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Resources, Conservation & Recycling 141 (2019) 347–361
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Decision making on supplier selection based on social, ethical, and environmental criteria: A study in the textile industry
T
Patricia Guarnieria, , Flavio Trojanb ⁎
a
University of Brasilia, Faculty of Economy, Business, Accounting and Public Management. Department of Business, Postgraduation program in Business and Postgradutation program in Agribusiness, Brazil b Federal Technological University of Parana, Production Engineering Postgraduation Program, Brazil
ARTICLE INFO
ABSTRACT
Keywords: Decision making Environmental supply issues Ethical supply issues Social supply issues Supplier selection Sustainable supply chain management
The main objective of this paper is to balance social, environmental and economic criteria, alongside related ethical issues, in the supplier selection process when outsourcing activities in the textile industry. We propose a multi-criteria model to support supplier selection process, whereby suppliers are allocated to classes based on sustainability, integrating the opinions of customers and managers. The model has three phases: i) criteria definition, in which the Copeland method is used to aggregate criteria reported in the literature for a group of decision makers (customers and expert managers); ii) elicitation of the perceptions of decision-makers, about criteria and the definition of weights for these criteria using the AHP method, and iii) multi-criteria classification of suppliers performed using the ELECTRE-TRI method. A numerical application was performed in the textile sector in order to test results. The main results show that suppliers can be classified, balancing social, environmental and economic criteria and related ethical issues, considering opinions from customers and experts. The theoretical implications can benefit researchers who can apply the model in future research into economic, environmental and social interactions. Managers can use the structure presented in this model to improve supplier selection.
1. Introduction Social, environmental and ethical issues should be considered alongside economic concerns when looking at supplier selection, to incorporate customers’ and decision makers’ preferences. In addition, sustainable practices related to purchasing and supply management have been discussed in recent decades (Giunipero et al., 2012; Johnsen et al., 2014; Ghadimi et al., 2019). Corporations should integrate social, environmental, ethical, human rights and consumer concerns as a strategy of cooperation among their stakeholders (Niinimäki, 2015). Although its importance, a limited amount of empirical research in environmental and social responsibility issues was pointed out in a study related to future of purchasing and supply management (Zheng et al., 2007). Ehrgott et al. (2011) studied social standards in selecting suppliers in emerging economies. Tate et al. (2012) studied supplier involvement and supplier impact on environmental purchasing at the operating level. Giunipero et al. (2012) identified the drivers of and barriers to purchasing and supply management related to sustainability. Quarshie et al. (2016) examined and contrasted existing research and knowledge
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creation, focusing on sustainability and social responsibility issues in supply chains. Kim et al. (2016), performed a systematic literature review on ethical sourcing and identified some gaps and research directions. They found that studies on ethical sourcing are dominated by case studies or survey methods. The authors emphasize the need to employ other methods in this research field. International reports have reinforced the need to consider social, ethical and environmental issues in supply chain management. World Vision (2016) reported a new law to oblige companies to report annually on their measures to ensure that child labor is not used to make products for the Canadian marketplace. The United Kingdom and California have similar legislation. The United States Congress, according Larson et al. (2017), reviewed a proposed federal law with the same purpose. In the textile industry, the vast majority of fashion brands do not have their own manufacturing facilities. In the textile sector, many companies outsource their suppliers to developing countries such as, India, Africa, China and Brazil, to lower costs and avoid restrictive legislation on the environment and labor relations. Niinimäki (2015) states that about 80% of clothing exports are shipped from undeveloped countries to developed economies.
Corresponding author. E-mail address: [email protected] (P. Guarnieri).
https://doi.org/10.1016/j.resconrec.2018.10.023 Received 9 April 2018; Received in revised form 17 October 2018; Accepted 18 October 2018 0921-3449/ © 2018 Elsevier B.V. All rights reserved.
Resources, Conservation & Recycling 141 (2019) 347–361
P. Guarnieri, F. Trojan
Outsourcing production in global value chains for textiles, from developed to developing countries, emerged in the 1950s and 1960s, and increased subsequently to become the dominant form of industrial organization in the clothing sector (Palpacuer, 2005). Loo (2002) classifies this relation as the “cream” and “cake” layers. In the “cake”, the labor force suffers from low job mobility and low-paid blue-collar work. In the “cream” industrialists and governments enjoy high profits and export values. The management of textile supply chains is typically long and complex (Fashion Revolution CIC, 2016). This complexity is due to some fashion brands work with thousands of factories located in different parts of the world. These factories cut, sew and assemble garments. There are also facilities down the chain that dye, weave and finish materials and farms that grow fibers (Fashion Revolution CIC, 2016). Low paid, blue collar workers whose jobs are fragmented and who are mainly female, semi-skilled workers, are usual in the textile industry (Loo, 2002). Scandals related to large companies that outsource their production to suppliers in developing countries, abusing human rights, and damaging the environment and society in general have been reported in the media. Corporate images of companies have been damaged, because the non-ethical practices of suppliers tainted the entire supply chain. Some companies do not really know where their clothes are made. The vast majority of fashion brands do not own their manufacturing facilities, making it difficult to monitor or control working conditions throughout the supply chain. The orders are placed with one supplier, who subcontracts the work to other factories (Fashion Revolution CIC, 2016). Transparency is a great challenge, as people working in the supply chain become invisible. If the producer wants to address sustainability issues in the supply chain, subcontractor selection is an essential issue. Monitoring sustainability and working conditions in supply chains, mainly in emerging economies, is a huge challenge (Mamic, 2005; Ehrgott et al., 2011; Fashion Revolution CIC, 2016). Delmonico et al. (2018) found that organisational culture stands out as a particular barrier to sustainable procurement in public institutions in emerging economies. Wilson (2015) studied the future of the Scottish textiles sector, considering a circular economy model. Niinimäki (2015) provided an overview of ethical foundations in the fashion field and argued for the consideration of ethical, environmental, social and economic criteria, rather than economic criteria alone. Govindan et al. (2015) conducted a literature review on green supplier selection from a multi-criteria perspective and found the most widely used criterion for green supplier evaluation is the environmental management system. Khan and Islam (2015) identified environmental sustainability in the materials and manufacturing stages of branded Tshirts made in Bangladesh. Deschamps et al. (2016) presented an analysis of consumer attitudes towards ethical consumerism in relation to their socioeconomic class levels, public consciousness and willingness to embrace ethical consumption of textile products in Mexico. Calamari and Hyllegard (2016) explored designers’ perspectives on decisions made during the design process of textiles and whether impacted human health and the environment. This literature indicates recent studies focused on the incorporation of sustainable dimensions and ethical issues related to supply chain strategies and explains the relevance of this research topic. However, there are no studies specifically related to balancing ethical, social, environmental and economic criteria in supplier selection for outsourcing activities in the textile industry. The literature also shows that although there are several papers dealing with environmental criteria, the social and ethical aspects related to supplier selection are rarely approached and there is still a lack of studies presenting structured frameworks or models involving these aspects at the same time to improve the decision-making process. The main contribution of this paper is to propose a model to evaluate suppliers using the social and environmental dimensions of sustainability, as well related ethical practices, based on the Triple Bottom Line (TBL) concept, also considering the indicators proposed in the
mainstream literature. The approach follows a multi-criteria modeling process, which comprises: i) identification of objectives and criteria; ii) definition of weights for criteria; iii) evaluation of suppliers on the criteria; iv) sorting suppliers into classes of commitment. This model is important because it relates supplier selection to social, environmental and ethical issues, which are increasingly important to consumers. No paper (as presented in Table 2) deals with the methods used in our research, which demonstrates the novelty of this proposed approach. 2. Literature review 2.1. Multi-criteria selection of suppliers: traditional and sustainable perspectives The objective of supplier selection is to identify suppliers with the highest potential for meeting a manufacturer’s needs consistently and with an acceptable overall performance. Selecting suppliers from a large number of possible suppliers with various levels of capabilities and potential is a difficult task, mainly because it involves qualitative and quantitative criteria (Araz and Ozkarahan, 2007). This problem fits the MCDA approach (De Almeida, 2007). Several studies have examined the supplier selection problem (SSP) from a multi-criteria perspective (De Boer et al., 2001; Ku et al., 2010; Liao and Kao, 2010; De Almeida, 2007; Ha and Krishnan, 2008; Pi and Low, 2006). Although these are important studies, most of them used traditional criteria to select suppliers, but some considered sustainability in the supplier selection process. The consideration of sustainability increases in response to legislative demands and awareness among the population related to environmental protection. Guarnieri (2014), in an extensive literature review on multi-criteria methods for supplier selection, identified traditional the criteria to select suppliers: Cost, Quality, Delivery, Financial Stability, Technological Capacity, Ease of Communication, Flexibility, Management /Organization, Production Capacity / Facilities, Support, Compatible Cultures, Geographical Location, Organizational and Technical Capacity and R& D Mutual Confidence. The criteria were presented in order of appearance in the literature. In that research, sustainability related criteria were not listed by the author. The search terms did not include the keywords “sustainability, social, environmental and ethical criteria/ aspects”. We extend that research by adding to the keywords “supplier selection”, “vendor selection”, “multi-criteria or multiple criteria”, “supplier selection” and “multi-criteria/multiple criteria”, the specific keywords “sustainability” and/or “social or environmental or ethical criteria/aspects” in the Google Scholar and Science Direct databases, over the period of publication from 2000 to 2018. We found seven articles that report the use of sustainability criteria for supplier selection considering a multi-criteria approach. The main criteria and main methods used are reported in Tables 1 and 2, which served as a theoretical basis for the proposed model. Table 3 compiles the main social and environmental criteria and ethical issues in the literature. Supplier selection considering the balance between social, environmental, and ethical issues is a new topic, and contrasts with traditional supplier selection focused mainly on economic issues (Esfahbodi et al., 2016; Zhou and Xu, 2018; Vahidi et al. 2018). Choy et al. (2005) argued that evaluating and selecting business suppliers is strategic in nature. These tasks involve a variety of quantitative and qualitative criteria which need to be aggregated. Often these criteria conflict with each other (De Almeida, 2007). In this case, an approach which can aggregate the criteria and their evaluation is important. Husser et al. (2014) examined the decision-making processes among French buyers, when confronted with a dilemma involving an ethical choice. In that research, the participants answered a questionnaire related to five scenarios that described typical situations that arise in purchasing involving ethical issues. Table 2 shows some recent studies involving multi-criteria approaches and the methods used. 348
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Table 1 Social, ethical and environmental criteria in Supplier Selection Problem (SSP).
