130
RESEARCH JOURNAL OF FISHERIES AND HYDROBIOLOGY, 10(11) July 2015, Pages: 130-142
MaziyarKazempoor et al, 2015
RESEARCH JOURNAL OF FISHERIES AND HYDROBIOLOGY
© 2015 AENSI Publisher All rights reserved
ISSN:1816-9112
Open Access Journal
Copyright © 2015 by authors and American-Eurasian Network for Scientific Information.
This work is licensed under the Creative Commons Attribution International
License (CC BY). http://creativecommons.org/licenses/by/4.0/
Supplier selection using the AHP in JIT production process 1MaziyarKazempoor, 2Saeedhakaminasab and 3HadiHemmatian
ABSTRACT The purpose of this paper is to aid just-in-time (JIT) manufacturers in selecting the most appropriate suppliers using AHP approach. Many manufacturers employ the JIT philosophy in order to be more competitive in today’s global market. The success of JIT on the production floor has led many firms to expand the JIT philosophy to the entire supply chain. The procurement of parts and materials is a very important issue in the successful and effective implementation of JIT; thus, supplier selection in long-term relationships has become more critical in JIT production environments.An effective supplier selection process is veryimportant to the success of any manufacturing organization. Themain objective of supplier selection process is to reduce purchaserisk, maximize overall value to the purchaser, and develop closenessand long-term relationships between buyers and suppliers in today’scompetitive industrial scenario. Therefore the aim of this study is Supplier selection using the AHP in JIT production process.
KEY WORDS: supplier selection, SCM, AHP,JIT
1Department of management, Semnan branch, Islamic Azad University, Semnan, Iran 2Department of management, Semnan branch, Islamic Azad University, Semnan, Iran 3Department of management, Semnan branch, Islamic Azad University, Semnan, Iran Address For Correspondence: Saeedhakaminasab, Department of management, Semnan branch, Islamic Azad University, Semnan, Iran E-mail: [email protected] Received: 6 March 2015 Accepted: 25 June 2015 Published: 24 July 2015
INTRODUCTION
Today’s companies are faced with fierce competition, which is forcing them to increasingly consider new
applications to improve quality and to reduce cost and lead time (Boer, 2001). For this reason, manufacturers
must keep pace with the dynamic requirements of the market and be receptive to change (Chan, 2007). The aim
of many new manufacturing systems, like the just-in-time (JIT) philosophy, is to eliminate waste in the
production environment and to continue this process as a continuous cycle, always striving for the best (Lubben,
1988).The JIT philosophy is an important action in the supply chainmanagement (SCM) system (Swift, 1995).
The JIT purchasing system requiressmaller order quantities and tighter delivery times (Hamphyeys, 2003).
Hence, manufacturersdealing with the JIT philosophy must collaborate withtheir suppliers (Handfeild, 2002). In
order to achieve a successful JIT system, a relationshipbetween the supplier and buyer must be established
forclose business collaboration as strategic partners (Nydick, 1992).Matson and Matson (2007) suggested that,
for global competitiveness,further support is required for efficient JIT supply chainsand that it is critical that JIT
suppliers identify and address performanceissues as effectively as possible.Manufacturers practicing JIT require
suppliers that punctuallysupply materials and outsourced parts in the appropriate quantityand with consistent
(qualityKhurrum ,2003). Because reliable suppliers enablemanufacturers to reduce inventory costs and improve
productquality, it is understandable that manufacturers are increasinglyconcerned about supplier selection
(Braglia&Petroni, 2000). It isapparent that the selection of appropriate suppliers and effectivesupplier
relationship management are key factors in increasingthe competitiveness of firms (Choy, Lee, & Lo, 2003a;
Ghodsypour& O’Brien, 2001). In a long-term relationship, after selecting thesuppliers, purchasing departments
need to periodically evaluatethe performance of their suppliers in terms of critical (criteriaBayzit, 2005).As
business organizationsbecome more dependent on suppliers, the direct and indirect consequence of poor
decision-makingabout supplier selection becomes more (severeHandfield, 2002). As a result, an effective and
131
efficient supplierselection and evaluation process becomes very important to the success of any
manufacturing/Serviceorganization (Bhutta, 2002). In general, supplier selection problem falls under purchasing
department (Hwang, 2005).N most industries, the cost of raw materials and componentparts represents the
largest percentage of the total productcost (Vaidya, 2006). For instance, in high technology firms,
purchasedmaterials and services account for up to 80% of the totalproduct cost (Demirates, 2009). Therefore,
selecting the right suppliers is the keyto procurement process and represents a major opportunity forcompanies
to reduce costs across its entire supply chain (Gencer, 2007).Choosing the right method for supplier selection
effectivelyleads to a reduction in purchase risk and increases the numberof JIT suppliers and TQM production
(Razim, 2010). Supplier selectionproblem has become one of the most important issues forestablishing an
effective supply chain system (William, 2008).For many years, the traditional approach to supplierselection has
been to select suppliers solely on the basis ofprice (Kukungul, 2009). However, as companies have learned that
the soleemphasis on price as a single criterion for supplier selection isnot efficient, they have turned into to a
more comprehensivemulti-criteria approach (Yang ,2006). Recently, these criteria have becomeincreasingly
complex as environmental, social, political, andcustomer satisfaction concerns have been added to thetraditional
factors of quality, delivery, cost, and service (Wang, 2009). Therealization that a well-selected set of suppliers
can make astrategic difference to an organization's ability to providecontinuous improvement in customer
satisfaction drives thesearch for new and better ways to evaluate and select suppliers (Liao, 2010). One of the
well-known methods is the analytichierarchy process (AHP) as an intuitively easymethod for formulating and
analyzing decisions (Gencer, 2007). It was developed to solve a specificclass of problems that involves
prioritization ofpotential alternate solutions (Demirtas, 2009). This is achievedby evaluation of criteria elements
and sub criteriaelements through a series of pair wisecomparisons (Vinod, 2011). Numerous applications of
theAHP have been made since its developmentand it has been applied to many types ofdecision problems
(Bayzit, 2006).
