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Research Article

A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study

Roles Investigation

* E-mail: [email protected]

Affiliation Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

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Roles Methodology, Supervision

Affiliation Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Roles Conceptualization

Affiliation Institute for Management and Planning Studies, Tehran, Iran

  • Athena Forghani, 
  • Seyed Jafar Sadjadi, 
  • Babak Farhang Moghadam


  • Published: August 15, 2018
  • Reader Comments

Table 1

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.

Citation: Forghani A, Sadjadi SJ, Farhang Moghadam B (2018) A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study. PLoS ONE 13(8): e0201604.

Editor: Yong Deng, Southwest University, CHINA

Received: December 24, 2017; Accepted: June 21, 2018; Published: August 15, 2018

Copyright: © 2018 Forghani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.


Supplier selection is one of the most important activities for most enterprises and has a substantial impact on the efficiency and effectiveness of the entire supply chain [ 1 , 2 ]. It is likely that the manufacturer allocates more than 60% of its total sales on purchased services and materials [ 3 ]. Furthermore, material cost is up to 70% of the finished product expenses [ 4 ]. Therefore, selecting the appropriate suppliers can result in reduced purchasing cost, decreased supplying risk and improved product quality [ 5 ].

When it comes to select a suitable supplier, various criteria need to be contemplated. However, it would not be appropriate to recommend and sometimes even it would not be possible to take into account all the criteria upon final decision making due to the diversity of strategies among the industries concerning the supply chain regarding the product's characteristics.

Typically in dealing with the supplier selection discussion, two kinds of problems are propounded: First, the supplier is the one (natural or legal entity) who satisfies all the purchasers' requirements (single sourcing); in this type of supplier selection, the management should make the only decision to determine the best supplier. Second, there is no single supplier to meet all the purchasers' needs (multiple sourcing). Many companies encounter some disruption or inadequate supply capacity on the part of the supplier. Adopting the second model, which is the ‘multiple sourcing’ the purchasing company meanwhile using the business process, can resolve the unpredicted delay of supply by one of the numerous other suppliers.

It would not be convenient for the DM to choose suitable suppliers who can fulfill all the firms' demands based on different criteria. Another point to consider is that as a multiple criteria decision-making (MCDM) problem, the supplier selection would lie under the effect of many qualitative and quantitative contradictory factors. In order to maintain an equilibrium among such conflicting criteria, many studies have proposed various models, encompassing single to hybrid approaches. Adopting the single approach, the studies often consider locating a solution for the supplier selection problem through specifying the optimal quantities that are usually under the effect of a number of constraints. As an example, Zhang and Zhang [ 6 ] in their study for minimizing the total cost including the product and fixed costs with stochastic demand, developed a mixed-integer programming (MIP) model based on the assumption that all the existing suppliers could meet the qualitative criteria level. In another study on the supplier selection problem, Du et al . [ 7 ] considered the life-cycle cost through developing a bi-objective model that could account for the operational cost together with the purchasing cost since decreasing only the purchasing cost may result in more frequent failure of equipment, which results in increase of the maintenance costs. For model solving, they introduced a Pareto genetic algorithm hybridization, multi-intersection and similarity crossover strategy. In order to minimize the total cost of the product and maximize the quality of the products as well as the reliability of delivery, Karpak et al . [ 8 ] used goal programming (GP) in hydraulic pump division of a US based manufacturer. Karpak et al . [ 9 ] employed visual interactive GP in finding solution for single- and multiple-product supplier selection problems. In order to select the suppliers and allocating the required quantities based on the total cost of product, total quality and delivery reliability, Karpak et al . [ 10 ] designed a GP model subject to demand and capacity constraints. Fuzzy mixed-integer GP was developed by Kumar et al . [ 11 ] for solving the supplier selection problem of fuzzy nature. An MIP model for stochastic supplier selection was introduced by Amorim et al . [ 12 ] in the food industry. A Monte Carlo simulation was applied for fuzzy GP by Moghaddam [ 13 ] for the purpose of solving the supplier selection problem.

Amin et al . [ 14 ] was the first to consider the strategic perspectives in applying hybrid models by devising a two-stage integrated quantified SWOT analysis technique with fuzzy linear programming (fuzzy LP) to resolve the supplier selection problem. A number of studies have attempted to apply historical data for supplier selection problem; Faez et al . [ 15 ] for example introduced an integrated case-based reasoning with MIP for selecting the supplier and the required quantities of goods to order. An integrated approach of AHP, enhanced by rough set theory and multi-objectives mixed-integer linear programming (MILP), was introduced by Xia and Wu [ 16 ] for a multi-product supplier selection and order allocation problem, in which the suppliers will offer price discounts on sum of the trade volumes.

Demirtas and Üstün [ 17 ], based on the analytical network process (ANP) and the multi-objective mixed-integer programming (MOMIP), developed a two-stage supplier selection and order allocation model to minimize the purchasing value, the budget and defect rates. Using the Tchebycheff procedure the model was solved by the ϵ-constraint method and the reservation level. In order to deal with the supplier selection problem, Wu et al . [ 18 ] used the Delphi method, ANP, and the MOMIP model for supplier selection. In this model the criteria are first generated by experts using the Delphi method. Then, the obtained criteria are served as input for the ANP method and in the end, the MOMIP model is utilized for selecting the best suppliers and the relevant quantities.

Additionally, in a study undertaken by Lee et al . [ 19 ] the fuzzy AHP and fuzzy multiple goal planning were used for selecting the suppliers of a company producing thin-film-transistor liquid-crystal display products. A two-stage model was developed by Liao and Kao [ 20 ] by applying fuzzy TOPSIS and multi-choice GP for selection the appropriate suppliers and allocating the orders. Also fuzzy TOPSIS and multi-choice GP were used by Rouyendegh and Saputro [ 21 ] in a fertilizer and chemical producing company. In another study conducted by Kilic [ 22 ], the fuzzy TOPSIS was employed together with MILP for selecting the best supplier in a multi-item/multi-supplier problem. Perçin [ 23 ] used integrated AHP–GP for supplier selection problem. SWOT analysis was used in a study carried out by Ghorbani et al . [ 24 ] for evaluating the suppliers; they also used integer linear programming (ILP) model for selecting and determining the quantities. The group decision making with different voting power and linear programming (LP) were used by Sodenkamp et al . [ 25 ] for supplier selection problem. Simić [ 26 ] has reviewed the 50 years of fuzzy set theory and models for supplier selection.

This paper intends to adopt a qualitative method by using PCA, Z-TOPSIS algorithm [ 27 ] with triangular fuzzy number and a mixed integer linear programming for supplier selection. In order to show its applicability, the proposed methodology is implemented using a case study involving a pharmaceutical company.

The rest of the paper is organized as follows: Sections 2 and 3 present the supplier criteria in pharmaceutical companies and a questionnaire to gather information about the importance of the criteria; Section 4 reviews the methodology of PCA and its application in reducing the number of the criteria; Section 5 briefly reviews Z-TOPSIS method; Section 6 presents a new model; Section 7 gives a numerical example to show applicability of the proposed model; Section 8 presents sensitivity analysis of the results; and Section 9 concludes the paper.

Supplier criteria

In pharmaceutical companies, the main criteria are grouped under six categories, which are cost, quality, services, delivery, supplier profile and overall personnel capabilities. These primary criteria are decomposed into various sub-criteria as represented in Table 1 :


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In the next section, a questionnaire is used to collect information about the importance of these 24 criteria. Then, the PCA method is applied to reduce the number of criteria to ease the methodology.

To rate the supplier selection criteria ( c 1 , c 2 ,…, c 24 ) from 0 (the least important) to 10 (the most important), we asked the business managers of 34 pharmaceutical companies: Hakim( x 1 ), Aboureyhan( x 2 ), Behvazan( x 3 ), Akbarieh( x 4 ), Arya( x 5 ), Raha( x 6 ), Aryo Gen( x 7 ), Bakhtar Bioshimi( x 8 ), Cosar( x 9 ), Behsa( x 10 ), Caspian Tamin( x 11 ), Tehran Chemi( x 12 ), Cobel Darou( x 13 ), Doctor Abidi( x 14 ), Exir( x 15 ), Farabi( x 16 ), Iran Najou( x 17 ), Jaber Ebne Hayan( x 18 ), Kish Medipharm( x 19 ), Loghman( x 20 ), Alborz Darou( x 21 ), Osveh( x 22 ), Pars Darou( x 23 ), Ramofarmin( x 24 ), Chemi Darou( x 25 ), Razi( x 26 ), Shahid Ghazy( x 27 ), Sina Darou( x 28 ), Behestan Darou( x 29 ), Rooz Darou( x 30 ), Sobhan Darou( x 31 ), Zahraavi( x 32 ), Iran Darou( x 33 ), Shafa( x 34 ).

