This paper is designed to present the effectiveness of group multicriteria decision making in automotive manufacturing company focusing on the selection of suppliers in Malaysia. The process of selecting suppliers is one of the most critical and challenging endeavor in any supply chain management. There are five decision making tools being analyzed in this study, namely, analytical hierarchy process (AHP), fuzzy analytical hierarchy process (FAHP), technique for order performance by similarity to ideal solution (TOPSIS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), and fuzzy analytical hierarchy process integrated with fuzzy technique for order performance by similarity to ideal solution (FAHPiFTOPSIS). The scores of ranking among the suppliers in each MCDM tools (AHP, FAHP, TOPSIS, FTOPSIS, and FAHPiFTOPSIS) show significantly comparable variation. Scores of the best supplier is then compared to the lowest supplier for all MCDM tools whereby this reflects that the highest percentage goes to TOPSIS with scoring of 79.37%. On the contrary, FAHPiFTOPSIS demonstrated the lowest score variation of 22.42% which indicates that FAHPiFTOPSIS is able to eliminate biasness in supplier selection process.
A supply chain is a system which connects several departments from procurement of raw materials, to manufacturing, warehousing, and distribution of the products to the customers. Part of the contribution to supply chain complexity is the geographical outsourcing for cheaper supply and new market penetration. The complexity of supply chain is aggravated further when industry rely too much on multirange products and frequent introduction of new products as a strategy to meet different segmented market demands.
In automotive industry, such situation is rampant. The frequent introduction of new models and shorter product lifecycles compounded by fast order-delivery require high level of agility and flexibility of the suppliers, thereby, exacerbating the supply chain complexity. Hence, the right selection of supplier becomes more complicated. With the mounting complexity of supply chain, the selection of the suppliers becomes very challenging. The recent incident in Fukushima, Japan, devastated by massive earthquake and nuclear disaster, and major floods in Thailand, had affected severely many Malaysian industries as well as industries in other parts of the world [
In the previous years the automotive industry has witnessed an unprecedented turmoil. Such crises had affected the European and Asian automotive industry and had gravely stricken the American automobile industry. The first half of 2009 had indeed been a very faltering year due to economy recession. Based on press report released by MAA [
Vehicles Sales in 2008 and 2009.
Segment | September | Year-to-date-September | ||||
---|---|---|---|---|---|---|
2009 | 2008 | Variance | 2009 | 2008 | Variance | |
PV (passenger vehicles) | 42,039 | 46,476 | 0.90 | 361,463 | 392,393 | 0.92 |
CV (commercial vehicles) | 4,030 | 4,253 | 0.95 | 36,156 | 37,520 | 0.96 |
| ||||||
Total | 46,069 | 50,729 | 0.91 | 397,619 | 429,913 | 0.92 |
Vehicles sales in 2008 and 2009.
The economy is currently in transition from recession to recovery in tandem with financial markets improvement. Year 2010 shows an increase trend in automotive industry. In 2010, the total industry volume of vehicles has picked up and increased to 605,156 units and, in fact, surpassed the highest recorded volume of 552,316 units in 2005.
Table
Summary of passenger and commercial vehicles produced and assembled in Malaysia from 1980 to 2010.
Year | Passenger cars | Commercial vehicles | 4 × 4 vehicles | Total vehicles |
---|---|---|---|---|
1980 | 80,422 | 23,805 | — | 104,227 |
1985 | 69,769 | 37,261 | — | 107,030 |
1990 | 116,526 | 63,181 | 11,873 | 191,580 |
1995 | 231,280 | 45,805 | 11,253 | 288,338 |
2000 | 295,318 | 36,642 | 27,235 | 359,195 |
2005 | 422,225 | 95,662 | 45,623 | 563,510 |
2006 | 377,952 | 96,545 | 28,551 | 503,048 |
2007 | 403,245 | 38,433 | — | 441,678 |
2008 | 484,512 | 46,298 | — | 530,810 |
2009 | 447,002 | 42,267 | — | 489,269 |
2010 | 522,568 | 45,147 | — | 567,715 |
(Source: [
Note:
(i) Passenger vehicle industry reclassified in January 2007 and includes all passenger carrying vehicles, that is, passenger cars, 4WD/SUV, window van, and MPV models.
(ii) Commercial vehicles also reclassified on 1 January 2007 and include trucks, prime movers, pick-up, panel vans, buses and others.
Bar graph of passenger and commercial vehicles produced and assembled in Malaysia from 1980 to 2010 [
The data in Table
Line graph of passenger and commercial vehicles produced and assembled in Malaysia from 2005 to 2010 [
In Figure
In May 2011, based on a press release by Malaysian Automotive Association (MAA), automotive industry has shown a healthy increase of sales compared to the same period the year before (see Table
Summary of passenger and commercial vehicles produced and assembled in Malaysia from May 2010 to May 2011.
