INTRODUCTION
Reasons for choosing this topic
The fourth industrial revolution is about to begin throughout the world Besides the outstanding development of science and technology, environmental problems are the issues that need to be given top priority One of the business models criticized for its negative impact on the environment and society is fast fashion According to the United Nation (2019), Fast fashion is the second most polluting business behind the oil industry.
It contributes 10% to world emissions, generating more carbon dioxide annually - 1.2 billion tons - than the shipping and aviation sectors put together The industry's greenhouse gas emissions are anticipated to rise by more than 50% by 2030 if this rate is maintained These emissions are produced along the whole supply chain of the fast fashion sector, from the origin to the origin of the raw materials, manufacture, and processing, to shipping and in transit (Fleischmann, 2019).
Faced with concerns about the environmental situation and the increase in the amount of fashion waste Consumers are more and more interested in the green living movement and begin to worry about the origin of products Therefore, the supply chain of garment enterprises in general as well as fast fashion businesses in particular must pay attention to the "green" issue in the supply chain in their operations besides stopping at maximum cost saving, high profit An important aspect of this is the assessment and selection of green suppliers, which can help companies reduce their environmental impact and improve their sustainable performance.
Lo et al (2018) said about green purchasing is a crucial component of the growth of sustainable businesses, and it frequently has an impact on the operations and environmental protection strategies of an organization According to Kokangul and Susuz (2009) and Lee and Drake (2010), the percentage of high-tech firms that purchase raw materials and components can reach 80%, making buying techniques essential to the management of a green supply chain Choosing the suitable company as a product or case supplier requires consideration of many complex factors and is therefore considered a multi-criteria decision-making problem (Kumar, Rahman, & Chan, 2017).
Supplier selection has a direct impact on an organization's profitability and competitive position Therefore, this problem has been implemented by many researchers to build models to evaluate green suppliers in related industries such as textile industry, garment industry in the most effective way.
Utama (2021) conducts a case study of a garment company in Indonesia Research results shows that the quality criterion gives the largest weight The results of this study show that the integration of AHP and VIKOR can be used to solve green supplier selection problems Wang et al (2019) demonstrates how to choose the best sustainable suppliers in the garment industry using a hybrid approach combining FAHP and TOPSIS While, Roy et al (2020) suggests a framework to evaluate sustainable supplier selection techniques utilizing the preference ranking organization method for enrichment evaluation (PROMETHEE) and the fuzzy analytical hierarchy process (FAHP) Karami et al (2021) applying three approaches to evaluate the supplier in garment industry by integrated DEA– PCA–VIKOR In another research paper, Jia (2015) selected the optimal supplier to supply sustainable materials in fashion clothing production by using Fuzzy TOPSIS method to evaluate 12 criteria of sustainable suppliers Based on seven key factors—operational competence, product attribute, logistic warehousing, ethics, status, business competencies, and versatility – Kaushik et al (2022) selects suppliers for the online fashion retail industry. Prioritizing the criteria and choosing the most practical suppliers from a pool of potential suppliers required the application of an integrated Best Worst Method (BWM) - VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach.
In the context of the rapid development of the garment industry, which must ensure environmental issues, choosing a suitable green supplier for businesses is extremely important However, there are currently few research models on green supplier assessment in the fast-fashion industry As the best as my knowledge, there is lack of research on evaluating and selecting green suppliers by using Interval-valued neutrosophic AHP-TOPSIS in fast fashion industry.
For the above reasons, the author has conducted a research paper: "Evaluating and selecting green suppliers by integration of neutrosophic AHP - TOPSIS approach: a case of the fast-fashion industry." to conduct research on the criteria of the green suppliers in assesses the priorities of “green” issues, which can play an important role in sourcing and attempts to propose important environment variables that can be used in supplier selection.
This paper aims to contribute to the theoretical development of green supplier selection and to develop a systematic and effective framework for the selection of green suppliers in the fast fashion industry.
Research objectives
General objective: Evaluating and selecting green suppliers in the fast-fashion industry Detail objective:
- Identifying importance criteria and sub-criteria in the fast-fashion industry when choosing green suppliers.
- Evaluating criteria and sub criteria of green suppliers in the fast-fashion industry base on AHP – TOPSIS method and using the interval-valued neutrosophic number
- Raking green suppliers through the evaluation of criteria
Research object and scope
Research object: The object of the study is a green supplier in the fast-fashion industryResearch scope: Fast-fashion industry
Research methodology
In order to gather evaluation data on the relationships between the criteria, this study used the expert consultation method of data collecting The experts taking part in the interview process are individuals with extensive knowledge and experience in assessing and choosing suppliers, as well as those with expertise in the area of procuring raw materials for garment factories.
Interval-valued neutrosophic integration model AHP-TOPSIS
In this study, the Analytic Hierarchy Process method combines TOPSIS and interval-valued neutrosophic set to evaluate and select green suppliers Input data is through data collection method
The structure of the subject
The thesis consists of 5 chapters as follows:
Presentation of reasons for choosing topic, research objective, object and scope of research, methodology and structure of topic.
Outlining concepts and theories about green supplier, fast-fashion industry, basic theory of AHP-TOPSIS method and interval-valued neutrosophic number.
