ArticlePDF Available

SUPPLIER SELECTION WITH AHP AND 0-1 GOAL PROGRAMMING: AN APPLICATION IN HEALTHCARE INDUSTRY

Authors:

Abstract and Figures

In order for health institutions to continue their activities, the goods and services they need must be supplied at the right time, in the right amount, at the right quality, at an affordable price and from the right source. This is possible with an effective supply chain management and selection of the right supplier. Supplier selection studies in the health sector are almost nonexistent, therefore, it was wanted to contribute to the literature by studying in this sector. In this study, it was aimed to work with the right suppliers to ensure that a dental health center provides critical medical supplies. First of all, the products of vital importance were determined by ABC (Always, Better Control)-VED (Vital, Essential, Desirable) matrix analysis and a supplier list was created. The best suppliers were selected with the Zero-One Goal Programming method based on AHP priorities, one of the multi-criteria decision making methods, by determining the criteria suitable for the sector.It is thought that this model will contribute significantly to the literature and will save time in supplier selection studies in the health sector.
Content may be subject to copyright.
Mersin University
Journal of Maritime Faculty
50
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
ISSN 2687-6612, Turkey
DOI: 10.47512/meujmaf.815671
Research Article
SUPPLIER SELECTION WITH AHP AND 0-1 GOAL PROGRAMMING: AN
APPLICATION IN HEALTHCARE INDUSTRY
Res. Ass. Sena Kumcu *1, Asst. Prof. Dr. Bahar Özyörük 2
1 Gazi University, Faculty of Engineering, Department of Industrial Engineering, Ankara, Turkey
ORCID ID 0000-0002-9648-6281
senakumcu@gazi.edu.tr
2 Gazi University, Faculty of Engineering, Department of Industrial Engineering, Ankara, Turkey
ORCID ID 0000-0001-5434-6697
bahar@gazi.edu.tr
* Corresponding Author
Received: 23/10/2020 Accepted: 26/11/2020
ABSTRACT
In order for health institutions to continue their activities, the goods and services they need must be supplied at the right
time, in the right amount, at the right quality, at an affordable price and from the right source. This is possible with an
effective supply chain management and selection of the right supplier. Supplier selection studies in the health sector are
almost nonexistent, therefore, it was wanted to contribute to the literature by studying in this sector. In this study, it was
aimed to work with the right suppliers to ensure that a dental health center provides critical medical supplies. First of all,
the products of vital importance were determined by ABC (Always, Better Control)-VED (Vital, Essential, Desirable)
matrix analysis and a supplier list was created. The best suppliers were selected with the Zero-One Goal Programming
method based on AHP priorities, one of the multi-criteria decision making methods, by determining the criteria suitable for
the sector.It is thought that this model will contribute significantly to the literature and will save time in supplier selection
studies in the health sector.
Keywords: Supplier Selection, Healthcare Industry, Multiple Criteria Decision Making (MCDM), Goal
Programming
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
51
1. INTRODUCTION
In order for health institutions to continue their
activities, the goods and services they need must be supplied
at the right time, in the right amount, at the right quality, at
an affordable price and from the right source. This is
possible with an effective supply chain management (SCM).
For this reason, more emphasis is placed on supply chain
management in today's healthcare industry. SCM in
hospitals provides elimination of all activities, movements
and processes, minimizing errors, and increasing the
efficiency of the process between the inputs and outputs.
The procurement activities of the health institution,
where human health and even life is in question, should be
carried out without interruption, because there is no
compensation for the fault of logistics activities in health
institutions. Any disruption that may be experienced can
cost human life. Therefore, suppliers should be selected very
carefully in healthcare institutions (Aptel & Pourjalali 2001:
68).
One of the most important components in SCM is
supplier selection (Tookey and Thiruchelvam, 2011).
Because choosing an appropriate supplier reduces
purchasing costs, improves profits, reduces product delivery
time, increases customer satisfaction and strengthens
competitiveness (Frej et al., 2017).
Various supplier selection methods as observed in the
literature have been classified in main categories and sub-
categories. Table 1 summarizes the supplier selection
methods (Taherdoost and Brard, 2019). Among the supplier
selection studies, which have a very wide area in the
literature, only the literature review of Analytic Hierarchy
Process (AHP) and Goal Programming (GP) method are
used together are presented below;
Dağdeviren and Eren (2001) applied AHP and zero one
goal programming (ZOGP) method together in order to
perform supplier selection in their studies.
Wang et al. (2004) proposed an integrated AHP and
preemptive goal programming (PGP) model in their studies.
Perçin (2006) applied an integrated AHP and GP model
for supplier selection. The model was to determine the
optimal order quantity from the most appropriate supplier
while considering the capacities of potential suppliers.
Mızrak et al. (2008) applied a goal programming (GP)
approach with AHP priorities was utilized to solve the
problem of materials' supplier selection for a company
operating in textile industry.
Sivrikaya et al. (2015) presented an integrated
evaluation approach for decision support enabling effective
supplier selection and ordering processes in textile industry.
The integrated evaluation method in their studies includes
two phases that consist of fuzzy AHP and goal programming
approaches.
Ünal et al. (2019) proposed an approach for integrated
Fuzzy Analytical Hierarchy Process (FAHP) and GP method
for supplier selection in a hotel business in Antalya.
As a result of the literature research, it was seen that
there are very few studies in which ZOGP and AHP were
used together in supplier selection. Integrated AHP and
ZOGP method has been proposed because it is thought to
contribute to the literature.
In this study, an application has been made for the
selection of suppliers of high value and vital medical
supplies to be purchased by the oral health center. ABC
(Always Better Control) and VED (Vital, Essential,
Desirable) analysis methods were combined with the matrix
created to determine the vital and high value product group.
There are limited studies on ABC and VED matrix analysis
in the health sector. Some of the recent studies are
mentioned below;
Nigah et al. (2010), Yeşilyurt and Bayhan (2015),
Karagöz and Yıldız (2015), Fitriana et al. (2017), Guimarães
et al. (2019) applied the ABC-VED matrix analysis method
for inventory management in the health sector recently.