Segment | May | Year-to-date May | ||||
---|---|---|---|---|---|---|
2011 | 2010 | Variance | 2011 | 2010 | Variance | |
PV (passenger vehicles) | 40,936 | 46,259 | 0.88 | 228,816 | 222,977 | 1.03 |
CV (commercial vehicles) | 5,109 | 4,624 | 1.1 | 26,597 | 24,133 | 1.1 |
| ||||||
Total | 46,045 | 50,883 | 0.9 | 255,413 | 247,110 | 1.03 |
(Source: [
Currently, automotive industry is slowly and gradually shifting towards Asian countries, mainly due to high cost and saturation of automobile industry in the west and the increase in demand in Asia. The principal driving markets for Asian automobile industry are China, India, and ASEAN nations. The future of automotive industry in the Asian countries in particular Thailand, Philippines, Malaysia, and Indonesia is bright and promising because of the (ASEAN Free Trade Area) AFTA with tariffs currently at 0 to 5% [
Malaysia is a country that has a long history of making cars. There are many local and international cars assemblers and manufacturers in the country. The employees of the industry are widely regarded as skilled, well educated, and trainable. Located strategically in the ASEAN region which has a population of more than 500 million people, Malaysia offers vast opportunities for global automotive and component manufacturers and suppliers to set up their manufacturing and distribution operations in the country. Mercedes-Benz assembly plant has proven its great prospect where its plant located in Pekan, Pahang, initially assembled only 4 units per day for one model and today assembles the S-Class, E-Class, and C-Class Mercedes with annual volume reaching 5,000 units [
Supplier selection decision process considers qualitative and quantitative criteria [
One of the MCDM is the analytical hierarchy process (AHP) whereby it is a theory of mathematical for decision making and measurement introduced by Saaty [
In making decision for supplier selection, it is agreed by [
This system describes a matter with a certain degree of characteristic which is also known as membership function. Membership function is a graphical representation which associates with the magnitude of input and ultimately determines an output response. There are different membership functions associated with each input and output response. Details about membership function are explained in Section
The remainder of this paper is organized as follows: in Section
Fuzzy sets can be simply defined as a set with fuzzy boundaries whereby the values of boundaries is multivalued unlike the two-valued Boolean logic. In the fuzzy theory, fuzzy set
The above set explains the membership (characteristic) functions of
There are 2 types of TFN which are symmetry and unsymmetry. Symmetry TFN is used in this paper since it enables users to easily calculate, understand, and capture the vagueness in people’s verbal assessments [
TFN can be defined in three numbers, (
Symmetry TFN.
Throughout this study, the commonly used algebraic operations for fuzzy numbers are addition and multiplication. The fuzzy operators shown below were adapted from [
The analytical hierarchy process (AHP) was first developed by Saaty, in mid of 1970 [
AHP is simple, systematic, and a very useful approach which integrates the matrix theory. In defining the weights of criteria and comparing the alternatives, a set of pair-wise comparison has been developed by Saaty [
Linguistic variables for importance of the criteria.
Linguistic variables | Scale of fuzzy number |
---|---|
Very low (VL) | (0, 0, 0.1, 0.2) |
Low (L) | (0.1, 0.2, 0.2, 0.3) |
Medium low (ML) | (0.2, 0.3, 0.4, 0.5) |
Medium (M) | (0.4, 0.5, 0.5, 0.6) |
Medium high (MH) | (0.5, 0.6, 0.7, 0.8) |
High (H) | (0.7, 0.8, 0.8, 0.9) |
Very high (VH) | (0.8, 0.9, 1.0, 1.0) |
Linguistic variables for performance of the alternatives.
Linguistic variables | Scale of fuzzy number |
---|---|
Very poor (VP) | (0, 0, 1, 2) |
Poor (P) | (1, 2, 2, 3) |
Medium poor (MP) | (2, 3, 4, 5) |
Fair (F) | (4, 5, 5, 6) |
Medium good (MG) | (5, 6, 7, 8) |
Good (G) | (7, 8, 8, 9) |
Very good (VG) | (8, 9, 10, 10) |
There are three principles used in solving problems with AHP [ AHP establishes the priorities based on sets of pair-wise comparisons. AHP score is built on human attributes and judgements where the intensity of each attribute or judgment is set according to its hierarchy over the other. AHP synthesizes these judgments by using the hierarchy framework to obtain the overall priority of the elements or factors.
The concept of AHP and FAHP is mainly the same, and the difference of FAHP is that it analyses the numbers in terms of fuzzy numbers but as for AHP the numbers analyzed are crisp numbers. FAHP was firstly introduced by Van Laarhoven and Pedrycz in 1983 [
There are 6 steps in the process of decision making using FAHP. These steps are as follows. Firstly, form a decision matrix of the importance of each criterion with respect to each other. The membership function of triangular fuzzy number is defined by three real numbers Calculate the The next step is the calculation of Upon getting the result for The final stage is defuzzification whereby triangular fuzzy numbers are transformed into real numbers which is defined as weights (
The steps of 1 to 6 will be repeated for each alternative in terms of the criterion which will be called as the weight of factors in terms of alternatives.