1 2
3 4 5 6 7
Suggested Criteria
Authors
Compatible Cultures Environment; Diversity; Safety; Human rights; Philanthropy; Smaller Suppliers. Energy consumption environmental costs; Chemical waste generation; Solid waste generation; Greenhouse gas emissions; Water consumption Management Skills: environmental partners; Training; “Green Image”; Consumers’ Loyalty; Green Market Share; Stakeholder Relations Environmental Projects; Recycling; reusing; destination Environmental management; Environmental Planning; Environmental certification; Environmental policies; Environmental expertise; Lean technologies available; Use of environmentally friendly materials; Pollution reduction capacity. Compliance with legislation; Accident prevention program; Health and safety; Continuous improvement Public disclosure of environmental records; Environmental assessment of the 2nd chain link suppliers; Management of hazardous waste; Waste and toxic pollutants management; Use of hazardous materials Certification; Reverse logistic; Use of environmentally friendly packaging; Use of ozone layer aggravating substances; Managing the emission of dangerous gases Environment Management, Social Responsibility, Green Products, Technology Standards and Health and Safety Management ISO 14,001 certification, Technology level, Capability of using green technologies, Pollution production, Energy consumption, Amount of solid wastes, Usage of toxic substances, Worker safety and labor health, Occupational injury and illness Pollution production, Pollution control, Qualitative, Energy consumption, Ecologic design, Labor relations, Governmental relations, Community welfare investment.
Brammer and Walker (2011) Humphreys et al. (2003)
Yuanqiao (2008) Handfield et al. (2002) Hussain and Al-Aomar (2017) Vahidi et al. (2017) Zhou and Xu (2018).
Table 2 Supplier Selection studies by multi-criteria approach. Year / Title / Description
Used Methods
Authors
2018 - An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation - It proposes a criteria system for evaluating sustainable suppliers from three aspects and six dimensions, introducing an integrated evaluation model with hybrid information aggregation. The model is based on Triple Bottom Line theory, which can serve as a framework of sustainable supplier selection for manufacturing. It integrated three methods (the same considered by Kuo et al., 2015) to examine the interrelationships between the indicators; to calculate the criteria weights; to aggregate hybrid data type to describe quantitative and, finally, to rank and determine the optimal sustainable supplier. 2018 - Green Suppliers Performance Evaluation in Belt and Road Using Fuzzy Weighted Average with Social Media Information: It proposed a decision model used the membership grade of the criteria and sub-criteria and its relative weights, which consider the volume of social media, to establish an analysis matrix of green supplier selection. 2017 - Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company: In this study, the supplier selection was performed in a construction company, based on a new approach supported by a multi-criteria model. 2017 - Sustainable supplier selection and order allocation under operational and disruption risks – It proposes a bi-objective two-stage mixed possibilistic-stochastic programming model to deal with sustainable supplier selection and order allocation problem under operational and disruption risks. A hybrid systematic framework is suggested for choosing the most influential sustainability criteria. Numerical applications and a case study were carried out in order to test the model. 2017 – A model for assessing the impact of sustainable supplier selection on the performance of service supply 2017 - Chains – It develops and validates a model for assessing the impact of supplier sustainability on the performance of a service supply chain. Supplier sustainability is assessed based on five main criteria derived from literature and validated by industry experts. The impact of these sustainability aspects on the performance of service supply chains is assessed based on two sets of economic and competitiveness measures. 2017 - Green Supplier Evaluation and Selection Using Cloud Model Theory and the QUALIFLEX Method: It developed an integrated MCDM model based on the cloud model and QUALIFLEX (qualitative flexible multiple criteria method) approach to assess the green performance of companies under economic and environmental criteria. 2017 - Green Supplier Evaluation and Selection in Apparel Manufacturing Using a Fuzzy Multi-Criteria Decision-Making Approach: It addressed the green supplier evaluation and selection problem in global apparel manufacturing by developing a methodological framework for green supplier evaluation and selection based on the triple bottom line principle and a fuzzy multi-criteria decision-making (MCDM) model. 2017 - Using Fuzzy DEA for Green Suppliers Selection Considering Carbon Footprints: It aimed to establish a decision-making process for buyers with sustainability in mind. A fuzzy data envelopment analysis (FDEA) model was developed to select the most suitable supplier.
DEMATEL - Decision Making Trial and Evaluation Laboratory); ANP (Analytic Network Process) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje)
Zhou and Xu (2018)
FWA (Fuzzy Weight Average)
Lin et al. (2017)
DEMATEL (Decision Making Trial and Evaluation Laboratory), EDAS (Evaluation based on Distance from Average Solution)
Stević et al. (2017)
SWOT (Strengths, Weaknesses, Opportunities and Threats) and QFD (Quality Function Deployment) methods.
Vahidi et al. (2017)
SEM techniques (set of mathematical models and Statistical methods): Confirmatory Factor Analysis (CFA), path analysis, and other types of analytical methods (not informed in the paper)
Hussain and AlAomar (2017)
Cloud Model Theory and QUALIFLEX (Qualitative Flexible Multiple Criteria method)
Wang et al. (2017)
MCDM (Multiple Criteria Decision Methods) model
Guo et al. (2017)
FDEA (Fuzzy Data Envelopment Analysis)
Yu and Su (2017)
(continued on next page)
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Table 2 (continued) Year / Title / Description
Used Methods
Authors
2017 - Integrated Supplier Selection Framework in a Resilient Construction Supply Chain: An Approach via Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA): It proposed a framework worked by integrating building information modeling (BIM) and a geographic information system (GIS) in a RCSC. BIM and GIS together provide highly transparent construction material information, enhanced supply chain status visualization, and workable access information for supplier selection. 2015 - Developing a Green Supplier Selection Model by Using the DANP with VIKOR: It proposed a novel hybrid multiple-criteria decision-making (MCDM) method to evaluate green suppliers in an electronics company. Seventeen criteria in two dimensions concerning environmental and management systems were identified under the Code of Conduct of the Electronic Industry Citizenship Coalition (EICC). 2015 - Supplier Selection Problems in Fashion Business Operations with Sustainability Considerations: It had framed twelve criteria from the economic, environmental and social perspectives for evaluating suppliers.
