Waste Management 147 (2022) 36–47
Available online 19 May 2022
0956-053X/© 2022 Elsevier Ltd. All rights reserved.
Country report
Sustainability performance evaluation of the E-waste closed-loop supply chain with the SCOR model
Vipul Jain
a, Sameer Kumar
b,*, Amirhossein Mostofi
a, Mojtaba Arab Momeni
caWellington School of Business and Government, Victoria University of Wellington, Wellington, New Zealand
bOpus College of Business, Department of Operations and Supply Chain Management, University of St. Thomas, Minneapolis, MN 55403, USA
cDepartment of Industrial Engineering, Jam Faculty of Engineering, Persian Gulf University, Bushehr, Iran
A R T I C L E I N F O Keywords:
E-waste supply chain Performance management Sustainability
Best-Worst Method SCOR reference model
A B S T R A C T
From a sustainability perspective, the performance of a company’s supply chain will be satisfactory when it has reached in all aspects a desirable eco-environmentally friendly level. Assessing the sustainability performance in the closed-loop e-waste supply chain becomes vital because its activities are primarily targeted towards sus- tainability goals related to the process of production, supply, recycling, and disposal of electrical components.
This study evaluates the performance of e-waste supply chain sustainability and identifies its performance in- dicators as a framework for evaluating supply chain performance using the Best-Worst Method (BWM), which is a multi-criteria decision-making (MCDM) approach. For this, the supply chain operations reference (SCOR) model is considered the basic performance evaluation reference. Moreover, through reviewing the literature, the complementary indicators of this model, especially in terms of sustainability, are added to the performance evaluation indices using the Nominal Group Technique (NGT). After specifying and forming a performance evaluation hierarchy, the BWM method is used to determine the criteria score. The results of implementing the framework on some well-known supply chains in New Zealand indicate that the attributes of “Costs,” “Quality,” and “GreenScor” are crucial for achieving high performance, while in this developed country, there is less concern about social issues.
1. Introduction
Waste Electrical and Electronic Equipment (WEEE), known as E- waste, is one of the end-of-life (EOL) products that have significant economic and environmental impacts. It is a remarkable and growing source of waste due to the widespread use of electronic products, which has changed people’s lifestyles in today’s society (Menikpura, Santo, &
Hotta, 2014). The worrying reports, such as a 3–5% annual growth of e- waste (Afroz et al. 2013; Dwivedy & Mittal, 2013), generation of 44.7 million tons of e-waste in 2017 throughout the world, only 20% of which are recycled (Bald´e et al., 2017), or the disposal of 20–50 million tons of e-waste per year make the efficient management of e-waste necessary, not only in the organizational level but also globally (Shahrasbi et al.
2021). Moreover, it should be noted that e-waste contains valuable re- sources of different materials and metals such as silver, gold, and plat- inum (Oguchi et al. 2013).
One of the management’s responsibilities is to address
environmental and social objectives, establish efficient policies, and measure performances to economic, environmental, and social re- sponsibility dimensions, i.e., the triple bottom line of sustainability (Savino & Mazza, 2013). In this perspective, firms should plan to mea- sure their social and environmental impact and their financial perfor- mance (Walker et al. 2021). This aligns the organization toward a sustainable development that is defined as “development that meets the needs of the present without compromising the ability of future gener- ations to meet their own needs” (WCED, 1987). Reviewing the goals in e- waste management indicates that sustainable goals, such as recycling valuable materials, reducing the disposed waste (de Souza et al., 2016), decreasing carbon emission (Yang et al., 2020), and creating many job opportunities, are prominent (Yang et al., 2021). So, studying the per- formance of e-waste activities is better suited in the context of sustain- able supply chain performance evaluation (Debnath, 2020).
A supply chain is a linked network consisting of many facilities such as suppliers, production facilities, and distribution centers that deal with
* Corresponding author.
E-mail addresses: vipul.jain@vuw.ac.nz (V. Jain), skumar@stthomas.edu (S. Kumar), mostofiamirhossein@gmail.com (A. Mostofi), mojtaba.mam.sut@gmail.com (M. Arab Momeni).