TOPSIS has been used in various fields and a number of applications such as outsourcing of logistics service, weapon selection, supplier selection analyzing business competition, and many other applications [ Obtain normalized decision matrix for Construct weighted normalized decision matrix, Identify the ideal alternative (extreme performance on each criterion, Develop a distance measure over criterion to both ideal ( For each alternative, determine the relative closeness to the ideal solution,
TOPSIS only considers crisp values, whereas human judgments are usually uncertain and could not be evaluated using fix numbers. In spite of this, fuzzy numbers are used to replace all the crisp values in TOPSIS. In decision making, it is difficult to give a certain judgement, hence by integrating fuzzy logic and TOPSIS it will eliminate the uncertainty of the decision made [
In performing FTOPSIS, Chen in 2000 [
A committee of decision makers were formed to evaluate the alternatives based on the goal defined using the linguistic variables in Table
Linguistic variables for the importance weight of each criterion.
Linguistic variables | Fuzzy value |
---|---|
Very low (VL) | (0, 0, 0.1) |
Low (L) | (0, 0.1, 0.3) |
Medium low (ML) | (0.1, 0.3, 0.5) |
Medium high (MH) | (0.5, 0.7, 0.9) |
High (H) | (0.7, 0.9, 1.0) |
Very high (VH) | (0.9, 1.0, 1.0) |
Appropriate linguistic variables for the ratings of the alternative with respect to criteria were chosen and these linguistic ratings used are taken from Table
Linguistic variables for the ratings.
Linguistic variables | Fuzzy value |
---|---|
Very poor (VP) | (0, 0, 1) |
Poor (P) | (0, 1, 3) |
Medium poor (MP) | (1, 3, 5) |
Fair (F) | (3, 5, 7) |
Medium good (MG) | (5, 7, 9) |
Good (G) | (7, 9, 10) |
Very good (VG) | (9, 10, 10) |
The importance weights of criteria and the ratings of three candidates are converted into fuzzy values. In order to obtain the fuzzy decision matrix and fuzzy weights of three alternatives, average value of each criteria for 3 decision makers was taken.
The next step of the FTOPSIS analysis is the normalization of fuzzy decision matrix. This step is performed by taking each respective value and divide it with the maximum number of the particular criteria for all the 3 candidates.
Construction of fuzzy weighted normalized decision matrix was then generated. In order to generate these values, values in Step
Fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) were then defined based on the values of normalized positive and negative triangular fuzzy numbers.
Distance of each alternative from FPIS and FNIS was then calculated. The distance of two fuzzy numbers is calculated in (
After complete computing the positive and negative distance, these values were then summed up to obtain the total distance measurement for positive values,
Finally, obtain the final ranking of the alternative closeness coefficient, CC. In order to achieve the CC, (
As per quoted by Krohling and Campanharo in 2011 [
Integration between fuzzy AHP and fuzzy TOPSIS is believed to be able to make decision making more practical and reliable for decision makers due to its human-like thinking capability. For FAHPiFTOPSIS, the process of decision making comprises of 9 steps [ Construct pair-wise comparison matrices among all the elements or criteria in the dimensions of the hierarchy system and convert it into fuzzy. Define the fuzzy geometric mean as
Define the fuzzy weights as
Aggregating the ranking of criteria and suppliers’ performance criteria ranking as
Suppliers’ performance as
Normalizing the fuzzy decision matrix as
Weighted normalization of the fuzzy decision matrix as
Distance to positive and negative ideal solution using vertex method [ Closeness coefficient as
All the 5 models mentioned above are basically focusing on the theoretical part of the decision making. In this subsection the models are compared with respect to the differences. Based on Table
Summary of advantages of MCDM models.
Differences | AHP | FAHP | TOPSIS | FTOPSIS | FAHPiFTOPSIS |
---|---|---|---|---|---|
Evaluators are able to represent the relative importance and interaction of multiple criteria in the supplier selection process [ |
Y | ||||
Bias in decision making can be reduced by the flexibility and ability to check on inconsistency and able to decompose and problems into hierarchies of criteria. [ |
Y | Y | |||
Accurate, effective, and systematic decision support tool [ |
Y | ||||
Effectively handle both qualitative and quantitative data and easy to implement and understand [ |
Y | Y | Y | Y | Y |
No tedious pairwise comparison and weights can be directly assigned by decision makers which makes the practical application of the methodology very straightforward [ |
Y | Y | |||
TOPSIS has been proved to be one of the best methods addressing rank reversal issue, that is, the change in the ranking of the alternatives when a nonoptimal alternative is introduced [ |
Y | Y | Y | ||
Fuzzy AHP is preferable for widely spread hierarchies, where few importance/rating pair-wise comparisons are required at lower level trees [ |
Y | ||||
Can adopt linguistic variables [ |
Y | Y | Y | ||
By using fuzzy AHP and fuzzy TOPSIS, uncertainty and vagueness from subjective perception and the experiences of decision maker can be effectively represented and reached to a more effective decision [ |
Y | ||||
Ranking results for both methods are similar which shows that when decision makers are consistent in determining the data, two methods independently, and the ranking results will be the same and will handle fuzziness of data involved in decision making effectively [ |
Y | Y |
Note: Y means the differences are applicable for the respective MCDM.