Note that the research was accomplished during the year of 2017. The questionnaire ( S1 File ) was sent to the companies through e-mail address, and completion of the questionnaire was taken as consent. Table 2 presents the results of the completed questionnaires.


Next section reviews the methodology of PCA and its application in reducing the number of the supplier selection criteria.

Principal component analysis (PCA)

Pearson [ 28 ] was the first to introduce the PCA for the dimensionality reduction purpose. With respect to PCA, two main ideas can be enumerated; first, it provides an efficient data analysis tool for identifying and expressing data patterns and determining data similarities as well as differences. Second, in terms of its ability for data compression through minimizing the data set dimensionality which is constituted from a numerous interrelated variables with almost zero information loss [ 29 ]. Data compression can be performed through original data transformation into a new collection of variables and in fact into new set of principal components ( ζ s ) virtually uncorrelated to each other. Depending on the level of significance, the ζ s will be represented in declining order, so that only the first few number of important ones are retained. Using the PCA, the data dimensionality is decreased and the multicollinearity can be removed [ 30 ].


supplier selection case study


Here Table 2 presents the data used as the supplier’s attribute X ij , indicating that the selection criterion score i based on the business manager j will shape an n×p matrix X , in which n represents the number of the selected criteria and p denotes the number of business managers.

Thus, the matrix X is entered into the PCA calculation (using SPSS), and a set of ζ s is developed. The first five ζ s will have 86.278% cumulative percentage which is higher than 85 percent of total variance. Hence the variance percentage of extraction sums of squared loadings of the first five ζ s will be used in Eq ( 5 ) in order to develop the SCORE PCA of individual selection criterion. At this point, the optimum supplier criteria are selected based on the highest number.

Based on the results of these 24 cases, c 19 (certificate of GMP), c 4 (product quality), c 1 (product price), c 18 (past record documentation) and c 9 (CRM) have the highest SCORE PCA among the other criteria and they are selected as the best criteria by the scores 6.390, 6.305, 6.147, 5.997 and 5.700, respectively.

Since GMP is the primary condition for selecting the supplier and all firms must have this certificate, we therefore, set aside this criterion for the rest of the survey.

Next section reviews Z-TOPSIS. This method, using the scores resulted from the PCA method, will be applied to obtain the importance value of each supplier with respect to each product.

Z-TOPSIS method

A brief review of some principle definitions of fuzzy sets from Chen [ 31 ], Chen and Lee [ 32 ], and Sotoudeh-Anvari and Sadi-Nezhad [ 33 ] are given below.

Definition 1: Fuzzy set

supplier selection case study

Definition 2: Type-1 fuzzy number

supplier selection case study

Definition 3: Z-number

supplier selection case study

Chen [ 31 ] proposed linguistic numbers in the form of Table 3 and Table 4 . Additionally, Table 5 employs the Z-TOPSIS technique to address the reliability of DMs. The numbers in this table are proposed by the authors.




In order to determine the alternatives' ranking order, the following algorithm is operated, whereby the Step 1 is adopted from Kang et al . [ 35 ]; however it must be noticed that for the expert’s reliability, it uses the linguistics variable presented in Table 5 for the component B in Z-number, followed by Steps 2–7 from Chen [ 31 ].

Step 1: Using the information obtained from the Table 5 , the component is derived and then the Z-Number is converted to Type-1 fuzzy number

supplier selection case study

Step 2: Construct decision matrix, and weight matrix,

supplier selection case study

Step 3: Construct normalized fuzzy decision matrix,

To make different scales comparable, the linear scale transformation shall be used to create the normalized decision making matrix as represented in Eq ( 13 ).

supplier selection case study

The above mentioned technique is intended to retain the feature that the ranges of normalized fuzzy numbers are in the interval [0,1].

Step 4: Construct weighted normalized fuzzy decision matrix,

supplier selection case study

Step 5: Find fuzzy positive-ideal solution, A * and fuzzy negative-ideal solution, A -

supplier selection case study

Step 6: Find the distance of each alternative from A * and A -

supplier selection case study

Step 7: Find closeness coefficient, CC i

supplier selection case study

It is obvious that by the approaching of CC i to 1, an alternative A i gets closer to A * and farther from A - . Hence, based on the closeness coefficient, the ranking order of all alternatives can be determined and from a set of feasible alternatives, the best one can be selected.

In the next section, a new model is presented to determine the suppliers and the amount of the products provided from the related suppliers.

The proposed model

The proposed methodology consists of three steps. In the first step, using the PCA method, the number of supplier selection criteria is reduced. In the next step, using the scores resulted from the PCA method, the importance value of each supplier is obtained via Z-TOPSIS with respect to each item. Then, these values are used in the mixed integer linear programming (MILP) model [ 36 ] as explained below.


  • In this study, the purchaser would be allowed to buy from several suppliers.
  • A multi-product environment is considered for this study and several products can be supplied for the customer by the suppliers.
  • The product amounts and the number of suppliers are known.
  • The buyer is allowed to buy for only one single period.
  • Demand has been considered as constant, without any change during the planning period.
  • A fixed budget for purchasing all the products has been considered.

i and j represent the suppliers and the products, respectively.

d ij The mean of defective items of product i purchased from supplier j

SIV ij The Importance value of supplier j pertinent to item i resulting from the Z-TOPSIS method

P ij Price of product i purchased from supplier j

B T The total amount of budget available for procurement of various products.

D i Demand for product i

S ij Minimum capacity of product i purchased from supplier j

supplier selection case study

R ij Minimum order of product i purchased from supplier j

supplier selection case study

maxNS Maximum number of possible suppliers to be selected

minNS Minimum number of possible suppliers to be selected

Decision variables

x ij The amount of product i purchased from supplier j

y ij The binary variable, which is one in case the product i is purchased from the supplier j and will be zero, otherwise

supplier selection case study

The objective function is presented in Eq ( 19 ) which determines the highest importance value of the selected suppliers relative to each product item through maximizing the related expression.

In constraints (20). the sum of the required budget must be determined, Eq ( 21 ) is associated with demand, Eqs 22 and 23 determine whether an item, say the part i is ordered from supplier j , the ordered number of items must lie within the supplier capacity and the required demand, respectively. Finally, the selected number of suppliers must be restricted by a minimum and a maximum numbers given by Eq ( 24 ).

In the section, a numerical example is given to show applicability of the proposed model. Note that in the first step, the business managers of 34 pharmaceutical companies are asked to rate the 24 supplier selection criteria. Using the PCA method, these 24 supplier selection criteria are reduced to 4, which are: product quality, product price, past record documentation and CRM. Therefore, all the pharmaceutical companies can only consider these four criteria and the scores resulted from the PCA method are considered as input for the next steps for the implementation of Z-TOPSIS and MILP model. For the example of the proposed method, we consider one firm with two decision makers who used 4 criteria obtained from the previous method. In other words, all other pharmaceutical firms could use these 4 criteria for ranking purposes.

A case study

In the present study, Microsoft Excel is used for the suppliers' ranking. The evaluation of ranking and the suppliers' weights processes are described below. In this paper, we have used the linguistic numbers given in Table 2 to evaluate the criteria importance; also we have used the information of Table 5 for criteria reliability measurement represented in Table 6 in the Z-number form.


Next, in order to evaluate the suppliers’ rating corresponding to each criterion, the DMs use the linguistic rating variable presented in Table 4 and make use of the data given in Table 5 for measuring the reliability of the supplier performance evaluation corresponding to each individual criterion as represented in Table 7 and Table 8 .



Now for the purpose of supplier selection problem case study, the Z-TOPSIS Algorithm is applied. Table 9 below shows the final results:


As we can observe from the results of Table 9 , supplier 2 ( CC 2 = 0.635) is considered as the most important one followed by supplier 3 ( CC 3 = 0.526), supplier 1 ( CC 1 = 0.474) and supplier 4 ( CC 4 = 0.354).

