The competition between the companies in the dynamic market conditions has made the Supply Chain Management (SCM) a more important issue. The companies which have organized their supply chain effectively have obtained more flexibility in their manufacturing processes in addition to delivery of the customer demands. In this study, two different multicriteria decision making algorithms composed of the FAHP and a holistic hybrid method using FTOPSIS were utilized for an electronic company in wholly fuzzy processes. The FAHP is used for determination of the global weights of the factors and the performances of alternative suppliers are evaluated by using both FAHP-based and FAHP-FTOPSIS hybrid methods for synthetic extent values of pairwise comparisons. The sequences of the suppliers differed for the algorithms. The performances of the proposed approaches are quite successful and flexible in a narrow interval. The managerial advantages obtained from the proposed fuzzy algorithms are also analyzed and interpreted.
The aim of a manager is to efficiently transport the products and services to the customer in a Supply Chain Management (SCM) system. Ensuring continuity and comparative degree for the administration is only possible by supplying and using the resources with high productivity, high quality, and low price. The administration has to realize flexible manufacturing and effective management for the supply chain from manufacturer to the final customer in order to compensate for the changing customer demand in a highly competitive environment.
In recent years, the integration of management functions within the borders of the administration is inadequate in terms of competition. Hence, the integration and effective management of activities beyond administration in the chain are quite necessary. Furthermore, the competition of supply chains has become much more important than the competition of the firms themselves. SCM is getting more important to accomplish this integration for fast and flexible covering of changeable customer necessities.
A supply chain can be described as a network used to supply necessary materials for manufacturing and service, to transform these to final products, and to transport the final products to the customers (e.g., suppliers, factories, warehouses, and distribution depots). A supply chain also offers different alternatives for distribution of products [
Although SCM systems are quite important for the companies, enough importance is not given to the measurement and evaluation of performance of these processes. The existing research only focuses on the company’s performance in market or the purchaser-seller relationship instead of the suppliers in whole system [
The evaluation of the performances of suppliers is the most important phase in SCM system. The phase can be described as a multicriteria decision problem regarding several factors in evaluation processes. The multi-criteria decision analysis (MCDA) for structuring these decision problems and evaluation of alternative suppliers provides a rich collection of methods. However, MCDA methods are often criticized because of their inability to handle the uncertain and imprecise problems. Thus, the fuzzy decision making was proposed as a powerful tool. Human has good ability in qualitative data processing which helps him/her to make decisions in fuzzy environment [
Fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems in industry, nature, and humanity and facilitators for common sense reasoning in decision making in the absence of complete and precise information [
The fuzzy analytical hierarchy process (FAHP) and the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) methods are commonly used to address the multicriteria decision problems. The first study of FAHP is proposed by van Laarhoven and Pedrycz which compared fuzzy ratios described by triangular fuzzy numbers (TFNs) [
Chen and Hwang first applied fuzzy numbers to establish a prototype fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) [
As it is explained above, the evaluation of the supplier performance is one of the most important issues in management of supply chain. By using appropriate criteria and a systematic approach, the measurement of these performances is inevitable for the chain’s success and competitive advantage. In this study, two types of algorithms have been suggested for evaluation of suppliers’ performance and selection of the best possible supplier regarding its performance in one of the biggest electronic companies in Turkey. In this model, first, one of the techniques of fuzzy multicriteria decision making, that is, FAHP, is used to calculate global weights of the criteria and these weights are considered for both algorithms. Then, the FAHP-based performances and the rankings of FAHP-FTOPSIS hybrid algorithm are used to select the best alternative supplier, separately. In Section
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership function, which assigns to each object a grade of membership ranging from zero to one.
In the following, some basic important definitions of fuzzy sets related to this study are given [
A fuzzy set
A TFN
Let
A matrix
The study proposes two types of algorithms for evaluating the performance of suppliers in the fuzzy MCDA. The steps of the proposed holistic method can be outlined as follows.