As well as listing previous studies from which to point out the criteria and build a research model for the article Describing in detail the research methods In particular, the method is implemented based on the combination of AHP and TOPSIS.
Analyzing and interpreting data based on interviews and using the methods outlined, including interval-valued neutrosophic AHP and TOPSIS results.
From the results of chapter 4 on the ranking of options and make a green supplier recommendation for the fast-fashion industry.
Conducting research to find out how to solve the challenges of choosing green suppliers to minimize the impact on the environment In this study, the AHP-TOPSIS method combined with the interval-valued neutrosophic number was used to evaluate and select green suppliers for the fast fashion industry through interviews with experts with specialized knowledge The 5-chapter study will evaluate and select green suppliers based on the criteria that have been synthesized.
LITERATURE REVIEW
Green supply chain management
According to Srivastava (2007), green supply chain management is a combination of supply chain management and environmental management The goal of a green supply chain is to assist businesses in maintaining a balance between their environmental and economic performance, decrease the environmental effect of their goods and services, and promote a green image (Sarkis, 2001)
According to Sarkar et al (2017), the aim of green supply chain management is to integrate logistical and financial data thoroughly, in order to promote the competitiveness of supply chain units' products or services, which leads to sustainable firm development and enhanced environmental protection Many people in the industrial sector have changed the way they produce things and encouraged their companies to become more environmentally conscious Some companies have also created environmental guidelines for the handling of waste goods and take into account the recyclable nature of the raw materials they buy from suppliers (Chen, Shyur, Shih, & Wu, 2012).
Taking everything into account, a green supply chain can be defined as an efficient supply chain that still ensures environmental friendliness and efficient use of environmentally friendly inputs and turns products by-products of use into something that can be improved or recycled in the current environment This process makes it possible for the outputs and by-products to be reused at the end of their life cycle, thus creating a sustainable supply chain.
Green supplier selection
Sustainability is an expanded overview of strategies for governing the environment from multiple perspectives but at the level of enterprises The essential component of sustainability in the overall scheme can be considered to be green supplier selection. (Yazdani, M., Chatterjee, P., Zavadskas, E K., & Zolfani, S H., 2017).
The assessment and selection of green suppliers is applied in many different industries and approaches In high-tech industry, Lee et al., (2009) applied AHP method in conjunction with a fuzzy set of numbers to distinguish between the standards for evaluating
5 ordinary suppliers and green suppliers Freeman & Chen (2015) developed the AHP-Entropy-TOPSIS framework for green supplier selection at a Chinese-based electronic machinery manufacturer Çalık (2021) practical instance using the AHP and TOPSIS methodologies in the Pythagorean fuzzy environment to choose a green supplier for an agricultural machinery and tool firm To better deal with ambiguity and vagueness while addressing the supplier selection problem while taking into account green concepts in a home appliance manufacturer, Ecer (2022) uses an extension to the analytical hierarchy process (AHP) under interval type-2 fuzzy environment (IT2FAHP) model In the automobile manufacturing industry, Yu & Hou (2016) applied modified multiplicative analytic hierarchy process (MMAHP) method to solved green supplier selection issues Sharma & Rawani (2016) applied AHP approach to discuss the green supplier selection in Indian cement industry The manufacturing industry, it is possible to use a method such as the integrated AHP and TOPSIS method (Sukmawati & Setiawan, 2022), Fuzzy Axiomatic Design (FAD) approach (Kannan, D., Govindan, K., & Rajendran, S., 2015) In the agri-food industry to solving the evaluating green supplier, Banaein et al (2018) using fuzzy set integration into TOPSIS, VIKOR, and GRA method, Tirkolaee at al. (2021) applying AHP and Fuzzy TOPSIS method, Puška & Stojanović (2022) evaluated criteria by using fuzzy SWARA and ranked alternative by using fuzzy MABAC, MARCOS, and CRADIS techniques.
Fast-fashion industry
Fast fashion is defined as the conversion of trendy design into items that are available to the general public (Sull & Turconi, 2008) In order to improve the frequency with which customers buy trendy fashions, this business seeks to get customers into stores as frequently as possible (Barnes
& Lea‐Greenwood, 2006) This is accomplished by using inexpensive clothing that sells for less money and is available more quickly than conventional attire (Bhardwaj & Fairhurst, 2010) As a result, market cycles are shortened, design skills that can combine trendy clothing raw materials and suppliers (Barnes & Lea‐Greenwood, 2006).
Numerous earlier studies have critically examined the effects of the textile and fast-fashion industries on the environment and social welfare in the literature (Choi, Chiu,
& Govindan, 2014) According to Diabat et al (2014), In order to improve environmental performance, industries, particularly those in the textile industries, are under pressure to adopt sustainable practices in their operations Observe that fast fashion is a trend in the market and that an increasing number of fast fashion businesses are more responsive to customer needs Fulton & Lee (2010) emphasize in the document that the industry "Fast fashion" has an impact on both the fashion business and customers By studying their company reports and analyzing the present sustainability condition of fast fashion firms, Turker & Altuntas (2014) found that these companies also established sustainability standards for their suppliers Shen (2014) studied the H&M instance to get knowledge about sustainability initiatives in modern business practices According to the findings mentioned above, fast fashion and sustainability are highly associated, and this fact affects the industry's selection of suppliers.
