A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

Accepted Manuscript A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

Lauro Osiro, Francisco Rodrigues Lima-Junior, Luiz Cesar Ribeiro Carpinetti PII:

S0959-6526(18)30517-1

DOI:

10.1016/j.jclepro.2018.02.197

Reference:

JCLP 12144

To appear in:

Journal of Cleaner Production

Received Date:

22 September 2017

Revised Date:

12 January 2018

Accepted Date:

19 February 2018

Please cite this article as: Lauro Osiro, Francisco Rodrigues Lima-Junior, Luiz Cesar Ribeiro Carpinetti, A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics, Journal of Cleaner Production (2018), doi: 10.1016/j. jclepro.2018.02.197

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

1

Title: A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

First author: Lauro Osiro Affiliation: Federal University of Triangulo Mineiro Department of Production Engineering Av. Doutor Randolfo Borges Júnior, 1250 Univerdecidade CEP 38064-200. Uberaba – MG - Brazil Second author: Francisco Rodrigues Lima-Junior Affiliation: Federal University of Technology of Parana (UTFPR) Department of Management and Economy Av. Sete de Setembro, 3165 – Rebouças, CEP 80230-901 –Curitiba – PR - Brazil Third author: Luiz Cesar Ribeiro Carpinetti Affiliation: Production Engineering Department School of Engineering of São Carlos, University of São Paulo Av. Trabalhador Sancarlense, 400, CEP 13566-590, São Carlos, SP, Brazil. Corresponding author: Luiz Cesar Ribeiro Carpinetti School of Engineering of São Carlos, University of São Paulo Av. Trabalhador Sancarlense, 400 13566-590, São Carlos, SP, Brazil E-mail: [email protected] Phone number: +55 16 33739421

ACCEPTED MANUSCRIPT

2

A group decision model based on quality function deployment and hesitant fuzzy for selecting supply chain sustainability metrics

Abstract Supply chain sustainability management is gaining increasing importance. Several studies propose quantitative evaluation approaches to manage sustainable supply chains. However, none of the studies focus on the selection and weighting of the metrics as a group decision process and in a way that considers the degree of difficulty of collecting data for measuring performance on a particular metric. Therefore, this paper proposes a group decision model for selecting metrics for supply chain sustainability management. The proposal is based on the combination of Hesitant Fuzzy Linguistic Term Sets (HFLTS) with the prioritization procedure of the house of quality of the Quality Function Deployment (QFD) method. HFLTS are used to represent judgments of different decision makers about the importance of supply chain sustainable performance requirements and the relationship between selected metrics and requirements. Prioritization of requirements and metrics is based on the method of distance measures between HFLTSs. The degree of difficulty of data collection is also estimated based on judgments using linguistic expressions and on distance measures of HFLTSs. An illustrative application is presented based on a first tier automobile manufacturing company. Through this illustrative example it is possible to see the benefit of using hesitant fuzzy sets to aggregate the judgments of different decision makers. It is also evident the importance of considering the degree of difficulty of data collecting as an additional argument to select and prioritize metrics. The proposed decision model can also be applied to other decision problems such as selecting criteria for sustainable supplier selection and evaluation. Keywords: Supply chain sustainability metrics, hesitant fuzzy QFD, multicriteria group decision making, hesitant fuzzy linguistic terms sets.

ACCEPTED MANUSCRIPT

3

1. Introduction The concept of supply chain management (SCM) refers to the integration of planning, implementing and controlling of all the business processes related with material transformation and information flow (Lambert et al., 1998; Melo et al., 2009). Although cost reduction and additional value to customers are commonly mentioned objectives of SCM (Ko et al., 2010; Seuring, 2013), nowadays, SCM goals have evolved to consider the three dimensions of sustainable development (Ahi and Searcy, 2015a; Ansari and Kant, 2017). Evaluating the sustainability performance of a supply chain involves the use of metrics related to economic as well as social and environmental dimensions. General benefits of performance evaluation include highlighting progress, identifying potential problems and providing insights about future improvement plans, among others (Ahi and Searcy, 2015a). However, supply chain performance evaluation is a complex task mainly because of characteristics such as the involvement of several players, decentralization of historical data, lack of cohesion between metrics and generally poor communication between reporters and users (Lohman et al., 2004; Naini et al., 2011). The literature on supply chain sustainability evaluation presents several qualitative and quantitative studies, including literature review (Tahir and Darton, 2010; Ahi and Searcy, 2015a; Ansari and Kant, 2017), conceptual frameworks (Chardine-Baumann and BottaGenoulaz, 2014; Varsei et al., 2014), surveys about metrics mostly used (Bloemhof et al., 2015) and quantitative models to evaluate supply chain sustainability (Brandenburg et al., 2014). In recent years, quantitative models have been increasingly proposed as an approach to support supply chain sustainability evaluation. Many types of techniques have been studied, including multicriteria decision making (Büyüközkan and Çifçi, 2011; Govindan et al., 2013), statistical (Ahi and Searcy, 2015b), mathematical programming (Kannan et al., 2013), artificial intelligence (Kuo et al., 2010) and simulation techniques (Van der Vorst et al., 2009). Selecting a set of metrics to evaluate supply chain sustainability can be approached as a multicriteria decision making problem, whose alternatives to be assessed and selected are metrics from an initial list based on a literature review. Although the literature presents several quantitative models to supply chain sustainability evaluation, just a few of them focus on the problem of selecting and weighting sustainability metrics (Bai et al., 2012; Feil et al., 2015; Chen et al., 2015; Fritz et al., 2017). However, these studies present some limitations. First, the proposed methods for metric selection are only based on the relative importance of the initial set of alternative metrics. They do not consider the degree of difficulty to gather information necessary to evaluate the supply chain sustainability on each alternative metric. This is an important aspect since the effectiveness of the supply chain sustainability evaluation process depends on the accuracy of the information regarding budgetary and time constraints. Therefore, the decision making process to select sustainability metrics should consider the availability of

ACCEPTED MANUSCRIPT

4

information required for supply chain sustainability evaluation and other resources needed such as time, people and third party firms (Wu and Barnes, 2010, Lima Junior and Carpinetti, 2016). Due to the legal requirements for standardized financial reports, economic metrics are more consolidated. On the other hand, environmental and social regulations have been evolving more recently and are less consolidated; thus, gathering of this data is not an easy task. According to Ansari and Kant (2017), the difficulty of gathering reliable data without bias contributes to the predominance of qualitative research in sustainable supply chain. Thus, the assessment of the degree of difficulty of gathering information contributes to a better analysis of environmental and social metrics that is necessary for their development and consolidation. Therefore, the metric selection process should take into account at the same time the importance of the metric as well as the degree of difficulty of data collection. Another limitation of the studies identified in the literature is that just some of them allow the use of linguistic variables to weigh the alternative metrics (Amindoust et al., 2012; Erol et al., 2011; Govindan et al., 2013; LimaJunior and Carpinetti, 2016). Even using linguistic variables, they do not deal with the situation when, due to lack of information or uncertainty, the decision maker hesitates between different linguistic terms. To this purpose, the use of hesitant fuzzy linguistic term sets (HFLTS) can be advantageous since it takes into account more than one linguistic term to model the hesitation in the decision maker judgments. Hesitant fuzzy set is a technique particularly interesting to aggregate divergent opinions in group decision making. Therefore, the main objective of this paper is to propose a multicriteria decision making model to aid the selection and weighting of metrics for supply chain sustainability evaluation. It builds on the approach proposed by Lima-Junior and Carpinetti (2016) but combines the QFD (Quality Function Deployment) technique and the method of distance between two HFLTSs proposed by Liao et al. (2014). The metric selection is initially based on a general list of metrics divided in the environmental, economic and social dimensions. The importance of the metrics is based on their relationship with requirements and the relative importance of these requirements. This procedure is based on the QFD house of quality, which is a very sound prioritization technique based on relationships, in this case between requirements and metrics (Carnevalli and Miguel, 2008). The mathematical processing of the judgments from different experts adopts the HFLTS fuzzification procedure. The final evaluation and selection of each metric is based on the relationship of these two-dimensions: the importance and degree of difficulty of data collection. The evaluation of the degree of difficulty of data collection is based on information availability, human resources and time required and additional resources as proposed by Lima-Junior and Carpinetti (2016). The paper is organized as follows: Section 2 briefly revises the subject of evaluation of supply chain sustainability, focusing especially on sustainability evaluation models. Section 3 presents some fundamental concepts on hesitant fuzzy linguistic term sets theory. Section 4 comments

ACCEPTED MANUSCRIPT

5

the QFD method and studies based on QFD. Section 5 presents the research method followed by this study. Section 6 details the proposed model. Section 7 presents an illustrative application case. Section 8 discusses the results and managerial implications. Finally, conclusions about this study and suggestions for further work are made in Section 9. 2. Evaluation of Supply Chain Sustainability Over the past decades, due to pressures from government legislation, international standards, stakeholders as customers and shareholders, many organizations have been pushed to include in their strategic improvement plans sustainability issues as a way to minimize the negative environmental and social impacts of their business operations (Ahi and Searcy, 2015a; Varsei et al., 2014). This also applies to supply chain tiers such as suppliers, manufacturers, transporters, warehouses and retailers, which have been forced to integrate efforts to manage not only economic but also environmental and social aspects. In this context, methods and metrics to evaluate performance related to supply chain sustainability have been receiving increasing attention of practitioners and researchers (Ansari and Kant, 2017; Barbosa-Póvoa et al., 2017). Sustainable supply chain management or sustainability in supply chain management, often used as synonymous, are terms for which there are no unique concepts. Dubey et al. (2017), in a literature review on the subject, present 16 different definitions for sustainable supply chain management or sustainability in supply chain management. Ahi and Searcy (2013) define sustainable supply chain management as “the voluntary integration of social, economic, and environmental considerations with the key inter-organizational business systems to create a coordinated supply chain to effectively manage the material, information and capital flows associated with the procurement, production and distribution of products or services to fulfill short term and long term profitability, stakeholder requirements, competitiveness and resilience of the organization”. Haake and Seuring (2009), in a more concise and focused way, define sustainable supply chain management as “the set of well-defined supply chain management policies, actions taken, and the relationships formed to solve the social and environmental issues related to design, acquisition, production, distribution, use, reuse and disposal of the goods and services of a firm”. Other authors define sustainability in supply chain management as “the ability of an organization to mitigate, detect, respond, and to recover from growing global threats related to supply chain and to enhance the long-term value” (Closs et al., 2011). Very close to the definition of sustainable supply chain management proposed by Haake and Seuring (2009), Linton et al. (2007) define sustainability in supply chain management as “the integration of flows by taking care of things such as product design, manufacturing by-products, byproducts produced during product use, product life extension, product end-of-life, and recovery processes at end-of-life to solve the core supply chain management issues”.