Let
The degree of possibility for a convex fuzzy number to be greater than
The proposed model is applied for four alternative suppliers of an electronic company which share very similar features. The company produced several types of electronic cards with more than 400 employees. It is one of the biggest electronic companies in the Middle East region which realizes a significant amount of exports to many countries. The aim of this application is to propose MCDA approach to evaluate the performance of the alternative suppliers of the company in the chain. Schematic diagram of the proposed algorithms is provided in Figure
Schematic diagram of the proposed algorithms.
As it is given in Figure establishing the decision team and identifying the factors (first level: main factors) and criteria (second level: subfactors) to be used in the model, using FAHP method, calculating the local weights of the factors and criteria by using fuzzy pairwise comparison matrices, and calculating the global weights then, evaluating the alternatives with FTOPSIS by using FAHP weights and determination of the final ranking.
The proposed models are applied to a real life problem in order to measure the performance of suppliers in the chain. This application created proprietary solutions for the electronic company. This company, which has one of the biggest annual sales turnovers, is working with approximately 50 inside or abroad suppliers and trying to manage this complex supply chain. The four of these suppliers selected have similar technical specifications to test the proposed approaches.
First, a team is established for the SCM performance evaluation system improvement and the company’s related managers and academicians experienced in SCM are included in this team. Then, the factors and the criteria which will be used in the chain performance are determined and the strategic goals are used for the critical success of the company’s necessities. In gathering this information, a simultaneous study is done with both department managers and workers of the alternative companies. The process factors are evaluated on the basis of sales/marketing, logistics, manufacturing, and finance according to the literature and company’s features and necessities.
After determining the factors and criteria, weighting of the factors has been calculated for which the FAHP technique is used. The pairwise comparison judgment matrices are formed for the factors comparison. The values were obtained from the consensus of entire team. These matrices are evaluated and the local weights of the factors and the criteria are calculated. The main factors and 14 criteria included factor groups are given in Figure
The factors and criteria used in SCM performance evaluating system.
Initially, the fuzzy scale regarding relative importance to measure the relative weights in FAHP is given in Table
Linguistic variables for the important weight of each criterion.
Linguistic values | Triangular fuzzy numbers |
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Just equal | (1, 1, 1) |
Equally important (EI) | (1/2, 1, 3/2) |
Weakly more important (WMI) | (1, 3/2, 2) |
Strongly more important (SMI) | (3/2, 2, 5/2) |
Very strongly more important (VSMI) | (2, 5/2, 3) |
Absolutely more important (AMI) | (5/2, 3, 7/2) |
Linguistic variables for the ratings.
Linguistic values | Triangular fuzzy numbers |
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Very low (VL) | (0, 0, 0.2) |
Low (L) | (0, 0.2, 0.4) |
Medium (M) | (0.2, 0.4, 0.6) |
High (H) | (0.4, 0.6, 0.8) |
Very high (VH) | (0.6, 0.8, 1) |
Excellent (E) | (0.8, 1, 1) |
The fuzzy pairwise comparison matrix of the factors is stated in Table
Pairwise comparison matrix of main factors.
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(1, 1, 1) | (3/2, 2, 5/2) | (1, 3/2, 2) | (2, 5/2, 3) |
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(2/5, 1/2, 2/3) | (1, 1, 1) | (1/2, 1, 3/2) | (1/2, 1, 3/2) |
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(1/2, 2/3, 1) | (2/3, 1, 2) | (1, 1, 1) | (1, 3/2, 2) |
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(1/3, 2/5, 1/2) | (2/3, 1, 2) | (1/2, 2/3, 1) | (1, 1, 1) |
The global weights of criteria used in the evaluation process are calculated using FAHP. The results obtained from the calculations based on the pairwise comparison matrices are presented in Table
Calculated global weights of the criteria with FAHP.