AHP (Analytic Hierarchy Process and GRA (Grey Relational Analysis)
Wang et al. (2017)
DEMATEL (Decision Making Trial and Evaluation Laboratory); ANP (Analytic Network Process) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje)
Kuo et al. (2015)
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Jia et al. (2015)
In decisions involving several dimensions, the Multi-Criteria Decision Aid (MCDA) is suitable (De Almeida, 2007). The MCDA approach can model unstructured problems and support the decision making process in a systematic way. Several authors have explored the multi-criteria approach related to sustainability aspects. Table 2 presents several individual or combined methods found in literature that can be used for the selection of suppliers based on sustainability aspects and ethical issues. No two papers use the same structure to consider the opinions of consumers and managers, or a procedure to identify social, ethical and environmental criteria. To fill this gap, the methodology of our proposal is demonstrated in Sections 3 and 4. The gap identified in Table 3 drives the present study to structure a model in which the relevant criteria are considered through an integrative process addressed to customers and managers. Wong et al. (2015) carried out a systematic literature review of 142 academic articles and found that stakeholder and resource orchestration theories can be used to develop an integrative approach of environmental management in supply chains. Brammer and Walker (2011); Yuanqiao (2008) and Huang and Keskar (2007) provided a mechanism to integrate in a comprehensive set of metrics arranged hierarchically, taking into account product type, supplier type, and original equipment manufacturers’ supplier integration level. Humphreys et al. (2003) and Handfield et al. (2002) identified socioenvironmental criteria in order to select suppliers. Zhou and Xu (2018) proposed criteria for evaluating sustainable suppliers, using three aspects and six dimensions in an integrated evaluation model with hybrid information aggregation, based on the Triple Bottom Line. Hussain and Al-Aomar (2017) developed and tested a model for assessing the impact of supplier sustainability on the performance of a service supply chain. When organizations manage their supply chain, they are responsible for the environmental and social performance of their suppliers and partners. According to Seuring and Müller (2008), many pressures related to social responsibility have derived from a number of internal and external sources including employees and management, socially aware organizations, communities, governments, and non-governmental organizations. Mamic (2005); Preuss (2009) and (Scheiber, 2013) state that some companies have implemented Codes of Conduct for their suppliers. Mamic (2005) and Preuss (2009) argue that these programs must ensure compliance and monitor performance in relation to the standards in the code. Although the social and ethical dimensions of sustainability are important, they are still considered emerging topics in academic works (Sarkis et al., 2010). Building on earlier studies, the present paper develops a model to support decision-making related to supplier classification in the textile
industry, considering social, ethical and environmental issues, and procedures that involve the identification of consumers’ and managers’ preferences. By using this model, it is possible for the decision makers to find the most appropriate suppliers for their business goals, especially suppliers committed to environmental and social aspects, considering the related ethical issues. 3. Methods and techniques From the literature review described in Section 2, aspects of criteria weight, criteria structure, interrelations, definition of classes and applied methods, were developed into an idealized model structure, as illustrated in Fig. 1. The proposed model is structured in three phases. In Phase 1, criteria are selected that meet the needs of customers and managers (decision-makers). The criteria were chosen from the literature review and were defined and filtered taking to account the preferences of decision-makers. The model incorporates criteria following the characteristics of the consulted group of decision-makers. In other words, researchers took the general set of criteria from the literature, but each application adapts the criteria to fit the specific context. In the numerical application, consumers were surveyed to find their preferences in the moment of purchasing of textile goods. A sample of 250 customers/final consumers of the textile sector in Brazil responded to a questionnaire by a structured media communication. The preferences were measured on a Likert Scale, ranging from 0 – Totally Disagree to 5 – Totally Agree. Many companies plan their strategies based on the preferences of consumers, and they should integrate social, environmental, ethical, human rights and consumer concerns in their strategy in cooperation with stakeholders in their supply chains (Niinimäki, 2015). The main advantage of this procedure is the ability to find the preferences of specific consumers in different scenarios. The results were submitted to another procedure conducted with experts in marketing, finance, production and engineering, in order to confirm the results obtained with customers. The results were fed into an aggregation process supported by the Copeland method, as explained in the numerical application. In Phase 2, the selected criteria were used to sort suppliers. In this phase the criteria weights were calculated based on the original data collected. The calculation of weights used the AHP (Analytic Hierarchy Process) method, as explained in the numerical application. In Phase 3, suppliers were sorted into groups, according to their commitment to different criteria, their limits and the weights from the AHP method. The sorting calculation was performed with the software ELECTRE TRI 2.0a, available in Lamsade (Paris-Dauphine University).
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Fig. 1. Model Structure.
The Copeland method was chosen for the aggregation of criteria, and it uses the Condorcet matrix for pairwise comparison. With this method it is possible to aggregate preferences related to criteria definition. After criteria definition it is necessary to know weight the criteria. Using the responses of managers and the AHP method, the weights were defined. Other methods could be utilized to this purpose, like the Macbeth method, or the Swing Weights procedure, but the AHP allows for trade-offs between criteria. Finally, to generate ordered classes, the ELECTRE TRI method was used as it can generate an allocation with a non-compensatory approach aligned with the evaluation in this phase. Some other data for the numerical application were introduced, based on perceptions of experts. In applications in the companies, these data can be gathered from decision-makers, in order to capture their
preferences. Decision makers must be able to express objectives for supplier selection that meet customers’ requirements and the priorities of the company, regarding ethical practices related to sustainability dimensions. 4. Numerical application In this section we describe the survey of customers from the textile industry to find their views on social, environmental and ethical issues, with a sample of 250 Brazilian customers. The respondents were selected based on accessibility and convenience. The questionnaires were available in social media. The sample was non-probabilistic, so the results cannot be generalized to the universe of customers from this segment, either in Brazil or other countries. The results are used to
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Table 3 Evaluations Average attributed by the Decision-Makers Group. Importance Degree for Criteria Traditional Criteria
Social
Economic
Socio-environmental Criteria
Social
Environmental
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Effective communication Meeting customer requirements Support Compatible cultures Mutual trust Geographic location Cost Quality On-time delivery Efficiency of Service Financial stability Technological capacity Delivery delays Flexibility Productivity and production capacity Technical and organizational capacity Manag. capacity and organization R & D Level Human Rights Philanthropy Public Disclosure Certification Management Skills Accordance with the law Continuous Improvement Smaller Suppliers Environmental impact Reverse Logistic Environmentally Friendly packaging Emission of dangerous gases Environmental Assessment of suppliers Management of hazardous waste Environmental Management Projects for environment Image Green Diversity Environmental Costs Security