2. Literature review:
There are several papers regarding the implementation ofJIT systems and buyer–supplier relationships
under JIT systemsin the literature.Dong, Carter, and Dresner (2001) reported that the implementationof JIT
purchasing systems can result, on average, in reducedinventory costs, shorter lead times and improved
productivity forbuying organizations. Dong et al. (2001) also stated that JIT purchasingstrategies are aimed at a
synchronized and timely productflow from the supplier to the buyer.Boer, Labro, and Morlacchi (2001)
suggested that with increasingsignificance of the purchasing function, purchasing decisionsbecome more
important. As organizations become more dependenton suppliers, the direct and indirect consequences of
poordecision-making become more severe (Wu, 2009). In addition, several developmentsfurther complicate
purchasing decision-making (Razmi, 2010). Theglobalization of trade and the Internet enlarge a purchaser’s
choiceset (Chia-Wei, 2009). Changing customer preferences require a broader and fastersupplier selection
(Manoj, 2004).In the supplier selection process, it is not always easy to recognizeprecise rules, but there is, in
general, a coherent way to solvethe problem (Kahraman, 2003). The choice of supplier is then a problem
usuallysolved by subjective criteria, based on personal experiences andbeliefs, on the available information and,
sometimes, on techniquesand algorithms supporting the decision process (Albino &Garavelli, 1998). The key to
enhancing the quality of decision-makingin the supplier selection function is to take advantage ofthe powerful
computer-related concepts, tools and techniques thathave become available in the last years (Wei, Zhang, & Li,
1997).Chao, Scheuing, and Ruch (1993) concluded that quality and on timedelivery are the most important
attributes of purchasing performance.Ghodsypour and O’Brien (1998) agreed that cost, qualityand service are
the three main categories to consider when determiningsupplier selection parameters. Briggs (1994) stated
thatjoint development, culture, forward engineering, trust, supplychain management, quality and communication
are the keyrequirements of a supplier partnership, apart from optimum cost.Petroni and Braglia (2000) evaluated
the relative performance ofsuppliers that have multiple outputs and inputs, based on capabilitiesrelating to
management, production facilities, technology,price, quality and delivery compliance. Wei et al. (1997)
determinedthat factors such as a supplier’s supply history, productprice, technology ability and transport cost
have effects on theselection of suppliers.
2.1. Variouscriteriaforsupplierselection:
On the basis of the literature reviewed above it has been observed that the basic criteria typically utilized for
selecting the suppliers are pricing structure, delivery, product quality, and service etc. While most buyers still
consider cost to be their primary concern, few more interactive and interdependent selection criteria are
increasingly being used by the manufacturers (Chen- Tung, 2006). The various important criteria for the
supplier selection as observed from the literature reviewed above are:
�Price (Lee, 2009, Famuyiwa, 2008, Vinod, 2011, Liao, 2010)
�Quality (Boran, 2009, Wang, 2009, Bayrak, 2007, Chan, 2007, Yucel, 2011, Wang, 2008)
�Delivery (Shahanaghi, 2009, Sevkli, 2010, Punniyamoorthy, 2011, Pang, 2006)
132
�Performance History (Lee, 2009, Ku, 2009, Guneri, 2009, Chamodrakas, 2010, Amin, 2011)
�Warranties & Claims Policies (Charles, 1996, Liu, 2003, Ramanathan, 2007, Ghodsypour, 1998)
�Production Facilities and Capacity (Ozgen, 2006, Guneri, 2009, Amid, 2006, Vencheh, 2011, Razmi, 2010)
�Technical Capability (lee, 2009, Ku, 2010, Famuyiwa, 2008 Bay, 1992, Hockey, 1994, Weber, 1993)
�Financial Position (Lasch, 2005, Dulmin, 2003 Thomas, 2008, Ozgen, 2006, Sanayei, 2010, Demirtas, 2008,
Liao, 2007)
�Procedural Compliance (Desheng, 2010, Shou-yan, 2008, Yeh, 2011, Surjandari, 2010, Sawik, 2010)
�Reputation and Position in Industry (Weber, 2000, Ding, 2005, Yeh, 2011, Liao, 2010, Lee, 2009, Ku, 2009,
Mohammadi, 2009, Chen, 2010)
�Desire for Business (Weber ,1991, Swift ,1995, Naude ,1993, Houshyar ,1992, Ellram ,1995)
�Repair Service (Weber ,1998, Verma ,1998, Patton ,1996, Motwani ,1999, Maloni ,1997)
�Attitude (Gunasekaran ,2000, Degraeve ,2000, Choi ,1996, Chakraborty ,1996, Carr ,1999)
�Packaging Ability (Zarandi, 2002, Tracey, 2001, Semra, 2003, Meixell, 2005, Lummus, 2003)
�Labor Relations Record (Keah, 2001, Kannan, 2002, Humhreys, 2003, Hong, 2005, Handfield, 2002)
�Geographical Location (Davidrajuh, 2003, Choy, 2002, Chan, 2004, Boer, 2001, Bharadwaj, 2004)
�Amount of Past Business (Basnet, 2005, Altinoz, 2001, Agrell, 2004, Aaronson, 2004, Lee, 2001)
�Reciprocal Arrangement (Ya ,2010, Xu , 2010, William ,2010, Wagner ,2007, Wadhwa ,2007,Vanteddu
,2011, Thomas ,2008, Tarofder ,2007, Tahiri ,2008, Sevkli ,2010, Saen ,2007),
It has been observed from the literature that the price,delivery, and quality continued to be considered
mostimportant criteria by most of the researchers (Punniyamoorthy, 2011). With economicglobalization,
companies choose suppliers globally fromanywhere in the world (Liu, 2011). For instance, developing countries
arebecoming more competitive because of their low labor andoperating costs (Lee ,2009).