ACCEPTED MANUSCRIPT

6

In a parallel to the definition of performance evaluation proposed by Neely et al. (1995), evaluation of sustainability in supply chain refers to the process of evaluating quantitatively and/or qualitatively the effectiveness and efficiency of supply chain management to achieve the environmental, social and economic sustainability goals. For this purpose, a large variety of supply chain sustainability metrics has been suggested in the literature (Ahi and Searcy, 2015, Rahdari and Rostamy, 2015, Searcy et al., 2016, Valenzuela-Venegas et al., 2016). Since there are so many metrics available, authors such as Braz et al. (2011), Nudurupati et al. (2011) and Melnyk et al. (2014) recommend choosing a not-so-large number of metrics in order to avoid a time-consuming and costly evaluation process. Other suggestions presented in the literature on performance measurement include: set a target for each metric (Melnyk et al., 2014); define the sources of information used to quantifying the supply chain performance on each metric (Neely et al., 2000); define how often measurement should be made (Melnyk et al., 2014); and review performance metrics used over time, as supply chain competitive strategy changes (Braz et al., 2011). 2.1 Metrics and Methods for Supply Chain Sustainability Evaluation Supply chain sustainability metrics are quantitative or qualitative attributes used to quantify the performance of the supply chain tiers in relation to economics, environmental and social factors (Melnyk et al., 2014; Ahmadi et al., 2017). They enable to measure achievements related to strategic and operational objectives considering financial and non-financial aspects (Braz et al., 2011; Searcy et al., 2016). Thus, supply chain sustainability evaluation requires collecting performance data from a focal company, their distributors, warehouses, logistic providers and other suppliers. Since these historical performance data are commonly lacking, incomplete and/or decentralized (Nudurupati et al., 2011), supply chain evaluation regarding some sustainability metrics is commonly based on specialists’ knowledge and experience (LimaJunior and Carpinetti, 2017). Several authors have proposed either metrics or frameworks for supply chain sustainability evaluation as summarized in Table 1. Based on literature review, Ahi and Searcy (2015a) propose some metrics to measure performance in green and sustainable supply chains such as air emission, energy use and consumption. Rahdari and Rostamy (2015) proposed a general set of sustainability metrics at the corporate level based on the analysis of several sustainability normative frameworks. These authors suggest 70 different metrics related to factors such as productivity, health and safety management, human rights, risk management, pollution, among others. Searcy et al. (2016) analyzed a hundred corporate social responsibility reports of Canadian companies and identified 657 different metrics and concluded that the most commonly used metrics are related to issues that are regulated, such as safety. ValenzuelaVenegas et al. (2016) focus their study on the identification and classification of sustainability

ACCEPTED MANUSCRIPT

7

indicators for the assessment of eco-industrial parks. They classify 249 indicators according to the environmental, social and economic dimensions and also according to four criteria: difficulty of understanding; pragmatism of use; relevance and; partial representation of sustainability, when comparison between firms in the park is supported by the indicator. Fritz et al. (2017) propose a rank of aspects (or metrics) that can be used for sustainability data exchange and assessment over the supply chain of the automotive and electronics industries. The aspects are initially identified through literature review and then they are ordered according to the judgments of specialists within companies and several industry stakeholders. The judgments were collected through interviews and surveys. Bloemhof et al. (2015) present a framework for food chain logistics considering drivers, strategies, performance indicators and improvement opportunities to measure and potentially enhance sustainability performances. Their study was based on explorative web-based research and semi-structured interviews with the best practice players in logistic service providers in the Netherlands, the UK and France. Tahir and Darton (2010) propose a step-by-step process for selecting sustainability indicators, which was illustratively applied to the oil palm fruit production business. Take in Table 1 Quantitative techniques to the purpose of supply chain performance evaluation focused on sustainability metrics are largely proposed in the literature. Brandenburg et al. (2014) reviewed the literature and found 134 studies proposing quantitative models for sustainable supply chain management. The modelling approaches are mainly based on multicriteria decision making (MCDM) and mathematical programming methods. The most used techniques include analytic hierarchy process (AHP), analytic network process (ANP), linear programming and multiobjective linear programming. Barbosa-Póvoa et al. (2017) analyzed 220 operational research models for supporting sustainable supply chain decisions. The results indicate that mathematical programming methods and simulation are the most adopted modelling approaches. Ansari and Kant (2017) reviewed 286 papers on sustainable supply chain management. Similarly to Brandenburg et al. (2014), they conclude that the most used decision techniques are linear programming, multiobjective linear programming, analytic hierarchy process (AHP) and data envelopment analysis (DEA). According to the results showed by Brandenburg et al. (2014), Ansari and Kant (2017) and Barbosa-Póvoa et al. (2017), artificial intelligence approaches are not so much explored in the reviewed models. On the other hand, studies based on quantitative techniques applied to the selection and weighting of supply chain sustainability evaluation metrics are not so frequent in the literature. Table 2 presents some quantitative models found in the literature.

ACCEPTED MANUSCRIPT

8

Take in Table 2 Feil et al. (2015) propose a procedure based on Delphi method to identify and select indicators to measure sustainability in micro and small furniture industries. Bai et al. (2015) propose the use of grey-based neighborhood rough set theory to evaluate, select and monitor sustainability metrics. Chen et al. (2015) propose an approach to select sustainable development indicators based on fuzzy Delphi and fuzzy TODIM. Linke et al. (2013) focus on the selection of simple and relevant sustainability metrics; they also discuss different means of normalization. Haghighi et al. (2016) present a framework that combines DEA method and balanced scorecard (BSC) for performance evaluation of sustainable supply chains. Ahmadi et al. (2017) developed a model to analyze the social sustainability of manufacturing companies using the Best Worst Method. While Linke et al. (2013), Feil et al. (2015), Haghighi et al. (2016) and Ahmadi et al. (2017) use crisp numbers to the mathematical representation of the judgments of the participants regarding the weights of the metrics, Bai et al. (2015) and Chen et al. (2015) use linguistic terms based on interval numbers. However, these models do not support the situation when the decision maker hesitates between different linguistic terms due to lack of information or uncertainty. To this purpose, the use of hesitant fuzzy linguistic term sets can be advantageous since they take into account more than one linguistic term to modelling decision making. 3. Hesitant Fuzzy Linguistic Term Sets The literature presents many approaches for computing with words (CWW) to address group decision making problems in which the experts choose one linguistic term to represent their preference (Xu, 2012). It includes linguistic models based on fuzzy sets (Xu, 2009), type-2 fuzzy sets, fuzzy two tuple (Herrera and Martínez, 2000, Santos et al., 2017) and intuitionistic fuzzy sets (Atanassov, 1986). However, according to Rodríguez et al. (2012), there are some decision problems subjected to uncertainty in which the experts cannot easily provide a judgment based on a single linguistic term, as they hesitate between two or more terms to represent their judgments. It happens when the decision makers wish to express their judgments using several terms at the same time or using a more complex linguistic expression that is not included in the linguistic evaluation scale. An approach to deal with more than one linguistic term to express the decision maker judgments, named Hesitant Fuzzy Linguistic Term Sets (HFLTS) was proposed by Rodriguez et al. (2012), based on hesitant fuzzy sets (Torra, 2010). This method is more suitable to elicit linguistic preferences when decision makers hesitate between several terms of a linguistic variable. Some fundamental definitions on HFLTS are presented as follows. 3.1. Symmetric linguistic term set

ACCEPTED MANUSCRIPT

9

In order to facilitate the measurement of the distance between two HFLTS, this paper uses the symmetric linguistic term set proposed by Xu (2005), as shown in Equation (1). 𝑆 = {𝑠 ‒ 𝜏, ⋯,𝑠 ‒ 1,𝑠0,𝑠1,⋯,𝑠𝜏}

(1)

Where 𝜏 is a positive integer, 𝑠 ‒ 𝜏 and 𝑠𝜏 are respectively the lower and the upper limits of linguistic terms. The term set 𝑆 must satisfy the following conditions: 1) If 𝛼 < 𝛽, then 𝑠𝛼 < 𝑠𝛽. 2) The negation operator is defined as 𝑛𝑒𝑔(𝑠𝛼) = 𝑠 ‒ 𝛼; especially 𝑛𝑒𝑔(𝑠0) = 𝑠0. 3.2. Definition of hesitant fuzzy linguistic term sets (HFLTS) Let 𝜗 be a linguistic variable and 𝑆 be a linguistic term set represented in Equation (2) and Fig. 1. 𝑆 = {𝑆 ‒ 𝜏, ⋯,𝑆0,⋯,𝑆𝜏} = {𝑛𝑜𝑡ℎ𝑖𝑛𝑔, 𝑣𝑒𝑟𝑦 𝑙𝑜𝑤, 𝑙𝑜𝑤, 𝑚𝑒𝑑𝑖𝑢𝑚, ℎ𝑖𝑔ℎ, 𝑣𝑒𝑟𝑦 ℎ𝑖𝑔ℎ, 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒}

(2)

Take in Fig. 1 An HFLTS 𝐻𝑆(𝜗) is an ordered finite subset of the consecutive linguistic terms of 𝑆 (Rodriguez et al. 2012). For instance, an HFLTS might be 𝐻𝑆(𝜗) = {𝑛𝑜𝑡ℎ𝑖𝑛𝑔, 𝑣𝑒𝑟𝑦 𝑙𝑜𝑤, 𝑙𝑜𝑤} or 𝐻𝑆(𝜗) =

{𝑣𝑒𝑟𝑦 𝑙𝑜𝑤, 𝑙𝑜𝑤, 𝑚𝑒𝑑𝑖𝑢𝑚,ℎ𝑖𝑔ℎ}. Frequently, decision makers prefer to evaluate the alternatives by means of linguistic expressions instead of only one linguistic term. Rodriguez et al. (2012) proposed a context-free grammar to extract HFLTS from human linguistic expressions. 3.3. Transformation of linguistic expressions into HFLTS Rodriguez et al. (2012) and Rodriguez et al. (2013) propose a function 𝐸𝐺 :𝑙𝑙→𝐻𝑆 to transform 𝐻

the linguistic expressions 𝑙𝑙 into HFLTS (𝐻𝑆), according to their meaning, as follows: 1) 𝐸𝐺 (𝑠𝑖)={𝑠𝑖 / 𝑠𝑖 ∈ 𝑆}; 𝐻