demonstrate the constructs in the model. The model could be applied in any country or sector. The fact that the sample is not representative does not undermine the possibility of the model being customized and generalized. Despite this limitation, the results of this survey may still be relevant. It demonstrates the preferences of a considerable number of consumers from a developing country (Brazil), with a recent increase in purchasing power and change of attitudes in terms of environmental and social awareness, and related ethical issues. In the last six years, the number of Brazilian consumers who bought sustainable products jumped from 29% to 48% (Akatu Research, 2018). In the sample, 47.6% were 17 to 22 years old, 42.4% were 23 to 30 years old, 1.6% were 30 to 36 years old and 8.4% were over 36 years old. 36.8% were female and 63.2% male. 56.8% had an income over US $ 2,500. 16% had an income from US$ 1,900 to US$ 2,500. 18.4% had an income from US$ 450 to US$ 1,900. 7.2% had an income from US $280 to US$ 450. And, 1.6% had in income below US$ 280. The questionnaire had 62 closed questions related to ethical practices in the social, environmental and economic context. The Likert Scale (1–5) was used in the questionnaire, in order to evaluate the importance of each criterion, in which NOT MPORTANT had the value 1, LESS IMPORTANT values 2, INDIFFERENT value 3, VERY IMPORTANT value 4, and EXTREMELY IMPORTANT value 5. In the model structure we use three MCDA methods: i) Copeland’s method in order to define the relevant criteria based on those identified
Customers
Marketing
Financial
ProducTion
Engineering
2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 3.0 2.2 2.8 1.3 3.3 3.1 2.8 3.1 3.6 3.0 2.8 1.3 1.3 3.6 3.5 3.3 2.2 1.3 2.5 2.6
4.3 3.1 2.2 2.0 3.3 1.4 2.5 3.8 2.9 1.8 1.6 2.6 2.2 1.5 2.0 1.7 2.8 2.8 3.6 2.0 3.5 2.5 3.5 3.5 3.5 2.5 3.8 2.5 2.5 2.5 1.5 3.9 3.0 3.0 2.5 1.5 2.1 2.5
2.1 1.8 1.8 1.8 3.1 3.6 4.8 2.3 3.3 1.5 4.4 2.5 1.8 1.3 1.5 2.1 3.6 1.3 2.3 1.9 2.5 2.5 2.5 4.3 3.3 2.5 3.4 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 1.5 1.7 2.5
2.8 4.1 4.1 2.3 3.8 3.7 2.6 4.5 4.9 3.9 1.8 3.6 4.6 4.1 4.5 3.5 3.0 2.8 2.8 2.1 2.5 2.5 2.5 2.5 4.0 2.5 2.5 3.3 2.5 2.5 2.5 2.5 3.1 2.5 2.5 1.5 2.3 2.5
3.0 4.5 3.8 4.3 2.1 2.8 3.6 3.7 2.6 4.5 2.2 4.1 3.6 4.0 3.8 4.4 2.9 3.7 1.5 1.3 2.5 2.5 2.5 3.5 3.5 2.5 2.5 3.3 2.5 2.5 2.5 2.5 2.5 3.5 2.5 1.5 2.3 2.5
in the literature, ii) AHP method to calculate weights capturing criteria importance and, iii) ELECTRE TRI method in order to sort suppliers in classes, considering a balance among traditional and socio-environmental criteria. Copeland’s method computes the number of victories and defeats in pairwise comparisons. This method is a compromise between the opposing concepts of Borda and Condorcet, combining the advantages of both methods. If inconsistencies appear, Copeland’s method can be used to obtain collective decisions. After the application of the Copeland’s method, the results can be expressed by the global ranking that represents the aggregation of the group’s preferences. The evaluation instrument is presented to the decision maker(s) at this stage, in order to capture important criteria, involving traditional and social, ethical and environmental features of the sustainability concept. From the literature review, the traditional and socio-environmental criteria were organized in Table 3. The evaluation of decision makers from strategic areas of the company was based on expert perceptions. This procedure is widely used in papers dealing with models from the operations research area (soft and hard). The level of importance for a group of decision makers from several strategic areas in the company is essential in the decision-making process. Table 4 shows the average evaluations from the data collected. The average had the same value of median in this case, which presupposes a symmetrical distribution. So we use the average, considering that the median better fits when the data have asymmetric distribution. In the first phase the data were obtained mainly from managers and
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0 1 1 −1 −1 0 0 1 1 0 1 3 0 1 1 0 −1 1
Costumers Marketing Financial Production Engineering SUM Costumers Marketing Financial Production Engineering SUM Costumers Marketing Financial Production Engineering SUM
−1 1 −1 1 1 1
Cr14
Criteria
Costumers Marketing Financial Production Engineering SUM
0 −1 −1 1 1 0 0 −1 −1 1 1 0
Cr27
1
Costumers Marketing Financial Production Engineering SUM Costumers Marketing Financial Production Engineering SUM Costumers Marketing Financial Production Engineering SUM
Criteria
Cr1
Criteria
−1 1 −1 −1 −1 −3
Cr28
0 1 1 −1 −1 0 0 1 1 −1 1 2 0 1 1 −1 1 2
Cr15
0 −1 0 0 −1 −2
0 1 1 −1 −1 0 2
Cr2
0 1 0 −1 −1 −1 0 1 −1 1 1 2 0 1 −1 1 −1 0
−1 1 −1 1 1 1
Cr29
Cr16
0 1 1 −1 −1 0 0 1 0 0 1 2 3
Cr3
Table 4 Fragment for Copeland Pairwise Comparison (Example).
0 1 −1 −1 1 0 0 1 −1 1 1 2 0 −1 −1 1 1 0
Cr17
0 1 1 1 −1 2 0 1 0 1 1 3 0 1 0 1 −1 1
Cr 4
1 1 −1 1 1 3
Cr 30
0 1 1 0 −1 1 0 1 1 1 1 4 0 −1 1 1 1 2
Cr18
0 1 −1 −1 1 0 0 −1 −1 1 1 0 0 −1 −1 1 1 0
Cr5
1 1 −1 1 1 3
Cr 31
−1 1 −1 0 1 0 −1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
Cr19
0 1 −1 −1 1 0 0 1 −1 1 1 2 0 1 −1 1 1 2
Cr6
−1 1 −1 1 1 1
Cr32
1 1 1 1 1 5 1 1 −1 1 1 3 1 1 −1 1 1 3
Cr 20
0 1 −1 1 −1 0 0 1 −1 1 1 2 0 −1 −1 1 1 0
Cr7
−1 1 −1 −1 1 −1
Cr33
−1 1 −1 1 1 1 −1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
Cr21
0 1 −1 −1 −1 −2 0 −1 −1 −1 1 −2 0 −1 −1 −1 1 −2
Cr8
−1 1 −1 1 −1 −1
Cr34
1 1 −1 1 1 3 1 1 −1 1 1 3 1 −1 −1 1 1 1
Cr 22
0 1 −1 −1 1 0 0 1 −1 −1 1 0 0 −1 −1 −1 1 −2
Cr9
1 1 −1 1 1 3
Cr 35
−1 1 −1 1 1 1 −1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
Cr23
0 1 1 −1 −1 0 0 1 1 1 0 3 0 1 1 1 −1 2
Cr10
1 1 1 1 1 5
Cr 36
−1 1 −1 1 −1 −1 −1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
Cr24
0 1 −1 1 1 2 0 1 −1 1 1 2 0 1 −1 1 1 2
Cr 11
1 1 1 1 1 5
Cr 37
−1 1 −1 −1 −1 −3 −1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
Cr25
0 1 −1 −1 −1 −2 0 1 −1 1 1 2 0 −1 −1 1 −1 −2
Cr12
(continued on next page)
−1 1 −1 1 1 1
Cr38
−1 1 −1 1 1 1 −1 1 −1 1 1 1 −1 −1 −1 1 1 −1
Cr26
0 1 1 −1 −1 0 0 1 0 −1 1 1 0 −1 0 −1 1 −1
Cr13
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customers. The results were normalized into the scale between 1 and 5. The data were organized in Table 3, considering the average evaluations in the aggregation process. Fig. 2 shows customer evaluations of criteria in a socio-environmental context. The results presented in Table 4 were aggregates of criteria. In the Copeland method, according to Saari and Merlin (1996), in a pairwise comparison between cj and ck as Eq. (1):
These results are interpreted as a voting process and the adapted Copeland parameters are used to define Cr(j,k), as follows. They were compared in a Condorcet Matrix, in that each Cr(j,k), was defined in Eq. (3):
−1 1 −1 1 1 1 −1 −1 −1 1 1 −1 1 1 1 1 1 5 1 1 1 1 1 5 1 1 −1 1 1 3 1 −1 −1 1 1 1 −1 1 −1 1 1 1 −1 −1 −1 1 1 −1 Costumers Marketing Financial Production Engineering SUM Costumers Marketing Financial Production Engineering SUM
−1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
−1 1 −1 1 1 1 −1 −1 −1 1 1 −1
1 1 −1 1 1 3 1 −1 −1 1 1 1
1 1 −1 1 1 3 1 1 −1 1 1 3
−1 −1 −1 1 1 −1 −1 −1 −1 1 1 −1
−1 1 −1 1 1 1 −1 −1 −1 1 1 −1
−1 1 −1 1 1 1 −1 −1 −1 1 1 −1
1 1 1 1 1 5 1 1 1 1 1 5
Cr38 Cr 35 Cr29 Cr28 Cr27 Criteria
Table 4 (continued)
Cr 30
Cr 31
Cr32
Cr33
Cr34
Cr 36
Cr 37
P. Guarnieri, F. Trojan
cj beats ck 1 if 0 if cj and ck are tied 1 if ck beats cj
S(j, k ) =
(1)
The Copeland value for each c(j,k), is defined as Eq. (2):
C(j, k ) =
Sjk
(2)
k j
where, C(j,k)= the sum of comparisons results among every criteria; Sjk= pairwise score based on the Copeland’s method parameters. Copeland’s method was adapted to evaluate comparisons between alternatives (Criteria evaluated for the customers and decision makers from relevant areas in the company). As presented in Table 4, each criterion was compared with all the others, considering the decision makers’ preferences. In future studies it would be desirable to include representatives of logistics, marketing, finance, production, and engineering.