Table 1. Classification of Supplier Selection Methods (source Taherdoost and Brard, 2019)
Supplier Selection Methods
Statistical/Probabilistic (Cluster Analysis)
Fuzzy Set Theory
Multi Attribute Decision
Making (Categorical Method)
AHP
ANP (Analytic Network Process)
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
MAUT (Multi-Attribute Utility Theory)
Outranking Methods:
ELECTRE (Elimination and Choice Expressing Reality)
PROMETHEE (Preference Ranking Organization Method for Enrichment
Evaluations)
Methods Based on Costs ABC (Activity Based Costing)
TCO (Total Cost of Ownership)
Mathematical Programming Linear Programming
MOLP (Multi-Objective Linear Programming)
Goal Programming
Artificial Intelligence CBR
(Case-Based Reasoning)
ANN (Artificial Neural Network)
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
52
A model has been developed by integrating the priorities
of AHP, one of the MCDM methods, into a zero-one goal
programming model for selecting the best supplier to
provide this product group. The zero-one goal programming
model is a type of GP method, in which the decision
variable values can either result in one or zero. The
advantage of ZOGP is that the model can help the decision
makers to select an optimal allocation solution for limited
resources.
In Chapter 2, the literature review for supplier selection
in the healthcare sector is examined. In Chapter 3, the
methodology of the study is given and the methods used are
explained in detail. Chapter 4 includes the application
section. Finally, Chapter 5 includes the results of the study
and the findings obtained.
2. SUPPLY CHAIN MANAGEMENT IN
HEALTCARE INDUSTRY
Today, healthcare industry grows rapidly. Therefore
healthcare delivery systems has become a major priority in
the field. (Fashoto et al., 2016). The healthcare sector supply
chain is characterized by its complexity, which results on the
one hand from the multitude of different supplies used by
the institutions.
A major characteristic of the healthcare sector supply chain
is the simultaneous presence of two chains: one external and
the other internal (Rivard-Royer et al.,2002). (see Figure 1).
Figure 1. Supply Chain in Healtcare Sector (Source: Rivard-Royer, Landry and Beaulieu, 2002)
In the health sector supply chain structure, producers
are divided into two as primary and secondary producers.
Primary manufacture involves the creation of the active
ingredient contained within the medication. Secondary
Production converted the active ingredient into usable
products. The final products are distributed to healthcare
organizations by distributors, wholesalers and
manufacturers and there is a backward flow from them.
(Kritchanchai, 2014). Figure 2 summarizes the health
sector supply chain structure.
Figure 2. Healthcare Supply Chain Structure (Source: Mustaffa and Potter, 2009)
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
53
2.1. Literature Review Of Supplier Selection
Studies And The Criteria Used In The Health
Sector
Supplier selection problem is described as a complex
multi-criteria decision problem that can contain many
quantitative and qualitative variables together.
Therefore, the systematic evaluation of such a
problem is important in terms of producing correct
solutions. One of the first studies on supplier selection
was conducted by Dickson (1966) in America. Dickson
sent a questionnaire to 273 selected people from the
purchasing agent and the executives of the National
Association of Purchasing. Here, 23 criteria were used
and the most important criteria were determined as
product quality, on-time delivery and warranty policy
(Dickson, 1966: 16-17).
It is seen that various criteria are used in the studies
on the supplier selection problem in the literature. In this
study, the criteria used in the health sector were examined.
The literature review in this field is given in Table 2.
Kirytopoulos, Leopoulos, and Voulgaridou (2008)
presented a comprehensive method for evaluating and
selecting proposals in pharmaceutical industry clusters in
their work. The best supplier was selected in line with the
criteria determined by the analytical network process
(ANP).
Enyinda, Dunu, and Bell-Hanyes (2010) made use of
the analytical hierarchy process (AHP) model in their
articles. They developed the Expert Selection Software
by conducting a case study to solve the supplier selection
process problem in a pharmaceutical company.
Vankatesh et al. (2015) addressed the problem of
selecting suppliers for blood bag purchase, which is
critical in the health sector. They made their supplier
selection with TOPSIS method in line with the criteria
determined by the literature review and expert opinions.
Fashoto, Akimuwesi, Owalabi, and Adelekan (2016)
used analytical hierarchy process (AHP) and artificial
neural network (ANN) in their studies. They developed a
decision support model for evaluating and selecting the
healthcare providers of tertiary institutions.
Bahadori et al. (2017) used a combination of ANN
and fuzzy VIKOR in their study. They have developed a
model for selecting the best supplier in the hospital. The
results obtained from the model showed that the most
effective factor in supplier selection is 'quality'.
Forghani et al. (2018) worked in a multi-supplier
pharmaceutical company.In order to improve supplier
selection, they first used the principal component analysis
(PCA) method to reduce the number of supplier selection
criteria. Then, they obtained the importance value of each
supplier for each product using the method based on the
concept of Z-numbers called Z-TOPSIS. Finally, they
used these values as input in mixed integer linear
programming (MILP). With the developed model, they
determined the suppliers and the amount of products
supplied from the relevant suppliers.
Manivel and Ranganathan (2019) analyzed the
Supplier Selection process in line with the interviews
with the pharmacy manager. They have applied the
combination of Fuzzy Analytical Hierarchy Process
(FAHP) and Fuzzy Ideal Solution methods (FTOPSIS)
for the selection of suppliers.
Table 2. Supplier Selection Criteria in Healthcare Industry
Supplier Selection Criteria
Authors Criteria
Kirytopoulos, Leopoulos and Voulgaridou (2008) Price, Quality, Service, Supplier’s Profile, Risk
Enyinda,Dunu ve Bell-Hanyes (2010)
Quality, Cost, Compliance with Legislation, Service,
Supplier Reliability, Risk Management, Supplier’s
Profile, Green Purchasing
Venkatesh and diğ. (2015) Purchasing Cost, Production Quality, Financial Status
Fashoto, Akimuwesi, Owalabi and Adelekan
(2016) Cost, Service, Risk, Quality, Delivery
Bahadori and et al. (2017)
Price, Quality, Delivery Time, Payment Terms, The
Suppliers Background, Packaging and Transport
Quality
Forghani, Sadjadi, Farhang ve Morhadam (2018) Cost, Quality, Service, Delivery, Supplier Profile
Manivel and Ranganathan (2019)
Cost, Delivery, Service, Flexibility, Supplier
Reliability
Doğan and Akbal (2019) Price, Technical Competence, Service Quality,
Repair Service and Guarantee Policy
Yazdani et al. (2020) Offer Price, Supplier's Stock Capacity, Batch
Volume, Flexibility, Technology and Quality
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
54
Doğan and Akbal (2019) discussed the selection of a
medical company for a university hospital in their study
and used the AHP method, which is one of the multi
criteria decision making methods, to determine the most
suitable supplier for both the patient and the hospital.
Yazdani, Torkayesh, Chatterjee (2020) conducted
their studies in order to realize the sustainable supplier
selection in a hospital in Spain. They determined the
importance weights of alternative suppliers using the
DEMATEL and BWM (Best Worst Method) method.
The best supplier; They determined it using the EDAS
(Evaluation According to Average Solution Distance)
method.