2) 𝐸𝐺 (𝑎𝑡 𝑚𝑜𝑠𝑡 𝑠𝑖)={𝑠𝑗 / 𝑠𝑗 ∈ 𝑆 𝑎𝑛𝑑 𝑠𝑗 ≤ 𝑠𝑖}; 𝐻

3) 𝐸𝐺 (𝑙𝑜𝑤𝑒𝑟 𝑡ℎ𝑎𝑛 𝑠𝑖)={𝑠𝑗 / 𝑠𝑗 ∈ 𝑆 𝑎𝑛𝑑 𝑠𝑗 < 𝑠𝑖}; 𝐻

4) 𝐸𝐺 (𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑠𝑖)={𝑠𝑗 / 𝑠𝑗 ∈ 𝑆 𝑎𝑛𝑑 𝑠𝑗 ≥ 𝑠𝑖}; 𝐻

5) 𝐸𝐺 (𝑔𝑟𝑒𝑎𝑡𝑒𝑟 𝑡ℎ𝑎𝑛 𝑠𝑖)={𝑠𝑗 / 𝑠𝑗 ∈ 𝑆 𝑎𝑛𝑑 𝑠𝑗 > 𝑠𝑖}; 𝐻

6) 𝐸𝐺 (𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑠𝑖 𝑎𝑛𝑑 𝑠𝑗)={𝑠𝑘 / 𝑠𝑘 ∈ 𝑆 𝑎𝑛𝑑 𝑠𝑖 ≤ 𝑠𝑘 ≤ 𝑠𝑗}; 𝐻

ACCEPTED MANUSCRIPT

10

Using the linguistic term set of Equation (2) and linguistic expressions denoted by 𝑙𝑙, examples of HFLTS could be: 𝐻𝑆 = 𝐸𝐺 (𝑙𝑙1 = 𝑣𝑒𝑟𝑦 ℎ𝑖𝑔ℎ) = {𝑣𝑒𝑟𝑦 ℎ𝑖𝑔ℎ} 𝐻

𝐻𝑆 = 𝐸 (𝑙𝑙 = 𝑔𝑟𝑒𝑎𝑡𝑒𝑟 𝑡ℎ𝑎𝑛 𝑙𝑜𝑤) = {𝑚𝑒𝑑𝑖𝑢𝑚, ℎ𝑖𝑔ℎ, 𝑣𝑒𝑟𝑦 ℎ𝑖𝑔ℎ, 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒} 𝐺𝐻

2

𝐻𝑆 = 𝐸 (𝑙𝑙 = 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑣𝑒𝑟𝑦 𝑙𝑜𝑤 𝑎𝑛𝑑 𝑚𝑒𝑑𝑖𝑢𝑚) = {𝑣𝑒𝑟𝑦 𝑙𝑜𝑤, 𝑙𝑜𝑤, 𝑚𝑒𝑑𝑖𝑢𝑚} 𝐺𝐻

3

The literature presents several developments of the hesitant fuzzy set theory applied to decision problems. Liao et al. (2014) proposes ordering alternatives in MCDM problems using the distance between two HFLTs, as presented in the next section. 3.4. Distance between two collections of HFLTSs Distance measures are the base of some MCDM methods, e.g. TOPSIS and VIKOR. In order to use HFLTSs in MCDM problems more effectively, Liao et al. (2014) presented a family of distance measures between two collections of HFLTSs. In a MCDM problem, each alternative 𝑖 𝑖

{

𝑖1

𝑖2

𝑖𝑚

}

has a collection of HFLTSs, one for each criterion. For instance, ℍ𝑠 = 𝐻 𝑠 ,𝐻 𝑠 ,…,𝐻 𝑠 , where 𝑖

𝑖𝑗

ℍ𝑠 is a collection of HFLTSs and 𝐻 𝑠 is the HFLTSs that represents the evaluation using the 𝑗th criterion. With the ordered structure for the linguistic term set described above, the generalized 1

2

distance measure between ℍ 𝑠 and ℍ 𝑠 is defined as in Equation (3):

(∑ 𝑚

(

)

1 2 dgd ℍ 𝑠 ,ℍ 𝑠

|

1j

∑

(

|

2j λ

L δl ‒δl 1 = m × Ll = 1 2τ + 1 𝑗=1

))

1 λ

(3)

Where: 𝜆 is a parameter to determine different distance measures. For Hamming distance 𝜆 = 1; for Euclidean distance 𝜆 = 2. Based on Liao et al. (2014), this study uses 𝜆 = 2. 1𝑗

2𝑗

𝐿 is the number of linguistic terms in 𝐻 𝑆 or 𝐻 𝑆 . 1𝑗

1𝑗

2𝑗

2𝑗

𝛿 𝑙 is 𝑙𝑡ℎ term of 𝐻 𝑆 𝛿 𝑙 is 𝑙𝑡ℎ term of 𝐻 𝑆

2𝜏 + 1 is the number of linguistic terms in S For situations where the criteria have different weights, Liao et al. (2014) define the generalized 1

2

weighted distance measure between ℍ 𝑠 and ℍ 𝑠 according to Equation (4).

ACCEPTED MANUSCRIPT

(

m

wj

L

)= ∑ L ∑

(

1 2 dgwd ℍ 𝑠 ,ℍ 𝑠

j=1

(

|δ1jl ‒ δ2jl| λ 2τ + 1

l=1

))

1

11

λ

(4)

𝑡 𝑚 Where 𝑊 = (𝑤1,𝑤2,⋯,𝑤𝑚) is the weighting vector that satisfies 0 ≤ 𝑤𝑗 ≤ 1 and ∑𝑗 = 1𝑤𝑗 = 1.

To use the Equation (3) and Equation (4), the number of linguistic terms in two HFLTSs must be the same. However, the use of a different number of linguistic terms is common between different HFLTSs. Zhu and Xu (2014) introduced a method to add linguistic terms to a

HFLTS with a lower number of linguistic terms. Similarly, Liao et al. (2014) proposed the positive and negative ideal solutions based on the lower bound 𝐻 bound 𝐻

𝑖𝑗 + 𝑆

𝑖𝑗 ‒ 𝑆

and the upper

to calculate the distance between different HFLTSs.

3.5. Positive and negative ideal solutions Suppose that it is needed to evaluate a set of alternatives 𝑋 = {𝑥𝑖|𝑖 = 1,⋯,𝑛} with respect to a set of criteria 𝐶 = {𝑐𝑗|𝑗 = 1,⋯,𝑚}. Using HFLTSs, the judgment matrix is given by Equation (5).

[

11

12

𝐻𝑆

𝐻𝑆

21

22

1𝑚 𝑆 2𝑚 𝐻𝑆

⋯ 𝐻

𝐻𝑆 𝐻𝑆 ⋯ ⋮ ⋮ ⋱ ⋮ 𝑛1 𝑛2 𝑛𝑚 𝐻𝑆 𝐻𝑆 ⋯ 𝐻 𝑆

]

(5)

𝑖𝑗

Where 𝐻 𝑆 is a HFLTS which represents the evaluation of alternative 𝑥𝑖 with respect to criterion 𝑖𝑗

𝑐𝑗. Considering that 𝐻 𝑆 is a linguistic term set, its upper bound is 𝐻 its lower bound is 𝐻 solution 𝑥

+

𝑖𝑗 ‒ 𝑆

{

𝑖𝑗

𝑖𝑗 + 𝑆

{

𝑖𝑗

}

= 𝑚𝑎𝑥 𝑠𝑖𝑗|𝑠𝑖𝑗 ∈ 𝐻 𝑆 and

}

= 𝑚𝑖𝑛 𝑠𝑖𝑗|𝑠𝑖𝑗 ∈ 𝐻 𝑆 . Then, the hesitant fuzzy linguistic positive ideal

is given by Equation (6) and hesitant fuzzy linguistic negative solution 𝑥

‒

is given

by Equation (7).

𝑥

+

= 𝐻

{

𝑗+ 𝑆 |𝑗 = 1,⋯,𝑚

‒

{

𝑗‒ 𝑆 |𝑗 = 1,⋯,𝑚

𝑥 = 𝐻

Where

}

(6)

}

(7)

ACCEPTED MANUSCRIPT

𝐻

𝑚𝑎𝑥{𝑠𝑖𝑗|𝑖 = 1,⋯,𝑛} 𝑓𝑜𝑟 𝑏𝑒𝑛𝑒𝑓𝑖𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛 𝑐𝑗 𝑚𝑖𝑛{𝑠𝑖𝑗|𝑖 = 1,⋯,𝑛} 𝑓𝑜𝑟 𝑐𝑜𝑠𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛 𝑐𝑗

𝑗+ 𝑆

=

{

𝑗‒ 𝑆

=

𝑚𝑖𝑛{𝑠𝑖𝑗|𝑖 = 1,⋯,𝑛} 𝑓𝑜𝑟 𝑏𝑒𝑛𝑒𝑓𝑖𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛 𝑐𝑗 𝑚𝑎𝑥{𝑠𝑖𝑗|𝑖 = 1,⋯,𝑛} 𝑓𝑜𝑟 𝑐𝑜𝑠𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛 𝑐𝑗

𝐻

{

12

The distance between each alternative 𝑥𝑖 and its hesitant fuzzy linguistic positive ideal solution 𝑥

+

or/and hesitant fuzzy linguistic negative solution 𝑥

‒

can be used to choose the best

(

alternative considering the set of criteria. The best alternative has the smaller distance 𝑑 𝑥𝑖,𝑥

(

or the larger distance 𝑑 𝑥𝑖,𝑥

‒

+

)

).

3.6. Satisfaction degree of an alternative Based on the TOPSIS method, Liao et al. (2014) proposed the use of both distances measured simultaneously rather than separately. The satisfaction degree of an alternative 𝑥𝑖 is calculated by Equation (8), in which 𝜃 is a risk parameter. 𝜃 > 0.5 implies a pessimistic view of the decision maker while 𝜃 < 0.5 implies the opposite. .