Factors | Weights | Criteria | Local weights | Global weights |
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Focus-on customers (pcs/sec) | 0.447 | (1.1) | 0.226 | 0.101 |
(1.2) | 0.189 | 0.084 | ||
(1.3) | 0.270 | 0.121 | ||
(1.4) | 0.315 | 0.141 | ||
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Product (ratio/pcs) | 0.161 | (2.1) | 0.501 | 0.081 |
(2.2) | 0.248 | 0.040 | ||
(2.3) | 0.251 | 0.040 | ||
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Process (pcs, $) | 0.254 | (3.1) | 0.315 | 0.080 |
(3.2) | 0.270 | 0.069 | ||
(3.3) | 0.189 | 0.048 | ||
(3.4) | 0.226 | 0.057 | ||
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Stuff (ratio, pcs) | 0.138 | (4.1) | 0.764 | 0.105 |
(4.2) | 0.083 | 0.011 | ||
(4.3) | 0.153 | 0.021 | ||
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Total |
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The obtained global weights and determined linguistic variables are multiplied and the total performances are calculated. The results are defuzzified and the crisp performance values are obtained. The results of the algorithm at the end of the FAHP-based approach are shown in Table
Fuzzy performance of Supplier #1 (
Criteria | Global weights | Linguistic performance value | Scale value | Fuzzy performance points |
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(1.1) | 0.101 | M | (0.2, 0.4, 0.6) | (0.020, 0.040, 0.061) |
(1.2) | 0.084 | M | (0.2, 0.4, 0.6) | (0.017, 0.034, 0.051) |
(1.3) | 0.121 | VL | (0, 0, 0.2) | (0, 0, 0.024) |
(1.4) | 0.141 | H | (0.4, 0.6, 0.8) | (0.056, 0.085, 0.113) |
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(2.1) | 0.081 | VL | (0, 0, 0.2) | (0, 0, 0.016) |
(2.2) | 0.040 | M | (0.2, 0.4, 0.6) | (0.008, 0.016, 0.024) |
(2.3) | 0.040 | VH | (0.6, 0.8, 1) | (0.024, 0.032, 0.040) |
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(3.1) | 0.080 | H | (0.4, 0.6, 0.8) | (0.032, 0.048, 0.064) |
(3.2) | 0.069 | H | (0.4, 0.6, 0.8) | (0.027, 0.041, 0.055) |
(3.3) | 0.048 | M | (0.2, 0.4, 0.6) | (0.010, 0.019, 0.029) |
(3.4) | 0.057 | H | (0.4, 0.6, 0.8) | (0.023, 0.034, 0.046) |
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(4.1) | 0.105 | VH | (0.6, 0.8, 1) | (0.063, 0.084, 0.105) |
(4.2) | 0.011 | E | (0.8, 1, 1) | (0.009, 0.011, 0.011) |
(4.3) | 0.021 | VH | (0.6, 0.8, 1) | (0.013, 0.017, 0.021) |
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Total points |
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Crisp values and sequences of the alternative suppliers’ performances.
Alternatives | Total fuzzy points | Defuzzification | Rank |
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(0.303, 0.463, 0.660) | 0.469 | 4 |
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(0.421, 0.621, 0.748) | 0.609 | 2 |
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(0.510, 0.710, 0.902) | 0.709 | 1 |
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(0.413, 0.613, 0.768) | 0.606 | 3 |
Defuzzification formulation is given here as
Fuzzy initial decision matrix of TOPSIS method constructed for the evaluation of four suppliers (alternatives) of the electronic company by linguistic variables defined in Table
Initial FTOPSIS decision matrix for four alternative companies.
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(0.20, 0.40, 0.60) | (0.20, 0.40, 0.60) |
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(0.80, 1, 1) | (0.60, 0.80, 1) |
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(0.40, 0.60, 0.80) | (0.40, 0.60, 0.80) |
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(0.40, 0.60, 0.80) | (0.80, 1, 1) |
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(0.60, 0.80, 1) | (0.20, 0.40, 0.60) |
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(0.60, 0.80, 1) | (0.40, 0.60, 0.80) |
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(0.40, 0.60, 0.80) | (0.80, 1, 1) |
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(0, 0.20, 0.40) | (0.20, 0.40, 0.60) |
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Global weights | 0.101 | 0.084 | 0.011 | 0.021 |
Using the global weights of criteria in Table
Weighted evaluation matrix for the alternatives.
