1 A Decision Analysis Model for Supplier Selection Using Fuzzy-AHP IMS 2005, Kunming, China July 1-10, 2005 Prof. Heung Suk Hwang, Department of Business Management, Kainan University, Taiwan Tel : ext Kainan University

2 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

3 1. Introduction ☞ develop a supplier’s performance evaluation model for a thi r 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

4 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

5 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

6 ☞ 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.

7 Dong-eui University, Korea Web-based Decision Support System Internet/Intranet Web-based Integrated Decision Support System Web-based Integrated Decision Support System Information System Group-Joint Work Web-based Integrated Decision Support System 3. A Decision Analysis Model for Supplier ’ s Selection

8 Fig 3. Web-based Integrated Decision Model Three-step Approach of Decision Alternative Analysis

9 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

10 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. -

11 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.

12 An example is shown as follows : = =

13 = 1 - Max(0.0) = = 1.0 = 1 - Max(1.0) = = 0.0 = 1 - Max(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.

14 6 3) Internet /Intranet Based Solution Builder for Decision Support System - Brainstorming - AHP, Fuzzy-AHP Aggregate Priorities - 3-step Algorithm for Optimal Solution Brainstorming - AHP, Fuzzy-AHP Aggregate Priorities Figure 2. 3-stepapproach ofDecision Support System ☞ Developed asolution builder usingGUI-type Simulation Software. ☞ Three stepsofthissolution builder.

15 Figure 4. Client and Server in Decision Support System

Evaluation for Supplier Selection (Example) Major indicatorsSub-indicators Rem 1. ServiceabilityMeet the lead time Inventor rotation rate Lead time Customer satisfaction Market share 2.Supply capabilityProduction flexibility Multi-item production capability New item development/production capability 3. QualityQuality assurance Return penalty After service level Table 1. Supplier Selection Indicators

17 Cellular Manufacturing Sys. performance Service LevelSupply CapabilityQuality Meet Lead Time Inv Rot. Rate Lead Time Cust. Satis. Market Share Prod. Capa. Multi- Item Prod. New. Item Devel. Quality Assure Return Penalty A/S Cellular 1Cellular 2Cellular 3Cellular 4

18 Service Level Supply Capability Quality Cellular 1Supplier 2Supplier 3Supplier 4 C C C C C C C C C C C D Meet Lead Time Inv Rot. Rate Lead Time Cust. Satis. Market Share Prod. Capa. Multi- Item Prod. New. Item Devel. Quality Assure Return Penalty A/S Ci Cellular Manufacturing Sys. Performance

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20 Level 1 Supplier Perf. Level 2 Cellular Manufacturing Sys. Performance Service LevelSupply CapabilityQuality Meet Lead Time Inv Rot. Rate Lead Time Cust. Satis. Market Share Prod. Capa. Multi- Item Prod. New. Item Devel. Quality Assure Return Penalty A/S Cellular 1Cellular 2Cellular 3Cellular 4 Service Level Supply Capability Quality Meet lead time Inv Rot. rate Lead time Cust. Sati Market share Prod. Capa. Multi-item New item QA Return penalty AS Level 3 Level 4

21 Evaluation factorsWeighted value 1. Serviceability, 0.48 Meet the lead time Inventory rotation rate Lead time Customer satisfaction Market share Supply Capability, 0.25 Production flexibility Multi-item Prod. Capa New item dev./ prod Quality, 0.27 Quality assurance Return penalty A/S Table 2. Suppliers data for evaluation indicators

22 Table 3. Results of integrated priority Indicator Sup. 1Sup. 2Sup. 3Sup. 4 Meet the lead time91%80%85%90% Inventory rotation rate15 times12 times16 times13 times Lead time15 days17 days16 days 143 days Customer satisfaction Market share12%12%18%19%15% Production flexibility20 days27 days16 days18 days Multi-item Prod. Capa.2 ea4 ea3 ea1 ea New item dev./ prod.1 ea2 ea1 ea Quality assuranceISO9001 none Return penalty12%3%3%1%1%4%4% A/S 3 days 6 days 2 days 5 days

23 Indicator Weighted value Supplier 1Supplier 2Supplier 3 Supplier 4 P 1 : Meet the lead time , , , , P 2 : Inventory rotation rate , , , , P 3 : Lead time , , , , P 4 : Customer satisfaction , , , , P 5 : Market share , , , , P 6 : Production flexibility , , , , P 7 :Multi-item Prod. capacity , , , , P 8 : New item dev./ prod , , , P 9 : Quality assurance , , , , P 10 : Return penalty , , , , P 11 : A/S , , , , Total Table 4. The weighted value for each suppliers candidates for sub-factors

24 Evaluation method Priority of Suppliers and Weighted Values of factors Selected Supplier 1. Fuzzy Set Ranking Method S 1 (0.368), S 3 (0.243), S 2 (0.180), S 4 (0.179) P 9 (0.160), P 2 (0.151), P 4 (0.145), P 7 (0.125), P 1 (0.091), P 8 (0.085), P 3 (0.058), P 10 (0.057), P 11 (0.053), P 6 (0.040), P 5 (0.035), S 1 : Supplier #1 2. AHP Method S 3 (0.342), S 1 (0.330), S 2 (0.180), S 4 (0.148) P 2 (0.170), P 9 (0.141), P 1 (0.140), P 5 (0.125), P 4 (0.101), P 3 (0.090), P 10 (0.062), P 8 (0.060), P 9 (0.041), P 7 (0.040), P 5 (0.030), S 3 : Supplier #3 Table 5. Results of Sample problem by both AHP and fuzzy set ranking method

25 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.

26 ☞ 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.

27 Kainan University, Taiwan Prof. Heung-Suk Hwnag Thank You Kainan University