The selection and evaluation of green suppliers in the industry Fast-fashion is a multi-criteria decision-making problem with many different approaches and methods. Here are some research articles on the selection and evaluation of green suppliers in textile and garment industries:
According to Utama et al (2021), the authors used the AHP method to evaluate the weight of 8 main criteria which are 15 sub-criteria of green suppliers along with which integrates the MOORA method to rank the best green suppliers in the Indonesia textile industry Celik et al (2021) also used the combined Best worst method andTODIM approaches, which were combined under an improved fuzzy notion of interval type-2 fuzzy sets, to evaluate green suppliers in the Turkish textile industry Fuzzy analysis hierarchical process and level analysis method are used to evaluate and select green suppliers based on three main criteria and six sub criteria in textile industry (Zafar,A., Zafar, M., Sarwar, A., Raza, H., & Khan, M T , 2019) ệZBEK & Yildiz (2020) employed the interval type-2 fuzzy TOPSIS approach with the goal of choosing the best provider among the suppliers of a parent company working in the garment industry who are digitalized utilizing industry 4.0 technology.
Interval-Valued Neutrosophic Sets
A green supplier selection process is becoming more and more important due to the subjectivity, ambiguity, and uncertainty (Sun, Y., & Cai, Y., 2021) In recent years, researchers' interest in how to proceed with the best supplier selection judgements has grown Fuzzy uncertainty numbers are one of the well-known and often employed approaches to solving the issue in multi-criteria decision-making problem (Mallik, Mohanty, & Mishra, 2022).
Zadeh (1965) proposed the fuzzy set and suggested fuzzy numbers were useful for gauging subjectivity in people The truth values of variables in classical logic are either 0 or 1 The result of a statement is either true (1) or false (0) Contrarily, fuzzy logic captures the degree to which something is true Fuzzy Set has been used to provide some context, but it is unable to account for the information's level of bias and indeterminacy. Atanassov (1986) created the intuitionistic fuzzy set (IFS) to close this gap, where in addition to the degree of membership, a degree non-member is also used to solve issues intuitively The truth-membership function and the false-membership function represent an intuitionistic fuzzy set Later, interval-valued intuitionistic fuzzy sets were added to intuitionistic fuzzy sets, according to Atanassov (1989) However, intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets are only able to handle incomplete data, not the ambiguous and inconsistent data that are frequently present in belief systems. Smarandache (1998) developed neutrosophic set which is a more sophisticated variation of this strategy to manage uncertainty more effectively.
Neutrosophic logic provides a new parameter called "uncertainty" that depicts ambiguity better by carrying more information than fuzzy logic (Kahraman, Oztaysi, & Cevik Onar, 2020) Fuzzy logic assigns uncertainty to membership variables between 0 and 1 Wang et al (2011) used the technical definition of neutrosophic sets to describe the idea of a single-valued neutrosophic set Interval valued neutrosophic set (IVNS) were proposed by Wang et al (2005) We refer to it “interval" since we only take into account the subunitary interval [0, 1] and it is a subclass of the neutrosophic set.
Interval valued Intuitionstic fuzzzy set
Intuitionstic fuzzy set Fuzzy set
Figure 2 1 From crisp sets to neutrosophic sets
There are many green supplier selection studies using different variations of the interval-valued neutrosophic in the literature Abdel et al (2019) using interval neutrosophic set combine with ANP to weighting the 3 main criteria, 12 sub-criteria and TOPSIS method to ranking green supplier in An Egyptian dairy and foodstuff corporation Van et al (2018) applied Interval Neutrosophic Set QFD approach was used to the case of Transportation Parts Company Limited in northern Vietnam for the assessment and selection of green suppliers An SS framework based on several criteria and the interval-valued fuzzy neutrosophic model was provided by Yazdani et al (2021) and evaluated in the supplier assessment of an Iranian dairy industry Two effective decision-making techniques, ANP and TODIM, have been combined by Yalcin et al(2020) in an interval-neutosophic environment In a case study of a business that manufactures filters, the IVN-ANP was used to balance the factors for assessing sustainable suppliers There are many studies in the implementation of green supplier evaluation and selection, however, there is no study using interval-neutrosophic to perform green supplier assessment in the fast-fashion industry.
The Analytic Hierarchy Process (AHP)
The Analytic Hierarchy Process (AHP) is a pairwise comparison measurement theory that bases priority scales on the opinions of experts (Saaty T L., 2008) Myers and Alpert (1968) created the groundwork for the AHP approach, and Thomas L Saaty expanded and systematized it in the 1970s As of now, AHP is one of the most often utilized approaches in multi-criteria decision-making in numerous fields According to de FSM Russo et al (2015), Saaty developed this technique in an effort to enable complicated decision-making and identify priorities in a methodical manner.
Analytical Hierarchy Process (AHP) is performed according to the following steps:
Step 1: Define the issue and decide what knowledge is needed.
Step 2: Build the decision hierarchy from the top down, starting with the objective, then the intermediate levels (criteria on which the succeeding parts depend), and finally the lowest level (which is often a collection of alternatives).
Figure 2 2 Structure diagram of AHP
Step 3: Create a set of matrices for pairwise comparisons by scale of absolute numbers of
Saaty (1980) Every element at a higher level is compared to every element in the level directly beneath it.