Table
Referring to Table
Summary of disadvantages of MCDM models.
Differences | AHP | FAHP | TOPSIS | FTOPSIS | FAHPiFTOPSIS |
---|---|---|---|---|---|
When a problem is decomposed into subsystems, the decision problem might become |
Y | ||||
AHP’s using crisp number, hence not able to reflect human thinking style [ |
Y | ||||
When a number of alternatives and criteria increased, pair-wise comparison becomes cumbersome and risk of inconsistencies grows [ |
Y | ||||
Problem is not decomposed into hierarchy hence decision maker might encounter difficulty to simplify the problem | Y | Y | |||
Integration with FAHP resulted in a number of extra steps to be followed | Y | ||||
Does not take into account the uncertainty associated with the mapping of one’s judgment to a number [ |
Y | ||||
FAHP requires more complex computations than FTOPSIS which includes pair wise comparison [ |
Y | ||||
In the extent analysis of FAHP, the priority weights of criterion or alternative can be equal to zero [ |
Y |
Note: Y means the differences are applicable for the respective MCDM.
Since FAHPiFTOPSIS integrated with FAHP and FTOPSIS, there are more steps that need to be performed. When comparing FAHP to FTOPSIS, FAHP requires more complex computation which includes pairwise comparison. When the number of alternatives and criteria increased, pairwise comparison becomes cumbersome. For AHP and FAHP besides the advantage of decomposing the problems, this might also lead to a large and lengthy process. AHP and TOPSIS are not integrated; hence, the models will not be able to reflect human thinking style.
Companies involved in this study are automotive supply companies, Malaysian automotive manufacturers, and foreign automotive manufacturing plants operating in Malaysia together with the cooperation of four major tyre manufacturers in Malaysia. The process of the study started with the determination of the important criteria and their ranking which was made through surveys conducted on supply companies for automotive manufacturers. The data and information collected were analysed in depth and compared against traditional criteria.
Five methods of MCDM which are AHP, FAHP, TOPSIS, FTOPSIS, and an integrated FAHPiFTOPSIS model will be investigated. The effectiveness of all the five MCDM models will be compared. This study will be conducted through questionnaires distributed to Malaysian automotive manufacturers and foreign companies which supply preassembled parts to automotive manufacturers. The respondents of this study comprise 4 major tyre manufacturers and 12 automotive manufacturing plants.
This phase starts with the design of the data collection protocol, which concerns data to be collected and how to collect the data. The data used in this paper are collected and obtained from several sources such as questionnaires, face-to-face interviews, and phone interviews. The questionnaire aimed at identifying the priorities of various criteria highlighted in selecting suppliers as attached in Appendix
In this phase, real quantitative data are used. After identifying the criteria which are critical and important in selecting suppliers, the next stage of the project will be structuring the existing AHP, FAHP, TOPSIS, FTOPSIS, and FAHPiFTOPSIS models for supplier selection using data obtained from the questionnaire. Results obtained from the survey which consists of goal, criteria, subcriteria, and alternatives are used in structuring the models.
The models used for AHP and TOPSIS are from the founders themselves which are Saaty [
Once these five models are structured, the results produced by the models are then analysed. The purpose of this analysis is to determine the qualities, shortcomings, and biasness of the models. For AHP, FAHP, and FAHPiFTOPSIS, bias in decision making can be eliminated due to its flexibility to check the inconsistency and ability to decompose problems into hierarchies [
The final step of this paper is to validate the models using existing data collected from the questionnaire. This is to prove the consistency of the results for the five models with the results of the supplier selection of in-house procedures of the companies under study. The results of the models are then analyzed with the current practice of the selected companies. The main purpose of this step is to test if the results are consistent with the models and also to observe the best method relevant to the companies in obtaining the best supplier for automotive industry.
The system architecture example of the models is illustrated in Appendix “ “ “
In the first component (
In Tables
Weight and level of importance of criteria 1 to 9 of 12. Major automotive manufacturers.