Contents lists available at ScienceDirect
Waste Management
journal homepage: www.elsevier.com/locate/wasman
https://doi.org/10.1016/j.wasman.2022.05.010
Received 16 December 2021; Received in revised form 10 May 2022; Accepted 11 May 2022
the processes of the logistic, production, distribution, and delivery of material and items. When these processes involve the downstream feed- forward flow of materials (deliveries) and the upstream feedback flow of information (orders), the supply chain is referred to as a forward supply chain (FSC) (Stevens, 1989). On the other hand, when the flow of ma- terial and information in a supply chain is reversed to process the returned and used items of an FSC and create additional value by recovering activities such as recycling and remanufacturing, the reverse supply chain (RSC) is relevant. Moreover, when both the forward and reverse flows in a supply chain are integrated, the resulting configura- tion is a closed-loop supply chain (Guide and Van Wassenhove, 2009).
Due to the definition of e-waste, these scarce components’ major value recovery activities can be evaluated during the processes of a reverse supply chain (Asim, Jalil, et al. 2019; Fu, Qiang, et al. 2021). However, the potential economic value of e-waste and the lack of electronic components, which in recent years has emerged in ways such as the microchip crisis (Russo et al., 2022), encourage manufacturers of core components to participate in reverse supply chain activities with more enthusiasm. This makes the closed-loop supply chain management more important and applicable in the context of e-waste supply chains. Islam and Huda (2018) underlined the unique features of the e-waste supply as various collection centers and collection methods, the various lifespan of WEEE, the variety of available disposition alternatives (e.g. reuse, repair, remanufacture, and recycling), the complex material structure of WEEE that contains both hazardous substances to the environment (e.g.
mercury, cadmium, lead, and ozone-depleting chemicals) and valuable and scarce raw materials, such as silver and gold.
The stream in the closed-loop supply chain of e-waste starts with companies that produce the electronic equipment or their suppliers and continues with the supply chain distributors until the products reach the customers. Then, it is followed by collection centers that collect end-of- life products and sell them to recycling centers. Finally, it ends with recycling companies that recycle usable components of products and resell them in the secondary markets or resend them to the producers.
The e-waste operations entail the shared cooperation, integration, and coordination of all members of the supply chain (Reis, Friede, & Lopes, 2018; Liu & Xiao, 2019) in response to globalization, outsourcing, and information technology (David & Shalle, 2014; Metta & Badurdeen, 2012). This, in turn, shifts the focus from managing internal business processes to managing organizations at the supply chain level (Balfaqih et al., 2016). Such a viewpoint plays an essential role in achieving and maintaining a sustainable competitive advantage in the business context (Bolstorff & Rosenbaum, 2007; Long et al., 2019). Accordingly, the present study evaluates the performance of the entire supply chain of e- waste instead of a separate part or individual organization within it.
The performance management system (PMS) plays a significant role in the business management of the supply chain. To be the system in accordance with the expected goals, it should provide linkage between organizational strategies, and the indicators should be relevant with the customers’ value. Also, performance metrics must be based on business goals and have clear definitions, scope, and purpose for the data collection process and measurement methods to function correctly (Ptak
& Schragenheim, 2003). The supply chain pursues various goals in
developing the performance appraisal system, which can include iden- tifying achievements, understanding the needs of customers, deter- mining business processes, making decisions based on facts, enabling progress, tracking progress, and identifying stated bottlenecks, coordi- nating members, and aligning them with goals of the supply chain, and identifying opportunities for improvement (Gunasekaran & Kobu, 2007, Feng et al., 2019; Zheng et al., 2019). Therefore, it is necessary to consider the supply chain as a whole in designing the performance management system instead of evaluating each involved firm sepa- rately. However, most research in the e-waste supply chain performance evaluation has examined the performance of only one function or part of it. Therefore, given the potential economic and environmental oppor- tunities that the e-waste supply chain provides, a system that evaluates
supply chain performance in a coordinated and integrated manner, such as the one presented in the present study, plays a significant role in achieving sustainable goals.
Some global tools such as Odette EVALOG, Efficient Consumer Response, the Oliver Wight Class A Checklist, and the SCOR model have been developed by the contribution of expert teams for evaluating and auditing the supply chain (Chardine-Baumann & Botta-Genoulaz, 2014).