In this study used of some criteria such as: qualitative abilities, Acquiring and adapting to new technologies
abilities, financial abilities and managerial ability.
2.2. Supplier selection methods in the literature:
The literature presents several methods for selecting a supplier.Categorical methods are qualitative models.
Based on the buyer’sexperience and historical data, suppliers are evaluated by a set ofcriteria. The evaluations
actually consist of categorizing the supplier’sperformance based on a set of criteria as either ‘positive’,‘neutral’
or ‘negative’ (Boer et al., 2001). After a supplier has beenrated on all criteria, the buyer gives an overall rating,
such thatthe suppliers are sorted into three categories (Kuo, 2010).Data envelopment analysis (DEA) is
concerned with the efficiencyof a decision alternative (Kelmenis, 2010). The DEA method aids the buyerin
classifying the suppliers into two categories: efficient suppliersand inefficient suppliers. Liu, Ding, and Lall
(2000) used DEA inthe supplier selection process. They evaluated the overall performancesof suppliers by using
DEA. Saen (2007) used IDEA (ImpreciseData Envelopment Analysis) to select the best suppliers in thepresence
of both cardinal and ordinal data.Cluster analysis (CA) represents a class of statistical techniquesthat can be
applied to data that exhibit ‘‘natural’’ groupings (Boeret al., 2001).Case-based reasoning systems (CBR)
combine a cognitive modeldescribing how people use and reason from past experience with atechnology for
finding and presenting experience (Choy et al.,2003a). Choy, Lee, and Lo (2002b) enhanced a CBR-based
supplierselection tool by combining the supplier management network(SMN) and supplier selection workflow
(SSW). Choy, Lee, Lau,and Choy (2005) used CBR to select suppliers in the new productdevelopment
process.In linear weighting methods, criteria are weighted and the criterionthat has the largest weight is given
the highest importance (Jain, 2009).Ghodsypour and O’Brien (1998) integrated AHP and linear programmingto
consider both tangible and intangible factors inchoosing the best suppliers and placing the optimum order
quantities.Lee, Sungdo, and Kim (2001) used only AHP for supplierselection. They determined the supplier
selection criteria basedon the purchasing strategy and criteria weights by using AHP. Liuand Hai (2005) used
DEA for determining the supplier selection criteria.Then, they interviewed 60 administrators to determine
thecriterion priorities and they used AHP for selecting suppliers.Ting and Cho (2008) presented a two-step
decision-makingprocedure – AHP for selecting a set of a firm’s candidate suppliers,followed a multi-objective
linear programming (MOLP) model foroptimal allocations of order quantities to the candidate suppliers.Boer,
Wegen, and Telgen (1998) used the ELECTRE 1 techniqueto evaluate five supplier candidates. Xia and Wu
(2007) used anintegrated approach of AHP improved by rough sets theory andmulti-objective mixed integer
programming, which was proposedto simultaneously determine the number of suppliers to employand the order
quantity allocated to these suppliers in the case ofmultiple sourcing and multiple products, with multiple
criteriaand with the supplier’s capacity constraints. Wang, Huang, andDismukes (2004) used an integrated AHP
and preemptive goalprogramming (PGP)-based multi-criteria decision-making methodologyto take into account
both qualitative and quantitativefactors in supplier selection. Liu and Hai (2005) compared thevoting analytic
hierarchy process (VAHP) and AHP for supplierselection process. Chan and Kumar (2007) identified some of
theimportant and critical decision criteria including risk factors forthe development of an efficient system for
global supplierselection. They used fuzzy extended analytic hierarchy process(FEAHP)-based methodology to
133
select suppliers.Total cost of ownership (TCO) based models include all costsrelated to the supplier selection
process that are incurred duringa purchased item’s life-cycle. Degraeve and Roodhooft (1999)evaluated the
suppliers based on quality, price and delivery performanceby using TCO. They emphasized that the uncertainty
ofdemand, delivery, quality and price must be reflected in thedecision problem. Ramanathan (2007) proposed an
integratedDEA-TCO-AHP model for the supplier selection problem.
Various supplier selection methods as observed in theliterature have been classified in to a number of
broadercategories. Fig. 1 presents various supplier selection methodsas discussed in the literature. Some of the
most commonlyused methods for supplier selection are discussed briefly here.
Fig. 1: Various Supplier Selection Methods.
2.3. Methodsforprequalificationofsuppliers:
Prequalification is the process of reducing the set of all suppliers to a smaller set of acceptable suppliers.
The various methods available under this category are:
A. Categorical Methods:
Basically, categorical methods are qualitative models.Based on historical data and the buyer's experience,
current orfamiliar suppliers are evaluated on a set of criteria. After asupplier has been rated on all criteria, the
buyer gives anoverall rating. The primary advantage of the categoricalapproach is that it helps structure the
evaluation process in aclear and systematic way (Huang, 2007).
B. Data Envelopment Analysis (DEA):
DEA is a classification system that splits suppliers betweentwo categories, ‘efficient’ or ‘inefficient’.
Suppliers are judgedon two sets of criteria, i.e. outputs and inputs. DEA considersa supplier to have a relative
efficiency of 100% if he producesa set of output factors that is not produced by other supplierswith a given set
of input factors. Weber et al. have primarily discussed the application of DEA insupplier selection in several
publications (Chunguang, 2010).