3. METODOLOGY
In this study, ABC-VED matrix analysis method was
used to determine critical product groups. Then these
products are grouped according to their application areas.
Later, alternative suppliers were determined for these
product groups. Later, in order to determine the priority
values of the suppliers, the AHP method was preferred
because the interactions of the criteria with each other are
not taken into account in the decision-making process and
because it can compare more than one quantitative and
qualitative criteria at the same time. In solving the
problem, 0-1 Goal Programming method was preferred
because it realizes many goals at the same time and offers
an effective solution method.
Figures or Tables should be sized the whole width of
a column, as shown in Table 1 or Fig. 1 (Figs. 1 and/to n)
in the present example, or the whole width over two
columns. Do not place any text besides the figures or
tables. Do not place them altogether at the end of
manuscripts.
3.1. ABC Analysis
ABC analysis is defined to the inventory control
model that separates the products in inventory according
to the number of use and cost value in a year. The
principle that forms the basis of the analysis was first put
forward by H. Ford Dickie, one of the employees of
General Electric. This method, which was developed in
1896 by an Italian economist named Vilfredo Pareto, is
also known as the pareto rule (Demiral 2013: 48).
The following steps are followed in classifying the
stocks according to the ABC principle:
1. All inventory items are listed.
2. The investment made in these elements; It is calculated
as (Unit price / cost) x Annual Demand.
3. Annual investment values are put in order from large
to small.
4. The investment made to each element is calculated as
what% of the total investment is.
5. The cumulative sums of the ratios in (4) are found.
6. By examining the cumulative percentages,
The elements that make up 70-80% of the investment are
defined as A group, 20-25% as B group, and the
remainder as C group (Yenersoy, 2011).
3.2. VED Analysis
Errors and lack of materials in hospital facilities can
cause patient losses or disabilities. Therefore, sometimes
the lack of a low cost material in hospitals can be of vital
importance. Although the cost of the medical equipment
used for vascular access is very low, its value for the
patient is much greater. Lack of such materials may cause
disruption or failure of treatments. Therefore, inventory
control methods of hospital enterprises take into account
not only cost but also vital importance (Karagöz and
Yıldız 2015: 319).
While ABC method classifies inventories according
to their cost; VED classifies medical supplies, especially
drugs and consumables, according to the vital needs of
the patient. (Kaptanoğlu, 2013: 32).
VED analysis classifies the inventory items in the
pharmaceutical and medical supplies inventory list of
hospital enterprises as vital (V) essential (E) and
desirable (D). İnventory with critical importance for
survival of patients are defined as V, inventory materials
with lower critical importance than V are defined as E
and inventory materials with the lowest usage
requirement are defined as D group (Vaz, et al. 2008:
120).
3.3. ABC-VED Matrix Analysis
The ABC-VED matrix is a method that considers
both the critical values and the economic and importance
levels of drugs and medical supplies. It also categorizes
the control of inventories according to priority (Pund et
al., 2016: 469-470).
The ABC-VED matrix is formulated by cross-
tabulating ABC and VED analysis. The combination
obtained is classified into three groups (Vaz, et al., 2008:
120).After determining the groups to be checked and
evaluated in the ABC-VED matrix, the materials in the
V, E, D groups are ABC classified.
First, in the first group, all vital (V) inventory
materials and A group inventory materials are handled.
This group includes AV, BV, CV, AE and AD
subclasses. Second, among the remaining inventory
materials, all subclasses of essential (E) and B group are
gathered into a group. Accordingly, in this second group
there will be BE, BD and CE subclasses. Finally, the
third category consists of the CD group (Gupta et al.,
2010: 201-205).
Table 3 shows the ABC-VED matrix analysis.
Table 3.ABC-VED Matris
Category V E D
A AV AE AD
B BV BE BD
C CV CE CD
3.4. AHP
AHP, which is one of the multi-criteria decision
making methods in selecting the right supplier and was
introduced by Thomas Saaty in the second half of the
1900s, is an effective tool to deal with complex decision
making and helps the decision maker to set priorities and
make the best decision. In addition, AHP is a useful
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
55
technique to check the consistency of the decision
maker's evaluations and thus reduce bias in the decision-
making process (Saaty, 1980).
The steps of AHP are shown in Table 4.
Table 4. Steps of AHP
1.Step: Decision making problem is defined.
2.Step: The hierarchy of the problem is created.
3.Step:
The Criteria Are Compared Between Each
Other.
𝑎 ⋯ 𝑎
⋮ ⋱
 (1)
4.Step: Assigning Weights and Priorities
𝑏

=


 (2)
W =

(3)
5.Step: Calculation of Consistency Ratio
CR= 
 (4)
𝐶𝐼
=


(5)
6.Step: Evaluation of Consistency Rate
𝜋 computed average from values of divided
weighed sum vector elements by associated priority value.
n – the number of criteria.
RI-the value for the corresponding size of matrix
proposed by Saaty (1980) can be found in Table 5.
Table 5. Randomness Index
Matrix size
Random Consistency
index (RI)
1 0,0
2 0,0
3 0,58
4 0,90
5 1,12
6 1,24
7 1,32
8 1,41
9 1,45
10 1,49
In the AHP method, after the problem definition and
target are determined, alternatives and criteria are
determined. Saaty (2008) developed a scale to compare
the determined criteria and determine the advantages. If
one criterion is more important than another, the scale
acts with the logic of giving importance to a value from 1
to 9 (Equation 1).
A paired comparison matrix is created between the
criteria determined in line with this scale. After the
comparison matrix is created, the eigenvector showing
the importance of each item relative to the other items is
created (Equation 2 and 3). The "Consistency Index (CI)",
which is an indicator of consistency, is calculated and
divided by the Randomness index (Equation 4). If CR>
0.1, the decision matrix is considered inconsistent, if
CR≤0.1, the decision matrix is considered consistent
(Equation 5).
3.5 0-1 Goal Programming
GP tries to come up with a compromise solution that
takes into account the importance of multiple conflicting
objectives.
Unwanted deviation variables are minimized by
target programming. In goal programming, each goal
requested from the decision maker is formulated to
achieve a certain numerical goal, minimizing the total
penalty arising from missing these goals, that is, the
weighted sum of the deviations of each of the goal
functions from their goals (Öztürk, 2009: 273). Its main
purpose is to transform a multi-purpose problem into a
single-purpose problem. The result of the model is
generally called effective solution (Taha, 2007: 343).
Charnes and Cooper (1961) were the first researchers
to introduce the goal programming (GP) method. Later,
scientists such as Lee (1972), Flavell (1976) Ignizio
(1985), Tamiz (1998), Vitoriano and Romero (2001),
Chang (2002) developed the goal programming method
(Karaatlı and Davras, 2014).