Applying such standards could overcome the main challenge of supply chain performance management, i.e., the lack of understanding of the metrics and applying them in a multi-organizational context (Peng Wong & Yew Wong, 2008; Akkawuttiwanich & Yenradee, 2018). A well- known and widely used standard framework for evaluating the perfor- mance of the supply chain is Supply-Chain Operations Reference (SCOR) model. The SCOR is based on a consulting team’s experience comprising various organizations’ practitioners. SCOR divides the supply chain processes into Plan, Source, Make, Deliver, and Return. The plan bal- ances the demand and supply to meet actual or planned demand and include order management, transportation management, and distribu- tion management processes. Source involves all processes related to preparing the raw materials and other purchased inventories. Make addresses the process required to produce demand, such as manufacturing, testing, and packaging. In Deliver, the process after producing and packaging are regarded. Finally, Return manages the return and receipt of products for any reason and supports customers after product delivery (Lambert, 2008). As observed, SCOR covers most of the processes within a supply chain. Also, A set of metrics for supply chain performance has been introduced in SCOR, which is based on the experience of a consulting team comprising of various organizations’
practitioners and later was incorporated into the existing supply chain standards. In the present study, the SCOR model and its metrics are utilized. A brief description of the attributes and measures utilized in the proposed PMS of the e-waste supply chain will be provided in the methodology section of the paper.
Despite the great advantages of SCOR for the performance evalua- tions of supply chains, some additional adjustments should be initiated to make it customized for special purposes, such as the case of the sus- tainable e-waste supply chain in this paper. First, it should also be noted that, although SCOR provides the necessary explanations for perfor- mance metrics and their measurement methods, it does not prioritize the performance metrics used by organizations. In this manner, SCOR has abandoned the importance of performance metrics as an internal deci- sion of organizations and appropriate to their context. However, to evaluate the performance of organizations, it is necessary to identify the performance metrics and their importance weight. So, adopting SCOR, one of the paper’s main objectives is to provide a way for determining the scores of the attributes and measures of PMS in the case study of the paper. Second, despite the great importance of the environmental and social dimensions in the e-waste supply chain, SCOR has paid less attention to these dimensions of sustainability. However, for moving to sustainable waste management, a targeted policy that encourages the application of the recycling technology within a resource efficiency- oriented framework is necessary (Cole et al., 2019). Hence, enriching the SCOR model by exploiting and extending the results of other studies that have dealt with the latter subject in the context of the e-waste supply chain is another objective of the present study. For these pur- poses, the Nominal Group Technique (NGT) and Best-Worst Method (BWM) are used as the techniques adjusting the SCOR model for the PMS of the case under study.
The rest of the paper is structured as follows. Section 2 includes the literature review and the comparison of the current study with previous studies. The methodology of the paper is described in Section 3 and its sub-sections. The numerical results of the paper that utilized the experts’ opinions are presented in Section 4. Finally, the research implications, managerial insights, and the paper’s conclusions are expressed in Sec- tions 5, 6 and 7, respectively.
2. Literature review
(Balfaqih et al., 2016) classified the ways of evaluating the supply chain performance into 1,) approaches and 2) techniques. Also, they divided the performance appraisal system approaches into three cate- gories: 1) the Perspective-Based approach that examines the overall performance appraisal, along with the causal hypotheses that determine the relationship between performance measures, 2) the Process-Based approach that helps to understand the critical operational aspects of the supply chain and seek to develop new ways to incorporate critical processes into the supply chain, and 3) A Hierarchical-Based that ana- lyzes supply chain performance at tactical, operational, and strategic levels of the decision- making process (Algren & Kotzab, 2011). Also, various techniques have been proposed by researchers to evaluate sup- ply chain performance in a second way. Some of these techniques are the Analytic Hierarchy Process (AHP) (Balfaqih et al., 2016), Analytic Network Process (ANP) (Pramod and Banwet, 2011), Data Envelopment Analysis (DEA), Delphi (Bigliardi and Bottani, 2010), and simulation techniques (Chan et al., 2014). According to the basis of the supply chain operations reference (SCOR) model, the performance management sys- tems are among Perspective-Based approaches that shed light on a unique vision of the supply chain through SCOR attributes, i.e., reli- ability, responsiveness, flexibility, and cost, and assets. Moreover, the best-worst method (BWM), an MCDM model, is used to determine the score of the performance attributes and measures in the proposed PMS is a technique that provides consistent judgment about the PMS explained later.
