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A Decision Analysis Model for Supplier Selection Using Fuzzy-AHP

IMS 2005, Kunming, ChinaJuly 1-10, 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. Conventional Suppliers Performance

Evaluation and Third Party Logistic

3. A Decision Analysis Model for Supplier Selection  3.1 Fuzzy-Fuzzy AHP Method

3.2 Evaluation for Supplier Selection (Example) 4. Summary and Conclusions

Kainan University

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1. Introduction

☞ develop a supplier’s performance evaluation model for a thir 3rd party logistics (TPL) in supply chain management (SCM).

☞we use the solution methodology of analytic hierarchy process

(AHP, Fuzzy-AHP)

☞ Developed three-step decision analysis model which converts

the qualitative factors of suppliers into the quantitative

measures, reliability.

☞ Developed the computer program and successful

applications are shown in the field of supplier’s selection

problem.Dong-eui University, Korea

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2. Conventional Suppliers Performance Evaluation and Third Party Logistic

Public Tender

Step 1: Basic survey on logistics works characteristics

Step 2: Interview of executive of supplier company, and survey on possible amount of supply, cost information, management status.

Step 3: Second interview of executive of supplier company, and visit to supplier’s company. Finally decide the supplier

Figure 1. Conventional process of supplier selection

☞ Conventional process of supplier selection

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Supplier Selection Evaluation Model

Step 1: Basic Supplier Selection Evaluation Indicators

Step 2: Data Collection for Each Indicators

Step 3: Compute Weighted Value of Each Suppliers, 1) Fuzzy-AHP 2) Comparison with the other methods

Step 4: Validation the Results and Final Decision for Supplier Selection

☞ Proposed supplier selection model

Figure 2. Proposed Supplier selection model

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☞ The Difficulties in Analyzing Supplier’s Selection Problem

- The increasing of factors to be considered, - Difficulties for holding in common the SCM information between related industries, - Difficulties of evaluation for the supplier’s performance, - Strategic priority of objects and weighted values.

○ we propose a systematic approach and evaluation method using AHP and fuzzy-AHP methods to consider the hierarchical decision○ Structure considering all the related factors we develop computer software for the proposed method.

7Dong-eui University, Korea

Web-based Decision Support System

Internet/Intranet

Web-based Integrated Decision Support SystemWeb-based Integrated

Decision Support SystemInformation

SystemGroup-Joint

Work

Web-based Integrated Decision Support System

3. A Decision Analysis Model for Supplier’s Selection

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Fig 3. Web-based Integrated Decision Model

? 프로그램개발? 사용자위주의프로그램개발

- Web 기반의Netork

System확장성활용성- 개별

- 종합우선순위산출

단계1 : Stochastic Set-? 적정보급센터의소요보급센터의위치결정적정보급지원수준결정

Step 1 :Brainstorming Model- Generate Alternatives and - Define the Performance Factors- Relationship Between Factors

단계2 : Secter-Clustering Model

보급지원영역활당Zone-Based

Step 2 :AHP,Analytic Hierarchy Process- Construction Evaluation Structure- Evaluation of Alternatives Using AHP and Fuzzy AHP methodologies

3 : GA-VRP Model

운송Mode의 선정

Step 3 : Aggregating Model

-시각화GYI-Type

프로그램개발사용자 위주의프로그램개발

-통합화

System

확장성,

활용성

-Visual Program• GUI-Type• SW Developed

• Customer Responsive

- Web-based Network• System System• Flexibility• Usability-Evaluation of Alternatives Using AHP

and Fuzzy set ranking methodologies- Prioritize the Prioritized Sets

Three-step Approach of Decision Alternative Analysis

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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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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 = 1 – for all cells. The fuzzy matrix complement cell values sum to 1 and fuzzy set difference matrix is defined as follows : - = 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.

ijA jiA-

TRR

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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 - = 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 project that is not dominated as follows : = 1 - max( , , … , )

Step 5 : The priority of the fuzzy set is then the rank order of XND values with a decreasing order.