C. Cluster Analysis (CA):
CA is a basic method from statistics which uses aclassification algorithm to group a number of items which
aredescribed by a set of numerical attribute scores into a numberof clusters such that the differences between
items within acluster are minimal and the differences between items fromdifferent clusters are maximal. This
classification is used toreduce a larger set of suppliers into smaller more manageablesubsets. Hinkle et al. were
the first to report this (Chang, 2011).
2.4. Multiattributedecisionmaking (MADM) techniques:
A vendor selection problem usually involves more than onecriterion and these criteria often conflict with
each other. SoMADM techniques are implemented to solve the problem.Some of the MADM techniques are:
A. Analytical Hierarchical Process (AHP):
Analytical Hierarchical Process (AHP) is a decision-makingmethod developed for prioritizing alternatives
when multiplecriteria must be considered and allows the decision maker tostructure complex problems in the
form of a hierarchy, or a setof integrated levels. This method incorporates qualitative andquantitative criteria.
The hierarchy usually consists of threedifferent levels, which include goals, criteria, and alternatives.Because
AHP utilizes a ratio scale for human judgments, thealternatives weights reflect the relative importance of
thecriteria in achieving the goal of the hierarchy (Behzadian, 2010).
134
B.Analytic Network Process (ANP):
Analytic Network Process (ANP) is a comprehensivedecision-making technique that captures the outcome
of thedependence and feedback within and between the clusters ofelements. Analytical Hierarchy Process
(AHP) serves as astarting point for ANP. Analytical Network Process (ANP) isa more general form of AHP,
incorporating feedback andinterdependent relationships among decision attributes andalternatives. ANP is a
coupling of two parts, where the firstconsists of a control hierarchy or network of criteria and subcriteriathat
controls the interactions, while the second part is anetwork of influences among the elements and clusters
(Aksoy, 2011).
C. Total Cost of Ownership (TCO) Models:
TCO-based models for supplier choice basically consists ofsummarization and quantification of all or
several costsassociated with the choice of vendors and subsequentlyadjusting or penalizing the unit price quoted
by the supplier.Total Cost of Ownership (TCO) as stated by Ellram is amethodology and philosophy, which
looks beyond the price ofa purchase to include many other purchase-related costs (Adiel ,2007).
D. Technique for the Order Performance by Similarity toIdeal Solution (TOPSIS):
Another favorable technique for solving MADM problemsis the TOPSIS. According to the concept of the
TOPSIS, acloseness coefficient is defined to determine the ranking orderof all suppliers and linguistic values are
used to assess theratings and weights of the factors. TOPSIS is based on theconcept that the optimal alternative
should have the shortestdistance from the positive ideal solution (PIS) and the farthestdistance from the negative
ideal solution (NIS) (Aissaoui, 2007).
E. Multiple Attribute Utility Theory (MAUT):
The MAUT proposed by Min, H. is also considered alinear weighting technique. The MAUT method has
theadvantage that it enables purchasing professionals to formulateviable sourcing strategies and is capable of
handling multipleconflicting attributes. However, this method is only used forinternational supplier selection,
where the environment ismore complicated and risky (Punniyamoorthy, 2011).
F. Outranking Methods:
Outranking methods are useful decision tool to solve multicriteriaproblems. These methods are only
partiallycompensatory and are capable of dealing with situations inwhich imprecision is present. Lot of attention
has been paid tooutranking models, primarily in Europe. However, so far, inthe purchasing literature there is no
evidence of applications ofoutranking models in purchasing decisions (Satty ,1970).
2.5. Mathematicalprograming (MP) models:
Mathematical programming models often consider only thequantitative criteria. Mathematical programming
models allowdecision makers to consider different constraints in selectingthe best set of suppliers. Most
importantly, mathematicalprogramming models are ideal for solving the supplierselection problem because they
can optimize results usingeither single objective models or multiple objective models(Bhutta et al., 2002). Some
of these models are:
A.Multi-Objective Models:
These models deal with optimization problems involvingtwo or more coinciding criteria.
B.Goal Programming Models:
Another important tool is Goal Programming (GP). Unlikemost mathematical programming models, goal
programmingprovides the decision maker (DM) with enough flexibility toset target levels on the different
criteria and obtain the bestcompromise solution that comes as close as possible to eachone of the defined
targets(Nydick et al., 1992).
2.6. Artificialintelligencemethods:
Artificial Intelligence (AI) models are computer-basedsystems trained by the decision maker using
historical dataand experience. These systems usually cope very well with thecomplexity and uncertainty
involved in the supplier selectionprocess. Some of the AI models are:
A. Case-Based-Reasoning (CBR) Systems:
CBR systems fall in the category of the so-called artificialintelligence (AI) approach. Basically, a CBR
system is asoftware-driven database which provides a decision-makerwith useful information and experiences
135
from similar,previous decision situations. CBR is still very new and onlyfew systems have been developed for
purchasing decisionmaking (Akarte et al. (2001).
B.Artificial Neural Network (ANN):
The ANN model saves money and time. The weakness ofthis model is that it demands specialized software
and requiresqualified personnel who are expert (Chan, 2003).
2.7. Fuzzylogicapproach:
In this method, linguistic values are used to assess the ratings and weights for various factors. These
linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Since human judgments including
preferences are often vague and cannot estimate his preference with an exact numerical value. The ratings and
weights of the criteria in the problem are assessed by means of linguistic variables. One can convert the decision
matrix into a fuzzy decision matrix and construct a weighted-normalized fuzzy decision matrix once the
decision-makers’ fuzzy ratings have been pooled. Finally a closeness coefficient of each alternative is defined
todetermine the ranking order of all alternatives (Liu and Hai, 2005).