4. APPLICATION
In this study was carried out in an oral and dental
health center operating in Ankara. It is aimed to provide
the materials needed by the enterprise in order to provide
a quality health service on time and on site. For this
purpose, materials with critical importance that must be
included in the inventory of medical products to be
purchased were determined using the ABC-VED Matrix
method. Later, the suppliers of tooth extraction tools
grouped by the application area among these materials
have been determined.
In order to determine the priority values of the
suppliers, the AHP method was preferred because the
interactions of the criteria with each other are not taken
into account in the decision-making process and because
it can compare more than one quantitative and qualitative
criteria at the same time. The solution was implemented
with the program Super Decision (2.10.0). In solving the
problem, 0-1 Goal Programming method was preferred
because it realizes many goals at the same time and
provides an effective solution method. The 0-1 Goal
Programming model was developed by transforming the
determined goals into constraints and adding the priority
values obtained from AHP as constraints. The model was
solved with Lindo 6.1 program and the right suppliers
were selected for critical product groups.
4.1. Finding Critical Product Groups with ABC-
VED Matrix Analysis
It is planned to purchase 104 products of dental
consumables in the oral and dental health center where
the application is performed. ABC-VED analysis method
was used to determine the critical materials that must be
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
56
Supplier Selection of Tooth
Extraction Tools
Price Delivery Quality Supplier Reliability
Supplier 1 Supplier 2 Supplier 3 Supplier 5
Supplier
6
Supplier 4
kept in the center among 104 items to be ordered.
ABC-VED Matrix analysis was created by combining
104 items of materials according to whether they are
critical or not. The results of the analysis are shown in
Table 6. According to these results, the products in
category I, which must be kept in the oral and dental
health center, correspond to 71.15% of the total materials
and 91.26% of the total material value.
The materials in the category II correspond to 21.15%
of the total materials and 8.34% of the total value. So,
materials in category I are lesser importance than the
materials in category II in terms of both amount and
value.
The least important of the materials in the category
III correspond to 7.7% of the total materials and 0.4% of
the total value.
According to the result of ABC-VED Matrix
analysis, 74 items of materials in Category I were
identified as critical materials. For this reason, these
materials should be provided with priority.
In this study, among 74 critical products, the products
used in tooth extraction, created according to the
application area, were taken into consideration.
In the next stage, the priorities of the suppliers in this
group with AHP will be determined.
Table 6. ABC-VED Matrix
Group Products Products
Ratio
Value (TL) Value Ratio
I.Category (AV+AE+AD+BV+CV) 74 %71,15 31.722.422 %91,26
II.Category (BE+CE+BD) 22 %21,15 2.899.915 %8,34
III.Category (CD) 8 %7.7 138.529 %0,40
TOTAL 104 %100 34.760,87 %100
4.2. Determining the weights of criteria and
ranking of suppliers with AHP
As a result of the ABC-VED Matrix analysis, criteria
were determined by the experts to select the right
suppliers to supply the tooth extraction tools in Category
I.
Criteria;
Price; It is aimed to find the supplier with the most
suitable offer.
Delivery; The supplier's ability to deliver the right
amount of products at the desired time has been taken
into account.
Quality; An evaluation was made by taking into
account the improper product percentages of the
suppliers.
Supplier Reliability; The past performance of the
suppliers has been taken into account.
The Analytical Hierarchical structure created for
tooth extraction tools is shown in Figure 3.
Figure 3. AHP Structure
4.2.1. Comparison of Criteria with AHP
Criteria were evaluated by experts using Saaty's 1-9
point preference scale, and the geometric mean of the
results is shown in Table 7.
Table 7. Compration Matrix
Criteria Price Quality Supplier
Reliability Delivery
Price 1 0.215 0.203 0.382
Quality 4.64 1 2.3 3.3
Supplier
Reliability 4.93 0.438 1 2.28
Delivery 2.62 0.30 0.438 1
The comparison matrix of the criteria has been solved
by Super Decision (2.10). The consistency ratio of the
criteria was calculated as 0.03348. A consistency ratio of
less than 0.1 indicates that the criteria were evaluated
consistently.
The weights of the criteria are included in Table 8.
Table 8. Weights of Criteria
Criteria Weights of Criteria
Price 0.072
Quality 0.484
Supplier Reliability 0.293
Delivery 0.150
According to the results obtained by the evaluations
of experts, it has been observed that the quality criterion
is the most important in the selection of the supplier for
the product group that has critical importance in the
health sector, and the price criterion is the least important.
4.2.2. Comparison of Suppliers by Criteria
Comparison of suppliers by each criterion is included
in Table 9-12. As a result of the comparisons, weights of
the suppliers were calculated according to the criteria.
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
57
4.2.3. Sorting Alternatives with AHP
As the last step in AHP, the priorities of suppliers are
obtained by multiplying the criteria weights of the
suppliers and the weight of each criterion. The sorting of
suppliers by AHP are shown in Table 13. According to
the AHP result, the first priority was Supplier 3, followed
by suppliers with number 6,1,5,4,2, respectively.
