Semra B�Industrial Engineering Department

Istanbul Commerce University Istanbul, TURKEY sbirgun@iticu.edu.tr

Emrah C�Engineering Management Department

Marmara University Istanbul, TURKEY

emrahcihan@gmail.com

Abstract— In today’s competitive environment, effective supplier selection process is very important for the success of manufacturing organizations. In this context, supplier selection represents one of the most important functions to be performed by the purchasing departments. Supplier selection is a multi-criteria problem including many factors (criteria). In the literature, a number of models and techniques have been developed to deal with selecting and evaluating suppliers. In this paper, ELECTRE, which is one of these methods, is discussed with a case study illustration.

Keywords-supplier selection, supplier selection methods, ELECTRE method

I. INTRODUCTION Supplier selection and evaluation have become one of the

major topics in production and operations management literature, especially in advanced manufacturing technologies and environment [1]. The main objective of supplier selection process is to reduce purchase risk, maximize overall value to the purchaser, and develop closeness and long-term relationships between buyers and suppliers, which is effective in helping the company to achieve “Just-In-Time” (JIT) production [2]. Additionally, with the increase in use of Total Quality Management (TQM) and Just-In-Time (JIT) concepts by a wide range of firms, the supplier selection question has become extremely important [3]. Choosing the right method for supplier selection effectively leads to a reduction in purchase risk and increases the number of JIT suppliers and TQM production.

Supplier selection is a multiple criteria decision-making (MCDM) problem which is affected by several conflicting factors. Consequently, a purchasing manager must analyze the trade-off between the several criteria. MCDM techniques support the decision-makers (DMs) in evaluating a set of alternatives [4]. Supplier selection problem has become one of the most important issues for establishing an effective supply chain system. The supplier selection problem in a supply chain system is a group decision according to multiple criteria from which a number of criteria have been considered for supplier selection in previous and present decision models [5]. The purchasing manager must know a suitable method, and then use the best method from the different types of methods to select the right supplier.

II. LITERATURE REVIEW There are several supplier selection methods available in

the literature. Some authors propose linear weighting models in which suppliers are rated on several criteria and in which these ratings are combined into a single score. These models include the categorical, the weighted point [6] and the analytical hierarchical process [7]. Total cost approaches attempt to quantify all costs related to the selection of a vendor in monetary units, this approach includes cost ratio [6] and total cost of ownership [8]. Mathematical programming models often consider only the more quantitative criteria; this approach includes the principal component analysis [3] and neural network [9].

The categorical method relies heavily on the experience and ability of the individual buyer [6]. People in charge of purchasing, quality, production, and sales all express their opinions about the supplier’s performance on the basis criteria which are important to them. These departments assign either a preferred, unsatisfactory, or neutral rating for each of the selected attributes for every contending supplier. At periodic evaluation meetings, the buyer discusses the rating with department members. The buyer then determines the supplier’s overall scores. This method is quite simple; it is not supported by objective criteria, and rarely leads to performance improvements. The main drawback of this method is that the identified attributes are weighted equally and the decisions made using this system tend to be fairly subjective.

Another method is the weighted point which considers attributes that are weighted by the buyer. The weight for each attribute is then multiplied by the performance score that is assigned. Finally, these products are totaled to determine a final rating for each supplier [6]. All measurement factors are weighted for importance in each purchasing situation. Typically this system is designed to utilize quantitative measurements.

The cost-ratio is an additional method that relates all identifiable purchasing costs to the monetary value of the goods received from vendors [6]. The higher the ratio of costs to value, the lower the rating applied to the vendor. The choices of costs to be incorporated in the evaluation depend on the products involved. The cost-ratio method establishes a “norm” of supplier services and evaluates vendors above and below the norm in relation to price. The subjective elements

RGÜN HAN

Supplier Selection Process using ELECTRE Method

___________________________________ 978-1-4244-6793-8/10/$26.00 ©2010 IEEE

common to other methods are thus reduced. The cost ratio method is based on cost analysis that considers cost ratios for product quality, delivery, customer service and price. The cost ratio measures the cost of each factor as a percentage of total purchase for the supplier. Due the flexibility of this method, any company in any market can adopt it. The drawback of the method is its complexity and requirement for a developed cost accounting system. Similarly the total cost of ownership method attempts to quantify all of the costs related to the purchase of a given quantity of products or services from a given supplier [10].