Table 2 1 Saaty’s pairwise comparison scale
2,4,6,8 Intermediate valued between two adjacent judgments. Reciprocals When activities i compared to j is assigned one of the above numers, then activities j compared to i assigned its reciprocal
Step 4: After completing the aforementioned matrix, determine the weights for the criterion by adding up each column's values, then dividing each result by the sum.
The weighted numbers shown here, however, should not be taken as the last word; rather, it is important check the consistency of the experts' opinions.
Step 5: The value of the correlation matrix coefficient completely depends on the subjectivity of the researcher in quantifying the weights for the goals, hence it is impossible to prevent certain discrepancies in the final judgment matrix.
AHP determines a consistency ratio (CR) to compare the consistency index of the relevant matrix (the one with our judgements) to the consistency index of a random-like matrix (RI) for the purposes of consistency testing (Saaty T , 1980)
Check consistency using the equation:
Where CI is consistency index, RI is Random Consistency Index, CR is consistency rate.
The equation of consistency index (CI) is given as:
Table 2 2 The RI value corresponds to the number of factors n n 1 2 3 4 5 6 7 8 9 10
The judgment matrix is acceptable if ≤ 0.1; otherwise, the pairwise comparison matrix must be re-evaluated if > 0.1.
Below is a flowchart of the steps of the AHP method
Establish criteria Build a hierarchical structure Make a pair-wise comparisons
Final weight of each evaluation index
Figure 2 3 Flowchart of AHP method
The Technique for Order of Preference by Similarity to Ideal Solution
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision-making method introduced by Hwang and Yoon (1981).The TOPSIS principle concerns the definition of a positive ideal solution and a negative ideal solution When a choice is the furthest away from the negative ideal solution and the closest to the positive ideal solution, it is said to be the best alternative.
The TOPSIS method consist of these steps:
Decision makers construct the evaluation matrix
Determined the Weight normalized matrix
Determined the Distance of Negative ideal solution to Weight normalized matrix
Calculate the relative closeness to the ideal solution
Ranking the alternatives and chosen the best
Figure 2 4 Flowchart of TOPSIS method
Summary of related work
After a brief overview of previous research papers related to the topic "Evaluating case of the fast-fashion industry" Below is a summary of related studies regarding green supplier selection.
Table 2 3 Summary of related work in green supplier selection
Lee et al., (2009) Fuzzy AHP High-tech industry
Freeman & Chen (2015) AHP-Entropy-TOPSIS Chinese electronic machinery manufacturer Çalık (2021) Pythagorean fuzzy AHP Agricultural machinery and and TOPSIS tool company
Ecer (2022) IT2F-AHP Home appliance manufacturer
Yu & Hou (2016) MM-AHP Automobile manufacturing industry Sharma & Rawani (2016) AHP Indian cement industry
Sukmawati & Setiawan AHP and TOPSIS Manufacturing industry
Kannan et al (2015) FAD Manufacturing industry
Banaein et al (2018) Fuzzy TOPSIS, VIKOR, Agri-food industry and GRA Tirkolaee at al (2021) AHP and Fuzzy TOPSIS Agri-food industry
Puška & Stojanović Fuzzy SWARA and fuzzy Agri-food industry
CRADIS Utama et al (2021) AHP and MOORA Indonesia textile industry
Celik et al (2021) Type-2 fuzzy BWM and Turkish textile industry
Zafar (2019) Fuzzy AHP Textile industry ệZBEK & Yildiz (2020) Interval type-2 fuzzy Garment industry
Abdel et al (2019) Interval neurtrosophic An Egyptian dairy and
Van et al (2018) Interval Neutrosophic Set Transportation Parts Company
QFD Limited in northern Vietnam
Yazdani et al (2021) Interval-valued fuzzy Iranian dairy industry neutrosophic
Yalcin et al (2020) IVN-ANP and TODIM Business and manufactures filters
In the context of a shifting business environment, successfully resolving environmental issues will open up new options for organizations to pursue sustainable development objectives and new competitive prospects in present As far as I have the knowledge, there is lacks of research on evaluating green suppliers in the fast-fashion industry utilizing interval-valued neutrosophic AHP-TOPSIS therefor author developing a decision-making model based on the interval-valued number to solve the green supplier selection problem.
This section is intended to present a rough background on previous research papers on green supplier in different environments, the applications of studies in the fast fashion industry Understanding the necessity and practice of environmental issues and having few studies on fast-fashion, the authors have carried out this topic.
METHODOLOGY
Research process
Selected criteria approved by expert
Selecting the suppliers of available alternative
Forming questionnaire Interview Aggregating data
Construct the decision matrix Normalized decision matrix
Calculate the weight decision matrix
Determine positive and negative ideal solution
Determine distances from ideal solution Ranking the alternative
The proposed method
Green supplier evaluation is a challenging decision In order to evaluate the most suitable expenditures, particularly for the fast fashion business, the author has analyzed and researched earlier research articles on green suppliers in the garment industry In conjunction with experts, a summary and conclusion are developed for these criteria. With the goal of evaluating and selecting green suppliers in the fast fashion industry, there are 3 main criteria and 11 sub-criteria shown in the table 3.1.