These importance values are input to the proposed mathematical model for the coefficients of the objective function. The parameter values that are required for supplying one item are presented below.

supplier selection case study

The mathematical model is coded in GAMS optimization program. The optimum solution and the amount of products that suppliers can provide are given below.

In order to code the mathematical model the GAMS optimization program has been used. The optimum solution and the product amounts that could be provided by the suppliers are shown below.

supplier selection case study

Sensitivity analysis

To investigate the effect of criteria weights on ranking of different suppliers, a sensitivity analysis is performed. Using varying degrees of criteria weights, resulted from the first step of Z-TOPSIS method, we have measured the changes in the outcome. Specifically, six cases were examined, but the ranking of the suppliers has remained unchanged. The details of the cases are presented in Table 10 , and the sensitivity results for the suppliers 1 to 4 are shown in Table 11 . The preliminary results have indicated that the proposed method could provide relatively robust results for supplier selection problem.




Contrary to the broad spectrum of studies undertaken on the supplier selection, the supplier assessment and selection through the application of specific measures relevant to the pharmaceutical industry were not extensively investigated. To bridge the gap, the current study has introduced a new selection model for the under study company aiming at presenting the ideas on the set up procedure of a Z-TOPSIS based selection model particularly designed to resolve the supplier selection problem in pharmaceutical industry. However, for the other industries, the relevant characteristics and requirements must be primarily studied. The questionnaire used in this paper was strongly recommended for determining the tailor-made supplier selection criteria in pharmaceutical industry. Obviously, for other industries, the questionnaire must be updated based on different criteria.

The PCA method has been used in this study to cut the number of supplier selection criteria, coupled with MILP model based on the Z-TOPSIS method. The merits development was described below.

The PCA can effectively remove the multicollinearity existing among the criteria ranking and would help reducing the trade-offs and the errors frequency in the questionnaire. The subjective errors also could efficiently be reduced using the PCA, meaning that the weight allocated to each ζ would be generated automatically. This model avoids complex and subjective pair-wise comparison for determining the ranking weights, which would eventually result in mitigation of this type of subjective errors. Moreover, it reduces the dimensionality of the questionnaire data meanwhile retaining the significant amount of information.

TOPSIS method using Z-numbers is used in this paper through extending the fuzzy rule based approach in multi-criteria decision making analysis. The proposed method, meanwhile providing a more useful way of handling vagueness and imperfection of information in decision making process, represents the expert knowledge more precisely. This method is more efficient compared with the current non-rule based TOPSIS in relation with ranking process.

Moreover, due to the software availability (SPSS, Microsoft Excel and GAMS), the proposed model is considered a user-friendly tool applicable for supplier selection.

As a future study, it is possible to use recent advances of robust optimization for supplier selection and we leave it for interested researchers. Furthermore, it would be interesting if the future work discusses the relationship between the results of this paper and the ones in the quotient space of fuzzy numbers [ 37 , 38 ] or symmetric fuzzy numbers [ 39 ].

Supporting information

S1 file. english questionnaire..

S2 File. Farsi questionnaire.

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  • 29. Fan L. Structural health monitoring base on principal components analysis implemented on a distributed and open system: City University of Hong Kong; 2006.

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Please note you do not have access to teaching notes, strategic supplier selection and order allocation for sustainable development of small and medium-sized enterprises: insights from a case study.

Journal of Global Operations and Strategic Sourcing

ISSN : 2398-5364

Article publication date: 15 February 2024

Efforts to implement supplier selection and order allocation (SSOA) approaches in small and medium-sized enterprises (SMEs) are quite restricted due to the lack of affordable and simple-to-use strategies. Although there is a huge amount of literature on SSOA techniques, very few studies have attempted to address the issues faced by SMEs and develop strategies from their point of view. The purpose of this study is to provide an effective, practical, and time-tested integrated SSOA framework for evaluating the performance of suppliers and allocating orders to them that can improve the efficiency and competitiveness of SMEs.


This study was conducted in two stages. First, an integrated supplier selection approach was designed, which consists of the analytic hierarchy process and newly developed measurement alternatives and ranking using compromise solution to evaluate supplier performance and rank them. Second, the Wagner-Whitin algorithm is used to determine optimal order quantities and optimize inventory carrying and ordering costs. The joint impact of quantity discounts is also evaluated at the end.

Insights derived from the case study proved that the proposed approach is capable of assisting purchase managers in the SSOA decision-making process. In addition, this case study resulted in 10.89% total cost savings and fewer stock-out situations.

Research limitations/implications

Criteria selected in this study are based on the advice of the managers in the selected manufacturing organizations. So the methods applied are limited to manufacturing SMEs. There were some aspects of the supplier selection process that this study could not explore. The development of an effective, reliable supplier selection procedure is a continuous process and it is indeed certainly possible that there are other aspects of supplier selection that are more crucial but are not considered in the proposed approach.

Practical implications

Purchase managers working in SMEs will be the primary beneficiaries of the developed approach. The suggested integrated approach can make a strategic difference in the working of SMEs.


A practical SSOA framework is developed for professionals working in SMEs. This approach will help SMEs to manage their operations effectively.

  • Small and medium-sized enterprises (SMEs)
  • Supplier selection and order allocation (SSOA)
  • Sustainable development
  • Wagner-Whitin (W-W) algorithm
  • Quantity discounts


Funding: No external funding is received for this study.

Disclosure statement: No potential conflict of interest was reported by the authors.

Data availability statement: No external data was used in this study.

Narkhede, G. (2024), "Strategic supplier selection and order allocation for sustainable development of small and medium-sized enterprises: insights from a case study", Journal of Global Operations and Strategic Sourcing , Vol. ahead-of-print No. ahead-of-print.

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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Analysis of key factors for green supplier selection: a case study of the electronics industry in vietnam.

supplier selection case study

1. Introduction

  • The creation of a framework for GSS in the electronics industry;
  • Determining and evaluating the factors influencing the GSS of Vietnamese electronics enterprises;
  • Suggesting the implementation of environmental practices for sustainable development to electronics companies in Vietnam and other developing countries.

2. Literature Review

2.1. environmental management and green supply chain trend, 2.2. recent research on gss, 2.3. selection of gss criteria, 3.1. modified delphi method.

  • Round 1: Experts are asked to determine the factors affecting GSS in electronics companies in Vietnam by grading their importance based on a five-level Likert scoring scale and explain their selection and suggestions for adjusting the criteria mentioned in the survey.
  • Round 2: The questionnaire is revised based on experts’ suggestions and comments and is re-sent to them with the summary and analysis of answers in round 1. Experts are asked to re-evaluate the criteria considering other panelists’ opinions.

3.2. Analytic Hierarchy Process (AHP)

4. results and discussion, 4.1. framework for gss, 4.2. ahp survey results and analysis, 4.2.1. participant demographics, 4.2.2. importance evaluation of dimensions and criteria, 4.2.3. discussions.

  • Enterprises should pay attention to energy-saving and recycled materials and require partners and suppliers to be more conscious about environmental management in their production. The different types of materials used in a product should emphasize recycling and reuse.
  • Enterprises should consider green product design and the economical and efficient use of raw materials. This can be achieved not only by the adjustment of input materials but the ability to recycle and reuse products in the future.
  • The production process should be eco-friendly by reducing the use of raw materials and energy and, therefore, minimizing the quantity of waste produced. In addition, reducing waste by controlling gas emissions and wastewater with treatment methods, using pollution control equipment, or alternative materials, recycling, and the application of advanced technology in the production process should be considered. Furthermore, the distribution network should have a less negative impact on the environment.
  • Enterprises can investigate potential suppliers for environmental concerns, educate suppliers on environmental issues, and emphasize environmental aspects when signing contracts with suppliers and logistics partners.
  • Enterprises can educate and orient employees to adhere to environmentally friendly principles. Companies should conduct regular training courses to convey more information about the impacts of environmental issues and climate change on socio-economic development and human life to increase their responsibility for environmental protection.