Table 9. Comparison of Suppliers by Criteria of Price and Priority Values
Suppliers
Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5 Supplier 6 Priority
Values
Consistency
Rate
Supplier 1 1 0.333 0.20 0.16 0.143 0.11 0.0265
0.05650<0.1
Supplier 2 3 1 0.33 0.16 0.143 0.11 0.0410
Supplier 3 5 3 1 0.33 0.25 0.20 0.0860
Supplier 4 5.9 5.9 3 1 0.5 0.33 0.1735
Supplier 5 7 7 4 2 1 0.33 0.2443
Supplier 6 9 9 5 3 3 1 0.4283
Table 10. Comparison of Suppliers by Criteria of Quality and Priority Values
Suppliers Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5 Supplier 6 Priority
Values
Consistency
Rate
Supplier 1 1 0.5 0.11 0.33 0.143 0.2 0.032
0.02970<0.1
Supplier 2 2 1 0.143 0.33 0.2 0.25 0.047
Supplier 3 9 7 1 7 2 3 0.421
Supplier 4 3 3 0.143 1 0.25 0.33 0.080
Supplier 5 7 5 0.5 4 1 2 0.255
Supplier 6 5 4 0.3 3 0.5 1 0.165
Table 11. Comparison of Suppliers by Criteria of Supplier Reliability and Priority Values
Suppliers Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5 Supplier 6 Priority
Values
Consistency
Rate
Supplier 1 1 0.33 0.25 0.25 0.33 0.143 0.039
0.07038<0.1
Supplier 2 3 1 0.33 0.33 1.28 0.33 0.090
Supplier 3 4 3 1 1.28 3 0.33 0.198
Supplier 4 4 3 0.781 1 0.5 0.25 0.144
Supplier 5 3 0.781 0.33 2 1 0.25 0.122
Supplier 6 7 3 3 4 4 1 0.406
Table 12. Comparison of Suppliers by Criteria of Delivery and Priority Values
Suppliers Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5 Supplier 6 Priority
Values
Consistency
Rate
Supplier 1 1 9 0.33 9 7 3 0.310
0.06604<0.1
Supplier 2 0.11 1 0.143 3 0.33 0.25 0.042
Supplier 3 3 7 1 9 5 3 0.413
Supplier 4 0.11 0.33 0.11 1 0.25 0.16 0.025
Supplier 5 0.143 3 0.2 4 1 0.5 0.077
Supplier 6 0.33 4 0.33 6 2 1 0.132
Table 13. The Sorting of Suppliers by AHP
Suppliers Price Quality Supplier
Reliability
Delivery Priority
Value
Sorting of
Suppliers
Supplier 1 0.0265 0.310 0.032 0.039 0.168 3
Supplier 2 0.0410 0.0420 0.047 0.090 0.051 6
Supplier 3 0.0860 0.413 0.421 0.198 0.360 1
Supplier 4 0.1735 0.025 0.080 0.144 0.069 5
Supplier 5 0.2443 0.077 0.255 0.122 0.147 4
Supplier 6 0.4283 0.132 0.165 0.406 0.204 2
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
58
4.3. AHP Priorities Integrated 0-1 Goal
Programming Model
In this section, the targets are determined by the
oral and dental health center about the material cost and
supply times. Then these constraints are formulated in
model. Later, the priority values of the suppliers
obtained from AHP were added as a constraint in the 0-
1 Goal Programming model.
The targets determined in 0-1 Goal Programming
are as follows;
Goal 1: The prices do not exceed the average
approximate cost.
Goal 2: Not exceeding the appropriate delivery time
for the product.
Goal 3: To protect the priority values obtained from
AHP.
The proposed model for 0-1 Programming, which
provides an effective solution method by meeting these
three targets at the same time, is as follows;
Min Z = (𝒅𝟏) + (𝒅𝟐)+( 𝒅𝟑)+(𝒅𝟑) (6)
Constrains:
𝑨𝒊𝒙𝒊
𝒏
𝒊𝟏 + 𝒅𝟏𝒅𝟏 = C (7)
𝒕𝒊𝒙𝒊
𝒏
𝒊𝟏 +𝒅𝟐 𝒅𝟐=T (8)
𝒘𝒊𝒙𝒊
𝒏
𝒊𝟏 + 𝒅𝟑𝒅𝟑 =1 (9)
𝒙𝒊
𝒏
𝒊𝟏 =1 (10)
𝒙𝒊= 0 or 1 𝒊 (11)
𝒅𝒋,𝒅𝒋𝟎 ∀𝒋 (12)
Decision Variables:
𝒙𝒊= if the order is to be given to the supplier i, takes
the value "1", if not, "0".
Deviation Variables:
𝒅𝟏: negative deviation from approximate cost,
𝒅𝟏: positive deviation from approximate cost,
𝒅𝟐: negative deviation from delivery time,
𝒅𝟐: positive deviation from delivery time,
𝒅𝟑: negative deviation from priority values
obtained from AHP
𝒅𝟑: pozitif deviation from priority values obtained
from AHP.
Model related parameters are shown in the Table 14.
Z= Sum of deviation variables,
𝑨𝒊=The amount of offered price by the supplier i,
C=Approximate cost amount determined by the
enterprise for the tools used in tooth extraction,
𝒕𝒊=Delivery time of supplier i,
T=Delivery time
𝒘𝒊=Priority value of supplier i obtained from AHP
Objective Function:
It is aimed to minimize the sum of deviations from
the determined targets.
Table 14. Parameters
Suppliers Prices
(
𝑨
𝒊
)
Delivery
Time
(
𝒕
𝒊
)
Priority
Values of
AHP
Supplier 1 53.134TL 7 0,16
Supplier 2 19.238 TL 5 0,05
Supplier 3 12.710 TL 4 0,36
Supplier 4 11.221 TL 4 0,071
Supplier 5 9.762 TL 5 0,152
Supplier 6 8.338 TL 3 0,204
Constrains:
Equation 7 is a approximate cost amount
constrains.
Equation 8 is a delivery time constrains.
Equation 9 is a priority value of supplier obtained
from AHP constrains.
Equation 10 is a restriction of selecting only one
supplier constrains.
Equation 11 is a deviation variables take a value of
0 or 1 constrains.
The formulation of the 0-1 goal programming
model with integrated AHP priorities with this
information is as follows,
Min Z = (𝒅𝟏) + (𝒅𝟐)+( 𝒅𝟑)+(𝒅𝟑) (12)
Equation 12 is aimed to minimize the sum of
deviations from the determined targets.
53.134𝒙𝟏+19.238 𝒙𝟐+12.710 𝒙𝟑+11.22 𝒙𝟒+9.762 𝒙𝟓+
8.338 𝒙𝟔+ 𝒅𝟏𝒅𝟏 =26.625 (13)
Equation 13 is a approximate cost amount
constrains.
7 𝒙𝟏+5 𝒙𝟐+4 𝒙𝟑+4 𝒙𝟒+5 𝒙𝟓+3 𝒙𝟔+𝒅𝟐 𝒅𝟐=10
(14)
Equation 14 is a delivery time constrains.
0,16 𝒙𝟏+ 0,05 𝒙𝟐+ 0,36 𝒙𝟑+ 0,071 𝒙𝟒+ 0,152 𝒙𝟓+
0,204 𝒙𝟔+ 𝒅𝟑𝒅𝟑 =1 (15)
Equation 15 is a priority value of supplier obtained
from AHP constrains.
𝒙𝒊
=1 (16)
Equation 16 is a restriction of selecting only one
supplier constrains.
𝒙𝒊=0 or 1 i=1,2,3,4,5,6 (17)
Equation 17 is a decision variable. İf the order is to
be given to the supplier i, takes the value "1", if not,
"0".
𝒅𝒋 ≥0 , 𝒅𝒋≥0 j=1,2,3 (18)
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
59
Equation 18 is a deviation variables take a value of
0 or 1 constrains.
4.4. Results of 0-1 Goal Programing
The model was solved in Lindo 6.1 program on a
64 bit operating system computer with Intel Core ™ i7-
7500U @ 2.70 GHz-2.90 GHz processor. The results
obtained from the program are shown in the Table 15.