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(0.02, 0.04, 0.06) | (0.02, 0.03, 0.05) |
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(0.01, 0.01, 0.01) | (0.01, 0.02, 0.02) |
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(0.04, 0.06, 0.08) | (0.03, 0.05, 0.07) |
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(0.00, 0.01, 0.01) | (0.02, 0.02, 0.02) |
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(0.06, 0.08, 0.1) | (0.02, 0.03, 0.05) |
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(0.01, 0.01, 0.01) | (0.01, 0.01, 0.02) |
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(0.04, 0.06, 0.08) | (0.07, 0.08, 0.08) |
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(0.00, 0.00, 0.00) | (0.00, 0.01, 0.01) |
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The distance of each alternative from
FTOPSIS results of ideal solutions and ranking of alternatives.
Alternatives |
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Rank |
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3.656 | 2.896 | 0.442 | 2 |
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3.657 | 2.794 | 0.433 | 4 |
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3.524 | 2.821 | 0.445 | 1 |
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3.625 | 2.820 | 0.438 | 3 |
The positive and negative ideal solutions and also similarities to ideal solution are calculated. As shown in Table
In the last step, the results are compared and analyzed for both approaches. It is seen that
The comparison of both FAHP-based and FAHP-TOPSIS hybrid algorithms.
Alternatives | FAHP | Hybrid (FAHP-FTOPSIS) | ||
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Rank | Scales | Rank | Scales | |
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4 | 0.469 | 2 | 0.442 |
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2 | 0.609 | 4 | 0.433 |
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1 | 0.709 | 1 | 0.445 |
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3 | 0.606 | 3 | 0.438 |
In a competitive market, which has many suppliers dispersed in a wide geographical area with lots of opportunities for purchasing and distribution, beside the managerial activities of the companies makes it inevitable to high-dimensioned critical decision making for the managers. Furthermore, the efficiency in decision making depends on exact evaluation of performance of suppliers in time windows.
SCM and the process of the evaluation of supplier performance can be defined as MCDA problems for the companies. The conventional evaluation methods are inadequate in dealing with the imprecise or vague nature of linguistic assessments. To overcome this difficulty, fuzzy multicriteria methods are proposed in all steps.
Though the purpose of AHP is to capture the expert’s knowledge, the conventional AHP cannot reflect the human thinking style. Therefore, FAHP and fuzzy extensions of AHP are developed to solve hierarchical fuzzy problems. This method has systematic approaches to alternative selection and problem justification by using the concepts of fuzzy theory and hierarchical structure analysis. Decision makers usually find that they are more confident to give interval judgments than fixed values judgments. This is because usually he/she is unable to explicit his/her preferences due to the fuzzy nature of the comparison process
Using the FTOPSIS method, the decision maker’s fuzzy assignments with different rating view points and tradeoffs among different criteria are considered in the aggregation procedure. to ensure more accurate decision making. This study uses TFN for both FAHP and FTOPSIS. The reason for using a TFN is that it is intuitively easy for the decision makers to use and calculate.
In this study, the evaluation of alternative suppliers’ performance for a big electronic company is realized in wholly fuzzy processes. Firstly, the main factors and criteria for evaluation of the performance are determined by the expert team according to the company’s necessities. The global weights of these criteria are calculated using FAHP technique. The supply chain performances of the alternative companies are evaluated using these weights and linguistic values of each supplier. The same weights are embedded in the FTOPSIS method and a hybrid algorithm is performed. In consideration of the results of the algorithms, the sequences differed. Regarding this difference, it is conceivable that the alternatives have very close technical features and/or the approximations of the methods into the problems are varied. The FTOPSIS method is substituted with the FAHP in most cases in the literature but there is no comparative degree with each other in the experiences up to now. In this manner, the proposed studies used in the application bring more flexibility to the company’s performance evaluation system.
The first rank, that is, the best possible performance with the scale value of 0.445, belongs to Supplier
As a future extension to this study, the relationships between the factors and criteria could be considered using Analytical Network Process (ANP), which is a powerful method. Hence, it is believed that both of the approaches could be used to obtain more accurate result in evaluating the chain performance.
The authors declare that there is no conflict of interests regarding the publication of this paper.