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Product costProduct priceThe production costs, such as the processing cost, maintenance cost and warranty cost.[ , , ]
Logistic costSum of lengthy distribution channel costs, transportation costs, handling and packaging costs, inventory costs, damage and insurance costs required during transportation.[ , , , ]
Quantity discountReduction in material cost based on purchase quantity.[ , ]
Product qualityQuality-related certificateWhether supplier has quality-related certificates (ISO 9000, QS 9000, etc.) to ensure that it has a certified quality management system.[ , , ]
Quality assuranceWhether suppliers carried out quality assessments on parts, whether they are certified for strict quality assurance and have a strong commitment to preventing quality failures.[ , ]
Handling abnormal quality capabilityThe capability of the supplier to solve abnormal quality problems.[ , ]
Reject rateThe number of parts rejected due to certain quality problems detected by incoming quality control and the production line.[ , , ]
Delivery & Service performanceLead timeThe amount of time from the placement to the arrival of an order.[ , , ]
On-time deliveryThe capability to meet delivery schedules.[ , ]
Order fulfillment rateThe capability of the supplier to comply with the predetermined order quantity.[ , ]
ResponsivenessThe ability to change according to the demand of customers, price structure, and the frequency of orders.[ , ]
Technology capabilityTechnology levelThe technology development level to meet company’s current and future demands.[ , , ]
Capability of R&DThe ability to research and develop to satisfy current and future demands of the company.[ , , ]
Capability of designThe ability of new product designs to satisfy current and future demands of the company.[ , , ]
Remanufacturing capabilityThe ability to reuse and repair components to rebuild a new product.[ , , ]
Environmental managementWaste managementThe quantity control and treatment of waste by the supplier.[ , ]
Environment-related certificatesWhether the supplier has relevant environment management certifications (such as ISO 14000).[ , , ]
Hazardous substances controlThe control of chemicals and hazardous materials used in production.[ , , ]
Energy consumption controlThe control of the amount of energy used in production.[ , , , ]
Green productRecyclingThe level to recycle products.[ , , ]
Eco-designThe capability to improve product design for products to be more environmentally friendly.[ , ]
Green packagingThe use of green materials in packaging reduces their energy consumption and negative impacts on the environment.[ , , ]
Supplier risksFinancial stabilityThe financial status of the supplier ensures they have a resilient financial system.[ , , ]
Social responsibilityThe integration of the supplier’s social and environmental concerns into their business operations.[ , , ]
Rule and regulation complianceThe compliance level of the supplier to government rules and regulations, customers’ requirements and environmental standards.[ , , , ]
Risk assessment and risk management capabilityThe ability to identify and evaluate the risks and implement appropriate practices for controlling identified risks.[ ]
No.TitleEducational LevelExperience in Electronics Industry
1Purchasing Team LeaderBachelor6 years
2Senior SQEBachelor5 years
3Procurement Deputy SupervisorBachelor5 years
4Operations Program ManagerMaster7.5 years
5Senior NPI ManagerMaster21 years
DimensionCriteriaLocal WeightLocal Ranking
Product qualityAbnormal quality handling capability0.358851
Quality assurance0.296642
Quality-related certificate0.188593
Reject rate0.155914
Cost and Service performanceProduct price0.457231
Lead time0.277902
Order fulfillment rate0.120344
Technology capabilityR&D capability0.444781
Technology level0.314672
Design capability0.120683
Remanufacturing capability0.119874
Environmental managementEnergy consumption control0.259731
Hazardous material management0.227503
Environment-related certificates0.144824
Green packaging0.133195
Supplier risksRule and regulation compliance0.358111
Financial stability0.306262
Social responsibility0.229533
Risk assessment and risk management capability0.106104
CriteriaGlobal WeightGlobal Ranking
Abnormal quality handling capability0.148501
Product price0.135742
Quality assurance0.106453
Quality-related certificate0.086644
Lead time0.083135
Reject rate0.049136
Energy consumption control0.045657
Hazardous material management0.0395910
Order fulfillment rate0.0388711
Capability of R&D0.0370812
Technology level0.0260813
Green packaging0.0229714
Rule and regulation compliance0.0195115
Environment-related certificates0.0185116
Financial stability0.0143217
Social responsibility0.0117218
Capability of design0.0103019
Remanufacturing capability0.0101520
Risk assessment and risk management capability0.0087621
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Tsai, J.-F.; Wu, S.-C.; Pham, T.K.L.; Lin, M.-H. Analysis of Key Factors for Green Supplier Selection: A Case Study of the Electronics Industry in Vietnam. Sustainability 2023 , 15 , 7885.

Tsai J-F, Wu S-C, Pham TKL, Lin M-H. Analysis of Key Factors for Green Supplier Selection: A Case Study of the Electronics Industry in Vietnam. Sustainability . 2023; 15(10):7885.

Tsai, Jung-Fa, Sheng-Che Wu, Thi Khanh Linh Pham, and Ming-Hua Lin. 2023. "Analysis of Key Factors for Green Supplier Selection: A Case Study of the Electronics Industry in Vietnam" Sustainability 15, no. 10: 7885.

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Total Cost Supplier Selection Model: A Case Study

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Case Studies: An Analysis of Supplier Evaluation

Case studies.

Boeing Commercial Airplanes Group Top Suppliers

Boeing Commercial Airplanes Group

Boeing Commercial Airplanes Group, BCAG , is a firm that demonstrates excellence in the area of supplier evaluation ( 22 ). It has a total of 3,100 suppliers. BCAG has put great emphasis on supplier evaluation since half of their total production costs come from suppliers. They have realized that suppliers are integral to the success of their business and established high-level supplier evaluation methodology to decrease their costs, improve their quality and introduce new technologies.

Each supplier has to be certified to become a BCAG supplier. Boeing has a “preferred supplier certification” process where suppliers are evaluated and rated against specific standards in the implementation of statistical process control, business processes and performance. Once they become a supplier, they are regularly given report cards and rated on a gold, silver or bronze status for meeting Boeing’s expectations.

Russ Bunio, vice president and general manager of supply management and procurement for ( BCAG ), says that suppliers who meet or exceed company standards are identified and recognized as preferred suppliers. In turn, they are rewarded by benefits such as selection preference, reduced inspections, industry recognition, and additional business opportunities. Only the very best are recognized publicly for their efforts and only a few suppliers can make it. The best suppliers deliver products of flawless quality and maintain a perfect on-time delivery schedule, consistently introduce new technology, provide Boeing with continual cost reductions, work as an extension of BCAG’s business and production systems, and focus on teamwork, risk sharing, continuous improvements and win-win attitudes.

Boeing has established a continuous cost-improvement program, CCIP , which is designed to achieve 3%-5% annual reductions in what BCAG pays for materials and parts. This target could not be achieved if BCAG did not evaluate its suppliers on a continuous basis and reward the best practices. The highest level of recognition is to be chosen as the “supplier of the year” which has a motivational intent. Of the Seattle-based company’s 3,100 suppliers in 1999, only 116 were recognized for meeting or exceeding continuous cost-improvement goals and just 13 were given “supplier of the year” recognition. Boeing then uses a small fraction of the suppliers of the year as benchmarks against the other suppliers. Only 0.5% of BCAG’s suppliers were chosen recently as the hallmarks against which other suppliers are measured ( 22 ).

h2. Top Suppliers

BFGoodrich Aerospace Aerostructures Group, Chula Vista, Calif. has been recognized for high performance in quality, competitive pricing and on-time deliveries. The company was one of the first suppliers to commit to reducing costs under bcag’s “continuous cost-improvement process” initiative.

Bummstead Manufacturing Inc., Auburn, Wash. is noted as providing top-quality machined detail and minor sub-assembly parts at competitive prices, with excellent product support, and a perfect delivery schedule. The company was recognized for exemplifying the meaning of “continuous quality improvement” and exhibiting a commitment to excellence by consistently improving processes.

DME Corp., Fort Lauderdale, Fla. has been commended for technical excellence, dependable performance, competitive pricing, and a highly trained and skilled work force. DME had a perfect on-time delivery record in 1999.