1 if Cj > 0 0 if Cj 0
Cr(j, k ) =
(3)
where, Cr(j,k)= resulted from the sum of Criteria pairwise comparisons. Finally, the Copeland Ranking was constructed in order to choose the most appropriate criteria from each R(j,k), defined in Eq. (4):
R (j , k ) =
(Lnk k j
Cl j )
(4)
where, R(j,k)= balance to ranking; Lnk= item of Lines; Clj= item of Column. The sum of each line (representing victories) minus the sum of each column (representing defeats) generated a cardinal value that represented a remainder from this evaluation, R(j,k). This was the final ranking with the chosen criteria, aggregating the preferences of all stakeholders involved in this problem. With this procedure, Copeland’s method included more social, ethical and environmental criteria in the application, because it is a voting process and it does not consider intensities of stakeholder views, which could cause distortions in this type of process (Table 5). The Copeland Ranking was considered the best for this type of problem. In Table 6, scores for supplier selection were defined considering the criteria chosen. The analysis is used to select suppliers that meet the needs of the company, considering socio-environmental features from the decision makers’ preferences. The AHP method was created by Saaty (1990) as a measuring method of preferences, using pairwise comparisons based on ratio scales. The AHP works with the concept of dominance presenting a matrix of judgments. The author proposed a consistency ratio (CR), defined as the ratio between the consistency index (CI) and the random index (RI) to verify the consistency by the subjective evaluations, as shown in the Eq. (5):
CR =
CI RI
(5)
The CI is calculated based on eigenvalue of A (λmax) and the number of elements (n), as presented in the Eq. (6):
CI = 354
n
max
n
1
(6)
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P. Guarnieri, F. Trojan
Fig. 2. Percentage Average for socio-environmentasl criteria by Customers.
The RI is obtained and according to the author, it should not exceed 10%. For this, the number of elements (n) must be under ten and CR ⩽0.1. And then, the weights were obtained with AHP method calculation, as presented in Table 7. In order to support the supplier evaluations in this application and clarify the scores for each criterion, the definition of some indicators is suggested in Table 8. Table 9 presents the evaluation carried out in this application, as classified by ELECTRE TRI. The evaluation was based on the experts’ perceptions. A more quantitative analysis can be performed using the indicators suggested in Table 8. The evaluation may depend on the data available. Many companies do not have the data to perform the necessary evaluation. Consequently, it was necessary to resort experts to generate a basic evaluation. The ELECTRE TRI is a multi-criteria method that categorizes alternatives in classes. This categorization results from a comparison between each alternative with predefined profiles and the limits of the categories (Mousseau et al., 2001). The method encompasses indexes of partial concordance cj(a,b), concordance c(a,b) and partial discordance dj (a,b) calculated by the Eqs. (7), (8) and (9):
0 if cj (a, b) = 1 if
gj (bh)
gj (a)
pj (bh)
gj (bh )
gj (a)
qj (bh)
pj (bh) + gj (a) pj (bh)
c (a , b ) =
dj (a, bh) =
gj (bh)
qj (bh)
, otherwise
j F kj c j (a, bh)
Fig. 3. 5. Results discussion The main findings demonstrate that it was possible to group suppliers into classes of commitment, considering the sustainability dimensions. In a practical solution, the suppliers evaluated by this numerical experiment could be classified in their position as reported by the company in which this application was conducted. This represented a different way to select suppliers in order to avoid future constraints and promote an efficient process of choice, at the same time promoting a balance of ethical, social, environmental and economic criteria in the supplier selection process related to outsourcing activities in the textile industry. It was possible to improve this process with use of quantitative indicators as suggested in Table 9, where subjectivity is used only on where essential. But, it was not easy to perform this analysis, because the company is not completely open to sharing confidential data. In this application we involve only evaluations from consumers and managers to overcome this difficulty. Each application of this method could generate results that are peculiar to its context, depending on the consulted group’s preferences, i.e., consumers and managers from a specific sector. At the same time, the model could be used in different segments or contexts, with small alterations. The main advantage of this procedure is that the preferences of consumers and experts are captured and integrated into the decision process, guaranteeing an efficient choice of suppliers, committed to sustainable practices and market expectations. The model is an organized way to classify suppliers, considering the most important criteria in the evaluation. In this case the results represent the classification for textile industry suppliers, but the model could be adapted for other scenarios. For this study, customers’ perceptions are essential when companies make sustainable decisions and help in the balancing of ethical, social, environmental and economic criteria in supplier selection. Many companies fail to provide adequate information to customers on practices that keep their supply chains free of unethical labor practices. When companies consider the sustainability dimensions and classify suppliers with these priorities, they avoid companies that use unethical practices. In addition, Delmonico et al. (2018) found in the context of Brazil, that to the extent that leaders or senior management considers sustainability-related actions as essential, the failures related to the procurement practices are minimized. Sustainability in large firms has been well researched and SMEs have been the focus less often in terms of sustainability in RCR (Table 10).
(7) (8)
j F kj
g (a) gj (bh) + pj (bh) 0 if j if gj (a) > gj (bh ) + vj (bh ) 1 [0, 1] , otherwise
(9)
The index σ (a,bh) represents the degree of credibility of the assertion aSbh, a∈ A, h∈ B, as shown in Eq. (10).
(a, bh) = c (a, bh ). j F __
1
dj (a, bh)
1
c (a , bh)
(10)
where: F = {j F : dj (a, bh ) > cj (a, bh )} According to the needs of the company, classes were pre-established: High, Medium and Low commitment. Allocation was performed according to the views of an expert in the company. Each class represented the orientation for a set of actions. Table 11 shows each class with their respective limiting profiles, and also presents the criteria weights, from the AHP method. After the data were processed in the ELECTRE TRI software, and suppliers allocated to commitment classes, the results are presented in 355
356
Cr1 Cr2 Cr3 Cr4 Cr5 Cr6 Cr7 Cr8 Cr9
Cr1 Cr2 Cr3 Cr4 Cr5 Cr6 Cr7 Cr8 Cr9 Cr10 Cr11 Cr12 Cr13 Cr14 Cr15 Cr16 Cr17 Cr18 Cr19 Cr20 Cr21 Cr22 Cr23 Cr24 Cr25 Cr26 Cr27 Cr28 Cr29 Cr30 Cr31 Cr32 Cr33 Cr34 Cr35 Cr36 Cr37 Cr38 Column Sum (L-C)
1 1 1 1 1 1 1 1 1
Cr 20
0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 8 9
Cr1
1 0 0 0 0 1 1 1 1
Cr21
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 8 18
0
Cr2
1 1 1 0 1 1 1 1 1
Cr 22
0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 18 −4
0 1
Cr3
Table 5 Condorcet Matrix to make Copeland Ranking.