2.8. Combinedapproaches/ hybridmethods:
Some authors have combined decision models from different steps in the supplier selection process.
Degraeve and Roodhoft developed a model combining mathematical programming model and TCO.
Ghodsupour and O’Brien had integrated AHP and Linear Programming to consider bothtangible and intangible
factors in choosing the best suppliers.Sanayei et al. presented an effective model using both MAUT and LP for
solving the supplier selection problem. Shyur present an effective model using both ANP and modified TOPSIS,
to accommodate the criteria with interdependencies. Boranhas proposed a multi criteria group decision making
approach using fuzzy TOPSIS, to deal with uncertainty (Bayzit, 2006).
On the basis of above literature the tree below section are presented:
1. Research objectives:
A: supplier selection using AHP method in JIT
B: investigating qualitative abilities criteria in supplier selection using AHP method in JIT
C: investigating Acquiring and adapting to new technologies abilities criteria in supplier selection using AHP
method in JIT
D: investigating financial abilities criteria in supplier selection using AHP method in JIT
E: investigating managerial abilities criteria in supplier selection using AHP method in JIT
2. Research questions:
A: What prioritize the criteria for supplier selection using AHP method in JIT can be settled?
B: A measure of the qualitative abilities in supplier selection using AHP method in JIT in which priority is?
C: A measure of the Acquiring and adapting to new technologiesin supplier selection using AHP method in JIT
in which priority is?
D: A measure of the financial abilities in supplier selection using AHP method in JIT in which priority is?
E: A measure of the managerial abilities in supplier selection using AHP method in JIT in which priority is?
3. Research hypothesis:
A: seems to prioritize the criteria for supplier selection using AHP method in JIT settled.
B: It seems to qualitative abilities criteria in supplier selection using AHP method in JIT can be made in priority.
C: It seems to Acquiring and adapting to new technologies abilities criteria in supplier selection using AHP
method in JIT can be made in priority.
D: It seems to financial abilities criteria in supplier selection using AHP method in JIT can be made in priority.
E: It seems to managerial abilities criteria in supplier selection using AHP method in JIT can be made in
priority.
3. Methodology:
The current study from propose perspective is an applied study and from methodology perspective is an
analytical – descriptive study. The method library to gather information about the history of foreign and
domestic studies has been used. Moreover AHP method will be used to assess suppliers and the decision support
model for the selection of suppliers will be used. This will be done with MATLAB and Expert Choice.
4. Conceptual model:
The study sought to examine the choice of supplier by AHP method in the production process has been
updated to address this issue, the conceptual model is designed: see fig 2.
136
Fig. 2: Conceptual model.
5. Data analysis:
5.1. Paired comparison matrix:
Paired comparisons consolidated matrix of qualitative capabilities of suppliers:
Table 1 Consolidated matrix of paired comparisons qualitative capabilities of suppliers to the
incompatibility rate is 0.02 shows:
Table 1: Consolidated matrix of paired comparisons to the qualitative capabilities of suppliers.
Paired comparisons matrix consolidated financial capabilities of suppliers:
Table 2 paired comparisons matrix consolidated financial capabilities of the incompatibility rate its
suppliers is 0.01 shows:
Table 2: paired comparisons matrix consolidated financial capabilities of suppliers.
.Consolidated matrix paired comparisons and the ability to achieve compliance with the new technology
suppliers:
Table 3 Consolidated matrix of paired comparisons and the ability to achieve compliance with the new
technology suppliers to the incompatibility rate is 0.02 shows: Table 3: Consolidated matrix of paired comparisons and the ability to achieve compliance with the new technology suppliers
137
Consolidated managerial capabilities paired comparisons matrix suppliers:
Table 4 Consolidated matrix of paired comparisons supplier managerial capabilities of the incompatibility
rate is 0.02 shows:
Table 4: Consolidated matrix of paired comparisons managerial capabilities of suppliers.
The main criteria for selecting suppliers paired comparison matrix:
Table 5 paired comparisons matrix main criteria for selection of suppliers of the incompatibility rate is 0.01
shows:
Table 5: paired comparisons matrix main factors agility Saipa
5.2. Prioritization criteria:
Ranking and prioritizing the main criteria for selecting suppliers:
Table 6: ranking and prioritizing the main criteria for selecting suppliers
Ranking and prioritization of qualitative abilities of suppliers:
138
Table 7: ranking and prioritization of qualitative abilities of suppliers.
Ranking and prioritizing financial capabilities of suppliers:
Table 8: ranking and prioritization of financial capabilities of suppliers.
Ranking and prioritizing ability to achieve and adapt to new technologies suppliers:
Table 9: ranking and prioritization of access and the ability to adapt to new technology suppliers.
Ranking and priority managerial abilities suppliers:
Table 10: ranking and prioritization of supplier managerial capabilities
139
6. Hypothesis test results:
H1: seems to prioritize the criteria for supplier selection using AHP method in JIT settled.
The results of AHP hierarchical analysis shows that, among the main criteria for selecting the supplier as
follows: managerial ability, quantitative ability, financial capability, the ability to acquire and adapt new
technologies, are most important.
H2: It seems to qualitative abilities criteria in supplier selection using AHP method in JIT can be made in
priority.
The results of AHP hierarchical analysis show that the criterion of quality to weight (0.230) the second priority
is the supplier of choice. Also the ability of quality criteria in the selection of suppliers, as follows: Product
qualities, service quality, timely delivery of the product, after-sales service are most important.
H3: It seems to Acquiring and adapting to new technologies abilities criteria in supplier selection using AHP
method in JIT can be made in priority.