Table 15. Results of The Model
Decision
Variable
Value Deviation
Variable
Value
𝒙
𝟏
0
𝒅𝟏

13 915
𝒙
𝟐
0
𝒅𝟏
0
𝒙
𝟑
1
𝒅𝟐

6
𝒙
𝟒
0
𝒅𝟐
0
𝒙
𝟓
0
𝒅𝟑

0,64
𝒙
𝟔
0
𝒅𝟑
0
According to the results obtained from the program,
it was found that the desired targets were achieved and
an order should be provided from Supplier 3. The
effects of deviation variables on constraints are as
follows;
A gain of 13 915 TL was obtained from the cost
amount.
A saving of 6 days from the delivery time.
It seems that in order to reach the AHP priorities
goal, the enterprise must make a purchasing decision.
5. CONCLUSION
Medical materials used in diagnosis, treatment and
examination procedures of patients in health
institutions are of vital importance. Correct decisions
should be made in the procurement of these materials
needed in service provision. Since the number of
suppliers of health institutions is high, making a
decision becomes more difficult.
The aim of this study is to select the most
appropriate supplier among the medical equipment
suppliers of an oral dental health center operating in
Ankara by using the AHP method, one of the multi-
criteria decision making methods, and the 0-1 goal
programming method in an integrated manner.
In this context, in order to decide on the most
suitable supplier, the procurement department manager
and employees of the hospital were interviewed and
their experiences were used.
While determining the medical company suppliers,
price, supplier reliability, quality and delivery criteria
were taken as basis. The criterion quality criterion with
the highest priority value at the end of the study; The
criterion with the lowest priority was the price criterion.
According to this result, it was revealed that the
hospital made a quality-oriented decision while
choosing its medical supplier. Considering the weight
of the alternatives in terms of criteria, it was decided
that supplier 3 should be selected as the most suitable
supplier. Created using data obtained from AHP and
other constraints
The zero one goal programming model created
using the data obtained from AHP and other constraints
was solved with Lindo (6.1). As a result of the solution,
it was found that order from Supplier 3 should be
consistent with AHP.
Supplier selection studies in the health sector are
almost nonexistent, so this study emphasized the
importance of working with the right suppliers to find
the products that are critical in the health sector at the
right quality at the right time. With this study, an
enterprise operating in the health sector has determined
its suppliers, which are determined according to critical
product groups, using effective stock control methods,
with an AHP priority integrated 0-1 goal programming
model. It is thought that this model will contribute
significantly to the literature and will save time in
supplier selection studies in the health sector. In this
respect, the study differs from other studies because it
deals with a real life problem.
In the future studies, different criteria are used in
the supplier selection and evaluations in the health
sector and different multi criteria decision making
methods by modeling with goal programming and the
results can be compared.
REFERENCES
Aptel, O., & Pourjalali, H. (2001). Improving activities
and decreasing costs of logistics in hospitals: a
comparison of US and French hospitals. The
international journal of accounting, Vol.36, No.1, pp.
65-90.
Bahadori, M., Hosseini, S. M., Teymourzadeh, E.,
Ravangard, R., Raadabadi, M., & Alimohammadzadeh,
K. (2017). A supplier selection model for hospitals
using a combination of artificial neural network and
fuzzy VIKOR. International Journal of Healthcare
Management, Vol. 5, pp. 1-9.
Dağdeviren, M., & Eren, T. (2001). Tedarikçi firma
seçiminde analitik hiyerarşi prosesi ve 0-1 hedef
programlama yöntemlerinin kullanılması. Gazi
Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi,
Vol. 16, No. 2, pp. 41-52.
Demiral, P. (2013). Hastanelerde Malzeme Yönetimi ve
İki Hastanenin Malzeme Yönetim Sistemlerinin
Karşılaştırılmasına Yönelik Bir Uygulama. Ms Thesis,
TC Gazi Üniversitesi, Sosyal Bilimler Enstitüsü,
Ankara.
Dickson, G. W. (1966). An analysis of vendor selection
systems and decisions. Journal of purchasing, Vol.2,
No.1, pp. 5-17.
Doğan, N. Ö., & Akbal, H. (2019). Sağlık Sektöründe
Tedarikçi Seçim Kararının AHP Yöntemi ile
İncelenmesi: Bir Üniversite Hastanesi Örneği. Celal
Bayar University Journal of Social Sciences, Vol.17,
No.4, pp. 440-456.
Enyinda, C., Dunu, Bell Haynes, J. (2010). A model for
quantifying strategic supplier selection: Evidence from
a generic pharmaceutical firm supply chain.
International Journal of Business, Marketing, and
Decision Science. Vol.3, No.2, pp.23-44.
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
60
Fashoto, S. G., Akinnuwesi, B., Owolabi, O., &
Adelekan, D. (2016). Decision support model for
supplier selection in healthcare service delivery using
analytical hierarchy process and artificial neural
network. African journal of business Management, Vol.
10, No.9, pp.209-232.
Fitriana, I., Satria, R. G. D., & Setiawan, D. C. B.
(2018). Medicine Inventory Management by ABC-
VED Analysis in the Pharmacy Store of Veterinary
Hospital, Yogyakarta, Indonesia. Asian Journal of
Animal and Veterinary Advances, Vol.13, No.1, pp. 85-
90.
Forghani, A., Sadjadi, S. J., & Farhang Moghadam, B.
(2018). A supplier selection model in pharmaceutical
supply chain using PCA, Z-TOPSIS and MILP: A case
study. PloS one, Vol.13, No.8, Article ID: e0201604.
Frej, E. A., Roselli, L. R. P., Araújo de Almeida, J., &
de Almeida, A. T. (2017). A multicriteria decision
model for supplier selection in a food industry based on
FITradeoff method. International Series in Operations
Research & Management Science, Vol. 274, pp. 257-
280.
Guimarães, F. S., dos Santos, C. B., Gonçalves, L. B.,
Thurow, L. L., & Silveira, M. P. T. (2019). Tools For
İnventory Control Of Dental Supplies Of A Municipal
Health Department: A Case Study. Brazilian Journal
Of Oral Sciences, Vol. 18, pp. 1-8.
Kaptanoğlu, A. (2013). Sağlık İşletmelerinde Maliyet
Depo Stok ve Envanter Yönetimi. 1. Baskı, İstanbul:
Beşir Kitabevi.
Karaatlı, M., & Davras, G. (2014). Tedarikçi seçiminde
analitik hiyerarşi prosesi ve hedef programlama
yöntemlerinin kombinasyonu: otel işletmelerinde bir
uygulama. Yönetim ve Ekonomi Araştırmaları Dergisi,
Vol.12, No. 24, pp.182-196.