𝜂(𝑥𝑖) =

(

) + ‒ θ 𝑑(𝑥𝑖,𝑥 ) + (1 ‒ 𝜃)𝑑(𝑥𝑖,𝑥 ) (1 ‒ 𝜃)𝑑 𝑥𝑖,𝑥 ‒

(8)

4. Quality Function Deployment (QFD) The first QFD concepts were formalized in Japanese firms such as Bridgestone and Matsushita in 1960s. However, the QFD method used in the product development process was spread to the West by the paper entitled Quality Deployment (hinshitsu tenkai) wrote by Akao in 1972. This work proposed the use of a quality table to analyse the correlation between customer requirements and the counterpart engineering characteristics (Chan and Wu, 2002). Since the first publication researchers and practitioners have proposed improvements and novel applications for QFD. In a literature review Carnevalli and Miguel (2008) classified most of the works about QFD as “conceptual research” whose objectives and scope indicating efforts to suit this tool in specific applications or to improve the method and facilitate its application. Although it has been originally developed for applications in product development planning, its applications have expanded to different process such as management, decision-making,

ACCEPTED MANUSCRIPT

13

engineering, planning, teamwork and costing (Chan and Wu, 2002). Moreover, some of these approaches combine QFD concepts with MCDM techniques such as AHP, ANP, TOPSIS and fuzzy methods to deal with the subjectivity of the analysis of the quality matrix (Chan and Wu, 2002; Carnevalli and Miguel, 2008; Juan et al., 2009; Wang, 2010). Different proposals have been developed in the context of supply chain management. Bevilacqua et al. (2006) suggested a model using fuzzy numbers and QFD to a supplier selection process for a medium-to-large industry. Buyukӧzkan and Berkol (2011) use analytic network process (ANP) and goal programming in a QFD approach to identify the most important requirements for sustainable supply chain. Dursun and Karsak (2013) propose using the QFD concept in a fuzzy multi-criteria group decision making model for supplier selection process. This model identifies the relevant supplier evaluation criteria based on the characteristics that the purchased items should possess to satisfy the firms requirements. Buyukӧzkan and Çifçi (2013) apply QFD concepts and fuzzy set theory in a group decision making (GDM) model for sustainable supply chain. The approach gathers incomplete information and multiple preference formats from different decision makers and aggregate them using fuzzy set theory. Lima-Junior and Carpinetti (2016) combine QFD and fuzzy sets to propose a model to support the choice and weighting of criteria for supplier selection process. 5. Research Method According to Bertrand and Fransoo (2002), the study presented in this paper is classified as axiomatic prescriptive model-based research. It is axiomatic since it is guided by the conceptualized prescriptive rules and policies for managerial decision making. Therefore, the research method of this study is much related with the model itself and its implementation and test. Fig. 2. illustrates the main elements of the quantitative model proposed in this paper in connection with the research method discussed here. The quantitative model is detailed in the next section, with the proposed rules and policies organized in a process of 4 main steps. The QFD procedures are embedded in steps 1 and 2. In steps 1 to 3, the mathematical processing of the judgments from different experts adopts the HFLTS fuzzification procedure. Distance measures from positive and negative ideal solution based on HFLTS are used in steps 1 to 3 in the evaluation of the importance of variables and relationships between requirements and metrics. All the mathematical formulations are presented in section 3. Apart from the quantitative model, the research procedure also included developing and testing of the computational model, which was implemented in Microsoft Excel©, so as to facilitate replication. Take in Fig. 2

ACCEPTED MANUSCRIPT

14

6. Proposed Model for Selecting Supply Chain Sustainability Metrics Fig. 3 presents the proposed model for selecting supply chain sustainability metrics. The involvement of experts from different functional area is important since there are many tradeoffs between metrics of the sustainability dimensions. It is desirable that the decision makers be from purchasing, logistic, quality, financial and product engineering. The proposed model presents four steps. The aim of the first step is selecting and weighing requirements for supply chain sustainability based on the judgments of the experts. The objective of step two is to deploy metrics from requirements and then ponder them based on the intensity of their relationship with each requirement. In step three, the objective is the evaluation of the degree of difficulty of data collection for each metric previously selected. In last step, each metric is classified into two groups based on importance and degree of difficulty of data collection. The next paragraphs describe these steps in detail. Take in Fig. 3 6.1. Step 1: Requirement selection and weighting In the first step, the decision makers should select the set of 𝑚 requirements for supply chain sustainability assessment. They can extract the requirements from the list showed in Table 3, as suggested in the literature, or identify new ones according to their specific supply chain strategy. The set of requirements should consider the economic, environmental and social dimensions. Take in Table 3 Once the set of the 𝑚 requirements has been defined, their importance is assessed by judgements of each decision maker using linguistic terms and expressions. The judgments can be based on the basic linguistic term set (BLTS) showed in Fig. 1, as suggested by Herrera and Martinez (2000). The linguistic terms and expressions are then transformed into HFLTSs as in section 3.3. Next, the distances of each requirement evaluation from its positive ideal solution Equation (6) and its negative ideal solution Equation (7) are determined using Equation (3). In order to determine the weight of each requirement, its degree of satisfaction is calculated following Equation (8) and using 𝜃 = 0.5, as suggested by Liao e al (2014). Finally, the degree of satisfaction should be normalized to define the weighting vector 𝑊 = (𝑤1,𝑤2,⋯,𝑤𝑚) 𝑚

satisfies 0 ≤ 𝑤𝑗 ≤ 1 and ∑𝑗 = 1𝑤𝑗 = 1.

𝑡

that

ACCEPTED MANUSCRIPT

15

6.2. Step 2: Metric selection and evaluation In the second step, the experts should define a set of 𝑛 metrics which are related to the requirements selected in the previous step. This selection of metrics should consider the triple bottom line (TBL), the industry segment and the strategy of the supply chain. Based on the literature review, Table 4 presents a list of different metrics grouped into the dimensions of the triple bottom line. It is important to point out that Table 4 is not an ultimate or complete list of metrics. If necessary, the group decision can adopt new metrics to satisfy specifics needs of the stakeholders. Take in Table 4 After the initial selection of the 𝑛 metrics, each participant evaluates the relationship between each metric and each requirement. Similarly to Step 1, they use the BLTS presented in Fig.1 as linguistic terms and expressions in their judgments. It is suggested that the decision makers try to reach consensus in their assessments. In case it is not possible, the divergent opinions are aggregated as suggested by Liao et al. (2014). For example, one decision maker uses the term 𝑠3 in his judgment, and another person uses the expression that result in the sub-set {𝑠4,𝑠5}. If they do not reach consensus, then the representation of the assessment as a HFLTS could be

{𝑠3,𝑠4,𝑠5}. Once the relationship between each metric and each requirement has been evaluated using HFLTS, the weight of each metric is determined by calculating the distance from its positive ideal solution and from its negative ideal solution, according to Equations (4), (6) and (7). The weights of the 𝑚 requirements used in Equation (4) were determined in step 1. Then, the degree of satisfaction is computed according to Equation (8) to define the relative importance of each metric. 6.3. Step 3: Evaluation of difficulty of data collection The objective in this step is to assess the degree of difficulty to collect data to evaluate supply chain sustainability in regard to each one of the 𝑛 metrics. Based on Lima Junior and Carpinetti (2016), evaluation of the difficulty of data collection by the decision makers takes into account: 

Information availability: the information available, either historical records or tacit knowledge of the decision makers.



Human resource and time required: the number of people involved in the process and the time necessary for evaluation.



Other resources: takes into account any other required resource such as hiring services from third party.

ACCEPTED MANUSCRIPT

16

Again, as assessment of these criteria is mostly of qualitative nature, requiring judgments by the decision makers, HFLTS can also be used. The five linguistic terms presented in Fig. 4 are used to judge these criteria (as proposed by Lima-Junior and Carpinetti (2016)). Similarly to step 2, the decision makers should try to reach consensus in their assessments or aggregate the different terms according to Liao et al. (2014). Take in Fig. 4 To determine the distance of each metric from the positive and negative ideal solution in each parameter, it should be considered that information availability is a cost criterion because the difficulty of data collection decreases with the increase of information availability. Therefore, information availability uses the lower bound to define its positive ideal solution, as in Equation (6), and the upper bound to define its negative ideal solution, as in Equation (7). On the other hand, human resource and time required and additional resources are benefit criteria and therefore these criteria use the upper bound to define their positive ideal solution, Equation (6), and the lower bound to define their negative ideal solution, Equation (7). Since the three criteria have the same weight, the distance of each metric from its positive and negative ideal solutions is as in Equation (3). The difficulty of data collection of each metric is determined by its degree of satisfaction using Equation (8). 6.4. Step 4: Metrics classification and final selection The aim of step 4 is the categorization and final selection of the set of metrics for supply chain sustainability evaluation. The categorization of the metrics is based on a two-dimensional model as illustrated in Fig. 5, where the line 𝑦 = 𝑥 divides the Euclidean space into two zones. Metrics that fall in the upper zone have a favourable relation between importance (vertical axis) and difficulty of data collection (horizontal axis). On the contrary, metrics with unfavourable relation between importance and difficulty of data collection will fall in the lower zone. Take in Fig. 5 The outputs from steps 2 and 3, evaluations of importance and difficulty of data collection, are normalized into an interval [0, 1] using a sigmoid normalization, as in Equation (9). In this equation, 𝑣𝑛 is the normalized value, while 𝑣 is the original value, 𝑣 is the mean and 𝜎𝑣 is the standard deviation from the set of original values.