1 0 0 0 0 1 1 1 1
Cr23
1 0 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 27 −20
1 1 1
Cr 4
0 0 0 0 0 0 1 1 0
Cr24
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 8 10
0 0 0 0
Cr5
0 0 0 0 0 0 0 1 0
Cr25
1 1 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 12 4
0 1 1 0 0
Cr6
1 1 0 0 1 1 1 1 1
Cr26
1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 7 12
0 1 0 0 0 0
Cr7
1 0 0 0 0 1 1 0 0
Cr27
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 2 30
0 0 0 0 0 0 0
Cr8
0 1 0 0 1 0 0 1 1
Cr28
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 4 23
0 0 0 0 0 0 0 0
Cr9
1 1 0 0 1 1 1 1 1
Cr29
0 0 1 0 1 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 19 −7
0 1 1 0 0 0 0 1 1
Cr10
1 1 1 0 1 1 1 1 1
Cr 30
1 1 1 1 1 1 1 1 1
Cr 31
1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 31 −28
1 1 1 1 0 0 1 1 1 1
Cr 11
0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 6 15
0 1 0 0 1 0 0 0 1 0 0
1 0 0 0 0 1 1 0 1
Cr32
Cr12
0 1 0 0 1 1 0 1 1
Cr33
0 0 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 17 −3
0 1 0 0 0 0 0 1 1 0 0 1
Cr13
0 1 0 0 1 0 1 1 0
Cr34
1 1 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 23 −14
0 1 1 1 0 0 0 1 1 1 0 1 1
Cr14
1 1 1 0 1 1 1 1 1
Cr 35
0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 20 −8
0 1 1 1 0 0 0 1 1 0 0 1 1 0
Cr15
1 1 1 1 1 1 1 1 1
Cr 36
0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 19 −8
0 1 0 0 1 0 0 1 1 1 0 1 0 0 0
Cr16
1 1 1 1 1 1 1 1 1
1 1 0 0 1 1 1 1 1
Cr38
1 0 1 0 1 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 22 −10
1 1 1 0 1 0 0 1 1 1 0 1 0 1 1 1 1
Cr18
17 26 14 7 18 16 19 32 27
Line Sum
0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 0 15 6
0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0
Cr19
(continued on next page)
Cr 37
0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 7 11
0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Cr17
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Resources, Conservation & Recycling 141 (2019) 347–361
Cr10 Cr11 Cr12 Cr13 Cr14 Cr15 Cr16 Cr17 Cr18 Cr19 Cr20 Cr21 Cr22 Cr23 Cr24 Cr25 Cr26 Cr27 Cr28 Cr29 Cr30 Cr31 Cr32 Cr33 Cr34 Cr35 Cr36 Cr37 Cr38 Column Sum (L-C)
Table 5 (continued)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 36 −35
1 1 1 1 1 1 1 1 1 1
Cr 20
0 1 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 15 5
0 0 0 0 0 0 0 1 0 1 0
Cr21
1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1 30 −24
1 0 1 1 1 1 1 1 1 1 0 1
Cr 22
1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 11 12
0 0 0 0 0 0 0 1 0 0 0 0 0
Cr23
0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 30
0 0 0 0 0 0 0 0 0 0 0 0 0 0
Cr24
0 1 0 0 0 0 0 0 0 0 0 0 0 3 30
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Cr25
1 1 0 0 0 1 1 1 0 0 0 0 18 0
0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 1
Cr26
0 0 0 0 1 0 0 0 0 0 0 5 26
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Cr27
0 0 0 0 0 1 0 0 0 0 9 14
0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1
Cr28
0 0 1 1 1 0 0 0 0 21 −5
0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1
Cr29
0 1 1 1 1 0 0 1 30 −24
1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1
Cr 30
1 1 1 1 0 0 1 34 −31
1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1
Cr 31
0 0 0 0 0 0 6 22
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Cr32
0 0 0 0 0 9 16
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1
Cr33
0 0 0 0 9 16
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1
Cr34
357
0 0 1 29 −21
1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1
Cr 35
1 1 37 −37
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Cr 36
1 34 −31
1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
Cr 37
22 −7
0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0
Cr38 12 3 21 14 9 12 11 18 12 21 1 20 6 23 32 33 18 31 23 16 6 3 28 25 25 8 0 3 15
Line Sum
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Cr.8
358
Cr.2 Cr.8 Cr.9 Cr.12 Cr.24 Cr.25 Cr.27 Cr.32 Cr.33 SUM λmax =
1 4 4 1/2 5 5 1 1/2 1/2 21.50 9.79
Cr.2
Pairwise Comparison – Saaty Scale
1/4 1 1/3 1/5 2 2 1/3 1/5 1/5 6.52
1/4 3 1 1/3 5 5 1/2 1/5 1/5 15.48
Cr.9
Very Low Low Medium High Very High
All the process Three-quarters Half the process A Quarter Noneof the process
Table 7 Weights Definition by AHP Method.
Cr 27 Environmental impact
High Good Medium Poor Low
Always Three-quarters Half A Quarter Never
Cr 25 Continuous Improvement
Cr 8 Quality
Cr 2 Meeting customer requirements
Table 6 Results for Criteria Chosen by Copeland Method and proposed scales.
2 5 3 1 5 5 2 1/4 1/5 23.45 CR =
Cr.12
Very high High Medium Low Very Low
Cr 12 Technological capacity
1/5 1/2 1/5 1/5 1 1 1/5 1/5 1/5 3.70 0.0683
Cr.24
Excellent Good Medium Poor Bad
1/5 1/2 1/5 1/5 1 1 1/4 1/5 1/5 3.75
Cr.25
1 3 2 1/2 5 4 1 1/5 1/5 16.90
Cr.27
Cr 32 Management of hazardous waste and pollutants
Always Three-quarters Half A Quarter Never
Cr 9 On-time delivery
2 5 5 4 5 5 5 1 1/2 32.50
Cr.32
Excellent Good Medium Poor Bad
2 5 5 5 5 5 5 2 1 35.00
Cr.33
Cr 33 Environmental Management
Totally in accordance With few issues With serious problems With ongoing claims Totally in Discordance
Cr 24 Accordance with the law
0.65 2.18 1.26 0.62 3.16 3.08 0.91 0.35 0.29 12.50
A-Vector
5.2% 17.4% 10.1% 5.0% 25.3% 24.6% 7.3% 2.8% 2.3% 100.0%
Weights
100% 75% 50% 25% 0%
Score
100% 75% 50% 25% 0%
Score
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Table 8 Suggestion for Criteria Indicators (quantitative evaluation). Indicator for Criterion 8:
QL (%) =
(
QL(%) – Quality nS = Number of sectors considered with quality 100% tS= Total of the sectors considered in the evaluation QL(%) > 75% 50% < QL(%) < 75% 25% < QL(%) < 50% 5% < QL(%) < 25% QL(%) < 5%
)
nS *100 tS
Interpretation
Indicator for Criterion 24:
Acc(%) =
(
…….………High; …….…… Good; ……… Medium; …………. Poor; …….………Bad.
100% 75% 50% 25% 0%
Acc(%) - Accordance with law nC = Number of cases on trial by law tC= Total of cases which could generate issues with law Acc(%) = 0 …… Totally in accordance; Acc(%) < 5% ….…………With few issues; 5% < Acc(%) < 50% …….With serious problems; 50% < Acc(%) < 75% ……….With ongoing claims; Acc(%) > 75% ….…Totally in discordance.
)
nC *100 tC
Interpretation
100% 75% 50% 25% 0%
Table 9 Suppliers Evaluation. Criteria Evaluation Copeland Method
Cr.2 - Meeting customer requirements
Cr.8 Quality
Cr.9 On-time delivery
Cr.12 Technological capacity
Cr.24 Accordance with the law
Cr.25 Continuous Improvement
Cr.27 Environmental impact
Cr.32 Management hazardous waste
Cr.33 Environmental Management
Weights Supl. 1 Supl. 2 Supl. 3 Supl. 4 Supl. 5 Supl. 6 Supl. 7 Supl. 8 Supl. 9 Supl. 10
5.2 % 100 % 100 % 75 % 50 % 50 % 75 % 75 % 25 % 50 % 25 %
17.4 % 75 % 50 % 100 % 25 % 25 % 75 % 50 % 0% 50 % 25 %
10.1 % 50 % 100 % 75 % 50 % 75 % 75 % 75 % 25 % 50 % 50 %
5.0 % 50 % 75 % 75 % 50 % 50 % 75 % 100 % 50 % 50 % 50 %
25.3 % 100 % 50 % 100 % 75 % 75 % 100 % 50 % 50 % 75 % 25 %
24.6 % 50 % 25 % 100 % 100 % 50 % 100 % 50 % 75 % 100 % 50 %
7.3 % 25 % 100 % 50 % 75 % 50 % 100 % 75 % 50 % 25 % 50 %
2.8 % 25 % 75 % 50 % 75 % 75 % 75 % 100 % 75 % 50 % 75 %
2.3 % 25 % 50 % 100 % 50 % 50 % 75 % 50 % 50 % 50 % 50 %
6. Final remarks
management and the understanding how to promote the balancing of ethical, social, environmental and economic criteria in the supplier selection decision to outsource activities in the textile industry. We performed: i) a systematic review of the literature to identify the main traditional and socio-environmental criteria used to select suppliers; ii)
The main objective of this paper was to propose a model to support decision-making in supplier selection based on the socio-environmental criteria and related to targets of sustainability in supply chain
Fig. 3. Sorting Results. 359
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Table 10 Parameters for the boundaries between classes. Classes / Boundaries CL1 CL2 CL3 Weights Limits
Criteria
High Medium Low
g1
g2
g3
g4
g5
g6
g7
g8
g9
b2 b1
100% 65 %
90 % 80 %
80 % 75 %
100% 75 %
100% 80 %
100% 80 %
90% 60 %
100% 80 %
100% 70 %
Preference Indifference
5.2% 0% 0%
17.4% 5% 5%
10.1% 0% 0%
5.0% 0% 0%
25.3% 0% 0%
24.6% 0% 0%
7.3% 5% 5%
2.8% 0% 0%
2.3% 5% 5%