The results of AHP hierarchical analysis shows that the criterion of ability to achieve and adapt to new
technologies by weight (0.144) in the fourth priority of the provider. As well as the ability to acquire and adapt
new technology standards in the selection of providers, respectively: technical ability, capability, features
packed, communication systems, education assistance, are most important.
H4: It seems to financial abilities criteria in supplier selection using AHP method in JIT can be made in
priority.
The results of AHP hierarchical analysis show that the financial measures weight (0.177) in the third
priority is the supplier of choice. As well as the financial criteria in selecting the supplier, in order: price,
financial stability, business efforts are most important.
H5: It seems to managerial abilities criteria in supplier selection using AHP method in JIT can be made in
priority.
The results of AHP AHP show that measure the ability to choose the provider of weight management
(0.499) Supplier selection is the first priority. Also among the measures management's ability to choose the
provider, as follows: control of operations, the ability to solve problems, meet customer relationships, how are
most important.
Conclusion:
The supplier selection problem is of vital importance for operation of every firm because the solution of this
problem can directly and substantially affect costs and quality. Indeed, for many organizations effective supplier
evaluation and purchasing processes are critical success factors. A great deal of research has been conducted
todetermine what criteria should be used to evaluate suppliers. In practice, any set of criteria must be considered
in light of real-life constraints, making the supplier selection a complicated decision problem that involves
balancing many tradeoffs and satisfying conflicting desiderata.From a decision support system perspective, the
research on the supplier selection problem can be divided into two parts. First, multi-attribute decision making
models that give grades to suppliers on a set of criteria, and then use a weighting scheme to arrive at a supplier
score. Second, mathematical programming techniques that model the constraints and an objective function to
select the optimal supplier. The grading method is easy and intuitive but remains simplistic in that it does not
consider any constraints explicitly. On the other hand, mathematical program-ming methods accommodate both
constraints and supplier selection criteria, but must make restrictive assumptions to reduce inordinate
complexity. As such, supplier selection criteria play an integral role in both approaches.In this study, a detailed
evaluation and selection of suppliers through the use AHP is hierarchical analysis. In this study, the results of
the analysis show that, Myartvanayy management Supplier selection process at the time of greatest importance.
REFERENCES
140
Aaronson, D., W. Bostic, P. Huck, R. Townsend, 2004. Supplier relationships and small business use of
trade credit. Journal of Urban Economics, 55: 46-67.
Abginechi, S., R.Z. Farahani, 2010. Modeling and analysis for determining optimal suppliers under
stochastic lead times. Applied Mathematical Modeling, 34: 1311-1328.
Adiel, T., 2007. Multi-criteria decision model for outsourcing contracts selection based on utility function
and ELECTRE method. Computers & Operations Research, 34: 3569-3574.
Agrell, P.J., R. Lindroth, A. Norrman, 2004. Risk, information and incentives in telecom supply chains.
International Journal of Production Economics, 90: 1-16.
Aissaoui, N., M. Haouari, E. Hassini, 2007. Supplier selection and order lot sizing modeling: A review.
Computers & Operations Research, 34: 3516-3540.
Akarte, M.M., N.V. Surendra, B. Ravi, N. Rangaraj, 2001. Web based casting supplier evaluation using
analytical hierarchy process. Journal of the Operational Research Society, 52: 511-522.
Akinc, U., 1993. Selecting a set of vendors in a manufacturing environment.Journal of Operations
Management, 11: 107-122.
Aksoy, A., N. Ozturk, 2011. Supplier selection and performance evaluation in just-in-time production
environments. Expert Systems with Applications, 38: 6351-6359.
Altinoz, C., P. Kilduff, S.C. Winchester, 2001. Current issues and methods in supplier selection. Journal of
the Textile Institute, 92 (2): 128-141.
Amid, A., S.H. Ghodspour, C. O’Brien, 2006. Fuzzy multi-objective linear for supplier selection in a supply
chain. International Journal of production economics, 104: 394-407.
Amin, S.H., J. Razmi, G. Zhang, 2011. Supplier selection and order allocation based on fuzzy SWOT
analesis and fuzzy linear programming. Expert Systems with Applications, 38: 334-342.
Awasti, A., S.S. Chauhan, S.K. Goyal, J.M. Proth, 2009. Supplier selection problem for a single
manufacturing unit under stochastic demand. International Journal of Production Economics, 117: 229-233.
Basnet, C., J.M.Y. Leung, 2005. Inventory lot-sizing with supplier selection.Computers & Operations
Research, 32: 1-14.
Bay, A., I. Magid, 1992. A knowledge based decision support system for computer performance
management. Decision Support System, 8: 501-515.
Bayazit, O., 2006. Use of analytic network process in vendor selection decisions.Benchmarking: An
International Journal, 13(5): 566-579.
Bayazit, O., B. Karpak, 2005. An AHP application in vendor selection.ISAHP, Honolulu, Hawaii, 8-10.
Bayrak, M.Y., N. Celebi, H. Taskin, 2007. A fuzzy approach method for supplier selection. Production
Planning & Control, 18(1): 54-63.
Beamon, B.M., 1999. Measuring supply chain performance.International Journal of Operations &
Production Management, 19(3): 275-292.
Behzadian, M., R.B. Kazemzadeh, A. Albadvi, M. Aghdasi, 2010. PROMETHEE: A comprehensive
literature review on methodologies and applications. European Journal of Operational Research, 200: 198-215.
Bharaadwaj, N., 2004. Investigating the decision criteria used in electronic component procurement.
Industrial Marketing Management, 33: 317-323.
Bhutta, K.S., F. Huq, 2002. Supplier selection problem: a comparison of the total cost of ownership and
analytic hierarchy process approaches. Supply Chain Management: An International Journal, 7(3): 126-135.