Environment factor (C1): One of the key considerations when choosing a green supplier is the environment According to Yu et al (2019), the control of the exhaustion of both renewable and non-renewable resources as well as the production of pollution are issues that fall under the scope of the environmental aspect Three of this criterion's sub- criteria are presented in this study: Pollution control (C12), an environmental management system (C11), and green products (C13).
Economics factor (C2): Like environmental factors, economic criteria are also important criteria Economic aspects aim to maximize potential income flow while minimizing the capital required to produce that income (Gửren, 2018) This criterion consisted of 5 sub-criteria including: Quality (C21), Delivery (C22), Service (C23), Cost (C24), and Strategic alliance (C25).
Social factor (C3): Social problems are affected by social issues Sun & Cai
(2021) said about the social perspective, which is also an essential perspective that needs to be considered seriously, is overlooked while analyzing an alternative from the conventional and green views, according to some academics This criterion consisted of 3 sub-criteria: ethical issue and legal compliance (C31), vocational health and safety systems (C32), and information revelation (C33).
Table 3 1 Criteria for evaluating and selecting green suppliers
A set of systematic processes Acar et al (2016), and practices that enable a Heari et al (2019), Environmental supplier to reduce its Azadi et al (2015), environmental impacts, which Yu et al (2019)
C11 management includes the organizational system structure, planning and implementing policy for environmental protection.
Environment Controlling waste disposal Acar et al (2016)
Pollution Heari et al (2019), factor (C1) C12 properly, using harmful
Azadi et al (2015), control materials
Green packaging and product recyclability, remanufacturing which represents a large
C13 Green product quantity of products restored to a new state, is reusing the capabilities of suppliers.
Acar et al (2016), Heari et al (2019), Azadi et al (2015), Sun & Cai (2021).
Raw materials that meet Acar et al (2016),
C21 Quality company standards are not Heari et al (2019), damaged Azadi et al (2015),
Efficient in delivering the Acar et al (2016),
C22 Delivery goods at the right time Heari et al (2019),
Business must be more responsive to client requests and develop an effective stock
C23 Service management system to fulfill orders from customers under volatile market circumstances.
Yu et al (2019), Hendiani et al. (2020), Sun & Cai (2021).
Lowest possible product price Acar et al (2016), without sacrificing quality Heari et al (2019),
The establishment of a mutually advantageous and Strategic
C25 vital connection between alliance businesses and suppliers to enhance their competitiveness
Acar et al (2016), Hendiani et al. (2020), Sun & Cai (2021).
Ethical issue Suppliers must adhere to
C31 and legal ethical principles and law, compliance reputation
Abdel et al (2019), Azadi et al (2015),
Monitor the workers’ health Vocational
C32 and safety, control the health and
Social factor underage employment in
Abdel et al (2019), Azadi et al (2015),
Giving its clients and stakeholders information on Information
C33 the materials utilized, carbon revelation emissions, toxins generated during production, etc.
Abdel et al (2019), Azadi et al (2015),
The alternatives correspond to companies:
The four options are material suppliers in the fast-fashion industry These companies have high recognition, experts who have approached and have certain knowledge about these companies should have experience to evaluate.
• Inditex：Inditex is the parent company of fast fashion brands such as Zara, Pull & Bear, Massimo Dutti, Bershka, and Stradivarius
• Fast Retailing：Subsidiaries under Fast Retailing include Uniqlo, GU, Theory, PLST, PRINCESSE tamãtam, etc.
• H&M：H&M is committed to employing recycled textiles and 100% organic cotton, as well as investing in cutting-edge production techniques, in order to reduce its environmental impact.
• Patagonia：Patagonia is an American-based outdoor fashion brand that has committed to using various recycled and synthetic materials to manufacture their clothing and accessories.
The theoretical basis and previous studies related to green supplier and fast-fashion industry are the basis for the author to apply in his research model The author proposes a
20 hierarchical model with the goal of evaluating and selecting green suppliers with 3 main criteria and 11 sub-criteria:
Environment factor (C1): This main-criteria consist of 3 sub-criteria: Environmental management system (C11), Pollution control (C12), and green product (C13)
Economics factor (C2): This main-criteria consist of 5 sub-criteria: Quality (C21),
Delivery (C22), Service (C23), Cost (C24), and Strategic alliance (C25).
Social factors (C3): Consist of 3 sub-criteria: ethical issue and legal compliance (C31), vocational health and safety systems (C32), and information revelation (C33)
Goal Evaluating and selecting green supplier
C11 Environmental management system C12 Pollution control C13 Green product
C21 Quality C22 Delivery C23 Service C24 Cost C25 Strategic alliance
C31 Ethical issue and legal compliance C32 Vocational health and safety systems C33 Information revelation
Figure 3 2 Hierarchical structure of application
3.3.4.1 Preliminaries of interval-valued neutrosophic
This section provides a broad overview of neutrosophic sets and interval-valued neutrosophic numbers A neutrosophic set defined in ̃ , it is represented by T, I, F are truth- membership function, indeterminacy-membership function, and falsity membership function respectively of neutrosophic set.