a survey with customers of the textile industry and, iii) an MCDA modeling with inputs from the literature, the survey of customers and the views of managers, in order to develop a model to support decision making related to supplier selection. The theoretical contributions of this work are the inclusion of concerns on social and environmental criteria and ethical issues related to supplier selection, integrating the opinions of customers and managers. Supplier selection is a well-structured process in many companies, but only considers economic aspects. Also, the systematization of the decision-making process using the mixed MCDA approaches (Copeland/AHP/ELECTRE-TRI) is novel. Some papers deal with combined multi-criteria methods for similar purposes, but no one proposes this combination. In addition, the consideration of opinions of final consumers is a novel contribution, considering that companies are market driven and focused on consumer preferences. In this sense, this paper is different from most papers found in literature, which consider only the perceptions of managers. As practical contributions we highlight the model proposed, which can support decision making by managers, using well-defined steps to gather all the relevant information in the context of supplier selection with social, environmental and ethical issues. Companies working with outsourcing can use this model to select appropriate suppliers. The model can be applied in real contexts using empirical data, to test its validity. Researchers can use this model as a basis for new research adapting the set of criteria, the methods used and the sector of application. Indicators related to the social dimension of sustainability, or the way the company treats its employees and the community, and aspects involving more human relationships, were the most highly rated this study. The overall survey results suggest that it is important to the respondents that companies in the textile sector select their suppliers considering sustainability aspects, especially those related to the social dimension. The main contribution of this paper is the presentation of a systematic and comprehensive model that can support the decisionmaking process related to supplier selection aligned with the sustainability dimensions in supply chain management. The inclusion of a suitable set of criteria, the consideration of preferences of customers, and the analysis of the problem using an MCDA approach, systematizes the decision process in well-defined steps, enabling more confident decisions, balancing ethical, social, environmental and economic criteria in the supplier selection process. The MCDA modeling process incorporates qualitative and quantitative criteria and also handles the inherent subjectivity involved in decision processes in contrast with methods based on Linear Programming which consider only quantitative inputs. This paper defines a set of criteria used in MCDA modeling from a systematic review of literature, considering papers focused on the supplier selection problem. The variables considered in the systematic review of the literature were the dimensions covered by the TBL concept: social, environmental and economic. The study considers the preferences of consumers in textile industry, capturing criteria that they consider important in the textile supply chain. According Fashion Revolution CIC (2016), transparency means
companies know who makes their products and also implies openness, communication and accountability across the supply chain and with the customers. This paper is limited to the study of a particular segment, the textile industry. The survey was carried out with customers from the textile industry in Brazil, so the numerical results cannot be generalized to other developing countries. This research is a quantitative empirical research, based on a survey with customers of the textile industry and numerical application. Qualitative empirical research, using the opinions of managers, could result in different findings, depending on the segment in which the model was applied. But the model could be generalized and adapted to be applied in other sectors. The use of specific methods to aggregate the preferences of decision makers, define weights and thus, classify de suppliers conducts to specific results. In this context, the consideration of other methods can generate different results. Future studies could consider other industrial segments in order to investigate sustainable practices in purchasing, supplier classification and supply management. Future research could also focus on other developing countries, which are more likely to host unsustainable practices in supplier selection due to lax laws in those countries. We also suggest empirical studies focusing in how TBL dimensions can be incorporated in the purchasing and supplier classification processes. Ghadimi et al. (2019) found that sustainability in large firms has been well researched, however in small companies still not. So, we suggest that future research to exploit sustainability issues in suppliers in textile sector, focusing in small companies. In addition, we suggest the proximity categories proposed by Dallasega and Sarkis (2018) to study the distances between buyers and suppliers in textile sector and how it influences environmental and social issues. Research using Problem Solving Methods (PSM), such as the Value Focused Thinking approach (VFT), Soft System Methodology (SSM), and Strategic Choice Approach (SCA), and others, could be developed in order to structure the decision problem of how to incorporate and improve the balance of the TBL dimensions in purchasing, supplier selection and supply management processes. It would also be desirable to include the views of experts from other parts of companies in the textile industry, including logistics, marketing, finance, production and engineering, in order to obtain a more comprehensive overview. This model could also be adapted to the supplier selection in other industries. References Akatu Institute, Akatu Research, 2018. Overview of Conscient Comsumption in Brazil: Challenges, Barriers and Motivations. (Accessed July 2018). https://www.akatu.org. br/noticia/pesquisa-akatu-2018-traca-panorama-do-consumo-consciente-no-brasil/. Araz, C., Ozkarahan, I., 2007. Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure. Int. J. Prod. Econ. 106 (2), 585–606. https://doi.org/10.1016/j.ijpe.2006.08.008. Brammer, S., Walker, H., 2011. Sustainable procurement in the public sector: an international comparative study. Int. J. Oper. Prod. Manage. 31 (4), 452–476. https://doi. org/10.1108/01443571111119551. Calamari, S., Hyllegard, K.H., 2016. An exploration of designers’ perspectives on human health and environmental impacts of interior textiles. Text. Cloth. Sustainability 2 (9), 1–16. https://doi.org/10.1186/s40689-016-0020-7.
360
Resources, Conservation & Recycling 141 (2019) 347–361
P. Guarnieri, F. Trojan
https://doi.org/10.1007/s11135-009-9223-1. Kuo, T.C., Hsu, C.W., Li, J.Y., 2015. Developing a green supplier selection model by using the DANP with VIKOR. Sustainability 7 (2), 1661–1689. https://doi.org/10.3390/ su7021661. Larson, A., 2017. Child Labor Law. Available at:. Expert Law [Retrieved 10 July, 2017]. https://www.expertlaw.com/library/employment/child_labor.html. Liao, C.N., Kao, H.P., 2010. Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming. Comp. Ind. Eng. 58 (4), 571–577. https://doi.org/10.1016/j.cie.2009.12.004. Lin, K.P., Hung, K.C., Lin, Y.T., Hsieh, Y.H., 2017. Green suppliers performance evaluation in belt and road using fuzzy weighted average with social media information. Sustainability 10 (1), 5. https://doi.org/10.3390/su10010005. Loo, B.P.Y., 2002. The textile and clothing industries under the fifth kondratieff wave: some insights from the case of Hong Kong. World Dev. 30 (5), 847–872. Mamic, I., 2005. Managing global supply chain: the sports footwear, apparel and retail sectors. J. Bus. Ethics 59 (1), 81–100. https://doi.org/10.1007/s10551-005-3415-y. Mousseau, V., Figueira, J., Naux, J.P., 2001. Using assignment examples to infer weights for ELECTRE TRI method: some experimental results. Eur. J. Oper. Res. 130 (2), 263–275. https://doi.org/10.1016/S0377-2217(00)00041-2. Niinimäki, K., 2015. Ethical foundations in sustainable fashion. Text. Cloth. Sustain. 1 (3), 1–11. https://doi.org/10.1186/s40689-015-0006-x. Palpacuer, F., 2005. New challenges for developing country suppliers in global clothing chains: a comparative european perspective. World Dev. 33 (3), 409–430. Pi, W.N., Low, C., 2006. Supplier evaluation and selection via Taguchi loss functions and an AHP. Int. J. Adv. Manuf. Technol. 