Authors such as (Beske-Janssen et al. 2015) emphasized the impor- tance of performance measurement in achieving goals such as collabo- ration, transparency, and benefiting from opportunities by providing sufficient management tools. On the other hand, performance mea- surement provides advantages for the supply chain such as elimination of risk, compliance with standards and regulations, reduction of costs, increasing efficiency, strengthening competitive advantages, facilitating sustainability reporting, sharpening operational performance, and sup- porting the implementation of the supply chain strategy (Qorri et al., 2018). As mentioned, e-waste has significant economic and environ- mental effects on societies. So, a well-designed PMS for the e-waste supply chain can enhance all the benefits of performance appraisal in this regard.
(Qorri et al., 2018) analyzed the sustainable performance evaluation methods of the supply chain and then provided a comprehensive framework to assess the supply chain performance by summarizing the strengths and weaknesses of previous studies. They categorized methods to evaluate the performance of supply chain sustainability by using environmental management standards (e.g., ISO 14001), international reporting standard (e.g., GRI – Global Reporting Incentive), SCOR framework, Balanced Score Card (BSC), Life Cycle Assessment (LCA), MCDM tools (e.g., AHP, ANP, DEA), Rough set theory, Fuzzy-set approach, Composite Indicators, and Conceptual Frameworks. Their conceptual model to evaluate supply chain performance described the interdependence between supply chain strategy and the goal, sustain- ability metrics, sustainable key performance indicators (KPIs), and in- ternal and external stakeholders. The proposed PMS of the e-waste supply chain also falls within the comprehensive framework of (Qorri et al., 2018) because it utilizes the SCOR method, as one of the most common models in this field, and also takes into account the link be- tween the PMS attributes and measures using the BWM method.
As stated, SCOR leaves the importance weights of performance at- tributes and measures on the opinion of businesses managements.
Hence, several decision-making methods have been investigated for this purpose. In this regard, the Gray-based Neighbourhood Rough Set Theory (Cucchiella & Koh, 2012), Data Envelopment Analysis (DEA) method (Bai & Sarkis, 2014), Fuzzy Inference Systems (Zanon et al., 2019), and fuzzy DEMATEL method (Kiris¸ et al., 2019) could be referred. There are also studies that attempt to enhance the SCOR model
by attaching more comprehensive measures, especially in sustain- ability’s environmental and social dimensions. For example, (Stohler et al. 2018) added two input-oriented metrics: source reduction and energy usage, into the GreenScor metrics. They also extended SCOR to the social dimension by introducing two metrics of “job satisfaction ratio” and “effectiveness of staff training programs.” (Liu et al., 2018) presented a green construction supply chain performance evaluation system wherein evaluation indexes were categorized into the Balanced Scorecard (BSC) viewpoints. However, to clarify the metrics used to measure the index, the metrics of SCOR were borrowed. (Kiris¸ et al., 2019) studied the performance evaluation criteria of an automobile spare parts company by taking into account cost, delivery performance, and quality from the SCOR model and some green and social criteria to address all triple-bottom-line (TBL) dimensions. This stream of studies reveals that incorporating other MCDM methods with the SCOR framework is a relevant approach in the supply chain management studies, as done in this study. This scheme strengthens the performance evaluation of the e-waste supply chain by focusing on the reverse and closed-loop operations.
Some authors have not evaluated the e-waste supply chain perfor- mance as a whole and examined only some specific aspects or parts. The assessment of e-waste management options (de Souza et al., 2016), the cost-benefit analysis of recycling handling methods and programs (Wibowo and Deng, 2015; Ikhlayel, 2017; Shaikh et al., 2020), the design of a household e-waste collection network by scheming efficient public advertising (Shi et al., 2020), the location of e-waste collections using multi-objective models (Shi et al., 2020), the evaluation of online recycling platforms using game theory (Chen and Gao, 2021), and the risk-based performance evaluation of improvement strategies for sus- tainable e-waste management (Xu et al., 2020) are among the studies in this regard.