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Optimizing construction supplier selection in conflict-affected regions: a hybrid multi-criteria framework

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supplier selection case study

  • Jamil Hallak   ORCID: 1  

Conflicts and wars profoundly impact infrastructure, exacerbating the adversity already caused by natural disasters. Therefore, it is imperative that the reconstruction process be both effective and efficient to expedite a return to normalcy. This study aims to enhance the efficacy of reconstruction efforts through improved construction supplier evaluation and selection. It introduces an innovative hybrid multi-objective decision-making model that integrates a broad spectrum of economic, technical, and humanitarian criteria. The model is designed to optimally select and assign construction suppliers in regions affected by human and natural conflicts and crises. Fifteen criteria have been incorporated into the evaluation process to validate its effectiveness and maximize its contribution to local communities. This methodology streamlines decision-making and enhances transparency in conflict zones, aligning with the interests of all stakeholders. The study incorporates advanced methodologies, including Fuzzy Goal Programming (F-GP), Geographic Information System (GIS)-based Risk Assessment, and Fuzzy Analytic Hierarchy Process (F-AHP), leveraging real-world data and a case study. Additionally, a sensitivity analysis examines the impact of varying inputs on the model's output. The findings attest to the model's utility in conflict-affected regions and its potential applicability in stable settings.

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1 Introduction

In the wake of conflicts and crises, the efficient and effective reconstruction of infrastructure is crucial. The supplier evaluation and allocation process play a critical role in this context, yet traditional procurement methods often fail to meet the dynamic and complex needs of post-conflict environments (Khaled et al. 2011 ; Pal et al. 2013 ). Various multi-criteria decision making (MCDM) methods have been used in different industries such as food, automotive, healthcare, and petrochemical; however, research on the construction industry in conflict areas is rare (Tushar et al. 2022 ). To address this gap, the research is structured around the following central research questions (CRQs):

CRQ1: What supplier selection criteria are considered important for the construction industry in conflict areas? CRQ2: How can we best incorporate the identified supplier selection criteria into a supplier selection process in conflict areas? CRQ3: How can we best conduct a risk analysis in conflict areas based on GIS? CRQ4: How does the integration of multiple evaluation criteria and GIS-based risk analysis into the supplier evaluation and allocation process utilizing fuzzy multi-criteria decision-making affect the efficiency and outcomes of construction projects in conflict zones?

We hypothesize that incorporating various humanitarian, economic, and technical criteria into the evaluation process will make it more comprehensive. Utilizing GIS-based risk analysis will lead to a more realistic assessment of risks in conflict areas. Employing fuzzy multi-criteria decision-making (incorporating humanitarian, economic, technical, and risk criteria) will result in more effective and transparent supplier evaluations and allocations, thereby enhancing project success in these environments.

To address these CRQs, this study develops a structured hybrid fuzzy MCDM framework based on GIS-based risk assessment, F-AHP, and F-GP to tackle the supplier evaluation and allocation problem in the construction industry of northern Syria, a conflict area.

Motivated by the significant impact that procurement efficiency has on rehabilitation efforts, this research seeks to refine the procurement process in conflict-affected regions. Current methods often fall short due to the unpredictable nature of supply chains and the pressing needs of these areas (Sarkis and Talluri 2002 ; Christopher 2016 ). By developing a robust supplier evaluation and allocation model that accounts for a broad spectrum of criteria, this study aims to significantly improve transparency and effectiveness, impacting the success of reconstruction projects and the effective utilization of funds.

This study is significant as it addresses a notable gap in the literature concerning the application of fuzzy multi-objective decision-making models in post-conflict supplier selection (Ghodsypour and O’Brien 2001 ). The study aims to achieve the following main research objectives (MROs):

MRO1: Identify the supplier selection criteria relevant for the construction industry in conflict areas. MRO2: Calculate weights of identified construction supplier selection criteria using the F-AHP method. MRO3: Conduct GIS-based risk analysis in conflict areas. MRO4: Evaluate and allocate construction suppliers using mathematical fuzzy goal programming. MRO5: Discuss the implications of the proposed research.

This research contributes to the theoretical and practical understanding of procurement challenges and evaluation processes in conflict zones. It boosts transparency in the multi-criteria selection process, leading to a comprehensive framework, sustainability, and effective resource utilization, accelerating the return to normal life for affected populations, and contributing to the wider goal of effective reconstruction after crises. The findings are particularly relevant for non-governmental organizations, donors, local authorities, and other stakeholders engaged in reconstruction, offering them a novel approach that can lead to more informed and strategic decisions in construction supplier evaluation and allocation.

The paper is organized as follows: The literature review section outlines current methodologies and identifies their limitations. The methodology section introduces our innovative approach to supplier evaluation and allocation, while the application of this model is demonstrated through a detailed case study in section four. The final section discusses the implications of our findings and suggests potential avenues for future research.

2 Literature review

2.1 mcdm in supplier evaluation.

MCDM represents a methodical process aimed at identifying the most favorable option among various feasible alternatives based on a set of defined criteria or attributes (Garg 2016a , b , 2017 ; Hallak et al. 2019 , 2021 ; Hallak and Polat 2021 ). Selecting the correct supplier can significantly lower purchase costs and enhance an enterprise’s competitiveness, illustrating why many researchers believe that selecting a supplier is one of the most important activities of the purchasing department (Ghodsypour and O’Brien 2001 ).

In recent years, several supplier selection and evaluation methods have been developed. A number of MCDM methods have been employed in this research area: the analytical hierarchy process (AHP) (Etlanda and Sutawidjaya 2022 ); the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) (Cakar and Çavuş 2021 ); the best–worst method (BWM) (Amiri et al. 2020 ); the fuzzy decision-making trial and evaluation laboratory (FDEMATEL) (Giri et al. 2022 ); and the multi-criteria optimization and compromise solution (VIKOR) (Gupta and Kumar 2022 ). To maximize the advantages and minimize the weaknesses of specific MCDM methods, researchers often integrate two or more MCDM methods into a hybrid method (Govindan et al. 2020 ). The literature related to supplier selection is dominated by these hybrid MCDM approaches. Further, researchers have begun employing combinations of more than two MCDM methods, such as F-AHP, F-TOPSIS and Fuzzy inference systems (FIS) (Mina et al. 2021 ), and integrating F-AHP with PROMETHEE II for selecting suppliers based on circular economy principles (Tushar et al. 2022 ).

This research follows a novel approach by surveying the affected population to define criteria, using F-AHP to calculate weights, and implementing GIS-based risk assessments to evaluate risks associated with each supplier. Finally, all parameters are integrated into fuzzy goal programming to optimally evaluate and allocate construction suppliers for projects in the area.

2.2 Criteria used in the supplier evaluation

Although supplier selection criteria often differ from industry to industry, surveys of the literature show that criteria related to delivery, quality, and price are commonly considered across various sectors. The quality of materials and services has been a focal point in several studies (Dickson 1966 ; WEBER et al. 1991 ; Tam 2001 ; Chan and Kumar 2007 ; Gencer and Guerpinar 2007 ; Guo et al. 2009 ; Wang 2010 ; Balezentis and Balezentis 2011 ; Zeydan et al. 2011 ; Kilic 2013 ; Cristea and Cristea 2017 ; Tamosaitiene et al. 2017 ; Wang et al. 2017 ; Fallahpour et al. 2017 ), while aspects such as total price and delivery time have been emphasized by others (Dickson 1966 ; Weber et al. 1991 ; Tam 2001 ; Chan and Kumar 2007 ; Gencer and Guerpinar 2007 ; Lee 2009 ; Guo et al. 2009 ; Lam et al. 2010 ; Wang 2010 ; Balezentis and Balezentis 2011 ; Kilic 2013 ; Hruska et al. 2014 ; Cristea and Cristea 2017 ; Wang et al. 2017 ; Buyukozkan and Gocer 2017 ; Fallahpour et al. 2017 ). Certifications of products and materials (Ting and Cho 2008 ; Hudymacova et al. 2010 ; Cristea and Cristea 2017 ), supplier reputation (Lin and Chang 2008 ; Rezaei et al. 2014 ), and warranty periods post-delivery have also been considered key factors in supplier evaluations (Cristea and Cristea 2017 ; Wang 2010 ). Consistent performance and reliability are crucial, as emphasized in the literature (Wang et al. 2004 ; Chan and Kumar 2007 ; Gencer and Guerpinar 2007 ; Lee 2009 ; Cristea and Cristea 2017 ). Despite the evolving landscape and increasing emphasis on qualitative criteria, financial parameters, delivery, and quality consistently emerge as core criteria in nearly all research on supplier selection. This trend is affirmed by the aforementioned studies, which significantly influence decision-making in the supplier evaluation process.