Boer, L.D., L.V.D. Wegen, J. Telgen, 1998. Outranking methods in support of supplier selection.European
Journal of Purchasing & Supply Management, 4: 109-118.
Chakraborty, S., 1996. Vendor development strategies.International Journal of Operations & Production
Management, 16(10): 54-66.
Chamodrakas, I., D. Batis, D. Martakos, 2010. Supplier selection in electronic marketplaces using
satisficing and fuzzy-AHP. Expert Systems with Applications, 37: 490-498.
Chan, F.T.S., H.K. Chan, 2004. Development of the supplier selection mode- a case study in the advanced
technology industry. Proceedings of the Institution of Mechanical Engineers, 218: 1807-1824.
Che, Z.H., 2010. A genetic algorithm-based model for solving multi-period supplier selection problem with
assembly sequence. International Journal of Production Research, 48 (15), 4355-4377.
Chen-Tung., C., L. Ching-Torng, H. Sue-Fn, 2006. A fuzzy approach for supplier evaluation and selection
in supply chain management.International Journal of Production Economics, 102: 289-301.
Chia-Wei, H., H.H. Allen, 2009.Applying hazardous substance management to supplier selection using
analytic network process.Journal of Cleaner Production, 17: 255-264.
Choi, T.Y., J.L. Hartly, 1996. An exploration of supplier selection across the supply chain. Journal of
Operational Management, 14: 333-343.
Choy, K.L., W.B. Lee, V. Lo, 2002. An intelligent supplier management tool for benchmarking suppliers in
outsource manufacturing. Expert Systems with Applications, 22: 213-224.
141
Choy, K.L., W.B. Lee, V. Lo, 2004. An enterprise collaborative management system-a case study of
supplier relationship management. The Journal of Enterprise Information Management, 17: 191-207.
Chunguang, B., S. Joseph, 2010. Integrating sustainability into supplier selection with grey system and
rough set methodologies. International Journal of Production Economics, 124, 252-264.
Da Silva, R.V., G. Davies, P. Naude, 2002. Assessing customer orientation in the context of buyer/supplier
relationships using judgmental modeling. Industrial Marketing Management, 31: 241-252.
Demirtas, E.A., O. Ustun, 2008. An integrated multi-objective decision making process for supplier
selection and order allocation. Omega, 36: 76-90.
Ebrahim, R.M., J. Razmi, H. Haleh, 2009. Scatter search algorithm for supplier selection and order lot
sizing under multiple price discount environment. Advances in Engineering Software, 40: 766-776.
Ellram, L.M., 1995. Total cost of ownership: An analysis approach for purchasing. International Journal of
Physical Distribution & Logistics Management, 25(8): 4-23.
Farzad, T., R.O. Mohammad, A. Aidy, M.Y. Rosnah, 2008. A review of supplier selection methods in
manufacturing industries. Suranree Journal of Science Technology, 15(3): 201-208.
Ghodsypour, S.H., C. O’Brine, 1998. A decision support system for supplier selection using an integrated
analytic hierarchy process and linear programming. International Journal Production Economics, 56-57, 199-
212.
Golmohammadi, D., R.C. Creese, H. Valian, J. Kolassa, 2009. Supplier selection based on neural network
model using genetic algorithm. IEEE Transactions on Neural Networks, 20(9): 1504-1519.
Guneri, A.F., A. Kuzu, 2009. Supplier selection by using a fuzzy approach in just-in-time: A case study.
International Journal of Computer Integrated Manufacturing, 22 (8), 774-783.
Hakansson, H., B. Wootz, 1975. Supplier selection in an International environment- An experimental study.
Journal of Marketing Research, 12: 46-51.
Handfield, R., S.V. Walton, R. Scoufe, S.A. Melnyk, 2002. Applying environmental criteria to supplier
assessment: A study in the application of the analutical hierarchy process. European Journal of Operational
research, 141: 70-87.
Hokey, M., 1994. International supplier selection: A multi-attribute utility approach. International of
Physical Distribution & Logistics Management, 24(5): 24-33.
Hong, G.H., S.C. Park, D.S. Jang, H.M. Rho, 2005. An effective supplier selection method for constructing
a competitive supply-relationship. Expert System with Applications, 28: 629-639.
Houshyar, A., D. Lyth, 1992. A systematic supplier selection procedure.Computers and Industrial
Engineering, 23(1-4): 173-176.
Humphreys, P.K., Y.K. Wong, F.T.S. Chan, 2003. Integrated environmental criteria into the supplier
selection process. Journal of materials Processing Technology, 138: 349-356.
Hwang, H.S., C. Moon, C.L. Chuang, M.J. Goan, 2005. Supplier selection and planning model using AHP.
International Journal of the Information Systems for Logistics and Management, 1(1): 47-53.
Kannan, V., K.C. Tan, 2002. Supplier selection and assessment: Their impact on business performance. The
Journal of Supply Chain Management, 99(15): 11-21.
Keah, C.T., 2001. A framework of supply chain management literature.European Journal of Purchasing &
Supply Management, 7: 39-48.
Kelmenis, A., D. Askounis, 2010. A new TOPSIS-based multi-criteria approach to personnel selection.
Expert Systems with Applications, 37: 4999-5008.
Khurrum, S.B., H. Faizul, 2003. Supplier selection problem: a comparison of the total cost of ownership
and analytic hierarchy process approaches. Supply Chain Management “An International Journal, 7(3): 126-
135.
Kokangul, A., S. Zeynep, 2009. Integrated analytical hierarchy process and mathematical programming to
supplier selection problem with quantity discount. Applied Mathematical Modelling, 33: 1417-1429.