Furthermore, some research in recent years has investigated the performance evaluation of the e-waste supply chain with a broader perspective. (Tseng, Lim, & Wong, 2015) evaluated the performance of a closed-loop supply chain of e-waste in the balanced scorecard (BSC) viewpoints and using the Fuzzy Delphi and ANP methods. The results of that paper identified the most important sustainability criteria as green design, corporate sustainability, supplier cost-saving initiatives, and market share. (Isernia et al. 2019) investigated the performance of the WEEE management systems of Italian provinces toward the EU (Europe Union) WEEE collection target. They underlined the importance of e- waste management systems concerning the increase in environmental awareness, environmental protection, and sustainability. (Bruno et al., 2021) proposed a systematic method and introduced a dashboard of quantitative indicators to assess the users’ spatial access to WEEE net- works. (Maheswari et al., 2020) conducted a descriptive study to determine relevant and appropriate measures for the performance measurement of informal e-waste businesses in Indonesia. Their per- formance perspectives include financial, environmental, stakeholders values, internal business process, social and innovation, and growth performance measures, each containing several indicators. (Baidya et al., 2020) investigated and evaluated the drawback in the supply chain of e-waste using AHP. The main drawbacks in this regard are categorized as legislative requirements, environmental requirements, processing plant requirements, and social requirements. Some of the indicators in (Maheswari et al., 2020) and (Baidya et al., 2020) are borrowed in the proposed PMS. The research clearly indicated the need for assessing the e-waste supply chain toward a sustainable perspective.
(Saroha et al., 2020) evaluated the performance of sustainable practices in India and identified the supply chain performance indicators using the modified balanced scorecard technique.
Although the researchers have proposed advanced models for measuring the e-waste supply chain, the SCOR model has rarely been adopted in this supply chain. So, the proposed framework in this paper aligns the performance appraisal systems of the e-waste supply chain with the SCOR-based and sophisticated systems that many organizations
have acknowledged in terms of effectiveness and efficiency (Chardine- Baumann & Botta-Genoulaz, 2014). The need for a more systematic evaluation of the entire e-waste value chain regarding sustainable goals is also highlighted in (Ilankoon et al., 2018). So, the proposed PMS has innovation in both the perspective of the e-waste supply chain evalua- tion and the comprehensive viewpoint of that toward sustainability.
Finally, the application of MCDM methods in analyzing different aspects of e-waste management processes is referred to. (Biluca et al.,2020) exploited the GIS and ELECTRE TRI methods for sorting disposal areas of construction and demolition of waste. The environ- mental performance of e-waste management systems with four options of 1) landfill disposal, 2) direct incineration with energy recovery, 3) materials recovery without energy recovery, and 4) materials recovery with energy recovery were assessed by (Ismail and Hanafiah, 2021) using life cycle assessment (LCA) and material flow analysis (MFA) methods. They found that direct incineration with energy recovery is the prominent option in Malaysian companies dealing with e-waste. The raking of e-waste collection methods is explored in (Singh et al., 2021) using Fuzzy- Analytical Hierarchy Process (FAHP) and Fuzzy VIKOR techniques. Their results denote that social awareness and economic sustainability are the most impressive attributes for determining the ranking. The hybrid BWM and fuzzy TOPSIS approach are used in (Chen et al., 2020) to appraise Ghana’s barriers and pathways to implementing e-waste formalization management systems. To determine the interde- pendence among the e-waste mitigation strategies, the Grey concept and DEMATEL technique have been adopted by (Garg, 2021). As expressed, the BWM method is to evaluate the proposed PMS of the e-waste supply chain. The most important feature of this method is that it can achieve consistent judgments with a minimum of expert opinion polls. Hence, for a PMS with many measures and attributes, this method seems to be a good choice and the consistency of judgment increases the system’s reliability.
Table A1 in Appendix summarizes the reviewed research and the discussed topics. Accordingly, the present research is more compre- hensive than the reviewed studies in terms of approach and technique, being related to the e-waste supply chain, evaluating the performance of the entire supply chain instead of some parts, using the well-known model of SCOR as well as various objectives.
3. Methodology
In this section, the methodology used in the paper is described. This methodology includes the following stages. First, the overall framework to assess the e-waste supply chain performance is explained. A description of the SCOR model is provided in the next step. Then, the nominal group technique (NGT) for extracting the criteria relevant to the research community is illustrated. Also, with reference to the reviewed papers and the NGT technique, the final criteria extracted for the performance appraisal of the e-waste supply chain is presented.
Finally, the BWM method is described in detail, along with the way for measuring the consistency of experts’ opinions.
3.1. Theoretical framework
According to the purpose of the research, which is the evaluation of e-waste supply chain performance according to the SCOR method, the proposed method consists of five steps. This framework is illustrated in Fig. 1.
As shown in Fig. 1, first, the measures and attributes of the PMS are identified, referring to the SCOR model and other relevant studies in the literature. Second, the NGT technique is applied to extract the most relevant measures and attributes in the context of the e-waste supply chain. Third, to determine the importance weights of the measures and attributes, the BWM method is used. The fourth and fifth steps of the framework describe how the results of the NGT and BWM could be implemented; as a performance appraisal system; and will be explicated
in the numerical results of the paper.
