순위산출
- Prioritize
the Prioritized Sets
운송 Mode
의 선정
Fig 3. Web-based Integrated Decision Model
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3.1 Brainstorming
☞ 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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3.2 Fuzzy -AHP Method
☞ The concepts and rules of fuzzy decision making provide us with
the necessary tools for structuring a decision from a kind of
information.
☞ From the Shannon's summed frequency matrix for complementary
cells,
-
☞ an additional fuzzy set matrix was made by considering Aij = 1 – A ji
for all cells. The fuzzy matrix complement cell values sum to 1 and
fuzzy set difference matrix is defined as follows :
R - R T = 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.
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Five Steps Fuzzy AHP :
To obtain fuzzy preferences, the following five steps were considered:
Step 1 : Find the summed frequency matrix ( using Shannon method )
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 - R T = U(A, B)-U(B, A), if U(A, B) > U(B, A),
X
= 0
otherwise
where, for U(A, B) quantifies, A is preferable to B.
1.ColA
Step 4 : Determine the portion of each project that is not dominated
as follows :
ND
= 1 - max(
,…
)
X 1.ColA X, 2.ColA
X n,.ColA
AColA
Step 5 : The priority of the fuzzy set is then the rank order of XND
values with a decreasing order.
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An example is shown as follows :
 0 .0
 0 .2
R= 
 0 .4
0.4
 0 .0
R T =  0.8
 0 .6

0.6
 0 .0
 0 .0
T
RR 
 0 .0
0.0
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0 .6
0 .0
0 .0 
0 .4 
0 . 1 0 .0
0 .6 0 .6
0 .4