TRR

NDColAA

ColAX .1

1.ColAX 2.ColAX .n ColAX

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An example is shown as follows :

0.0 0.8 0.6 0.6

0.2 0.0 0.0 0.4

0.4 0.1 0.0 0.4

0.4 0.6 0.6 0.0

R =

0.04.04.06.0

6.00.00.06.0

6.01.00.08.0

4.04.02.00.0TR =

0.02.02.00.0

0.00.01.00.0

0.00.00.00.0

2.02.06.00.0

TRR

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= 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 : A > C > D > B.

ND

AX

ND

BX

ND

CX

ND

DX

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3) Internet /Intranet Based Solution Builderfor Decision Support System

-Brainstorming

-AHP, Fuzzy -AHP

AggregatePriorities-

3-step Algorithm for Optimal Solution

Brainstorming-

AHP, Fuzzy -AHP

AggregatePriorities

Figure 2. 3-step approach of Decision Support System

☞ Developed a solution builder usingGUI-type SimulationSoftware.

☞ Three steps of this solution builder.

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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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3.2 Evaluation for Supplier Selection (Example)

Major indicators Sub-indicators Rem

1. Serviceability Meet the lead time

Inventor rotation rate

Lead time

Customer satisfaction

Market share

2.Supply capability Production flexibility

Multi-item production capability

New item development/production capability

3. Quality Quality assurance

Return penalty

After service level

Table 1. Supplier Selection Indicators

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Cellular ManufacturingSys. performance

Service Level Supply Capability Quality

MeetLead Time

InvRot.Rate

Lead Time

Cust.Satis.

Market Share

Prod.Capa.

Multi-ItemProd.

New.Item

Devel.

QualityAssure

ReturnPenalty A/S

Cellular 1 Cellular 2 Cellular 3 Cellular 4

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Service Level Supply Capability Quality

Cellular 1 Supplier 2 Supplier 3 Supplier 4

C1 0.26 0.23 0.25 0.26 C2 0.26 0.21 0.29 0.24C3 0.08 0.09 0.08 0.75 C4 0.22 0.25 0.27 0.28C5 0.19 0.28 0.30 0.23 C6 0.25 0.33 0.20 0.22 C7 0.20 0.40 0.30 0.10 C8 0.20 0.40 0.20 0.20C9 0.28 0.31 0.30 0.11C10 0.60 0.15 0.05 0.20 C11 0.19 0.38 0.12 0.31

D1 0.24 0.28 0.21 0.27

MeetLead Time

InvRot.Rate

Lead Time

Cust.Satis.

Market Share

Prod.Capa.

Multi-ItemProd.

New.Item

Devel.

QualityAssure

ReturnPenalty A/S

Ci 0.25 0.396 0. 10 0.23 0.08 0.19 0. 495 0.23 0.58 0.189 0.21

Cellular ManufacturingSys. Performance

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20

Lev

el

1

Lev

el 1Supplier Perf.

Lev

el 2

Cellular ManufacturingSys. Performance

Service Level Supply Capability Quality

MeetLead Time

InvRot.Rate

Lead Time

Cust.Satis.

Market Share

Prod.Capa.

Multi-ItemProd.

New.Item

Devel.

QualityAssure

ReturnPenalty A/S

Cellular 1 Cellular 2 Cellular 3 Cellular 4

Service LevelSupply CapabilityQuality

Meet lead timeInv Rot. rateLead timeCust. SatiMarket shareProd. Capa.Multi-itemNew itemQAReturn penaltyAS

Lev

el 3

Lev

el 4

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Evaluation factors Weighted value

1. Serviceability, 0.48

Meet the lead time 0.190 0.091

Inventory rotation rate 0.315 0.151

Lead time 0.120 0.058

Customer satisfaction 0.301 0.145

Market share 0.074 0.035

2.Supply Capability, 0.25

Production flexibility 0.160 0.040

Multi-item Prod. Capa. 0.499 0.125

New item dev./ prod. 0.341 0.085

3. Quality, 0.27

Quality assurance 0.591 0.160

Return penalty 0.211 0.057

A/S 0.198 0.053

Table 2. Suppliers data for evaluation indicators

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Table 3. Results of integrated priority