3.2. SCOR model
The SCOR model includes three levels for addressing the details of processes. The first level defines the scope of an organization. The configuration and the type of supply chain are examined in the second level, and the details of the process elements, including the performance indicators, are defined in the third level. At this level, strategies for achieving competitive advantages are defined as a response to changes in the business environment (APICS, 2015; APICS, 2017).
SCOR metrics are organized hierarchically. As the layers of metrics are expanded to the lower level, a more detailed performance is measured. Higher-level metrics are complemented by computing lower- level metrics that take several detailed processes into account. Perfor- mance measures are used in SCOR along with performance attributes.
These attributes illustrate supply chain characteristics and enable managers to evaluate and compare the performance of a supply chain with competitors. A supply chain needs standard attributes to be described and compared to benchmarks. However, SCOR does not pro- vide comparative data for all types of supply chains. In SCOR, the whole supply chain will be improved. This, in turn, extends the bullwhip effect of the supply chain to the performance evaluation (Zimmermann, 2006).
In SCOR, five essential criteria are presented: reliability, respon- siveness, flexibility, cost, and asset management. These criteria have been expanded hierarchically. This means that we can identify gaps and improvement opportunities for the high-level criteria by evaluating low- level criteria. Therefore the diagnostics feature enhances the root cause analysis capability in SCOR (APICS, 2015).
With the increasing importance of sustainability criteria – especially environmental criteria, newer versions of SCOR (version 8 and above) have also developed greenness criteria. These criteria include carbon emission, liquid waste generated, air pollutant emission, solid waste generated, and recycled waste in version 11 of the SCOR model. Some authors have also developed social criteria for the SCOR model, though
Selection of attributes and measures to evaluate e-waste supply chain performance using the NGT method and expert opinion with the formation
of proposed hierarchy supply chain performance evaluation method
Identify the best and worst criteria at different levels of the supply chain hierarchy:Performance Attributes and Performance Measures Pairwise comparisons of criteria with the best and worst criteria Identify the importance weight of attribute and measure according to BWM
method
Providing the maximum rating of a supply chain in each of the measures according to their importance weight on a given scale to evaluate the
performance of the e-waste supply chain on that scale
Providing formula of the selected measures with reference to the SCOR model as a summary and completing the proposed e-waste
supply chain performance evaluation
Fig. 1.The general framework of the proposed method for assessing the e- waste supply chain performance.
they find it difficult (Kiris¸ et al., 2019; Stohler et al., 2018). The reason for its difficulty can be explained by the similarity or overlap of some of the criteria in the SCOR model with the sustainability criteria.
Five processes of Plan, Source, Make, Deliver, and Return are considered in SCOR. The plan balances the demand and supply to meet actual or planned demand and includes order management, trans- portation management, and distribution management processes (Lambert, 2008).
In this paper, the SCOR model is employed for the following reasons:
First, the main aim of this standard is performance evaluation of the whole supply chain rather than evaluating supply chain functions separately. Second, unlike other standards such asEfficient Consumer Response, which focuses on transportation operations, the scope of SCOR is much broader and covers almost all supply chain operations. Third, this model has been introduced as the primary supply chain manage- ment model in many studies, such that more than 800 firms have adopted this model as the reference model of their supply chain oper- ations (Chardine-Baumann & Botta-Genoulaz, 2014).
3.3. Nominal group technique (NGT)
The authors point out the importance of selecting appropriate met- rics for evaluating supply chain performance and its sustainability for the number of criteria (Qorri et al., 2018). The nominal group technique is among the techniques used to screen and select criteria from a set of criteria. The NGT method is one of the group decision-making methods widely used in selecting criteria for multi-criteria decision problems (Galankashi et al., 2015).
According to the methodology, first, the problem under investigation will be described to the experts at a meeting. The meeting manager then asks the experts to write their opinion on the sheets and submit them to the meeting manager. The meeting manager writes each expert’s opinion on the board in successive duplicates and discusses the announced opinion. At the end of the iteration, if the experts agree on the written opinion, that opinion is chosen as one of the solutions to the problem, and these duplicates continue until all comments have been reviewed. In the present study, the criteria are derived from the SCOR standard (APICS, 2015) and other researches in literature (Bai & Sarkis, 2014; Bai, Sarkis, Wei, & Koh, 2012; de Souza et al., 2016; Kiris¸ et al., 2019; Liu et al., 2018; Stohler et al., 2018; Tseng et al., 2015; Yeh & Xu, 2013; Maheswari et al., 2020; Baidya et al., 2020, Yang et al., 2021).