27 (5/6), 625–630. https://doi.org/10.1007/ s00170-004-2227-z. Preuss, L., 2009. Ethical sourcing codes of large UK-based corporations: prevalence, content, limitations. J. Bus. Ethics 88 (4), 735–747. https://doi.org/10.1007/s10551008-9978-7. Quarshie, A.M., Salmi, A., Leuschner, R., 2016. Sustainability and corporate social responsibility in supply chains: the state of research in supply chain management and business ethics journals. J. Purch. Supply Manage. 22, 82–97. https://doi.org/10. 1016/j.pursup.2015.11.001. Saari, D.G., Merlin, V.R., 1996. The Copeland method: relationships and the dictionary. Econ. Theory 8 (1), 51–76. https://doi.org/10.1007/BF01212012. Saaty, T.L., 1990. How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48 (1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I. Sarkis, J., Helms, M.M., Hervani, A.A., 2010. Reverse logistics and social sustainability. Corp. Soc. Responsib. Environ. Manage. 17 (6), 337–354. https://doi.org/10.1002/ csr.220. Scheiber, F., 2013. Dressing up for diffusion: codes of conduct in the German textile and apparel industry, 1997-2010. J. Bus. Ethics 1–22. https://doi.org/10.1007/s10551013-1964-z. Seuring, S., Müller, M., 2008. From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16 (15), 1699–1710. https:// doi.org/10.1016/j.jclepro.2008.04.020. Stević, Ž., Pamučar, D., Vasiljević, M., Stojić, G., Korica, S., 2017. Novel integrated multicriteria model for supplier selection: case study construction company. Symmetry 9 (11). https://doi.org/10.3390/sym9110279. Tate, W.L., Ellram, L.M., Dooley, K.J., 2012. Environmental purchasing and supplier management (EPSM): theory and practice. J. Purch. Supply Manage. 18, 173–188. https://doi.org/10.1016/j.pursup.2012.07.001. Vahidi, F., Torabi, S.A., Ramezankhani, M.J., 2017. Sustainable supplier selection and order allocation under operational and disruption risks. J. Clean. Prod. 174, 1351–1365. https://doi.org/10.1016/j.jclepro.2017.11.012. Wang, K.Q., Liu, H.C., Liu, L., Huang, J., 2017. Green supplier evaluation and selection using cloud model theory and the QUALIFLEX method. Sustainability 9 (5), 688. https://doi.org/10.3390/su9050688. Wilson, L., 2015. The sustainable future of the Scottish textiles sector: challenges and opportunities of introducing a circular economy model. Text. Cloth. Sustain. 1 (5), 1–9. https://doi.org/10.1186/s40689-015-0005-y. Wong, C.Y., Wong, C.W., Boon-Itt, S., 2015. Integrating environmental management into supply chains: a systematic literature review and theoretical framework. Int. J. Phys. Dist. Log. Man. 45 (1/2), 43–68. https://doi.org/10.1108/IJPDLM-05-2013-0110. World Vision, 2016. Supply Chain Risk Report Child and Forced Labour in Canadian Consumer Products. (Accessed July 2017). http://nochildforsale.ca/wp-content/ uploads/2016/06/Child-and-forced-labour-report_jun-08.pdf#. Yuanqiao, W., 2008. Green Purchasing to Achieve Corporate Sustainability. Unpublished Masters Thesis. Lund University, Sweden. Zheng, J., Knight, L., Harland, C., Humby, S., James, K., 2007. An analysis of research into the future of purchasing and supply management. J. Purch. Supply Manage. 13, 69–83. https://doi.org/10.1016/j.pursup.2007.03.004. Zhou, X., Xu, Z., 2018. An integrated sustainable supplier selection approach based on hybrid information aggregation. Sustainability 10 (7), 1–49. https://doi.org/10. 3390/su10072543.
Choy, K.L., Lee, W.B., Lau, H.C.W., Choy, L.L.C., 2005. A knowledge-based supplier intelligence retrieval system for outsource manufacturing. Knowledge Based Syst. 18 (1), 1–17. https://doi.org/10.1016/j.knosys.2004.05.003. Dallasega, P., Sarkis, J., 2018. Understanding greening supply chains: proximity analysis can help. Resour. Conserv. Recycl. 139, 76–77. De Almeida, A.T., 2007. Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method. Comput. Oper. Res. 34 (12), 3569–3574. https://doi.org/10.1016/j.cor.2006.01.003. De Boer, L., Labro, E., Morlacchi, P., 2001. A review of methods supporting supplier selection. Eur. J. Purch. Sup. Manage. 7 (2), 75–89. https://doi.org/10.1016/S09697012(00)00028-9. Delmonico, D., Jabbour, C.J.C., Pereira, S.C.F., de Sousa Jabbour, A.B.L., Renwick, D.W.S., Thomé, A.M.T., 2018. Unveiling barriers to sustainable public procurement in emerging economies: evidence from a leading sustainable supply chain initiative in Latin America. Resour. Conserv. Recycl. 134, 70–79. Deschamps, T.C., Carnie, B., Mao, N., 2016. Public consciousness and willingness to embrace ethical consumption of textile products in Mexico textiles and Clothing. Sustainability 2 (6), 2–16. https://doi.org/10.1186/s40689-016-0017-2. Ehrgott, M., Reimann, F., Kaufmann, L., Carter, C.R., 2011. Social sustainability in selecting emerging economy suppliers. J. Bus. Ethics 98 (1), 99–119. https://doi.org/ 10.1007/sl0551-010-0537-7. Esfahbodi, A., Zhang, Y., Watson, G., 2016. Sustainable supply chain management in emerging economies: trade-offs between environmental and cost performance. Int. J. Prod. Econ. 181, 350–366. https://doi.org/10.1016/j.ijpe.2016.02.013. Fashion Revolution CIC, 2016. Fashion Transparency Index. (Accessed July 2016). http://fashionrevolution.org/wp-content/uploads/2016/04/FR_ FashionTransparencyIndex.pdf. Ghadimi, P., Wang, C., Lim, M.K., 2019. Sustainable supply chain modeling and analysis: past debate, present problems and future challenges. Resour. Conserv. Recycl. 140, 72–84. Giunipero, L.C., Hooker, R.E., Denslow, D., 2012. Purchasing and supply management sustainability: drivers and barriers. J. Purch. Sup. Manage. 18, 258–269. https://doi. org/10.1016/j.pursup.2012.06.003. Govindan, K., Rajendran, S., Sarkis, J., Murugesan, P., 2015. Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J. Clean. Prod. 98 (1), 66–83. https://doi.org/10.1016/j.jclepro.2013.06.046. Guarnieri, P., 2014. Decision making regarding information sharing in collaborative relationships under an MCDA perspective. Int. J. Man. Decis. Mak. 13 (1), 77–98. https://doi.org/10.1504/IJMDM.2014.058469. Guo, Z., Liu, H., Zhang, D., Yang, J., 2017. Green supplier evaluation and selection in apparel manufacturing using a fuzzy multi-criteria decision-making approach. Sustainability 9 (4), 650. https://doi.org/10.3390/su9040650. Ha, S.H., Krishnan, R., 2008. A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert Syst. Appl. 34 (2), 1303–1311. https://doi.org/ 10.1016/j.eswa.2006.12.008. Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., 2002. Applying environmental criteria to supplier assessment: a study in the application of the analytical hierarchy process. Eur. J. Oper. Res. 141 (1), 70–87. https://doi.org/10.1016/S0377-2217(01) 00261-2. Huang, S.H., Keskar, H., 2007. Comprehensive and configurable metrics for supplier selection. Int. J. Prod. Econ. 105 (2), 510–523. https://doi.org/10.1016/j.ijpe.2006.04. 020. Humphreys, P.K., Wong, Y.K., Chan, F.T.S., 2003. Integrating environmental criteria into the supplier selection process. J. Mater. Proc. Technol. 138 (1–3), 349–356. https:// doi.org/10.1016/S0924-0136(03)00097-9. Hussain, M., Al-Aomar, R., 2017. A model for assessing the impact of sustainable supplier selection on the performance of service supply chains. Int. J. Sustain. Eng. 1–16. https://doi.org/10.1080/19397038.2017.1414898. Husser, J., Gautier, L., Andr, J.M., Lespinet-Najib, V., 2014. Linking purchasing to ethical decision-making: an empirical investigation. J. Bus. Ethics 123 (2), 327–338. https:// doi.org/10.1007/s10551-013-1838-4. Jia, P., Govindan, K., Choi, T.M., Rajendran, S., 2015. Supplier selection problems in fashion business operations with sustainability considerations. Sustainability 7 (2), 1603–1619. https://doi.org/10.3390/su7021603. Johnsen, T.E., Giannakis, M., Miemczyk, J., Kamann, D.J., Bernadin, E., 2014. Purchasing & supply management for a sustainable world: introduction to the IPSERA 2013 conference special issue. J. Purch. Supply Manage. 20, 71–73. https://doi.org/10. 1016/j.pursup.2014.04.001. Khan, M.R., Islam, M., 2015. Materials and manufacturing environmental sustainability evaluation of apparel product: knitted T-shirt case study. Text. Cloth. Sustain. 1–12. https://doi.org/10.1186/s40689-015-0008-8. Kim, S., Colicchia, C., Menachof, D., 2016. Ethical sourcing: an analysis of the literature and implications for future research. J. Bus. Ethics. https://doi.org/10.1007/s10551016-3266-8. Ku, C.Y., Chang, C.T., Ho, H.P., 2010. Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming. Qual. Quant. 44 (4), 623–640.
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