ACCEPTED MANUSCRIPT 𝑣𝑛 =

17

1 ‒

1+e

𝑣‒𝑣 𝜎𝑣

(9)

7. Illustrative application case In order to illustrate the proposed model, a pilot application was carried out in a clutch manufacturer. Three decision makers were interviewed to collect their perceptions about the set of requirements and metrics. They are from financial (DM1), purchasing (DM2) and quality department (DM3). The automobile supply chain can be classified as a functional supply chain (Fisher, 1997) or lean supply chain (Gattorna, 2010), so cost, delivery performance and continuous product and process improvement are its main competitive priorities. In step one, initially, the group of three decision makers defined the set of seven requirements for supply chain sustainability assessment, based on the list presented in Table 3. The set of requirements chosen regarding the three performance dimensions were: cost (R1), delivery reliability (R2), environmental aspects (R3), innovation (R4), management practices (R5), quality/customer satisfaction (R6) and social responsibility (R7). Analysing the set of requirements, the decision makers judged the importance of each requirement using linguistic terms and expressions. Table 5 shows the judgments about the importance of the seven requirements by the three decision makers. The judgments in table 5, transformed into HFLTS, are presented in Table 6. Take in Table 5 Take in Table 6 To determine the Euclidean distance of the importance evaluation of each requirement from its positive ideal solution and from its negative ideal solution, Equation (3) was used considering that the decision makers have the same weight, that is: 𝑤𝐷𝑀1 = 𝑤𝐷𝑀2 = 𝑤𝐷𝑀3 =

1

3. The

second and third columns of Table 7 show respectively the positive and negative distances of each requirement. Equation (8) was applied to calculate the degree of satisfaction of the requirements, showed in the fourth column. The degree of satisfaction is normalized to define 𝑡

7

the weighting vector 𝑊 = (𝑤1,𝑤2,⋯,𝑤7) , to satisfy ∑𝑗 = 1𝑤𝑗 = 1, as shown in the last column of Table 7. Take in Table 7

ACCEPTED MANUSCRIPT

18

In the second step, the decision makers selected a set of 15 metrics from the list of Table 4, considering the supply chain strategy of the company. The initial set of metrics included seven economic metrics: competitive price (I1), financial situation (I2), commitment to cost reduction (I3), delivery in full on time (I4), ability to problem resolution (I5), commitment to quality improvement (I6) and R&D capabilities (I7); five environmental metrics: consumption of nonrenewable materials (I8), waste disposal (I9), environmental certification (I10), environmental costs (I11) and clean technology availability (I13); and three social metrics: employee training and development (I12), working conditions (I14) and stakeholders relationship (I15). In order to assess the intensity of relationship between each metric and each requirement, the decision makers used linguistic terms and expressions based on the BLTS of Fig. 1. Table 8 shows the HFLTSs resulting from the aggregation of the decision maker judgments in regard to the intensity of relationship between metrics requirements. Take in Table 8 Equation (4) was used to determine the distance of the evaluation of the degree of relationship from its positive and negative ideal solutions considering the weights of each requirements. The second and third columns of Table 9 show the positive and negative distance of each metric, respectively. The degree of satisfaction of each metric, calculated by Equation (8), is showed in fourth column. Take in Table 9 In step 3, the decision makers evaluate the degree of difficulty of data collection for each metric considering the criteria information availability, human resource and time required and additional resources. The evaluation was made using linguistic terms and expressions based on the BLTS of Fig. 3. As in step 2, the judgments were aggregated as suggested by Liao et al. (2014). Table 10 presents the HFLTS of the metrics for each criterion. Equation (3) was used to determine the distance of each criterion from its positive and negative ideal solutions, which were determined according Equation (6) and (7) and taking into consideration that information availability is a cost criterion and human resource and time required and additional resources are benefit criteria. Table 11 show the positive and negative distances of each metric. Equation (8) was used to calculate the degree of satisfaction of the metrics, showing on the fourth column of Table 11. Take in Table 10

ACCEPTED MANUSCRIPT

19

Take in Table 11 Finally, in step 4, Equation (9) was used to normalize the degree of satisfaction related to the importance of the metric (output of step 2) and the difficulty of data collection (step 3). The normalized values are presented in Table 12. Based on these results, Fig. 6 shows the metrics distributed into the two-dimensional categorization model presented in section 6.4 and Fig. 5. Based on the judgments of the participants, the proposed decision model indicates as the preferred metrics: competitive price (I1), delivery in full on time (I4), financial situation (I2), environmental certification (I10), ability to problem resolution (I5), employee training and development (I12), commitment to quality improvement (I6) and commitment to cost reduction (I3).

Take in Table 12 Take in Fig. 6

8. Results and discussion In the application case developed in this study, the experts were more concerned with economic performance than environmental and social performance. Metrics such as competitive price, delivery in full on time and financial situation present low difficulty of data collection because the current procedures of logistics, purchasing and financial departments already use this kind of information. On the other hand, since environmental and social metrics are scarcely used, metrics such as stakeholder’s relationship (I15), environmental costs (I11), working conditions (I14) and consumption of non-renewable materials (I8) present high difficulty of data collection. Additionally, the Brazilian automotive industry had gone through several difficulties when the research was carried out because of the economic recession and sales reduction. Once the supply chain sustainability metrics are selected based on the relationship between importance and difficulty of data collection, the metrics with larger priority were the most traditional: competitive price (I1), delivery in full on time (I4) and financial situation (I2). Social and environmental metrics presented greater difficulty of data collection, because managing social and environmental aspects of the firm´s operations is still less urgent than managing economic and financial aspects. One exception was environmental certification (I10) that had a low difficulty of data collection due to the current practice of independent third-party audits. When compared with previous similar studies, results yielded by the proposed model are somewhat similar to ones obtained by Linke et al. (2013), in which economic metrics related to

ACCEPTED MANUSCRIPT

20

productivity and cost have also been considered as the most important for discrete manufacturing processes. Another similarity occurs with respect to the results found by Rahdari and Rostamy (2015) and Ahmadi et al. (2017), as both studies suggest the adoption of the metric employee training and development. In addition, in line with the results of this study, Ahi and Searcy (2015a) also recommend the use of metrics related to quality while Rahdari and Rostamy (2015) suggest consider the environmental certification for supply chain sustainability evaluation. On the other hand, results of this study differ from Ahi and Searcy (2015a) and Feil et al. (2015), since environmental metrics such as air emissions, energy use, greenhouse gas emissions, effluent treatment and recycling of waste were assessed as the most relevant. However, these results cannot be generalized since they depend on choices made by the decision makers such as the ones driven by company´s strategic priorities. The supply chain sustainability metric selection can be used for different decision-making processes like vendor selection, development of supply and distribution channels etc.; so a model for this purpose should be easy to use by experts from different functional areas. Application of the proposed model in the particular company (clutch manufacturer) did not present significant difficulties. The participants were familiarized with the QFD technique. The decision makers also agreed that the evaluation based on a range of linguistic expressions led to a better representation of their judgments. It supports the supposition that it is a more adequate technique to collect perceptions since it allows for hesitation in the judgments. Another aspect of the decision model considered relevant by the participants of the study was the consideration of the degree of difficulty of data collection in the process of metric selection. And again the use of HFLTS is also much adequate to capture a very subjective perception. Although it was just an illustrative application case, not part of a planning process, the decision makers demonstrated concordance about the preferred metrics resulting from the proposed decision model. 9. Conclusion This study proposed a new group decision approach for selection and weighting of supply chain sustainability metrics based on the combination of the QFD technique with the method of distance between two HFLTSs proposed by Liao et al. (2014). The most important metrics are those that present a better ratio between importance and difficulty of data collection. In the application case in a clutch manufacturer, the decision makers understood the model without any significant difficulty. Moreover, the use of linguistic expressions for the judgments of the decision makers has demonstrated to be an interesting alternative, since it allows the use of usual comparative expressions. Genuine contributions brought by this proposal are:

ACCEPTED MANUSCRIPT 

21

Unlike the previous hesitant fuzzy QFD approach proposed by Onar et al. (2016), in which the number of requirements is limited by the difficulty of the experts in making simultaneous comparative judgment, this model does not limit the number of requirements or metrics. In addition, the proposed model requires a smaller number of judgments from decision makers than comparative approaches, which brings agility of the decision making process;



It does not need parameterization of a base of rules, as in the QFD approaches based on fuzzy inference system;



It makes possible the use of linguistic expressions to assess the requirements, their relationships with the metrics and the difficulty of data collection. These expressions facilitate the evaluation processes since the decision makers have more freedom to judge the alternatives. The transformation from linguistic expressions to HFLTS and the determination of the satisfaction degree, using positive and negative distances, enabled the model to make aggregation operations with suitable modelling of uncertainty and vagueness;



Differently from previous studies on selecting supply chain sustainability metrics, it proposes that the final selection of metrics also takes into account the degree of difficulty of data collection, which is evaluated based on linguistic expressions regarding information availability, human resource and time required and other resources;



The decision model ranks the metrics according to the relationship between relative importance and degree of difficulty of data collection. In addition, it categorizes the metrics into segments so as to support the final selection.

Future researches can apply this model for selecting and weighting supply chain sustainability metrics in different industry sectors and considering the opinion of more decision makers. The proposed model also can be applied to selecting metrics for sustainable supplier selection and evaluation. In addition, further studies can test the hesitant fuzzy QFD approach proposed by this study to deal with decision making in problems involving the selection, ordering or categorization of alternatives. Acknowledgments To CNPq (445190/2014-0), FAPEMIG (APQ-00422-14) and FAPESP (2016/14618-4) for supporting this research project. References

ACCEPTED MANUSCRIPT

22

Ahi, P., Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329-341. Ahi, P., Searcy, C. (2015a). An analysis of metrics used to measure performance in green and sustainable supply chains. Journal of Cleaner Production, 86, 360-377. Ahi, P., Searcy, C. (2015b). Assessing sustainability in the supply chain: A triple bottom line approach. Applied Mathematical Modelling, 39, 2882-2896. Amindoust, A., Ahmed, S., Saghafinia, A., Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12 (6), 1668–1677. Ahmadi, H.B., Kusi-Sarpong, S., Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation & Recycling, 126, 99-106. Ansari, Z. N., Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of Cleaner Production, 142, 2524– 2543. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20, 87-96. Bai, C., Sarkis, J., Wei, X., Koh, L. (2012). Evaluating ecological sustainable performance measures for supply chain management. Supply chain management: An International Journal, 17 (1), 78-92. Barbosa-Póvoa, A.P., Silva, C., Carvalho, A. (2017). Opportunities and Challenges in Sustainable Supply Chain: An Operations Research Perspective. European Journal of Operational Research, 2017 (in press). https://doi.org/10.1016/j.ejor.2017.10.036 Bertrand, J.W.M., Fransoo, J.C. (2002). Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, 22 (2), 241–264. Bevilacqua, M., Ciarapica, F.E., Giaccheta, G. (2006). A fuzzy-QFD approach to supplier selection. Journal of Purchasing & Supply Management, 12 (1), 14-27. Bloemhof, J.M, van der Vorst, J.G.A.J., Bastl, M., Allaoui, H. (2015). Sustainability assessment of food chain logistics. International Journal of Logistics Research and Applications, 18 (2), 101-117. Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233 (2), 299–312. Braz, R.G.F.; Scavarda, L.F.; Martins, R.A. (2011). Reviewing and improving performance measurement systems: An action research. International Journal Production Economics, 133, 751-760. Büyüközkan, G., Berkol, Ç. (2011). Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment. Expert Systems with Applications, 38(11), 13731–13748. Büyüközkan, G., Çifçi, G. (2011). A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Computers in Industry, 62 (2), 164–174. Büyüközkan, G., Çifçi, G. (2013). An integrated QFD framework with multiple formatted and incomplete preferences: A sustainable supply chain application. Applied Soft Computing Journal, 13(9), 3931–3941. Carnevalli, J. A., Miguel, P. C. (2008). Review, analysis and classification of the literature on QFD-Types of research, difficulties and benefits. International Journal of Production Economics, 114(2), 737–754.