In this study, related literature served as the primary source for identifying supplier selection criteria, supplemented by feedback from surveying 32 NGOs implementing construction projects in Syria. These surveys focused more on risk and humanitarian factors in supplier evaluation in conflict areas, whereas previous studies often concentrated on stable communities and regions. Most earlier research predominantly relied on criteria weights determined through AHP, MCDM, and/or TOPSIS under normal and stable conditions. However, there has been a notable lack of research focused on developing optimal strategies for evaluating and allocating construction suppliers in crisis areas, especially considering humanitarian and risk factors assessed through GIS. This study extends existing methodologies by identifying and weighting criteria using F-AHP, performing GIS-based risk assessments, and deriving a risk value for each supplier. These values are integrated into a mathematical model that employs fuzzy goal programming to optimally allocate construction suppliers to projects. This approach aims to provide a robust framework for supplier selection in challenging environments, ensuring that projects are equipped with the best possible resources under demanding conditions.

3 Methodology

A novel hybrid approach has been introduced to address the complexities of this multi-criteria problem. To begin, a GIS-based risk assessment methodology was employed. This entailed determining the risk values associated with each potential construction supplier within the conflict-affected region through the integration of spatial analysis. Subsequently, a multi-criteria technique was applied utilizing F-GP to effectively solve the model and identify the optimal solution. Within this framework, particular emphasis was placed on the assignment of construction suppliers to the required projects that best align with a spectrum of goals encompassing financial, technical, spatial, humanitarian, environmental, and risk considerations. The ensuing sections provide a meticulous delineation of the steps and procedures undertaken in this approach, as shown in Fig.  1 :

Dual-Pronged Data Collection: This involves two methods, surveys and Focus Group Discussions (FGDs).

➢Surveys: We collected responses from 32 NGOs to obtain a broad perspective on different practices and standards in the field. These surveys provided quantifiable and comparable data about the NGOs' methods for evaluating suppliers and criteria utilized.

figure 1

The proposed methodology to solve the supplier problem

We employed a purposive sampling approach to select NGOs for our survey. Around 40 active local NGOs that conduct construction projects were invited to participate in the surveys, but 32 NGOs responded. This approach ensured that all participants were actively engaged in construction projects within Syria, allowing for a focused examination of construction supplier evaluation practices in this specific context. This method facilitated access to targeted insights into the challenges and practices peculiar to NGOs operating in conflict-affected regions.

Survey Validation: To ensure the reliability and relevance of our questionnaire for surveying NGOs, we implemented a validation process with input from both academic researchers and donors experienced in construction projects in Syria. The validation process included:

• Expert Selection: Two academics specializing in construction supplier evaluation, two experts from local NGOs, and two donors involved in construction project funding were selected for their extensive knowledge and field experience, as detailed in Table  1 .

• Review Process: Experts reviewed the draft questionnaire, assessing clarity, relevance to our objectives, and comprehensiveness in covering the NGO supplier evaluation process in conflict zones.

• Criteria for Validation:

(I) Clarity: Questions were designed to be clear and unambiguous.

(II) Relevance: All questions directly related to construction supplier selection and evaluation practices.

(III) Comprehensiveness: The questionnaire comprehensively covered all aspects critical to NGO decision-making in supplier evaluation and selection.

• Feedback and Revisions: Experts provided structured feedback suggesting necessary revisions to improve question focus. These changes were incorporated to enhance the supplier evaluation process.

• Final Validation: The revised questionnaire underwent a final review to confirm all modifications were effectively integrated. Approval from all experts confirmed the questionnaire was validated and ready for deployment. The whole survey is provided in Appendix 1 .

➢ FGDs: FGDs serve as a pivotal tool for delving deeper into issues initially highlighted through surveys. These discussions enable a nuanced exploration of the perspectives and practices of non-governmental organizations (NGOs), facilitating the identification of both common themes and divergent viewpoints. This process is crucial for developing a robust evaluation framework. For instance, when surveys reveal a lack of consensus on specific points, FGDs are instrumental in fostering collective agreement. Through FGDs, it was determined that the maximum acceptable distance for allocating a supplier to a construction project is 100 km. This limit is based on experiences showing that greater distances complicate project execution and increase logistical challenges. Similarly, it was established that suppliers within this region could be allocated to a maximum of three construction projects. This restriction reflects the administrative capacities of local suppliers who often lack the robust administrative structures necessary to manage multiple projects effectively. These conclusions are drawn from the practical insights and experiences of humanitarian actors and donors who have implemented construction projects in the region.

Prioritizing the criteria: Utilizing the F-AHP, a decision-making framework that allows for the incorporation of human judgment and uncertainty. It is used to determine the relative importance of a set of criteria that NGOs consider when evaluating suppliers. The "fuzzy" aspect allows it to handle imprecision, which is often the case in a crisis environment.

GIS Spatial Analysis: Geographic Information System (GIS) is a tool used to capture, store, manipulate, analyze, manage, and present spatial or geographic data. In this context, it helps in identifying and visualizing risks associated with different geographical locations where suppliers might operate, contributing to a comprehensive risk map. Two risks are defined: the first is the frontline risk, and the second is the hard access areas risk.

Building the Fuzzy Model: This involves creating a model based on fuzzy goal programming. This model can handle the complexity and ambiguity of real-world desired targets in each objective, making it suitable for evaluating suppliers where information may be incomplete or uncertain.

Model Validation: Before using the model for decision-making, it is critical to ensure that it accurately reflects the real-world scenario it's intended to represent. This involves testing the model against numerical examples to confirm its reliability.

Solving the model: The model is solved using GAMS, a high-level modeling system for mathematical programming problems. It is used to find the best solution or the most optimal supplier according to the criteria and data fed into the fuzzy model.

Sensitivity Analysis: After determining the optimal supplier, a sensitivity analysis is performed. This process involves changing one or more parameters in the model to see how these changes affect the outcome.

3.1 Building the model

3.1.1 model parameters.

Set of tenders; i ϵ I

Set of candidate construction suppliers; j ϵ J

Set of targeted objectives; n ϵ N

Distance between candidate construction supplier j and another (acquired from building road network dataset by GIS)

Submission matrix for tender i by each construction supplier j (not all supplier is submitting their offers to all tenders)

Financial offer submitted by supplier j for each tender i

Total value of previous contracts for each candidate supplier j.

Risk at each candidate location of Candidate supplier j in terms of proximity to frontlines.

Risk at each candidate location of Candidate supplier j in terms of hard access areas.

Quality of materials submitted by candidate supplier j for each tender.

Staff experience for Candidate supplier j.

Delivery time submitted by candidate supplier j for each tender.

Promptness response of quality and delivery issues for Candidate supplier j.

Capacity of each Candidate supplier j.

managemental capacity of each Candidate supplier j.

Financial capacity of each Candidate supplier j.

Recommendation letter submitted by third party for each Candidate supplier j.

Child labor involvement for each Candidate supplier j.

Commitment to humanitarian principles for each Candidate supplier j.

Commitment to environmental regulation and compliance for each Candidate supplier j.

Weights of each fuzzy goal n.

Aspiration level for each fuzzy goal n.

Maximum allowable deviation for each fuzzy goal n

3.1.2 Model decision variables

Amount of an overachieved for each fuzzy goal n

Amount of an underachieved for each fuzzy goal n

Degree of membership for each fuzzy goal n

3.1.3 Objective functions

Equations ( 1 ) to ( 2 ) delineate the objectives of the proposed model. Equation ( 1 ) is designed to maximize the overall quality of materials submitted by candidate suppliers. In contrast, Eq. ( 2 ) focuses on minimizing the financial bids tendered by the construction suppliers to conduct the construction works in the area. Equation ( 3 ) seeks to maximize the aggregate value of previous contracts held by the chosen suppliers, while Eq. ( 4 ) aims to enhance the cumulative experience of their staff. Equation ( 5 ) is intended to minimize the total delivery time for completing the construction projects as proposed by the suppliers.

Subsequent equations, namely Eqs. ( 6 ) through ( 7 ), are directed towards maximizing various operational capacities of the selected suppliers. These include promptness in response (Eq.  6 ), equipment capacity (Eq.  7 ), managerial capacity (Eq.  8 ), financial stability (Eq.  9 ), and the quantity of recommendation letters (Eq.  10 ). Equation ( 11 ) is oriented towards minimizing the incidence of child labor among the selected suppliers.