In addition, the principal component analysis (PCA) method is a multiobjective approach to vendor selection that attempts to provide a useful decision support system for a purchasing manager faced with multiple vendors and trade-offs such as price, delivery, reliability, and product quality [6]. This multivariate statistical method is a data reduction technique used to identify a small set of variable that account for a large portion of the total variance in the original variance. This technique is also used to identify “latent” dimensions in the data. In fact, the principal component analysis computes linear combinations of variables. The first linear combination of variables, accounts for the largest amount of variation in the sample; the second for the next largest amount of variance in a dimension independent of the first; and so on. This method is also a popular ranking method in multidimensional analysis. The principal component analysis methodology has the advantage to be fairly simple to exploit, since it has been accessible for decades. This method has proved to be capable of handling multiple conflicting attributes inherent in supplier selection while simultaneously trading-off key supplier selection criteria. To illustrate this method the following example is presented.

Another useful method is the Analytic Hierarchy Process (AHP), a decision-making method developed by [11] for prioritizing alternatives when multiple criteria must be considered and allows the decision maker to structure complex problems in the form of a hierarchy, or a set of integrated levels. Generally, the hierarchy has at least three levels: the goal, the criteria, and the alternatives. For the supplier selection problem, the goal is to select the best overall supplier [7]. The criteria can be quality, price, service, delivery, etc. The alternatives are the different proposals supplied by the suppliers.

The neural network for supplier selection is another method that has been developed to help selecting the best supplier.

Comparing to conventional models for decision support system, neural networks save a lot of time and money of system development. The supplier-selecting system includes two functions: one is the function measuring and evaluating performance of purchasing (quality, quantity, timing, price and costs) and storing the evaluation in a database to provide data sources to neural network [9]. The other is the function using neural network to select suppliers. Most of the neural-

network paradigms commonly used have three layers: input layer, output layer, and hidden layer. It should be decided which Artificial Neural Network (ANN) model should be used, and the number of nodes in the input layer, hidden layer and output layer. Back-propagation network (BPN) is the most popular neural network model and has the highest success rate. Although preliminary results appear very promising, some special cases and history data still need to be represented and its responses need to be evaluated.

One of the multi criteria decision making methods which can be used for supplier selection problems will be discussed in the following pages: ELECTRE. There are several case studies or illustrative example about ELECTRE models [12], [13], [14], [15], etc.

A. ELECTRE ELECTRE (Election et Choix Traduisant La Realite), was

conceived by Bernard Roy in response to deficiencies of existing decision making solution methods. ELECTRE is more than just a solution method; it is a philosophy of decision aid - the philosophy is discussed at length by Roy [16].

ELECTRE has evolved through a number of versions (I through IV); all are based on the same fundamental concepts but are operationally somewhat different. It is important to note that ELECTRE is not being presented as the "best" decision aid. It is one proven approach [16].

Two important concepts underscore the ELECTRE approach; thresholds and outranking. These will now be discussed. Assume that there exist defined criteria, gj, j=1,2,…,r and a set of alternatives, A. Traditional preference modeling assumes the following three relations hold for two alternatives (a, b) A [17]:

aPb (a is preferred to b) g(a) > g(b)

aIb (a is indifferent to b) g(a) = g(b)

aJb (a cannot be compared to b).

In contrast to the traditional approach, ELECTRE introduces the concept of an indifference threshold, q, and the preference relationships are redefined as follows:

aPb (a is preferred to b) g(a) > g(b) + q

aIb (a is indifferent to b) |g(a) - g(b)| � q, and

aJb (a cannot be compared to b) remains.

The indifference threshold is specified by the decision maker. While the introduction of this threshold goes some way toward incorporating how a decision maker actually does feel about realistic comparisons, a problem remains. There is a point at which the decision maker changes from indifference to strict preference. Conceptually, there is good reason to introduce a buffer zone between indifference and strict preference; an intermediary zone where the decision maker hesitates between preference and indifference [18].

B. Structure of ELECTRE Methods ELECTRE methods comprise two main procedures:

construction of one or several outranking relation(s) followed by an exploitation procedure. The construction of one or several outranking relation(s) aims at comparing in a comprehensive way each pair of actions. The exploitation procedure is used to elaborate recommendations from the results obtained in the first phase. The nature of the recommendations depends on the problematic (choosing, ranking or sorting). Hence, each method is characterized by its construction and its exploitation procedures [19].

1) ELECTRE I (Roy 1968): This method was built for multicriteria choice problems: Its aims is therefore to be able to obtain a subset N of actions such that any action which is not in N outranked by at least one action of N. The latter subset (which will be made as small as possible) is thus not the set of good actions, but it is the set in which the best compromise can certainly be found [19].

2) ELECTRE II ( Roy and Bertier, 1971, 1973): The main difference between this method and ELECTRE I lie in defining two outranking relations instead of one: the strong outranking and the weak outranking. The ELECTRE II method, on the other hand, aims to rank the actions from best to worst (ranking problem) [19].