Criteria | Level of importance | Respondent | Mean |
---|---|---|---|
(a) Delivery/lead time (DT) | |||
|
58 | 12 | 4.83 |
|
53 | 12 | 4.42 |
|
50 | 12 | 4.17 |
|
56 | 12 | 4.67 |
|
58 | 12 | 4.83 |
|
50 | 12 | 4.17 |
| |||
Total | 325 | 72 | |
| |||
Weightage (mean) | 4.5139 |
|
0.31 |
Weightage (mean) (%) | 11.7247 | Var | 0.10 |
Importance weight | 0.1175 | ||
| |||
(b) Support service (SS) | |||
|
56 | 12 | 4.67 |
|
57 | 12 | 4.75 |
|
56 | 12 | 4.67 |
| |||
Total | 169 | 36 | |
| |||
Weightage (mean) | 4.6944 |
|
0.05 |
Weightage (mean) (%) | 12.1937 | Var | 0.002 |
Importance weight | 0.12194 | ||
| |||
(c) Quality factor (QF) | |||
|
55 | 12 | 4.58 |
|
58 | 12 | 4.83 |
|
55 | 12 | 4.58 |
|
53 | 12 | 4.42 |
|
55 | 12 | 4.58 |
|
57 | 12 | 4.75 |
|
51 | 12 | 4.25 |
|
54 | 12 | 4.50 |
|
53 | 12 | 4.42 |
|
53 | 12 | 4.42 |
| |||
Total | 544 | 120 | |
| |||
Weightage (mean) | 4.5333 |
|
0.17 |
Weightage (mean) (%) | 11.7753 | Var | 0.03 |
Importance weight | 0.11775 | ||
| |||
(d) Technology (TE) | |||
|
56 | 12 | 4.67 |
|
48 | 12 | 4.00 |
|
47 | 12 | 3.92 |
|
51 | 12 | 4.25 |
|
45 | 12 | 3.75 |
|
46 | 12 | 3.83 |
|
50 | 12 | 4.17 |
| |||
Total | 343 | 84 | |
| |||
Weightage (mean) | 4.0833 |
|
0.31 |
Weightage (mean) (%) | 10.6064 | Var | 0.10 |
Importance weight | 0.10606 | ||
| |||
(e) Price/cost (PR) | |||
|
56 | 12 | 4.67 |
|
52 | 12 | 4.33 |
|
44 | 12 | 3.67 |
|
46 | 12 | 3.83 |
|
46 | 12 | 3.83 |
| |||
Total | 244 | 60 | |
| |||
Weightage (mean) | 4.0667 |
|
0.42 |
Weightage (mean) (%) | 10.5631 | Var | 0.18 |
Importance weight | 0.10563 | ||
| |||
(f) Factory capacity and capability (FC) | |||
|
51 | 12 | 4.25 |
|
49 | 12 | 4.08 |
|
49 | 12 | 4.08 |
|
54 | 12 | 4.50 |
|
52 | 12 | 4.33 |
|
51 | 12 | 4.25 |
| |||
Total | 306 | 72 | |
| |||
Weightage (mean) | 4.2500 |
|
0.16 |
Weightage (mean) (%) | 11.0393 | Var | 0.03 |
Importance weight | 0.11039 | ||
| |||
(g) Supplier background (SB) | |||
|
54 | 12 | 4.50 |
|
48 | 12 | 4.00 |
|
52 | 12 | 4.33 |
|
50 | 12 | 4.17 |
|
49 | 12 | 4.08 |
|
50 | 12 | 4.17 |
| |||
Total | 303 | 72 | |
| |||
Weightage (mean) | 4.2083 |
|
0.18 |
Weightage (mean) (%) | 10.9311 | Var | 0.03 |
Importance weight | 0.10931 | ||
| |||
(h) Flexibility (FL) | |||
|
47 | 12 | 3.92 |
|
50 | 12 | 4.17 |
|
45 | 12 | 3.75 |
|
48 | 12 | 4.00 |
| |||
Total | 190 | 48 | |
| |||
Weightage (mean) | 3.9583 |
|
0.17 |
Weightage (mean) (%) | 10.2817 | Var | 0.30 |
Importance weight | 0.10282 | ||
| |||
(i) Other management system requirements (MS) | |||
|
52 | 12 | 4.33 |
|
53 | 12 | 4.42 |
|
50 | 12 | 4.17 |
|
42 | 12 | 3.50 |
|
53 | 12 | 4.42 |
|
50 | 12 | 4.17 |
|
46 | 12 | 3.83 |
|
50 | 12 | 4.17 |
|
45 | 12 | 3.75 |
|
44 | 12 | 3.67 |
|
56 | 12 | 4.67 |
|
56 | 12 | 4.67 |
|
54 | 12 | 4.50 |
|
53 | 12 | 4.42 |
| |||
Total | 704 | 168 | |
| |||
Weightage (mean) | 4.1905 |
|
0.32 |
Weightage (mean) (%) | 10.8847 | Var | 0.56 |
Importance weight | 0.10885 | ||
| |||
Grand total weightage (mean) | 38.50 |
Weight and level of importance of nine (9) criteria of 4 major tyre suppliers.