Equation ( 12 ) addresses the commitment of suppliers to humanitarian principles, aiming for its maximization. Equations ( 13 ) and ( 14 ) target risk minimization related to the proximity of suppliers to frontlines and hard-to-access areas. Lastly, Eq. ( 15 ) is dedicated to maximizing adherence to environmental regulations by the selected construction suppliers.

Subject to:

The hard constraints of this model are encapsulated in Eqs. ( 16 ) to ( 20 ). Equation ( 16 ) mandates that each construction sub-project is to be allocated to only one supplier. Equation ( 17 ) specifies that a project can only be assigned to candidate suppliers who have submitted an offer for that particular project, as indicated by the submission matrix. Equation ( 18 ) limits the assignment of each construction supplier to a maximum of three projects. Equation ( 19 ) imposes a geographical constraint, ensuring that the projects assigned to a supplier are within a maximum distance of 100 km. Finally, Eq. ( 20 ) defines the binary variables associated with the selection of suppliers.

In existing literature, a variety of methods have been proposed to address multi-objective problems (Ulungu et al. 1994 ; Aiello et al. 2006 ; Ye and Zhou 2007 ; Singh and Singh 2011 ; Xu and Li 2012 ; Hathhorn et al. 2013 ; Emami and Nookabadi 2013 ; Xu et al. 2016 ; Li et al. 2017 ) In this particular study, the problem was addressed using fuzzy goal programming, F-AHP, GIS-based risk assessment, humanitarian and environmental context of the Syrian crisis.

3.2 Fuzzy goal programming

Goal programming is an approach used for solving multi-objective optimization problems that balances trade-offs in conflicting objectives. It allows for balancing all desired objectives (from Eq.  1 ) to Eq.  15 through direct trade-offs between all unwanted deviational variables by placing them in a normalized single-achievement function that includes all the objective deviations in just one equation (Jones and Tamiz 2010 ). In this study, a fuzzy goal programming model has been utilized because it provides a more realistic approximation to real case studies. This model was presented by Yaghoobi et al. ( 2008 ). The model consolidates all the objective functions into a single objective function, as formulated in Eq.  21 , where efforts are made to minimize the deviations from the desired goal values in each goal, taking into account the weight of each objective function.

Consecutively, we have converted each objective function into soft constraints by adding the deviations \({O}_{n}\) ​ for those objective functions that we are striving to minimize, and \({U}_{n}\) ​ ffor those objective functions that we are striving to maximize. The soft constraints are formulated in Eqs.  22 , 24 , 26 , 28 , 30 , 32 , 34 , 36 , 38 , 40 , 42 , 44 , 46 , 48 , and 50 , while Eqs.  23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , and 51 are constraints that ensure the sum of the normalized negative deviations, normalized positive deviations, and the membership variable \({\mu }_{\text{n}}\) ​ equals one. The fuzzy model is presented and validated by the Yaghoobi model (Yaghoobi et al. 2008 ).

3.3 Model validation

In the scholarly exposition of the case study, the validation of the proposed allocation model is meticulously articulated through a two-pronged data analysis approach. Initial validation is undertaken via synthetically constructed datasets, derived post-consultation with domain experts. This preliminary phase encompasses two distinct numerical examples: the first involving 10 construction projects and 20 potential suppliers, and the second encompassing 20 construction projects paired with 30 candidate suppliers. These scenarios are rigorously tested against varied weight assignments, prioritizing the dual objectives of optimizing material quality and minimizing project costs. The detailed datasets and the resultant computational outcomes are systematically documented in Appendix 2 .

To elucidate the model's operational efficacy, Fig.  2 is presented, delineating a comparative analysis of the synthetic datasets. This visual representation accentuates the model's capacity to negotiate between the competing objectives of material quality and cost-efficiency. Notably, the graphic illustrations within Fig.  2 delineate a positive correlation between the assigned weight to material quality and the model's propensity to enhance this particular objective. Conversely, an augmented emphasis on cost reduction is reciprocated by the model's inclination to curtail financial expenditures. Collectively, these outcomes substantiate the model's robustness and its capability to deliver balanced solutions within the complex operational landscape of Northern Syria's reconstruction endeavors.

figure 2

Model validation results ( a , b , c , d )

Subsequent to the synthetic trials, the model's validity is further corroborated through empirical data amassed from field surveys and focus group discussions within the specific contexts of Al-Bab and Ar-Ra'ee, Syria. The integration of real-world data provides a pragmatic dimension to the model's applicability, with a sensitivity analysis cementing its relevance. The congruence between the model's outcomes and the practical requirements observed in the field serves as a testament to its validity and effectiveness.

4 Results and discussions

4.1 case study.

In the midst of ongoing crises in Northern Syria, the tendering process for construction projects has evolved into a multifaceted challenge. Various suppliers, eager to contribute to the rebuilding efforts, have submitted proposals to undertake one or multiple projects dispersed across the northern region of Syria in two sub-districts (AlBab and Ar-Ra'ee). An NGO, acting as the steward of these efforts, is tasked with the rigorous evaluation of these proposals against a comprehensive set of criteria. These criteria span humanitarian considerations, risk assessment considerations, environmental impact, ensuring sustainability amidst reconstruction, and technical and financial competencies, alongside the capacity to effectively deliver on project commitments.

The evaluation matrix is composed of fifteen distinct criteria, carefully designed to holistically assess each supplier's offer based on data collected from stakeholders in the area. This systematic approach aims to align the selection process with overarching objectives by proposing a novel hybrid fuzzy model. As a result, this study strives to promote equitable development and adherence to environmental and humanitarian standards, achieving a transparent framework for all stakeholders in the crisis region.

The NGO will allocate each project to the supplier that demonstrates the highest congruence with the defined criteria described in Table  2 , thus ensuring the optimal alignment of project needs with supplier capabilities. It is stipulated that a single supplier may be awarded a maximum of three projects, with the stipulation that the geographical distance of these projects does not exceed 100 km for one supplier. This constraint is imposed to ensure logistical feasibility and effective project oversight, as lessons learned from previous projects as a result of FGDs.

Through this case study, the article elucidates the operational complexities and the intricate decision-making processes involved in post-conflict reconstruction. The narrative underscores the necessity of a transparent, balanced, and multi-objective approach that interweaves diverse evaluation criteria to foster comprehensive development and stability in crisis-afflicted regions.

4.1.1 Environmental regulation and compliance

Four factors were considered during the visits to each construction supplier, as shown in Fig.  3 :

Water Conservation: Using water-efficient construction techniques, as water resources might be scarce or contaminated in crisis regions.

Low-Impact Materials: Choosing construction materials that have minimal environmental impact, such as locally sourced materials, to reduce transportation emissions and support the local economy.

Waste Reduction: Implementing strategies to minimize construction waste and ensure proper disposal, as waste management systems in crisis areas might be compromised.

Energy Efficiency: Incorporating energy-efficient designs in construction to reduce the long-term environmental footprint, considering the limited energy resources in such areas or utilizing photovoltaic energy to produce electricity.

figure 3

Factor considered in the process of Environmental Regulation and Compliance

4.2 Findings

In this section, we present the results from applying the proposed hybrid methodology. We extracted the risk value for each candidate supplier by creating risk maps according to each criterion and its location on the related risk map, as shown in Fig.  4 .

figure 4

Risk values in the target area

After calculating the risk value for each candidate supplier, we integrated this value into the mathematical fuzzy goal programming model as previously described. We solved the model using the software package, incorporating all fifteen goals to determine the optimal solution for the entire problem.

The inclusion of more criteria in the model enhances its transparency for the affected populations and suppliers within the humanitarian context and yields more varied values. This results in a marked variance between the values of candidate suppliers, which facilitates the selection process for decision-makers.