ACCEPTED MANUSCRIPT

23

Chan, L.-K., Wu, M.-L. (2002). Quality function deployment: A literature review. European Journal of Operational Research (Vol. 143). Chang, B., Chang, C., Wu, C. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38, 1850-1858. Chardine-Baumann, E., Botta-Genoulaz, V. (2014). A framework for sustainable performance assessment of supply chain management practices. Computers & Industrial Engineering, 76, 138–147. Chen, R., Lin, Y., Tseng, M. (2015). Multicriteria analysis of sustainable development indicators in the construction minerals industry in China. Resource Policy, 46, 123133. Closs, D.J., Speier, C., Meacham, N. (2011). Sustainability to support end-to-end value chains: the role of supply chain management. Journal of the Academy of Marketing Science, 39, 101-116. Dempsey, W. A. (1978). Vendor Selection and the Buying Process. Industrial Marketing Management, 7 (4), 257-267. Dickson, G. W. (1966). An analysis of supplier selection systems and decisions. Journal of Purchasing 2 (1), 5-17. Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Shibin, K.T., Wamba, S.F. (2017). Sustainable Supply Chain Management: Framework and Further Research Directions. Journal of Cleaner Production, 142, 1119–1130. Dursun, M., Karsak, E.E. (2013). A QFD-based fuzzy MCDM approach for supplier selection. Applied Mathematical Modelling, 37 (8), 5864-5875. Erol, I., Sencer, S., Sari, R. (2011). A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain. Ecological Economics, 70 (6), 1088– 1100. Feil, A.A., Quevedo, D.M., Schreiber, D. (2015). Selection and identification of the indicators for quickly measuring sustainability in micro and small furniture industries. Sustainable Production and Consumption, 3, 34–44. Fisher, M. L. (1997). What is the right supply chain for your product? Harvard Business Review, March-April, 83-93. Fritz, M.M.C., Schöggl, J., Baumgartner, R.J. (2017). Selected sustainability aspects for supply chain data exchange: towards a supply chain-wide sustainability assessment. Journal of Cleaner Production, 141, 587-607. Gattorna, J. (2010). Dynamic supply chains: Delivering value through people. London, UK: Financial Times Prentice Hall. Germain, R., Dröge, C. (1990). Wholesale operations and vendor evaluation. Journal of Business Research, 21 (2), 119-129. Govindan, K., Khodaverdi, R., Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47, 345–354. Gunasekaran, A., Irani, Z., Choy, K., Filippi, L., Papadopoulos, T. (2015). Performance measures and metrics in outsourcing decisions: A review for research and applications. International Journal of Production Economics, v161, p. 153–166. Haake, H., Seuring, S. (2009). Sustainable procurement of minor items – exploring limits to sustainability. Sustainable Development, 17, 285-294.

ACCEPTED MANUSCRIPT

24

Haghighi, S.M., Torabi, S.A., Ghasemi, R. (2016). An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). Journal of Cleaner Production, 137, 579-597. Herrera, F., Martinez, L., (2000). An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decisionmaking. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 8 (5), 539-562. Ho, W., Xu, X., Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research 202 (1), 16-24. Huang, S. H., Keskar, H. (2007). Comprehensive and configurable metrics for supplier selection. International Journal of Production Economics, 105 (2), 510-523. Humphreys, P. K., Wong, Y.K., Chan, F.T.S. (2003). Integrating environmental criteria into the supplier selection process. Journal of Materials Processing Technology, 138 (1), 349356. Izadikhah, M., Saen, R.F. (2016). Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data. Transportation Research, 49, 110–126. Juan, Y.K., Perng, Y.P., Castro-Lacouture, D., Lu, K.S. (2009). Housing refurbishment contractors selection based on a hybrid fuzzy-QFD approach. Automation in Construction, 18 (1), 139-144. Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., Diabat, A. (2013). Integrated fuzzy multi criteria decision making method and multiobjective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner Production, 47, 355–367. Kannan, V. R., Tan, K. C. (2002). Supplier selection and assessment: their impact on business performance. Journal of Supply Chain Management, 38 (4), 11–21. Kar, A.K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23-33. Katsikeas, C.C., Paparoidamis, N.G., Katsikea, E. (2004). Supply source selection criteria: the impact of supplier performance on distributor performance. Industrial Marketing Management, 33 (8), 755-764. Khodakarami, M., Shabani, A., Saen, R.F., Azadi, M. (2015). Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management. Measurement, 70, 62–74. Kilincci, O., Onal, S.A. (2011). Fuzzy-AHP approach for supplier selection in a washing machine company. Expert Systems with Applications, 38 (8), 9656-9664. Ko, M., Tiwari, A., Mehnen, J. (2010). A review of soft computing applications in supply chain management. Applied Soft Computing, 10, 661-674. Ku, C., Chang, C., Ho, H. (2010). Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming. Quality and Quantity, 44 (4), 623-640. Kuo, R. J., Wang, Y. C., Tien, F. C. (2010). Integration of artificial neural network and MADA methods for green supplier selection. Journal of Cleaner Production, 18 (12), 1161– 1170.

ACCEPTED MANUSCRIPT

25

Lambert, D.M., Cooper, M.C., Pagh, J.D. (1998). Supply Chain Management: Implementation Issues and Research Opportunities. The International Journal of Logistics Management, 9, 1-20. Liao, H., Xu, Z., Zeng, X.-J. (2014). Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Information Sciences, 271, 125–142. Lima Junior, F. R., Carpinetti, L. C. R. (2016). A multicriteria approach based on fuzzy QFD for choosing criteria for supplier selection. Computers and Industrial Engineering, 101, 269–285. Lima Junior, F. R., Carpinetti, L. C. R. (2017). Quantitative models for supply chain performance evaluation: A literature review. Computers and Industrial Engineering, 113, 333-346. Linke, B.S., Corman, G.J., Dornfeld, D.A., Tönissen, S. (2013). Sustainability indicators for discrete manufacturing processes applied to grinding technology. Journal of Manufacturing Systems, 32, 556– 563. Linton, J.D., Klassen, R., Jayaraman, V. (2007). Sustainable supply chains: an introduction, Journal of Operations Management, 25, 1075-1082. Liu, H., Rodríguez, R. M. (2014). A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Information Sciences, 258, 220–238. Lohman, .C, Fortuin, L., Wouters, M. (2004). Designing a performance measurement system: a case study. European Journal of Operational Research, 156, 267-286. Mani, V., Agrawal, R., Sharma, V. (2014). Supplier selection using social sustainability: AHP based approach in India. International Strategic Management Review, 2 (2), 98-112. Melnyk, S. A.; Bititci, U., Platts, K., Tobias, J., Andersen, B. (2014). Is performance measurement and management fit for the future? Management Accounting Research, 25 (2), 173-186.

Melo, M.T., Nickel, S., Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196, 401-412. Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M. (2011). Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain. Resources, Conservation and Recycling, 55, 593–603. Neely, A., Gregory, M., Platts, K. (1995). Performance Measurement system design: a literature review and research agenda. International Journal of Operations and Production Management, 15, 80-166. Neely, A.; Mills, J.; Platts, K.; Richards, H.; Gregory, M.; Bourne, M.; Kennerley, M. (2000). Performance measurement system design: developing and testing a process-based approach. International Journal of Operations & Production Management, 20 (10), 1119-1145. Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S. (2011). State of the art literature review on performance measurement. Computers & Industrial Engineering, 60, 279-290. Omurca, S.I. (2013). An intelligent supplier evaluation, selection and development system. Applied Soft Computing, 13 (1), 690-697. Onar, S., Büyüközkan, G., Öztays¸ B., Kahraman, C., (2016). A new hesitant fuzzy QFD approach: An application to computer workstation selection. Applied Soft Computing, 46, 1–16.

ACCEPTED MANUSCRIPT

26

Osiro, L., Lima Junior, F. R. Carpinetti, L.C.R. (2014). A fuzzy logic approach to supplier evaluation for development. International Journal of Production Economics, 153, 95112. Rahdari, A.H., Rostamya, A.A.A. (2015). Designing a general set of sustainability indicators at the corporate level. Journal of Cleaner Production, 108, 757–771. Rajesh, R., Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343-359. Rezaei, J., Ortt, R. (2013). Multi-criteria supplier segmentation using a fuzzy preference relations based AHP. European Journal of Operational Research, 225 (1), 75-84. Rodriguez, R. M., Martinez, L., Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20 (1), 109–119. Rodríguez, R. M., Martínez, L., Herrera, F. (2013). A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Information Sciences, 241, 28–42. Santos, L. F. O. M., Osiro, L., Lima, R. H. P. (2017). A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria. Expert Systems With Applications, 79, 53–64. Searcy, C., Dixon, S.M., Neumann, W.P. (2016). The use of work environment performance indicators in corporate social responsibility reporting. Journal of Cleaner Production, 112 (4), 2907–292. Seuring S. (2013). A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54, 1513-1520. Subramanian, N., Gunasekaran, A. (2015). Cleaner supply-chain management practices for twenty-first-century organizational competitiveness: Practice-performance framework and research propositions. International Journal of Production Economics, 164, 216– 233. Tahir, A.C., Darton, R.C. (2010). The Process Analysis Method of selecting indicators to quantify the sustainability performance of a business operation. Journal of Cleaner Production, 18, 1598-1607. Torra, V. (2010). Hesitant Fuzzy Sets. International Journal of Intelligent Systems, 25, 529-539. Uysal, F. (2012). An integrated model for sustainable performance measurement in supply chain. Procedia - Social and Behavioral Sciences, 62, 689–694. Valenzuela-Venegas, G., Salgado, J.C., Díaz-Alvarado, F.A. (2016). Sustainability indicators for the assessment of eco-industrial parks: classification and criteria for selection. Journal of Cleaner Production, 133, 99-116. van der Vorst, J. G. A. J., Tromp, S. O., van der Zee, D. J. (2009). Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. International Journal of Production Research, 47 (23), 6611-663. Varsei M, Soosay C, Fahimnia B, Sarkis J. (2014). Framing sustainability performance of supply chains with multidimensional indicators. Supply chain management: An International Journal, 19, 242-257. Wang, S. Y. (2010). Constructing the complete linguistic-based and gap-oriented quality function deployment. Expert Systems with Applications, 37(2), 908–912. Weber, C. A., Current, J. R., Benton, W. C. (1991). Vendor selection criteria and methods. European Journal of Operational Research, 50 (1), 2-18.