0.0 
0 .2
0 .0
0 .4
0.1
0 .4 
0 .6 
0 .0
0 .4
0 .0
0 .4
0 .6

0 .0 

0.8
0 .0


0 .6 0 . 2 0 . 2 
0 .0 0 . 0 0 . 0 

0 .1 0 . 0 0 . 0

0.2 0.2 0.0
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ND
XB
ND
XA
ND
XC
ND
XD
= 1 - Max(0.0) = 1 - 0.0 = 1.0
= 1 - Max(1.0) = 1 - 1.0 = 0.0
= 1 - Max(0.2) = 1 - 0.2 = 0.8
= 1 - Max(0.2) = 1 - 0.2 = 0.8
The fuzzy set priority score : 1.0 > 0.0 > 0.8 > 0.8
and the alternative priority :
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A > C > D > B.
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3.3 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 evaluators 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,
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).
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3.4 In ternet /intranet Based Solution Builder
for Decision Support System
☞
Developed a solution builder using GUI-type Simulation
Software.
☞ Three steps of this solution builder.
3-step Algorithm for Optimal Solution
Brainstorming
AHP,
Fuzzy--AHP
Aggregate
Priorities
Figure 2. 3-step approach of Decision Support System
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Server
Protocol Encoding
Network
Internet/Intranet
Protocol Decoding
Client
Figure 4. Client and Server in Decision Support System
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Start
Start
Input Data
(Sub list)
Consistency
Test
Data Transform
Output
Frequency Matrix
Generate
Output
Output
Output
Heuristic
Borda Rank
1
Rank
order Order
Fuzzy
Priority
Consistency
Test
Summation
Frequency Matrix
Fuzzy
Matrix
Frequency
Matrix
Shannon
Heuristic 2
Copeland
Rank Order
Rank Order
Output
STOP
STOP
Fig 6. Schematic Flow Diagram of the Proposed Model
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The GUI-type program of Solution Builder-2001
Edit
File
New File
Show
Edit
Initial Size
Add
Enlargement
Reduce
Delete
Open
Tool Bar
Move
Close
Copy
Save
Standard
Search Node
Insert
Item
Status Bar
Save in Other Name
Select All
Project Information
Line Up
Character
Line up
Left - line
Encoding
Sort
Right - line
Printer Setup
AHP
Upper - line
Integrating Ranking
Change
Bottom - line
Pre-Show
Edit Levels
Centering
Print
Add Level
Centering Vertical
E-Mail Sending
Delete Level
Exit
Brainstorming
AHP
Integrate
Convert Brainstorming File into AHP File
AHP
Convert AHP File into Integrating Ranking File
Aggregating Ranking Method
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Aggregating Priority
AHP
Compute
Basic Information
Network
Server Setup
Tool
Copy in Clipboard
Partial Mode
Heuristic 1
Ideal Mode
Heuristic 2
Save in Image File
Perf. Sensitivity
Fuzzy Set Priority
Option
Dynamic Sensitivity
Trend Sensitivity
Node Information
Client Setup
Save in HTML
Help
Index
Homepage
Solution Builder?
Figure 5. Main-program of Solution Builder 2001
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☞ We used a brainstorming method and developed a GUI-type program
Edit
File
New File
Open
Close
Save
Save in Other Name
Project Information
Encoding
Printer Setup
Pre-Show
Print
E-Mail Sending
Exit
Show
Edit
Initial Size
Add
Enlargement
Delete
Move
Copy
Insert
Reduce
Tool Bar
Standard
Search Node
Item
Status Bar
Character
Select All
Line up
Line Up
Sort
Left - line
Change
Upper - line
Right - line
Bottom - line
Centering
Centering Vertical
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4. Make-or-Buy Decision Analysis in
Cellular Manufacturing Syatem
☞ Applied to Special decision problems; multi-objective,
multi-criterion, and multi-attributes structures for
Cellular manufacturing system
1) Make-or-buy decision making,
2) Determine the weighted value of each decision factors,
3) Resource allocation in manufacturing process,
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4.1 Cellular Manufacturing System
☞ Generally, the cellular manufacturing system uses many
kinds of machines and tools
☞ manufacturing process is a little bit complicated than
conventional production system
☞ In this study we used an oil pan manufacturing cell
☞ produces oil pan by 120 lot size, two workers,
and CNC machine:
- milling machine,
- boring machine,
- multi-spindle and drilling,
☞ CNC cell produces oil pan by 120 lot sizes.
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☞ Sample Example :
Oil pan manufacturing cell layout
CNC Drilling, Multi-spindle,
And tapping Machine
Governer
Assembly
Area
Finished
Oil pans
Oven
Casting
(Raw Material)
Dong-eui University, Korea
CNC Boring
Machine
CNC Milling,
Machine
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Figure 5. Sample output of AHP Structure
(Oil Pan Manufacturing Cell)
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AHP Structure
Figure 6. Sample Output of AHP structure of Cellular manufacturing System
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Level 1
A1 Final Object
(Final Object)
0.74
Make
Level 2
in house
B1
(Acq. Method)
0.38
Level 3
(Alternative)
P1 Proj. 1
B1
B2
B2
0.19
0.39
0.58
B2
0.26
P2 Proj. 2
0.29
0.32
0.16
0.20
0.06
Partial Make
Buy
tech import
0.19
P3 Proj. 3
0.21
0.21
0.15
B3
outsourcing
0.12
P4 Proj. 4
0.05
P5 Proj. 5
0.29
0.06
0.07
0.02
0.04
0.04
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Integration of Individual Evaluations :
Using the Heuristic 1, Heuristic 2, AHP, and Fuzzy Set Ranking methods,
we integrated the results of the individual reviewers as following,
where, B1: make in house not outsourcing,
B2: partial make in house and partial out sourcing for technology,
B3: all outsourcing,
P1, ···, P5 : cellular manufacturing alternatives
Table 4. Results of Integrated Priority
Majority Rule used Priority by Alternative
Methods
Priority by Alternatives
1. Heuristic Model 1
B1 (0.70), B2 (0.18), B3 (0.12)
P1 (0.29), P2 (0.30), P3 (0.18), P4 (0.15), P5 (0.08)
2. Heuristic model 2
B1 (0.73), B2 (0.23), B3 (0.05)
P1 (0.36), P2 (0.27), P3 (0.13), P4 (0.15), P5 (0.09)
3. Fuzzy Set Ranking
Method
B1 (0.74), B2 (0.20), B3 (0.06)
P1 (0.38), P2 (0.26), P3 (0.19), P4 (0.12), P5 (0.05)
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5. Resource Allocation in Cellular
Manufacturing System
☞ Using the AHP weighted value in resource allocation of
manufacturing works for Cellular manufacturing system
☞ For the budget allocation problem for this cellular
manufacturing works (alternatives) using the weighted
values of level 2, we formulated as following
optimization problem.
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n
m
i 1
j 1
Max  Wij X ij
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Formulation:
Max Z = 0.19X11 + 0.29X12 + 0.21X13 + 0.29X14 + 0.02X15
+ 0.39X21 + 0.32X22 + 0.21X23 + 0.06X24 + 0.04X25
+ 0.58X31 + 0.16X32 + 0.15X33 + 0.07X34 + 0.04X35
s.t. 11000X11 + 9000X12 + 12000X13 + 8000X14 + 7000X15 ≤ 25000
4000X21 + 5000X22 + 6000X23 + 5000X24 + 3000X25 ≤ 18000
4000X31 + 3000X32 + 5000X33 + 2000X34 + 1000X35 ≤ 11000
where, Xij = 0, 1
∀i, j
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6. Summary and Conclusion
☞
a three-step approach of web-based make-or-buy decision model
for multi-structured decision support system:
1) Brainstorming to define the alternatives and performance evaluation
factors,
2) Individual evaluation of the alternatives using fuzzy-AHP, heuristic
and fuzzy set reasoning methods, and
3) Integration of the individual evaluations using majority rule method.
☞ Developed a systematic and practical program
☞
The model was applied to a cellular manufacturing system problem
for the purpose of comparative validation.
☞
The results of various multi-structured decision support examples
for make-or-buy decision analysis and also resource allocation
problems are shown
By the sample results, the proposed model is a good method for the
performance evaluation of multi-attribute and multiple goals for
make-or-buy decision problems.
☞
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Thank You
Kainan University, Taiwan
Prof. Heung-Suk Hwnag
Kainan University
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