Criteria | Level of importance | Respondent | Mean |
---|---|---|---|
(a) Delivery/lead time (DT) | |||
|
19 | 4 | 4.75 |
|
17 | 4 | 4.25 |
|
15 | 4 | 3.75 |
|
19 | 4 | 4.75 |
|
19 | 4 | 4.75 |
|
16 | 4 | 4 |
| |||
Total | 105 | 24 | |
| |||
Weightage (mean) | 4.3750 |
|
0.44 |
Weightage (mean) (%) | 11.6600 | Var | 0.19 |
Importance weight | 0.11660 | ||
| |||
(b) Support service (SS) | |||
|
19 | 4 | 4.75 |
|
17 | 4 | 4.25 |
|
18 | 4 | 4.5 |
| |||
Total | 54 | 12 | |
| |||
Weightage (mean) | 4.5000 |
|
0.25 |
Weightage (mean) (%) | 11.9931 | Var | 0.06 |
Importance weight | 0.11993 | ||
| |||
(c) Quality factor (QF) | |||
|
19 | 4 | 4.75 |
|
19 | 4 | 4.75 |
|
15 | 4 | 3.75 |
|
15 | 4 | 3.75 |
|
15 | 4 | 3.75 |
|
19 | 4 | 4.75 |
|
14 | 4 | 3.5 |
|
15 | 4 | 3.75 |
|
15 | 4 | 3.75 |
|
17 | 4 | 4.25 |
| |||
Total | 163 | 40 | |
| |||
Weightage (mean) | 4.0750 |
|
0.50 |
Weightage (mean) (%) | 10.8605 | Var | 0.25 |
Importance weight | 0.10860 | ||
| |||
(d) Technology (TE) | |||
|
16 | 4 | 4 |
|
16 | 4 | 4 |
|
16 | 4 | 4 |
|
19 | 4 | 4.75 |
|
12 | 4 | 3 |
|
15 | 4 | 3.75 |
|
16 | 4 | 4 |
| |||
Total | 110 | 28 | |
| |||
Weightage (mean) | 3.9286 |
|
0.51 |
Weightage (mean) (%) | 10.4702 | Var | 0.26 |
Importance weight | 0.10470 | ||
| |||
(e) Price/cost (PR) | |||
|
18 | 4 | 4.5 |
|
15 | 4 | 3.75 |
|
13 | 4 | 3.25 |
|
13 | 4 | 3.25 |
|
16 | 4 | 4 |
| |||
Total | 75 | 20 | |
| |||
Weightage (mean) | 3.7500 |
|
0.53 |
Weightage (mean) (%) | 9.9943 | Var | 0.28 |
Importance weight | 0.09994 | ||
| |||
(f) Factory capacity and capability (FC) | |||
|
16 | 4 | 4.00 |
|
16 | 4 | 4.00 |
|
17 | 4 | 4.25 |
|
19 | 4 | 4.75 |
|
17 | 4 | 4.25 |
|
15 | 4 | 3.75 |
| |||
Total | 100 | 24 | |
| |||
Weightage (mean) | 4.1667 |
|
0.34 |
Weightage (mean) (%) | 11.1048 | Var | 0.12 |
Importance weight | 0.11105 | ||
| |||
(g) Supplier background (SB) | |||
|
16 | 4 | 4 |
|
15 | 4 | 3.75 |
|
19 | 4 | 4.75 |
|
16 | 4 | 4 |
|
16 | 4 | 4 |
|
16 | 4 | 4 |
| |||
Total | 98 | 24 | |
| |||
Weightage (mean) | 4.0833 |
|
0.34 |
Weightage (mean) (%) | 10.8827 | Var | 0.12 |
Importance weight | 0.10883 | ||
| |||
(h) Flexibility (FL) | |||
|
17 | 4 | 4.25 |
|
18 | 4 | 4.50 |
|
17 | 4 | 4.25 |
|
18 | 4 | 4.50 |
| |||
Total | 70 | 16 | |
| |||
Weightage (mean) | 4.3750 |
|
0.14 |
Weightage (mean) (%) | 11.6600 | Var | 0.02 |
Importance weight | 0.11660 | ||
| |||
(i) Other management system requirements (MS) | |||
|
17 | 4 | 4.25 |
|
18 | 4 | 4.50 |
|
17 | 4 | 4.25 |
|
16 | 4 | 4.00 |
|
19 | 4 | 4.75 |
|
16 | 4 | 4.00 |
|
15 | 4 | 3.75 |
|
17 | 4 | 4.25 |
|
18 | 4 | 4.50 |
|
18 | 4 | 4.50 |
|
17 | 4 | 4.25 |
|
17 | 4 | 4.25 |
|
17 | 4 | 4.25 |
|
17 | 4 | 4.25 |
| |||
Total | 239 | 56 | |
| |||
Weightage (mean) | 4.2679 | 0.25 | |
Weightage (mean) (%) | 11.3745 | 0.06 | |
Importance weight | 0.11374 | ||
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Grand total weightage (mean) | 37.5214 |
Upon analyzing the various decision making tools described in Section
Actual scores of Supplier 1, 2, 3, and 4 according to 5 decision making tools.