3) ELECTRE III (Roy, 1978): The ELECTRE I and ELECTRE II methods (at least their initial versions) concern problems involving true criteria. With the development of preference modeling, some new procedures appeared which take explicitly into account indifference and preference thresholds. The ELECTRE III method is a good example of the latter; furthermore, it has the peculiarity of being based upon a valued outranking relation which has the property, with respect to an ordinary relation, of being less sensitive to variations of the data and involved parameters [19].

4) ELECTRE IV (Hugonnard and Roy, 1982): The ELECTRE IV method, as the previous one, is based upon the consideration of a family of pseudo-criteria; it aims to rank the actions, but without introducing any weighting of the criteria. ELECTRE IV is currently the only version that that does not require such weights. The main difference is that in ELECTRE IV, instead of using a value of a membership function, the number of criteria in different outranking categories is used. A set of credibility degrees similar to ELECTRE III is defined to classify the alternatives based on the ascending and descending distillation processes. The method can be particularly useful when the decision maker is not able to assign a set of preference weights to reflect specific requirements of a given decision-making problem [19].

III. CASE STUDY

A computer hardware manufacturer company wants to choose a vendor for a new model’s sub-part. There are four

alternatives can be illustrated as Supplier A, Supplier B, Supplier C, Supplier D. The purchasing, supplier technical assistance, project and quality engineers of the company visit all the alternative’s plants and evaluate all of them based on some criteria which are already figured in the company strategically figures. Some of criteria are; quality, cost, delivery time & transportation, minimum order quantity, production capacity & flexibility, payment term, facility location etc. There are also sub criterias under key criteration such as for quality; quality inspection methods, percentage of refused products, high quality employee, product performance or for delivery time & transportaion criteria; delivery speed, just in time delivery, transportation cost, flexibility on delivery time.

The decision maker team hold a meeting and evaluate all the suppliers based on mentioned criterias with brain storm method. The result of this evaluation is shown in TABLE I.

Now, these data will be discussed step by step, to be able to reach the most suitable alternative, under ELECTRE approach.

A. Weighted Matrix In this step the weight of the factors are taken into

consideration. The weighted matrix’s ratings are calculated as multiplied the rates with the relevant weights.

B. Concordance and Discordance Sets For each pair of alternatives Ap and Aq (p,q=1,2,,m and

p�q), the set of attributes is divided into two different subsets. The concordance set, which is composed of all attributes for which alternative Ap is preferred to alternative Aq can be written as

C(p,q)={j, Vpj � Vqj} (1)

Where Vpj is weighted rating of alternative Ap with respect to the jth attribute. In other words, C(p,q) is the collection of attributes where Ap is better than or equal to Aq.

The complement of C(p,q), which is called the discordance set, contains all attributes for which Ap is worse than Aq. This can be written as

D(p,q)={j, Vpj > Vqj} (2)

Supplier Selection problem are obtained in table III.

C. Concordance and Discordance Indexes The relative power of each concordance set is measured by

means of the concordance index. The concordance index Cpq represent the degree of confidence in the pair wise judgments of ( Ap�Aq). The concordance index of C(p,q) is defined as

(3)

Where J* are attributes contained in the concordance set C(p,q).

The discordance index, on the other hand, measures the power of D(p,q). The discordance index of D(p,q), which represents the degree of disagreement in (Ap�Aq), can be defined as

=( - |) / ( - |) (4)

The complete list of concordance and discordance indexes is follows:

TABLE I. SUPPLIER EVALUATION TABLE

TABLE II. WEIGHTED MATRIX

.

.

The concordance and discordance sets for the

TABLE III. CONCORDANCE and DISCORDANCE SETS

TABLE IV. CONCORDANCE and DISCORDANCE INDEXES

D. Outranking Relationships:

The dominance relationship of alternative Ap over alternative Aq becomes stronger with a higher concordance index Cpq and a lower discordance index Dpq. The method defines that Ap outranks Aq when Cpq�Average(C) and Dpq�Average (D).

TABLE V. DETERMINATION of OUTRANKING RELATIONSHIP

Table V. also illustrates the determination of outranking relationships. Six outranking relationships are obtained: (SupA�SupB),(SupA�SupC),(SupA�SupD),(SupB�SupC), (SupB�SupD), (SupC�SupD). All relationships show us that Supplier A is the best alternative regarding to ELECTRE method.

IV. CONCLUSION Choosing the right supplier involves much more than

scanning price lists. The selection should depend on a wide range of factors. The strategic and analytical approaches can support the decision makers about vendor selection process.

There are many types of systematic methods to choose the vendors. Decision on the optimum method can be changed pursuant to the company structure.

In the case study part of this paper, we mentioned about ELECTRE method for supplier selection by using general acknowledges. The criteria can also be changed regarding to the parent company’s requirements.

In addition to choosing the best alternative, a second alternative might be considered in case any problem with the first one. ELECTRE method can also support to choose the second best alternative among all of them.

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