　 Indicator Sup. 1 Sup. 2 Sup. 3 Sup. 4

Meet the lead time 91% 80% 85% 90%

Inventory rotation rate 15 times 12 times 16 times 13 times

Lead time 15 days 17 days 16 days 143 days

Customer satisfaction 42 48 52 55

Market share 12% 18% 19% 15%

Production flexibility 20 days 27 days 16 days 18 days

Multi-item Prod. Capa. 2 ea 4 ea 3 ea 1 ea

New item dev./ prod. 1 ea 2 ea 1 ea 1 ea

Quality assurance ISO9001 ISO9001 ISO9001 none

Return penalty 12% 3% 1% 4%

A/S 3 days 6 days 2 days 5 days

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Indicator Weighted value Supplier 1 Supplier 2 Supplier 3 Supplier

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P1: Meet the lead time 0.091 0.26, 0.024 0.23, 0.021 0.25, 0.023 0.26, 0.024

P2: Inventory rotation rate 0.151 0.36, 0.054 0.21, 0.031 0.29, 0.044 0.14, 0.021

P3: Lead time 0.058 0.58, 0.034 0.09, 0.005 0.08, 0.005 0.25, 0.015

P4: Customer satisfaction 0.145 0.32, 0.046 0.25, 0.036 0.27, 0.039 0.18, 0.026

P5: Market share 0.035 0.19, 0.007 0.28, 0.010 0.30, 0.011 0.23, 0.008

P6: Production flexibility 0.040 0.25, 0.010 0.33, 0.013 0.20, 0.009 0.22, 0.009

P7:Multi-item Prod. capacity.

0.125 0.20, 0.050 0.40, 0.05 0.30, 0.038 0.10, 0.013

P8: New item dev./ prod. 0.085 0.20, 0.017 0.40, 0.034 0.20, 0.017 0.20, 0.017

P9: Quality assurance 0.160 0.48, 0.077 0.11, 0.018 0.30, 0.048 0.11, 0.018

P10: Return penalty 0.057 0.60, 0.034 0.15, 0.009 0.05, 0.003 0.20, 0.011

P11: A/S 0.053 0.19, 0.018 0.38, 0.020 0.12, 0.006 0.31, 0.017

Total 1.000 0.368 0.180 0.243 0.179

Table 4. The weighted value for each suppliers candidates for sub-factors

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Evaluation method

Priority of Suppliers and Weighted Values of factors

SelectedSupplier

1. Fuzzy Set Ranking Method

S1 (0.368), S3  (0.243), S2 (0.180), S4 (0.179)

P9 (0.160), P2 (0.151), P4 (0.145), P7 (0.125),

P1 (0.091), P8 (0.085), P3 (0.058), P10 (0.057),

P11 (0.053), P6 (0.040), P5 (0.035),

S1:

Supplier #1

2. AHP Method

S3 (0.342), S1  (0.330), S2 (0.180), S4 (0.148)

P2 (0.170), P9 (0.141), P1 (0.140), P5 (0.125),

P4 (0.101), P3 (0.090), P10 (0.062), P8 (0.060),

P9 (0.041), P7 (0.040), P5 (0.030),

S3:

Supplier #3

Table 5. Results of Sample problem by both AHP and fuzzy set ranking method

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4. CONCLUSION ☞ In this research, developed a three-step approach based on web-based supplier’s selection decision model using multi-structured decision support systems

☞ Those steps are : 1) brainstorming to define the alternatives and performance evaluation factors, 2) individual evaluation the alternatives using fuzzy-AHP, heuristic and fuzzy set reasoning methods, and 3) integration the individual evaluations using majority rule method.

☞ Developed a Supplier’s Selection Model

☞ For a simple and efficient computation, we developed a systematic and practical web-based program to calculate all the algorithms.

☞ The model was applied to a sample supplier’s selection problem in

Taoyuan area of Taiwan for a third party logistics considering the 11

evaluation factors and 4 supplier candidates.

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☞ By the sample results of both AHP and fuzzy set reasoning method, it is known that the proposed model is a good method for the performance evaluation of multi-attribute and multiple goals.

☞ For the academic users, we would provide this software and user manual. ☞ For the problems of data collecting and its analysis in hierarchical decision structures, the DHP (Delphic Hierarchy Process) method can be used in future study.

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Kainan University, Taiwan

Prof. Heung-Suk Hwnag

Thank You

Kainan University