AHP | FAHP | TOPSIS | FTOPSIS | FAHPiFTOPSIS | |
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Supplier 1 | 0.16451 | 0.18905 | 0.23400 | 0.31199 | 0.25827 |
Supplier 2 | 0.16414 | 0.11949 | 0.18632 | 0.29032 | 0.25693 |
Supplier 3 | 0.43261 | 0.41440 | 0.98365 | 0.92561 | 0.48116 |
Supplier 4 | 0.23874 | 0.25259 | 0.46652 | 0.49101 | 0.37255 |
Summary of various decision making tools based on actual score.
Referring to Table
Normalised scores of Supplier 1, 2, 3, and 4 according to 5 decision making tools.
AHP | FAHP | TOPSIS | FTOPSIS | FAHPiFTOPSIS | |
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0.16451 | 0.14389 | 0.12510 | 0.15453 | 0.18867 |
Supplier 2 | 0.16414 | 0.16597 | 0.09961 | 0.14380 | 0.18769 |
Supplier 3 | 0.43261 | 0.49307 | 0.52588 | 0.45847 | 0.35149 |
Supplier 4 | 0.23874 | 0.19707 | 0.24941 | 0.24320 | 0.27215 |
Summary of various decision making tools based on normalised priority.
Generally, it can be observed from Figure
The scores for the ranking among the four suppliers in each MCDM tool (AHP, FAHP, TOPSIS, and FTOPSIS) show significantly comparable variation. The percentage of variation scores for 4 suppliers is −36.9%, −40.33%, 26.8%, and 32.2% when comparing AHP, FAHP, TOPSIS, and FTOPSIS with FAHPiFTOPSIS, respectively. These variations are obtained from the average of the original scoring and then compared with respect to FAHPiFTOPSIS. Table
Variations of models with respect to each other.
AHP | FAHP | TOPSIS | FTOPSIS | FAHPiFTOPSIS | |
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With respect to AHP | — | −2.5% | 46.5% | 50.5% | 26.9% |
With respect to FAHP | 2.4% | — | 47.8% | 51.7% | 28.7% |
With respect to TOPSIS | −87% | −91.7% | — | 7.4% | −36.6% |
With respect to FTOPSIS | −101.9% | −107% | −7.9% | — | −47.5% |
With respect to FAHPiFTOPSIS | −36.9% | −40.33% | 26.8% | 32.2% | — |
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Total percentage of variations | −223.5% | 241.53% | 113.2% | 141.8% | −28.5% |
The following evaluation to determine the variation is based on the difference of scores achieved by the best supplier with the less preferred supplier, and Table
Difference of normalized scores for the best supplier (Supplier 3) and the less preferred supplier (Supplier 2).
AHP | FAHP | TOPSIS | FTOPSIS | FAHPiFTOPSIS | |
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Supplier 2 | 0.16414 | 0.16597 | 0.09961 | 0.14380 | 0.18769 |
Supplier 3 | 0.43261 | 0.49307 | 0.52588 | 0.45847 | 0.35149 |
Score difference | 0.26847 | 0.29491 | 0.79734 | 0.63529 | 0.22422 |
Percentage | 26.84 | 29.49 | 79.73 | 63.53 | 22.42 |
Difference in percentage comparison between the best supplier and the less preferred supplier.
This research has successfully demonstrated the applicability of the MCDM tools (AHP, FAHP, TOPSIS, FTOPSIS, and FAHPiFTOPSIS) by automotive industry in the selection of suppliers. As a result, few points can be concluded as follows. Six additional parameters were observed to have equal important criteria in automotive industry besides three classic criteria, namely, price, delivery, and quality which are support service, technology, factory capacity and capability, supplier background, flexibility, and other management system requirements. AHP, FAHP, TOPSIS, FTOPSIS, and FAHPiFTOPSIS are all applicable and accurate for supplier selection in automotive industry. For AHP and FAHP, when the number of suppliers and alternatives becomes big, more criteria will need to be considered and decision maker will face a challenge to perform pairwise comparison in a big matrix. For TOPSIS and FTOPSIS, it is a simpler method whereby weights are needed as the input of this decision making tool irrespective with the number of alternatives. The results from AHP, FAHP, TOPSIS, and FTOPSIS show great variation in the final ranking scores. However, FAHPiFTOPSIS decision making tool demonstrated the lowest score variation of 22.42%. This indicates that FAHPiFTOPSIS application is able to effectively eradicate any ambiguity or fuzziness in the process.
See Table
Supplier selection criteria.