The results obtained from solving the proposed hybrid fuzzy model revealed the optimal solution achieved for each goal. We have presented these in Table  3 to compare the actual values against the planned values (identified by three dedicated experts considering humanitarian aspects and similar previous projects in northern Syria). Additionally, we depict the results in Fig.  5 , showcasing the actual achieved values for each objective as a percentage and obtained value.

figure 5

Planned goals vs actual

For example, our cost target was approximately $300,000, but the actual value achieved was $321,526, reflecting an increase of 7%, which is considered relatively satisfactory as we strive to minimize this goal. While our aim was to reach a material quality scale of 66, we achieved a higher scale number of 94, which is approximately 44% higher. Our target for allocating suppliers with previous contracts was around $1,200,000, but after running the model, we achieved a very close value of $1,276,414. Throughout the model, we aimed to achieve 66 for staff experience, 150 for total delivery time to complete the projects, and scales of 66 for promptness response, equipment capacity, managerial capacity, financial capacity, recommendation letters, 40 for child labor involvement, 60 for commitment to humanitarian principles, 30 for risks related to frontlines, 30 for risks related to hard-to-access areas, and 60 for commitment to environmental regulations. In the results, we obtained 67, 260, 66, 67, 66, 94, 78, 44, 71, 26, 35, and 61, respectively.

The potential reasons for obtaining values that deviate significantly from the target values can be described as follows:

The decision-makers in the target area consider previous projects as a baseline and attempt to predict optimistic targets for this project based on that baseline.

Occasionally, the model may have already achieved the optimal values related to a specific criterion, indicating that there is no further possibility to improve the solution.

The model consistently strives to balance the achievement of goals according to weights determined by the F-AHP and does not focus on any single criterion in isolation.

4.3 Sensitivity analysis

4.3.1 scenario 1: financial offers changing.

In this Scenario, adjustments were made to the financial offers in response to the unstable market conditions in the area. Starting with a planned value of $300,000, the model endeavored to minimize this financial goal. A resultant value of $305,449.7 was obtained at a -5% change, as illustrated in Fig.  6 . When transitioning from a 0% to a 5% change, there was an observed increase in the financial offers. However, this increase still remained less than the 5% threshold relative to the 0% change scenario. This indicates that the model consistently prioritizes minimizing financial costs while simultaneously considering the fulfillment of other objectives.

figure 6

Changes of financial offer

4.3.2 Scenario 2: some projects and suppliers are outside the calculation because of its location withing the frontline region

In this scenario, stakeholders acknowledge the possibility that some areas on the frontlines may become uncontrollable. This implies that construction projects and suppliers in these areas would be excluded from consideration, necessitating a resolution of the problem without their involvement. In this case, the projects and suppliers in the frontline area can be characterized as follows:

The construction suppliers: S4, which was not selected under normal circumstances, and S5, S6, and S7, which were selected in the normal situation.

Project P3 is also excluded from consideration.

The results obtained after solving the model under these conditions are presented in Table  4 . It is observed that each project is allocated once, and the selected suppliers are assigned to a maximum of two construction projects each. In Table  5 , the achieved goals are compared against the planned values. The solution in this altered scenario is less favorable compared with the normal situation, particularly concerning the first three most critical factors (G1, G2, G3). This outcome is expected due to the exclusion of three suppliers initially chosen in the normal situation from Case 2.

4.4 Implications for theory and practice

4.4.1 theoretical implications.

This research enriches the theoretical landscape by blending diverse methodologies, notably integrating Fuzzy Goal Programming, GIS-based risk assessment, and the F-AHP. This multifaceted approach not only advances the understanding of MCDM but also tailors it specifically to the nuanced requirements of post-conflict reconstruction scenarios. This result is supported by Govindan et al. ( 2020 ), who emphasized the importance of using multiple methodologies to enhance decision-making processes. Such a synthesis is pivotal in offering a comprehensive framework that not only addresses but also adapts to the evolving complexities inherent in construction supplier evaluation within such zones. Additionally, the incorporation of real-world data and case studies enhances the theoretical relevance of the model, grounding abstract concepts in tangible scenarios that reflect the current challenges faced in supply chain management within conflict-impacted environments.

4.4.2 Managerial implications

From a practical standpoint, this study provides robust tools for improving decision-making in crisis situations. By systematizing the evaluation and allocation of suppliers to construction projects, the model underscores the critical importance of integrating risk and humanitarian considerations into procurement strategies. Such insights are invaluable for NGOs, donors, and local authorities engaged in reconstruction efforts, offering them a methodologically sound approach to enhance their operations. The application of this model not only promises enhanced efficiency and effectiveness in project implementations but also fosters greater transparency and accountability in environments that traditionally suffer from high uncertainty and risk.

Furthermore, the strategic recommendations outlined in this study serve as a guiding framework for entities involved in reconstruction efforts. By adopting the proposed methodologies, these organizations can better navigate the complexities of supplier selection and project allocation in ways that align with both immediate project goals and long-term developmental objectives. This dual focus on operational efficiency and strategic foresight exemplifies the practical applications of the research, providing a scalable and adaptable solution that can be customized for various conflict-affected regions worldwide.

5 Conclusion

This study has developed an innovative hybrid methodology aimed at optimizing supplier selection and assignment for construction projects critical to the reconstruction efforts in Syria post-crisis and following the February 2023 earthquake. By integrating Geographic Information Systems (GIS) and the F-AHP, the approach effectively identifies the risk values associated with each potential supplier, facilitating the selection of the most suitable entities. The subsequent application of fuzzy goal programming ensures that the selection and assignment processes are guided by a robust multi-objective optimization framework, which incorporates a comprehensive set of criteria covering technical, financial, humanitarian, and risk aspects.

The findings of this research significantly contribute to both theoretical understanding and practical applications in the field of crisis management and reconstruction. Theoretically, it bridges the gap in multi-criteria decision-making models by incorporating complex and dynamic environments such as conflict zones. Practically, it offers a transparent, systematic framework that enhances decision-making in supplier selection and project assignments, which is critical for effective reconstruction efforts. The real-case application using data collected from northern Syria not only demonstrates the methodology’s applicability and effectiveness but also its adaptability to other conflict-affected regions globally.

Addressing the research questions posed at the outset, the study highlights that a diverse array of supplier selection criteria, previously under-considered in conflict zones, are indeed crucial for the construction industry. The integration of GIS-based risk analysis and multi-criteria decision-making tools like F-AHP has proven effective in enhancing the efficiency and outcomes of construction projects in such challenging environments. These methodologies allow for a nuanced consideration of various risk factors and ensure that the most capable suppliers are selected and assigned to projects that they are best suited for.

Despite its innovative approach and contributions, the study acknowledges certain limitations, such as its focus on a relatively small geographic area and the omission of some potential risk factors. Future research could address these limitations by expanding the geographical scope of the study and incorporating more dynamic models that account for additional risk factors and real-time data. This would not only enhance the robustness of the model but also its applicability to a wider array of scenarios in conflict-affected regions.

In conclusion, this study not only enhances our understanding of supplier evaluation in post-conflict reconstruction but also contributes to more effective resource utilization, transparency in decision-making, and ultimately, the speedy recovery of affected communities. By continuing to refine and adapt this approach, it holds significant promise for aiding reconstruction efforts not just in Syria, but in any region emerging from crisis.

Data Availability

The data that support the findings is provided upon reasonable request to the first author.

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Received : 07 February 2024

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Authors : Le Thu Trang Nguyen; Thi Huyen Trang Nguyen; Duc Duy Nguyen

Addresses : Department of Industrial Systems Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam ' Department of Industrial Systems Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam ' Department of Industrial Systems Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

Abstract : Supplier selection plays a vital role in supplier management, which decides the success of the supply chain. This study proposes a supplier selection framework, FAHP-TOPSIS, to overcome the challenges associated with imprecise information in the decision-making process. A list of criteria is initially identified through an extensive literature review and expert interviews. Subsequently, the FAHP technique is employed to determine the weights assigned to each criterion. Following this, the TOPSIS method is applied to rank and select the most suitable alternatives from the pool of potential suppliers. To validate the effectiveness of the proposed supplier selection framework, a practical case study is conducted within the context of electrical explosion protection and sensor companies in Vietnam. A sensitivity analysis is performed to examine the robustness of the criterion weights and ranking of suppliers. The findings consistently demonstrate that product cost and quality consistently rank as the top-priority factors in supplier selection.

Keywords : supplier selection; fuzzy analytic hierarchy process; TOPSIS; fuzzy theory; procurement.

DOI : 10.1504/IJAMS.2024.140045

International Journal of Applied Management Science, 2024 Vol.16 No.3, pp.304 - 328

Received: 26 Oct 2023 Accepted: 05 Feb 2024 Published online: 16 Jul 2024 *

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