ACCEPTED MANUSCRIPT

27

Wu, C., Barnes, D. (2010). Formulating partner selection criteria for agile supply chains: A Dempster–Shafer belief acceptability optimization approach. International Journal of Production Economics, 125 (2), 284-293. Xu, Z. (2005). Deviation measures of linguistic preference relations in group decision making. Omega, 33 (3), 249–254. Xu, Z. (2009). Correlated linguistic information aggregation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17 (5), 633-647. Xu, Z. (2012). Linguistic Decision Making: theory and methods. Springer. Zhu, B., Xu, Z. (2014). Consistency Measures for Hesitant Fuzzy Linguistic Preference Relations. IEEE Transactions on Fuzzy Systems, 22 (1), 35–45.

ACCEPTED MANUSCRIPT

1.00

N

0.00 0.00

VL

L

M

H

VH

A

0.17

0.33

0.50

0.67

0.83

1.00

Fig. 1 – The basic linguistic term set with seven terms

x

ACCEPTED MANUSCRIPT

Fig. 2 – Illustrative view of the main elements related to the research method.

Fig. 3 – The proposed model for selecting supply chain sustainability metrics

ACCEPTED MANUSCRIPT

1.00

VL

0.00 0.00

L

M

H

VH

0.25

0.50

0.75

1.00

Fig. 4 – The basic linguistic term set with five terms used in step 3

Fig. 5 – Two-dimensional categorization model

x

ACCEPTED MANUSCRIPT

Fig. 6 – Categorization of supply chain sustainability metrics

ACCEPTED MANUSCRIPT

    

Group decision model for choosing supply chain sustainability metrics Selection based on importance and difficult of data gathering of each metric Metric importance evaluated by deployment of strategic requirements using QFD HFLTS enables evaluation based on ordinary linguistic expressions Social and environmental metrics present relevant difficulty of data collection

Table 1 – Qualitative studies about metrics for supply chain sustainability evaluation. Author(s) Tahir and Darton (2010) Ahi and Searcy (2015a) Rahdari and Rostamy (2015) Bloemhof et al. (2015) Searcy et al. (2016) Valenzuela-Venegas et al. (2016) Barbosa-Póvoa et al. (2017) Fritz et al. (2017)

Scope Selecting indicators to quantify the sustainability performance of a business An analysis of metrics used to measure performance in green and sustainable supply chains Designing a general set of sustainability indicators at the corporate level

Methodology Literature review and Process Analysis Method Literature review

Sustainability assessment of food chain logistics Analysis of performance indicators in corporate social responsibility reporting Sustainability indicators for the assessment of eco-industrial parks: classification and criteria for selection A review on OR methods and metrics to support sustainable supply chain decisions Identification and selection of sustainability aspects for performance assessment of supply chain

Semi-structured interviews Literature review and report analysis

Literature review and report analysis

Literature review

Metrics Product quality in terms of health and food safety, economic worth of products, profit and profitability, environmental incidents concerning products, among others. Quality, air emissions, energy use, energy consumption, recycling, solid waste, flexibility, environmental management system, life cycle assessment, profit, water consumption, among others. Investment risk management, internal controls and monitoring, risk management, political risk, financial disclosure and timeliness, non-financial information, shareholder rights, health and safety management, employee productivity, child labor and forced labor, waste, emission and pollution, energy efficiency, water use, among others. Water use, energy use, total waste production, accidents, renewable energy, recycling and recovery rate, trained employees, absenteeism, fuel use, among others. Total employees, compensation and benefits, supplier assessment, total training cost, total training hours, training hours per employee, turnover rate, full-time employees, fatalities, among others. Value added, payback, contribution to the gross domestic product, investment, air pollution, CO2 reduction, eco-efficiency, green packing, energy consumption per unit, long term vision, job creation, solid waste generation intensity, among others.

Literature review

Waste, recycling, renewable energies, biodiversity, CO2 emission, job creation, safety, discrimination, health, cost, risk, present value, profit, among others.

Literature review, survey and interviews

Avoidance of hazardous substances in products and process, product quality and safety, reuse, gas emissions, accidents, employee training, energy consumption, among others.

Table 2 – Quantitative models for selecting supply chain sustainability metrics.

Author(s) Erol et al. (2011)

Bai et al. (2012)

Uysal (2012)

Linke et al. (2013) Varsei et al. (2014) Ahi and Searcy (2015b) Chen et al. (2015)

Feil et al. (2015)

Khodakarami (2015)

Haghighi et al. (2016)

Scope A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain Evaluating ecological sustainable performance measures for supply chain management An integrated model for sustainable performance measurement in supply chain

Technique(s) used Fuzzy entropy combined with fuzzy multi-attribute utility

Metrics Annual water consumption, annual energy consumption, waste minimization, number of ISO standards developed, use of recycled materials, effectiveness of reverse logistics system, effectiveness of supplier monitoring, among others.

Rough set theory

Selection of sustainability metrics and analysis of different means of normalization A framework for sustainability development and assessment

Weighted sum

Assessing sustainability in the supply chain: A triple bottom line approach Multicriteria analysis of sustainable development indicators in the construction minerals industry in China

Probability theory

Supplier cost-saving initiatives, cost variance from expected costs, supplier lead time against industry norm, delivery reliability, satisfaction with knowledge transfer, response to product changes, information availability, mutual trust, technological capability levels, among others. Innovations created through supplier partnerships, total sales, the number of shareholders, promoting new investments, establishing new employment opportunities, total tax paid, waste minimization, number of ISO standards developed, fraction of facilities using renewable energy, effectiveness of reverse logistics system, effectiveness of supplier training in environmental issues, fraction of suppliers certified in ISO 14001, among others. Energy intensity, residual intensity, non-renewable materials intensity, restricted substances, water intensity, air releases intensity, investment costs, productivity and labor intensity. Supply chain cost, service level, labour practices and decent work, human rights, society, product responsibility, water usage, energy consumption, waste generation, use of hazardous and toxic substances. Not specified.

Selection and identification of the indicators for measuring sustainability in micro and small furniture industries Application of distinctive twostage data envelopment analysis models for evaluation of the sustainability of supply chain management An integrated approach for performance evaluation in sustainable supply chain

DEMATEL

Pairwise comparisons

Fuzzy Delphi method and fuzzy TODIM (an acronym in Portuguese for iterative multi-criteria decision making) Delphi method and weighted average Data envelopment analysis (DEA)

Data envelopment analysis (DEA)

Use of natural resources, health and safety, creation of employment, environmental impacts, emissions to air, generation of solid waste, use of energy, wealth creation, among others. Generation of dangerous waste, Waste disposal, Effluent treatment, Recycling of waste, Recycling of products, Employee satisfaction, Employee training and development, Child labor, Operating profit, Operational costs, Business ethics, among others. Annual cost, annual personnel turnover, environmental cost, partnership cost in green production plans, numbers of products from supplier to manufacture, revenue, number of green products, number of trained personnel in the fields of job, safety, and health. Delivery cost, supplier rejection rate, investment in sustainability design, hazardous materials, pollution prevention, number of green products, customers' satisfaction, service quality, ISO 14001, health and safety staff,

Izadikhah and Saen (2016) Ahmadi et al. (2017)

networks Evaluating sustainability of supply chains by two-stage range directional measure Assessing the social sustainability of supply chains using Best Worst Method

Data envelopment analysis (DEA) Best worst method

among others. Cost of work safety and labor health, environmental cost, rate of increasing partnership cost in green production plans, rate of increasing number of green products, number of obtained ISO certificates and number of trained personnel in the fields of job, safety, and health.

Work health and safety, training education and community influence, contractual stakeholders’ influence, occupational health and safety management system, the interests and rights of employees, the rights of stakeholders, information disclosure and employment practices.

ACCEPTED MANUSCRIPT Table 3 – Requirements for supply chain sustainability assessment Requirements Cost (Govindan et al., 2013) Delivery reliability (Chang, 2011) Environmental aspects (Huang and Keskar, 2007) Flexibility (Ahi and Searcy, 2015a) Health and security (Govindan et al., 2013) Information technology (Katsikeas et al., 2004) Innovation (Kar, 2015) Management practices (Kilincci and Onal, 2011) Product development (Osiro et al. 2014) Profit (Feil et al., 2015) Quality (Govindan et al., 2013) Relationship (Rezaei and Ortt, 2013) Social responsibility (Mani et al., 2014) Stability / continuity (Kar, 2015) Table 4 – Economic, environmental and social metrics Performance dimension Economic

Alternative metrics Commitment to cost reduction (Subramanian and Gunasekaran, 2015) Commitment to lead time reduction (Osiro et al., 2014) Competitive price (Katsikeas et al., 2004) Costs of used and returned (Subramanian and Gunasekaran, 2015) Cost of sharing (Gunasekaran et al., 2015) Transaction costs (Gunasekaran et al., 2015) Total training cost (Searcy et al, 2016) Return on average capital employed (Tahir and Darton, 2010) Investment in social responsibility (Rahdari and Rostamy, 2015) Financial power (Wang, 2010) Financial situation (Chang, 2011) Resilience (Rajesh and Ravi, 2015, Subramanian and Gunasekaran, 2015) Inventory turnover (Wu and Barnes, 2010) Delivery capacity (Wu and Barnes, 2010) Delivery in full on time (Wu and Barnes, 2010) Delivery lead-time (Govindan et al., 2013) Reliability of service (Katsikeas et al., 2004) Service level (Wu and Barnes, 2010) Responsiveness to demand change (Chang, 2011) Speed of problem resolution (Gattorna, 2010) Information systems (Wu and Barnes, 2010) Structure for information sharing (Rezaei and Ortt, 2013) Timely information (Germain and Dröge, 1990) RandD capabilities (Katsikeas et al., 2004) Technological structure (Rajesh and Ravi, 2015) Unique competencies (Wu and Barnes, 2010) Environmental costs (Ahi and Searcy, 2015a) Inventory reduction programs (Germain and Droge, 1990) Measurement tools and methods (Germain and Dröge, 1990) Strategic orientation (Wu and Barnes, 2010) Ability to co-design (Wang, 2010) Technical know-how (Katsikeas et al., 2004)