Category | Criteria |
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(i) Applies JIT concept |
(ii) Delivery lead time | |
(iii) Delivery quality | |
(iv) Packaging quality | |
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(i) Handled by expertise of the related field |
(ii) Availability | |
(iii) Promptness | |
(iv) Applies OEE concept | |
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(i) Quality performance |
(ii) Durability | |
(iii) Ergonomic qualities | |
(iv) Reliability | |
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(i) Collaboration with established R&D organization on referral designs |
(ii) Product certification | |
(iii) High technology machines and processes | |
(iv) Align with current technology (product/process/design) | |
(v) Suppliers capable of modifying product/design/process | |
(vi) Material availability | |
(vii) Alternative material and technologies | |
(viii) EDI capability | |
(a) inventory | |
(b) logistics | |
(c) production | |
(d) transaction | |
(ix) Design capability | |
(a) Flexibility to respond to design changes | |
(b) Product innovativeness | |
(c) Product performance | |
(d) Ability to modify product/process | |
(x) R&D | |
(a) Availability of testing laboratory | |
(b) Reliability of testing laboratory | |
(c) Calibration program | |
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(i) Flexibility in price reduction |
(ii) Competitive operating costs | |
(iii) Flexibility in payment | |
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(i) Level of capability to cope with rush orders |
(ii) Sufficient product capacity | |
(iii) Sufficient product facilities | |
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(i) Industry knowledge (specifically on the related process or parts) |
(ii) Geographical location of the supplier | |
(iii) Geographical condition such as labor cost and traffic congestion | |
(a) suppliers have dedicated supply point | |
(iv) Flexibility in freight | |
(a) suppliers have their own transportation | |
(v) Position in the industry and reputation | |
(a) suppliers supply to other established car manufacturers | |
(vi) Performance history | |
(a) suppliers have been blacklisted or not | |
(vii) Financial | |
(a) Table financial management system | |
(b) bankruptcy | |
(viii) Manning | |
(a) sufficient workers | |
(b) low turnover rate | |
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(i) Practices flexible manufacturing system in terms of design and process |
(ii) Flexibility of operation | |
(iii) Flexibility in production | |
(iv) Flexibility in order frequency and amount | |
(v) Flexibility to respond requirement volume changes | |
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(i) Kanban |
(ii) Kaizen | |
(iii) TPS (Toyota production system) | |
(iv) ISO 9000 | |
(v) Six sigma | |
(vi) 5S | |
(vii) FMEA | |
(viii) SPC | |
(ix) Strong safety processes and culture | |
(x) Environmental performance |
See Figure
The developed questionnaire is called:
Supplier selection evaluations are normally being conducted by companies in order to evaluate their suppliers’ performance and reliability. Traditionally, supplier selection evaluations are based on price/cost, quality, and delivery. However, in today’s management, most companies do not only consider the three primary criteria, but they also take into account technology, support service, flexibility, supplier background, management tool systems, and factory capacity and capability.
As part of my Master research in manufacturing engineering, I would like to seek you a great assistance to provide me your critical input on supplier selection. Below you will find the criteria listed. What you have to do is to fill in your inputs in each question by writing the numbers according to the ranking in the right box.
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Please state your level of importance of the supplier with respect to the criteria on the right boxes regarding the numbers below. See Table
Criteria | Supplier 1 | Supplier 2 | Supplier 3 | Supplier 4 |
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Criteria rating:
According to Saaty’s table, compare the 9 criteria accordingly and fill in the table. Criteria on the left side of the table need to be compared to the criteria at the horizontal columns. See Tables
Saaty’s pair-wise comparison table.
Numerical rating | Verbal comparison of preference |
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1 | Equal importance |
3 | Moderate importance of one over another |
5 | Strong or essential importance |
7 | Very strong or demonstrated importance |
9 | Extreme importance |
2, 4, 6, 8 | Intermediate values |
(Source: [
Criteria 1 | Criteria 2 | Criteria 3 | Criteria 4 | Criteria 5 | Criteria 6 | Criteria 7 | Criteria 8 | Criteria 9 | |
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Criteria 9 |
According to Saaty’s table, compare the suppliers (S1, S2, S3, and S4) with respect to the criteria. Suppliers on the left side columns are to be compared with the suppliers on the right columns. Kindly mark (/) at the preferred value when pairwise comparison is performed. See Tables
Saaty’s pair-wise comparison table.
Numerical rating | Verbal comparison of preference |
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1 | Equal importance |
3 | Moderate importance of one over another |
5 | Strong or essential importance |
7 | Very strong or demonstrated importance |
9 | Extreme importance |
2, 4, 6, 8 | Intermediate values |
(Source: [
Criterion 1: delivery/lead time.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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S1 | S2 | |||||||||||||||||
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S1 | S4 | |||||||||||||||||
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S2 | S4 | |||||||||||||||||
S3 | S4 |
Criterion 2: support service.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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S1 | S2 | |||||||||||||||||
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S3 | S4 |
Criterion 3: quality.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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Criterion 4: technology.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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S3 | S4 |
Criterion 5: price/cost.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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Criterion 6: factory capacity and capability.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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Criterion 7: supplier background.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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Criterion 8: flexibility.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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Criterion 9: management tool system.
9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
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S3 | S4 |
The authors would like to thank Associate Professor Dr. Mohd. Khaled Omar and all the survey respondents for their kind support and help contributed towards the implementation of this paper.