Definition 1: Smarandache (1998) let X be a universe A neutrosophic set ̃ in X is illustrated a truth-member function ( ) , indeterminacy-membership function ( ) , falsity membership function ( )
( ), ( ), ( ) are the real standard elements of [0,1], a neutrosophic set can be provided in Eq 1: ̃ + [ )}
The sum of ( ), ( ), and ( ) is unrestricted, so 0− ≤ ( ) + ( ) + ( )≤3 +
Definition 2: Liu et al (2016) let E be a space of point An interval-valued neutrosophic set ̃ in E is illustrated a truth-member function ( ) , indeterminacy-membership function ( ), falsity membership function ( )
An interval-valued neutrosophic set can defined as: ̃
Definition 3: Bolturk & Kahraman (2018) proposes the deneutrosophic of interval- valued neutrosophic, the equation is given as below:
Definition 4: Karasan & Kahraman (2018) and Zhang et al (2014) let 1
1 1 2 2 2 2 interval-valued neutrosophic numbers Their mathematical operations are provided as Equation (7)-(14) ̃ ], ] (7)
1 = 2 if and only if 1 ⊆ 2 and 2 ⊆ 1 ̃
Definition 5: Ye & Du (2019) defined between two interval-valued neutrosophic numbers ̃ 1 and ̃ 2 , there is a Euclidian distance. ̃ ̃
Definition 6: Ye & Du (2019) defined between two interval-valued neutrosophic numbers ̃ 1 and ̃ 2 , there is a Hamming distance.
3.3.4.2 Steps in interval-valued neutrosophic AHP
Step 1: Create a hierarchy of main criteria and sub-criteria for the problem The importance of the criteria and the level of evaluation of the suppliers is expressed by the linguistic variables Linguistic variables are represented as interval-valued neutrosophic.
Table 3 3 IVN number scales of linguistic variables.
Step 2: Using the interval-valued neutrosophic number to contruct the pair-wise comparison matrix According to Bolturk & Kahraman (2018), to check the consistency of interval- valued neutrosophic set, using the deneutrosophic equation (6) to scrip interval-neutrosophic number It can be claimed that the neutrosophic pairwise matrix is also
24 consistent if the deneutrosophicated pairwise comparison matrix is Equation (17) show the interval-valued neutrosophic pair-wise comparison matrix.
Step 3: To aggregated pair-wise comparison matrix, the author uses the equation proposed by Zhang et al (2014) is the interval-valued neutrosophic weighted arithmetic averaging (IVNWAA), assume that ( = 1, 2, , ) is the collection of interval-valued neutrosophic numbers and ( = 1,
2, , ) is weight of decision maker, ∑ =1 = 1.
Step 4: According to Bolturk & Kahraman (2018), to normalized the pairwise comparison matrix equation is given as follow: ̃
Step 5: According to Bolturk & Kahraman (2018), to calculate the importance weight of interval-valued neutrosophic number by using the arithmetic mean of each row: ̃ ∑ ∑ ∑ ∑ ∑ ∑ =1 =1 =1 =1 =1 =1
Step 6: For each sub-criteria, the aforementioned processes are repeated, and the sub- criteria's neutrosophic weights are obtained.
Step 7: To calculate the global-weight, multiply the main criteria weights by the local- weight
3.3.4 Integration of Interval-valued neutrosophic AHP-TOPSIS
Step 1: The evaluation matrix is constructed by decision makers including m alternative and p criterion Each alternative's intersection with a criterion is represented by x.
Table 3 4 Scale for IVN values for alternative evaluation
Step 2: To obtain the normalized evaluation matrix, use the equation is given as
Step 3: Structure the weighted matrix as follows by multiplying the weights of each criterion determined by interval-valued neutrosophic AHP ( ̃ ) by the normalized de cision matrix ( ⃛
Step 4: The interval valued neutrosophic positive ideal solution (IVNPIS) ̇ and the
+ interval valued neutrosophic negative ideal solution (IVNNIS) ̇
Step 5: Calculate distance from Positive ideal solution (PIS) and Negative ideal solution
(NIS) to weight normalized matrix by using the Euclidean distance equation.
Step 6: Compute revised closeness for each alternative (ℰ ) by using Equation
Step 7: Ranking the alternatives based on the revised closeness.
From the theoretical basis and previous studies, the author proposed the hierarchical model including 3 main criteria environment factor (C1), economic factor(C2), and social factor (C3) with 11 sub-criteria to evaluate 4 green suppliers have been discussed by experts In addition, this chapter provides preliminary formulas and steps of the Interval-valued neutrosophic AHP-TOPSIS method.
RESULT
Data description
The study selected 6 people with specialized knowledge of supply chain and knowledge of fast-fashion industry Interview from the perspective of the company's raw material purchaser Using a prepared questionnaire, the direct interview approach is performed, and the responses are used as data for the research article The application of the face-to-face interview method can guide and answer questions and answers in order to limit errors in the interview process Post-interview data will be analyzed by applying interval-valued neutrosophic AHP-TOPSIS method.
Table 4 1 Group of experts participating in the interview
Expert Gender Work experience Job position
1 Female More than 6 years supply chain and Merchandizer in production planning at garment company Phong Phu Corp.
2 Female 5 years of experience in the position of raw Material planner in material purchasing at garment company Phong Phu Corp.
3 Female 3 years of experience in support buyers, Quality control in work together with merchandiser to handle Phong Phu Corp. quality goods to suit the needs of buyers.