Ku, C.Y., C.T. Chang, H.P. Ho, 2010. Global supplier selection using fuzzy analytical hierarchy process
and fuzzy goal programming. Qual Quant, 44: 623-640.
Kuo, R.J., S.Y. Hong, Y.C. Huang, 2010. Integration of particle swarm optimization-based fuzzy neural
network and artificial neural network for supplier selection. Applied mathematical Modelling, 34: 3976-3990.
Lasch, R., G. Janker, 2005. Supplier selection and controlling using multivariate analysis. International
Journal of Physical Distribution & Logistics Management, 35(6): 409-425.
Lee, E.K., S. Ha, S.K. Kim, 2001. Supplier selection and management system considering relationships in
supply chain management. IEEE Transactions on Engineering Management, 48(3): 307-318.
Liao, C.N., H.P. Kao, 2010. Supplier selection model using Taguchi loss function, analytical hierarchy
process and multi-choice goal programming. Computers & Industrial Engineering, 58: 571-577.
Lin, R.H., C.L. Chuang, J.J.H. Liou, G.D. Wu, 2009. An integrated method for finding key suppliers in
SCM. Expert Systems with Applications, 36: 6461-6465.
142
Liu, F., F.Y. Ding, V. Lall, 2003. Using data envelopment analysis to compare suppliers for supplier
selection and performance improvement. Supply Chain Management: An International Journal, 5(3): 143-150.
Maloni, M.J., W.C. Benton, 1997. Supply chain partnerships: opportunities for operations research.
European Journal of operational Research, 101: 419-429.
Meixell, M.J., V.B. Gargeya, 2005. Global supply chain design: A literature review and critique.
Transportation Research Part E, 41: 531-550.
Mithat, Z., C. Cuneyt, C. Cemal, 2011. A combined methodology for supplier selection and performance
evaluation. Expert Systems with Applications, 8: 2741-2751.
Naude, P., G. Lockett, 1993. Market analysis via judgmental modeling: An application in the UK Chemical
Industry.European Journal of Marketing, 27(3): 5-22.
Ramanathan, R., 2007. Supplier selection problem: integrating DEA with the approaches of total cost of
ownership and AHP. Supply Chain Management: An International Journal, 12(4): 258-261.
Razmi, J., H. Rafiei, 2010. An integrated analytical network process with mixed-interger nonlinear
programming to supplier selection and order allocation.International Journal of Advanced manufacturing
Technology, 49: 1195-1208.
Sanayei, A., S.F. Mousavi, A. Yazdankhah, 2010. Group decision making process for supplier selection
with VIKOR under fuzzy environment. Expert Systems with Applications, 37: 24-30.
Sawik, T., 2010. Single vs. multiple obejective supplier selection in a make to order environment. Omega,
38: 203-212.
Shahanaghi, K., S.A. Yazdian, 2009. Vendor selection using a new fuzzy group TOPSIS approach. Journal
of Uncertain Systems, 3(3): 221-231.
Surjandari, I., S. Sudarto, S. Anggarini, 2010. Supplier selection in JIT automotive industry: a multivariate
approach. Operations and Supply Chain Management, 3(2): 83-93.
Swift, C.O., 1995. Preferences for single sourcing and supplier selection criteria.Journal of Business
Research, 32: 105-111.
Tahiri, F., Osman, M.R., Ali, A., &Yusuff, R.M. (2008).Areview of supplier selection methods in
manufacturing industries. Suranaree Journal of Science and Technology, 15 (3), 201-208.
Vaidya, O.S., S. Kumar, 2006. Analytical hierarchy process: An overview of applications. European
Journal of Operational Research, 169: 1-29.
Vanteddu, G., R.B. Chinnam, O. Gushikin, 2011. Supply chain focus dependent supplier selection problem.
International Journal of Production Economics, 129: 204-216.
Wadhwa, V., A.R. Ravindran, 2007. Vendor selection in outsourcing.Computers & Operations Research,
34: 3725-3737.
Wagner, S.M., G. Friedl, 2007. Supplier switching decisions.European Journal of Operational Research,
183, 700-717.
Wang, J.W., C.H. Cheng, H.K. Chen, 2009. Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft
Computing, 9: 377-386.
Weber, C.A., J.R. Current, W.C. Benton, 1991. Vendor selection criteria and methods.European Journal of
Operational Research, 50: 2-18.
William H., 2008. Integrated analytic hierarchy process and its applications- A literature review. European
Journal of operational Research, 186: 211-228.
William, Ho., X. Xiaowei, K.D. Prasanta, 2010. Multi-criteria decision making approaches for supplier
evaluation and selection: A literature review. European Journal of Operational Research, 202: 16-24.
Wu, W.Y., H.A. Shih, H.C. Chan, 2009. The analytic network process for partner selection criteria in
strategic alliances. Expert Systems with Applications, 36: 4646-4653.
Xu, X., J. Lin, 2010. Strategic supplier network for supplier selection.Journal of Computers, 5(6): 979-986.
Ya, L.T., J.Y. Yao, L. Chi-Hsiang, 2010. A dynamic approach for supplier selection using ant colony
system. Expert Systems with Applications, 37: 8313-8321.
Yang, C.C., B.S. Chen, 2006. Supplier selection using combined analytical hierarchy process and grey
relational analysis. Journal of Manufacturing Technology Management, 17(7): 926-941.
Yeh, W.C., M.C. Chuang, 2011. Using multi-objective genetic algorithm for partner selection in green
supply chain problems. Expert Systems with Applications, 38, 4244-4253.
Zarandi, M.H., I.B. Turksen, 2002. Supply chain: crisp and fuzzy aspects. International Journal of Applied
Mathematics and Computer Science, 12(3): 423-435.