ACCEPTED MANUSCRIPT

Ability to problem resolution (Osiro et al., 2014) Commitment to quality improvement (Kannan and Tan, 2002) Strategic position in the marketplace (Wu and Barnes, 2010) Process capability (Omurca, 2013) Sales (Gunasekaran et al., 2015) Market share (Ahi and Searcy, 2015a) Return on investment (Ahi and Searcy, 2015a) Return on asset (Gunasekaran et al., 2015) Value difference to quantity (Gunasekaran et al., 2015) Customers' satisfaction (Subramanian and Gunasekaran, 2015) Value at risk (SCC, 2012) Environmental

Air pollutant emission (Ahi and Searcy, 2015a) Carbon emission (Humphreys, 2003) Control of chemical waste (Humphreys, 2003) Clean technology availability (Humphreys, 2003) Environmental certification (Humphreys, 2003) Environmental policies (Humphreys, 2003) Use of environment friendly material (Humphreys, 2003) Recycling of waste (Subramanian and Gunasekaran, 2015) Returns handling capability (Humphreys, 2003) Waste disposal (Subramanian and Gunasekaran, 2015) Consumption of renewable materials (Feil et al., 2015) Consumption of non-renewable materials (Feil et al., 2015) Use of restricted substance (Linke et al. 2013) Water consumption (Ahi and Searcy, 2015a) Renewable energy consumption (Feil et al., 2015) Non-renewable energy consumption (Feil et al., 2015) Energy efficiency (Ahi and Searcy, 2015a) Reuse ratio (Subramanian and Gunasekaran, 2015) Solid waste (Humphreys, 2003) Generation of hazardous materials (Subramanian and Gunasekaran, 2015) Environmental incidents concerning products (Tahir and Darton, 2010) Environmental education (Rahdari and Rostamy, 2015)

Social

Human resource management skill (Wu and Barnes, 2010) Employee training and development (Feil et al., 2015) Employee satisfaction (Feil et al., 2015) Human capital investment (Subramanian and Gunasekaran, 2015) Share of operating revenues redistributed to local communities (Subramanian and Gunasekaran, 2015) Number of people employed per tone of non-renewable resource (Subramanian and Gunasekaran, 2015) Philanthropy (Rahdari and Rostamy, 2015) Employees work-life balance and family (Rahdari and Rostamy, 2015) Stakeholders relationship (Humphreys, 2003) Ease of communication (Wang, 2010; Santos et al., 2017) Government relationships (Wu and Barnes, 2010) Honesty (Kannan and Tan, 2002) Influence on industry (Rezaei and Ortt, 2013) Complexity of governance structures (Gunasekaran et al., 2015) Potential for collaboration (Rezaei and Ortt, 2013) Regular communications (Kannan and Tan, 2002) Reputation (Wu and Barnes, 2010) Refund policy (Katsikeas et al., 2004) Number of accidents (Rajesh and Ravi, 2015)

ACCEPTED MANUSCRIPT

Serious and fatal accidents (Feil et al., 2015) Health programs (Mani et al., 2014) Safety audits (Huang and Keskar, 2007) Safety programs (Mani et al., 2014) Safety training (Huang and Keskar, 2007) Child labor (Mani et al., 2014) Coordination of social projects (Govindan et al., 2013) Business Ethics (Rahdari and Rostamy, 2015) Corruption and Fraud (Rahdari and Rostamy, 2015) Educational institutions (Govindan et al., 2013) Working conditions (Govindan et al., 2013) Employee turnover (Rahdari and Rostamy, 2015) Supporting community projects (Govindan et al., 2013) Local community influence (Subramanian and Gunasekaran, 2015) Reward system (Subramanian and Gunasekaran, 2015) Employment opportunities for local community (Subramanian and Gunasekaran, 2015) Employee volunteer hours (Searcy et al., 2016) Average of training hours per employee (Searcy et al., 2016) Table 5 – Judgment of importance of the requirements by each decision maker Requirements R1 R2 R3 R4 R5 R6 R7

DM 1 At least very high At least very high Between medium and high Between low and high Between medium and high At least high Between medium and high

DM 2 Greater than high Very High Between medium and very high Between medium and high High Between high and very high Medium

Table 6 – HFLTS of importance of the requirements Requirements DM 1 DM 2 R1 VH, A VH, A R2 VH, A VH R3 M, H M, H, VH R4 L, M, H M, H R5 M, H H R6 H, VH, A H, VH R7 M, H M Table 7 – Requirement weighting calculation Requirements Positive Negative distance distance R1 0.1010 0.4165 R2 0.1166 0.3956 R3 0.3212 0.1875 R4 0.3704 0.1468 R5 0.2974 0.2020 R6 0.1782 0.3563 R7 0.3869 0.1429 Total -

DM 3 Greater than high Greater than high High High Between high and very high Greater than high Between medium and high

DM 3 VH, A VH, A H H H, VH VH, A M, H

Satisfaction degree 0.8048 0.7723 0.3686 0.2838 0.4045 0.6667 0.2697 3.5703

Weight 0.2254 0.2163 0.1032 0.0795 0.1133 0.1867 0.0755 1.0000

Table 8 – Judgments of Relationships between metrics and requirements Delivery Environmental Metrics Cost Innovation reliability aspects VH, A M, H, VH M, H, VH M, H, VH I1 H, VH VL, L, M, H M, H, VH VH, A I2 VH, A L, M, H M, H, VH L, M, H, VH I3 M, H A L, M L, M, H I4 H, VH M, H, VH L, M, H L, M, H I5 L, M, H, VH L, M, H L, M, H, VH M, H, VH I6 L, M N, VL, L M, H, VH A I7 VL, L, M N, VL H, VH, A L, M, H I8 VL, L, M N, VL VH, A VL, L I9 VL, L, M VL, L, M A VL, L, M, H I10 H, VH, A N, VL VH, A N, VL, L, M I11 VL, L, M, H L, M, H, VH VL, L, M L, M, H, VH I12 VL, L, M, H N, VL, L H, VH, A M, H, VH I13 VL, L VL, L L, M, H, VH VL, L, M, H, VH I14 L, M, H VL, L, M, H M, H, VH, A L, M, H, VH, A I15

Management practices

Quality/customer satisfaction

Social responsibility

H, VH M, H, VH H, VH, A H, VH, A H, VH, A H, VH, A M, H, VH, A N, VL, L VL, L, M M, H, VH N, VL, L, M H, VH, A L, M, H H, VH, A L, M, H, VH

M, H, VH L, M, H L, M, H, VH L, M, H, VH M, H, VH VH, A H, VH VL, L VL, L, M M, H, VH VL, L, M VL, L, M, H VL, L, M N, VL, L L, M, H, VH, A

L, M, H M, H, VH L, M, H N, VL, L, M VL, L, M, H M, H, VH L, M, H M, H, VH H, VH, A VH, A M, H, VH H, VH, A H, VH, A H, VH, A VH, A

ACCEPTED MANUSCRIPT Table 9 – Metric evaluations resulting from step 2 Metrics Positive distance Negative distance 0.281 0.584 I1 0.370 0.511 I2 0.338 0.552 I3 0.371 0.571 I4 0.333 0.539 I5 0.337 0.567 I6 0.462 0.490 I7 0.613 0.316 I8 0.592 0.354 I9 0.449 0.492 I10 0.546 0.423 I11 0.448 0.478 I12 0.524 0.407 I13 0.568 0.390 I14 0.403 0.509 I15

Satisfaction degree 0.675 0.581 0.620 0.606 0.619 0.627 0.515 0.340 0.374 0.523 0.437 0.516 0.437 0.407 0.558

Table 10 – HFLTSs of the judgments of the difficulty of data collection for each metric Metrics Information Human resource Additional resource availability and time required VH VL, L VL, L I1 H, VH VL, L, M VL, L I2 VL, L, M L, M VL, L, M, H I3 H, VL VL, L VL I4 M, H L, M L, M, H I5 L, M, H M, H VL, L, M I6 L, M, H M, H, VH L, M, H, VH I7 L, M, H L, M, H L, M I8 H, VH VL, L, M VL, L, M I9 H, VH VL VL I10 L, M, H L, M, H L, M I11 M, H, VH VL, L, M VL, L I12 M, H VL, L, M VL, L I13 L, M L, M, H L, M I14 L, M, H M, H L, M, H I15

ACCEPTED MANUSCRIPT Table 11 – Evaluation of the degree of difficulty of data collection Metrics Positive distance Negative distance Satisfaction degree 1.281 0.200 0.135 I1 1.178 0.327 0.217 I2 0.792 0.792 0.500 I3 1.281 0.200 0.135 I4 0.841 0.622 0.425 I5 0.821 0.716 0.466 I6 0.627 0.935 0.598 I7 0.796 0.688 0.464 I8 1.128 0.392 0.258 I9 1.334 0.141 0.096 I10 0.796 0.688 0.464 I11 1.128 0.392 0.258 I12 1.071 0.432 0.287 I13 0.739 0.739 0.500 I14 0.688 0.796 0.536 I15

Table 12 – Normalized values of importance of the metrics and the degree of difficulty of data collection Metrics Importance Difficulty of data collection 0,816 0,208 I1 0,638 0,301 I2 0,721 0,705 I3 0,693 0,208 I4 0,718 0,603 I5 0,735 0,661 I6 0,482 0,813 I7 0,145 0,658 I8 0,191 0,355 I9 0,501 0,171 I10 0,303 0,658 I11 0,486 0,355 I12 0,303 0,398 I13 0,246 0,705 I14 0,586 0,749 I15