4 Male 4 years of experience in manages the Material planner in sourcing, procurement of materials and Asmara Ltd. resources for the production
5 Female 6 years of experience in ensure the quality Quality control in product, audit the shipment before delivery Asmara Ltd. to the customer in garment company
6 Male 8 years of experience in position material Merchandiser in purchasing and sources for production Asmara Ltd.
Data analytic result
Step 1: A hierarchical framework of criteria and sub-criteria is used to design the problem as given in Figure 3 3.
Step 2: By utilizing the linguistic variables listed in Table 3 5, and expert evaluations, pairwise comparison matrices are created Equation (7) is used to determine the symmetrical equivalents of linguistic evaluations in paired comparison matrices.
Step 3: Then use equation (18) to aggregated the opinions of the experts The results of the table aggregating the opinions of the experts are presented in the Table 4.2
Table 4 2 Aggregated pairwise comparison matrix for main criteria
Step 4: Perform a pairwise comparison matrix normalization using the Equation (19)
The normalized pairwise comparison matrix for main criteria is shown in table 4.3
Table 4 3 Pairwise comparison matrix normalization for main criteria
Step 5: To calculate the interval-valued neutrosophic importance weights using Equation
(20) The result is given in table 4.4
Table 4 4 Importance weights for main criteria
Step 6: Repeat the above steps for sub-criteria Pair-wise comparison matrix, normalization anf importance weights of sub-criteria are shown in tables
Table 4 5 Aggregated pairwise comparison matrix for sub-criteria (C11-C13)
Source: Author Table 4 6 Pairwise comparison matrix normalization for sub-criteria (C11-C13)
Table 4 7 Aggregated pairwise comparison matrix for sub-criteria (C21-C25)
Table 4 8 Pairwise comparison matrix normalization for sub-criteria (C21-C25)
Table 4 9 Aggregated pairwise comparison matrix for sub-criteria (C31-C33)
Table 4 10 Pairwise comparison matrix normalization for sub-criteria (C31-C33)
Table 4 11 Importance weight for sub-criteria
Step 7: Calculate the global weight of the criteria by multiplying the weight of each main criterion by the corresponding local weights Using Equation (6) to deneutrosophic the global weight.
Based on Table 4.12 and the weighted of 3 main criteria is Environment factor (C1),Economics factor (C2), and social factor (C3) with 11 criteria: it can be seen that pollution control (C12) is the highest criterion with 13.16%, followed by green product (C13),Vocational health and safety systems (C32) with 12.98%, 12.87%, respectively In addition,low-weight criteria such as service (C23) with 4.65%, strategic alliance (C25) with only5.3% It can be said that the criterion with the highest volume has a higher importance.
Table 4 12 Global weight for each criteria.
Main criteria Sub- Local weight Global weight Deneutrosophic criteria
In this study, using TOPSIS for the purpose of ranking green suppliers in the Fast-fashion industry Review of green supplier evaluation criteria based on related works and expert opinion.
Step 1: Evaluators evaluate suppliers according to criteria using the linguistics term in Table 3 6 Using equation (18) to aggregate results from 6 experts and the results are presented in table (4.13 - 4.14)
Table 4 13 Aggregate the decision matrix for the criteria (C11 - C23)
Table 4 14 Aggregate the decision matrix for the criteria (C24 - C33)
Step 2: Using equation (21) to interval-valued neutrosophic normalize the evaluation matrix of step 1 The normalized decision matrix shown as table (4.15 – 4.16)
Table 4 15 IVN normalized decision matrix for the criteria (C11 - C23)
Table 4 16 IVN normalized decision matrix for the criteria (C24 - C33)
Step 3: To weighting the normalize decision matrix using global weight calculated in Interval-valued neutrosophic AHP in table (4.12) and equation (22) The weight normalize table is given in table (4.17 – 4.18)
Table 4 17 IVN weight normalized decision matrix for the criteria (C11 - C23)
Source: Author Table 4 18 IVN weight normalized decision matrix for the criteria (C24 - C33)
Step 4: Determining the IVN-NIS and IVN- PIS using equation (23-24)
Table 4 19 IVN Positive ideal solution and IVN Negative ideal solution
Step 5: Euclidean distance is obtained to find distance IVN-NIS to weight normalized matrix and IVN-PIS to weight normalized matrix show as table 4.20
Table 4 20 Euclidean distance from IVN-PIS and IVN-NIS
Step 6 - 7: Computing revised closeness for each green suppliers (ℰ ) by using Equation (25) Four green suppliers in the fast-fashion business are ranked according to their revised proximity (scores) for each alternative in Table 4.21.
Separation measure and the revised closeness for each supplier
Figure 4 1 Separation measure and the revised closeness for each supplier
As you can see in table 4.21, green suppliers with the highest star rating are S2 (Fast Retailing) with a score of 0.540, green suppliers are followed by S1 (Inditex), S4 (Patagonia), and low rank the most is S3 (H&M) with scores of 0.498, 0.467, 0.390, respectively H&M, a fashion brand from Sweden, one of the firms that have come under fire for increasing the quantity of clothing waste internationally Polyester fabric, a waste that decomposes slowly in the environment and poses a major threat to all living things, is a material that H&M frequently employs H&M recently introduced eco-friendly fashion collection However, it is still not possible to entirely ban clothes that are bad for the environment.