Karagöz, F., & Yıldız, M. S. (2015). Hastane
İşletmelerinde Stok Yönetimi İçin ABC ve VED
Analizlerinin Uygulanması. netim ve Ekonomi
Araştırmaları Dergisi, Vol.13, No.2, pp. 375-396.
Kirytopoulos, K., Leopoulos, V., & Voulgaridou, D.
(2008). Supplier selection in pharmaceutical industry:
An analytic network process approach. Benchmarking:
An International Journal. Vol. 15 No. 4, pp. 494-516
Kritchanchai, D. (2014). A framework for healthcare
supply chain improvement in Thailand. Operations and
Supply Chain Management: An International Journal,
Vol.5, No.2, pp. 103-113.
Mızrak Özfırat, P., & Öğüt, C. (2008). Application of
Analytic Hierarchy Process and Goal Programming in
Supplier Selection Problem. DEÜ Mühendislik
Fakültesi Fen ve Mühendislik Dergisi, Vol.10, No 1,
pp. 39-48
Mustaffa, N. H., & Potter, A. (2009). Healthcare supply
chain management in Malaysia: a case study. Supply
Chain Management: An International Journal, Vol. 14,
No. 3, pp. 234 –243.
Nigah, R., Devnani, M., & Gupta, A. K. (2010). ABC
and VED analysis of the pharmacy store of a tertiary
care teaching, research and referral healthcare institute
of India. Journal of young pharmacists, Vol. 2, No. 2,
pp. 201-205.
Öztürk, A. (2009). Yöneylem Araştırması, Ekin
Yayınevi.
Manivel, P., & Ranganathan, R. (2019). An Efficient
supplier selection Model for Hospital Pharmacy
through Fuzzy AHP and Fuzzy TOPSIS. International
Journal of Services and Operations Management,
Vol.33, No.4, pp.468–493.
Percin, S. (2006). An application of the integrated
AHP‐PGP model in supplier selection. Measuring
Business Excellence, Vol. 10, No. 4, pp. 34-49.
Pund, S. B., Kuril, B. M., Hashmi, S. J., Doibale, M.
K., & Doifode, S. M. (2016). ABC-VED matrix
analysis of Government Medical College, Aurangabad
drug store. Int J Community Med Public Health, Vol.3,
No.2, pp. 469-472.
Rivard‐Royer, H., Landry, S., & Beaulieu, M. (2002).
Hybrid stockless: a case study. International Journal of
Operations & Production Management, Vol. 22 No. 4,
pp. 412-424.
Saaty, T. L. (2008). Decision making with the analytic
hierarchy process. International Journal Of Services
Sciences, Vol.1, No. 1,pp. 83-98.
Saaty,T.L.(1980). The Analytic Hierarchy Process:
Planning, Priority Setting and Resource Allocation,
New York: McGraw-Hill.
Sivrikaya, B. T., Kaya, A., Dursun, M., & Çebi, F.
(2015). Fuzzy AHP–goal programming approach for a
supplier selection problem. Research in Logistics &
Production, Vol. 5, No. 3,pp. 271-285.
Taha, H. A. (2007). Yöneylem Araştırması, 4. basım.
Literatür Yayıncılık, İstanbul.
Taherdoost, H., & Brard, A. (2019). Analyzing the
process of supplier selection criteria and methods.
Procedia Manufacturing, Vol.32, pp.1024-1034.
Thiruchelvam, S., & Tookey, J. E. (2011). Evolving
trends of supplier selection criteria and methods.
International Journal of Automotive and Mechanical
Engineering, Vol.4, No.1, pp. 437-454.
Tooranloo, H. S., & Iranpour, A. (2017). Supplier
selection and evaluation using interval-valued
intuitionistic fuzzy AHP method. International Journal
of Procurement Management, Vol.10, No.5, pp. 539-
554.
Ünal, Z., Güven, S. & Çetin, E. İ. (2019). Otel
İşletmelerinin Tedarikçi Seçiminde Bulanık AHP ile
Mersin University Journal of Maritime Faculty (MEUJMAF)
Vol. 2, Issue 2, pp. 50-61, December 2020
61
Ağırlıklandırılmış Hedef Programlama Uygulaması.
Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi,
Vol.12, No.1, pp. 188-204.
Vaz, F. S., Ferreira, A. M., Kulkarni, M. S., Motghare,
D. D., & Pereira-Antao, I. (2008). A study of drug
expenditure at a tertiary care hospital: An ABC-VED
analysis. Journal of Health Management, Vol.10,
No.1,pp. 119-127.
Venkatesh, V.G. & Dubey, Rameshwar. (2015).
Supplier selection in blood bags manufacturing
industry using TOPSIS model. International Journal of
Operational Research. Vol.24, pp. 461-488.
Wang, G., Huang, S. H., & Dismukes, J. P. (2004).
Product-driven supply chain selection using integrated
multi-criteria decision-making methodology.
International journal of production economics, Vol. 91
No. 1, pp. 1-15.
Yazdani, M., Torkayesh, A. E., & Chatterjee, P. (2020).
An integrated decision-making model for supplier
evaluation in public healthcare system: the case study
of a Spanish hospital. Journal of Enterprise
Information Management. Vol. 33 No. 5, pp. 965-989
Yenersoy, G. (2011). Üretim Planlama ve Kontrol.
Papatya Yayıncılık.
Yeşilyurt, Ö., Sulak, H., & Bayhan, M. (2015). Sağlık
Sektöründe Stok Kontrol Faaliyetlerinin ABC ve VED
Analizleriyle Değerlendirilmesi: Isparta Devlet
Hastanesi Örneği. Süleyman Demirel Üniversitesi
İktisadi ve İdari Bilimler Fakültesi Dergisi. Vol. 20
No.1, pp.365-376.
... Determination of suppliers for a dental health centre was made by using 0-1 Goal Programming and AHP based model [9]. Importance of products was determined by a preliminary analysis by ABC-VED matrix analysis, and then the most appropriate suppliers were selected via the proposed model. ...