As can be shown, employing the AHP-TOPSIS technique to evaluate green suppliers offers decision-makers the chance to perform a speedy evaluation Additionally, it reveals the selection criteria used when important criteria are set It offers a framework for choosing green suppliers for the fast fashion industry, which is thought to be the second most polluting sector, by assessing green suppliers in accordance with their criteria. Offers a systematic approach to assess green suppliers for sectors including the apparel and textile industries, among other
Sensitivity analysis offers data on the ranking's consistency It is advised to carefully check the weights if the ranking is very sensitive to even minor changes in the criteria (Chang, Wu, Lin, & Chen, 2007).
To assess the efficiency of the suggested technique given the variations in distance measurements, a sensitivity analysis is conducted For this research paper to perform sensitivity analysis, the author changed the distance calculation method in step 5 of Interval-valued neutrosophic TOPSIS by changing from Euclidean distance to Hamming distance Hamming distance is calculated by the Equation (16), the result of distance from IVN-NIS and IVN-PIS to each green supplier The green suppliers are then ranked based on the final scores after the Hamming distances are used to compute each district's final score is shown as table 4.22.
Table 4 22 Hamming distance from IVN-PIS and IVN-NIS
After calculating each green supplier's final score using Hamming distances, the green suppliers are ordered based on the results Table 4.23 show the final score by using Hamming distance equation.
Table 4 23 Risk ranking of the supplier according to sensitivity analysis.
According to table 4.23, when using Hamming distance equation to evaluate the green supplier, the results are as follows: S2 (Fast Retailing) with a score of 0.574, green suppliers are followed by S1 (Inditex), S4 (Patagonia), and low rank the most is S3 (H&M) with scores of 0.527, 0.472, 0.343, respectively According to Figure 4.2, the use of different distance measures did not change the results with only a small difference.
By changing the method from Euclidean distance to using Hamming distance to check the sensitivity of information, there was no change in raking green suppliers.
This chapter gives us the results in the process of selecting a green supplier for the Fast-fashion industry The results show that the best green supplier is S2 (Fast Retailing), the ranking results are then in turn are S1 (Inditex), S4 (Patagonia), and low rank the most is S3 (H&M) In addition, the author performs sensitivity analysis to check the sensitivity of the parameter.
CONCLUSION
Conclusion
Although the fashion industry is notorious for its harmful effects on the environment, businesses are now moving in the direction of sustainability The study emphasizes the value of green suppliers since they are crucial to accomplishing sustainability objectives Environmental, economic, and societal considerations have emerged as extremely relevant and significant elements in supply chain management challenges for management choices and strategy execution.
This study emphasizes the significance of selecting environmentally friendly suppliers to lessen the industry's environmental effect Businesses may lessen their influence on the environment by choosing suppliers who employ environmentally friendly products and procedures The objective AHP-TOPSIS technique offers a means of evaluating Such fact-based rather than opinion-based evaluations can be utilized to create well-informed judgements.
The TOPSIS approach is used in this article to analyze and rank the providers in accordance with the given criteria after evaluating the relationship between the criteria to determine the weight of the criterion In the interval-valued neutrosophic set, new extensions of the AHP and TOPSIS methods are also introduced to take parameter uncertainty into account, lessen ambiguity in real-world data and information, and improve the accuracy and precision of results The fast-fashion industry's choice of a green supplier is addressed by the suggested approach.
Through the process of evaluating the criteria from the opinions of experts, from the interview data and performing data analysis by the AHP - TOPSIS method in the interval- valued neutrosophic environment, the result is a good green supplier especially Fast- retailing, Inditex ranked second, Patagonia ranked third and H&M in the last position In addition, the author also performed sensitivity analysis to check the data sensitivity.
Implication
It first emphasizes how crucial sustainability is to the fast fashion sector Because of the fashion industry's considerable environmental effect, businesses are starting to understand the importance of being more sustainable.
Second, the utilization of innovative technologies demonstrates how businesses are using technology to address challenging sustainability concerns One such example is the AHP-TOPSIS neutral method Fuzzy logic and decision-making processes, which are uncommon in conventional supplier assessment methodologies, are applied in this approach.
The study concludes by highlighting the possibility for other uses of the impartial AHP-TOPSIS approach Although this methodology was created to assess green suppliers in the fast fashion sector, it can be tailored to meet the unique requirements of other sectors that face sustainability-related challenges the same sustainability The method's adaptability makes it a useful evaluation technique that may be used in a variety of settings and industries.
The topic "Evaluating and selecting green suppliers by integrating neutrosophic AHP approach - TOPSIS: a case of the fast fashion industry" contains a number of significant implications It stresses the value of sustainability, impartial evaluation techniques, and cutting-edge technology, as well as the possibility for further uses of the unbiased AHP-TOPSIS methodology Businesses across a range of industries, not only the fast fashion sector, may take notes from this research and take action to become more sustainable.
The article analyses the advantages of choosing environmentally friendly suppliers and emphasizes the significance of sustainability in the fast fashion sector Based on predetermined criteria, the providers are analyzed and ranked using the AHP-TOPSIS approach The study's conclusion emphasizes the method's application to different industries dealing with sustainability-related issues The essay gives suggestions on how companies can use this model to choose the right green supplier for their company.
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Alternative evaluation of experts for sub-criteria