Chapter
Healthcare workers face the risk and the danger of contracting several infectious diseases. In order to prevent from the risk of being infected by blood, contact, respiratory droplets, etc., healthcare workers need to use Personal Protective Equipment (PPE). Because of the increasing demand for quality PPE after COVID-19 pandemic, it is important for healthcare institutions to determine the best supplier. Evaluation of suppliers is a complex decision, which contains a number of alternative suppliers and conflicting criteria. The conflicts between supplier evaluation factors require institutions to make a compromise choice among alternative suppliers. Therefore, the main aim of this study is to develop an analytic supplier evaluation model for PPE procurement to healthcare institutions. To that end, a hybrid multi-criteria decision making (MCDM) model based on Analytic Hierarchy Process (AHP) and VIse KriterijumsaOptimiz acija I Kompromisno Resenje (VIKOR) is proposed. A case study for surgical mask procurement to a hospital is presented to demonstrate the applicability of the proposed model. The application results show that the proposed model is a useful decision support tool for PPE procurements to policy-makers of healthcare institutions.
Article
Full-text available
Otel işletmelerinde yöneticilerin stratejik kararlarından bir tanesi belirtilen sürede işletmeye düşük maliyetli, yüksek kaliteli ürün sağlayacak tedarikçinin seçimidir. Birçok kriteri göz önünde bulundurmak zorunda olan yöneticiler, işletmenin çıkarlarına ve ihtiyaçlarına uygun en iyi alternatifi seçmek zorundadırlar. Bu çalışma ile faaliyetini Antalya’da sürdüren beş yıldızlı bir otel işletmesi için tedarikçi seçimi problemi bilimsel bir yaklaşımla ele alınmıştır. Çalışmada tedarikçi seçiminde Bulanık Analitik Hiyerarşi Süreci ile Hedef Programlama yönteminin bir arada kullanıldığı bir yaklaşım önerilmiştir. Uygulanan bütünleşik yöntemin hem konaklama işletmelerinde hem de farklı sektörlerde faaliyet gösteren işletmelerin tedarikçi seçimi sürecine fayda sağlayacağı ve yol göstereceği düşünülmüştür.
Article
Full-text available
Supplier selection is the process by which firms identify, evaluate, and contract with suppliers. The supplier selection process deploys an enormous amount of a firm’s financial resources and plays crucial role for the success of any organization. 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. The literature on supplier selection criteria and methods is full of various analytical approaches. Some researchers have developed hybrid models by combining more than one type of selection methods. The current paper provides an overall picture of research on supply chain management, supplier selection criteria and supplier selection evaluation methods (multi-criteria decision making). A summary of the process of supplier selection can be helpful for companies to have a clear understanding of the concept in order to improve their success and competitiveness. The results show that the application of a structured decision-making technique is vital, especially under the complex conditions that include both qualitative and quantitative criteria.
Article
Full-text available
Supplier selection is one of the critical processes in supplier chain management which is associated with the flow of goods and services from the supplier of raw material to the final consumer. The purpose of this paper is to present a novel approach and improves the supplier selection in a multi-item/multi-supplier environment, and provide the importance and the reliability of the criteria by handling vagueness and imperfection of information in decision making process. First, principal component analysis (PCA) method is used to reduce the number of supplier selection criteria in pharmaceutical companies. Next, using the most important criteria resulted from the PCA method, the importance and the reliability of the selected criteria are assessed by a group of decision-maker (DM). Then, the importance value of each supplier with respect to each product is obtained via the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) based on the concept of Z-numbers called Z-TOPSIS. Finally, these values are used as inputs in a mixed integer linear programming (MILP) to determine the suppliers and the amount of the products provided from the related suppliers. To validate the proposed methodology, an application is performed in a pharmaceutical company. The results show that the proposed method could provide promising results in decision making process more appropriately.
Article
Full-text available
This article investigates the significance of supplier selection in hospital pharmacy. In recent global scenario, the selection of supplier for the pharmacy is a crucial role to prompt service level as well as total value cost. Normally, high quality services, inventory and customer satisfaction is influenced mostly by the supplier selection process variable. The supplier selection normally depends on many criteria such as cost, delivery of the product, service, flexibility with the consumer, etc. The uncertainty during supplier selection could be handled by Multi-Criteria Decision Making (MCDM) techniques. A methodology is developed to analyze the qualitative and quantitative variables involved in the supplier selection process and it is illustrated with the case study of a hospital pharmacy. This paper analyze the alternatives, criteria and sub criteria of the supplier selection process by multi criteria decision making approach of Fuzzy Analytic Heuristic Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) methods. Based on discussion with the pharmacy manager, five criteria were chosen and analyzed. The decision makers determined the weights of the criteria and sub-criteria and evaluate the alternatives and ranks them by FAHP and FTOPSIS method. Finally compare the results obtained by FAHP and FTOPSIS methods and select the appropriate supplier which helps to improve the overall efficiency of the pharmacy.
Article
Full-text available
Supply chain management (SCM) is a sophisticated concept which contains all material-related activities of construction projects. In the last decade, construction supply chain management (CSCM) has become a new challenge for construction managers in order to procure right quantities of materials to construction site on time, and within the pre-defined budget. Supplier selection as a significant process in SCM is a multi-criteria decision making problem. There is a broad literature on supplier selection that examines selection of supplier evaluation criteria and multi-criteria decision making methods. Individual and integrated multi-criteria decision approaches are studied by many researchers. In this paper, supplier selection analysis for wall, cladding and roofing construction materials are researched. Whilst literature review and expert panel are employed in order to identify criteria, weights of each criterion are determined by an extensive questionnaire survey. Participants are selected from the experts in construction industry, universities and governmental institutions. Analytic Network Process (ANP) is utilized as multi-criteria decision making methodology and weights of criteria are obtained. This study is a section of an on-going doctoral research, in other words, a part of a more comprehensive model. The study is significant due to the development of a new approach on construction material supplier selection and the basis of the model that is able to provide decision support for construction project participants throughout the project life cycle.
Purpose In this study, an integrated decision-making model consisting of decision-making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and a modified version of evaluation based on distance from average solution (EDAS) methods is proposed for supplier selection problem in a public procurement system considering sustainable development goals. Design/methodology/approach DEMATEL and BWM methods are used to determine weights of the criteria that are defined for the supplier selection problem. Weight aggregation method is applied to combine the weights obtained from these two methods. A modified version of EDAS method is then used in order to rank the alternative suppliers. Findings The proposed decision-making model is investigated for a supplier selection problem for a hospital in Spain. The validity of the results is checked using comparison with other decision-making methods and several performance analysis tests. Practical implications The proposed multi-criteria decision-making (MCDM) model contributes to the healthcare supply chain management (SCM) and aims to lead the policy makers in selecting the best supplier. Originality/value There is no such study that combines DEMATEL and BWM together for weight generation. The application of the modified EDAS method is also new. In real time situations, the decision experts may confront to the difficulty of using BWM while identifying the best and the worst criteria choices. The idea of using DEMATEL is to aid the experts to make them enable in distinguishing between the best/worst criteria and handle BWM easily.