Accordingly, the final criteria are listed in Table A2 in Appendix.
The point to be noted about the measures outlined in Table A2 is that they are only addressed at levels (1) of the SCOR model, and levels (2) and (3) were not studied due to a large number of measures. However, the methodology proposed in the present study could extend to priori- tize and rate them. Another point is that many of the criteria introduced in the SCOR model have been applied in other studies. On the other hand, some of the criteria of the SCOR model had been adjusted in the mentioned studies, and hence in some categories, attributes, or mea- sures other than the SCOR model are mentioned. Some categories, such as quality and social, are generally outside the scope of the SCOR model and have been extracted from relevant criteria. Finally, it is worth pointing out that the attribute of quality means the quality of relation- ships and collaborative effort of the supply chain. Other quality concepts such as the quality of items delivered or the quality of information are referred to in the SCOR model’s attributes like reliability and are thus distinct from the quality presented in Table A2.
Notably, the research community of the paper is the e-waste recy- cling industry in New Zealand. For doing so, experts from three of the largest companies active in this field, namely, Remark-IT, Tech Collect NZ, and ITRECYCLA, contribute us by expressing their opinions about the relevance of attributes and measures in the NGT process and, and later in identifying the best and worst criteria as well as the pairwise comparison used in the BWM method. Remark-IT is at the forefront of the e-waste recycling industry. It is one of the pioneer organizations to
boost innovative sustainable solutions to combat the detrimental im- pacts of electronic waste. Its disposal process is tracked end to end within Remark-IT’s extranet system, of which all customers have full access to track collection and disposal data for any electronic waste that enters the chain of custody process. Tech Collect NZ is responsible for the highest standard recycling solutions of electronic waste in New Zealand. It focuses on keeping E-Waste out of the landfill and using environmentally friendly methods to recycle electronic waste, which protects the health and safety of the workers. It supports triple bottom line initiatives by recycling computer & electronic waste, surplus com- puter equipment, and redundant IT peripherals, minimizing the impact on the environment, reusing 99% of materials, and keeping hazardous substances. During the research process, 20 experts from the mentioned companies helped us, whose descriptive specifications are as described in Table A3 in Appendix.
The descriptive characteristics of the experts in Table A3 indicate that the research experts have the necessary knowledge, skills, and experience in the subject matter. It should also be noted that the NGT method is very effective when there is a need for expert judgments on an issue and agreement on the results. Also, this method does not require a large number of samples from the research community and emphasizes expertise rather than collective agreement.
3.4. Bwm method
This is a generalization of the hierarchical analysis method proposed by (Rezaei, 2016). The main idea behind this approach is to compare only the best and worst decision criteria instead of the pairwise com- parison of all criteria. In this case, experts can make pairwise compari- sons more consistent. If the criteria are high, then pairing all the criteria together will result in inconsistencies and the inability of experts to analyze a myriad of criteria.
In the present study, after identifying the attributes and measures of the e-waste supply chain using the NGT, we use the BWM method at both levels of performance hierarchy in Table A2. This means that first, the method applies for determining the weight of each performance attri- bute, and second, the importance weight of performance measures for each attribute will be calculated using the method at the second level.
Finally, the weight of importance of each measure that represents the framework of the performance evaluation methodology used in the present study is calculated by multiplying the weight of that measure by the attribute weight.
The implementation of the BWM method can be explained as follows (Rezaei, 2016):
1. Performance indicators are presented with c1, c2, …, cn.
2. Determine the most important and least important performance in- dicators according to the decision-maker’s opinion.
3. Pairwise comparison for the best performance indicator.
4. Pairwise comparison of the best performance indicator with other performance indicators. This comparison is made on a scale of 1 to 9 in which 1 means equal importance, and 9 means a completely su- perior performance of the best performance indicator compared to the target performance indicator. The results of this comparison are vectorized asAB = (aB1,aB2, ...,aBn), which aBj shows the preference of the best performance indicator over the performance index j.
5. Pairwise comparison of other performance indicators with the least important performance indicators. Here, too, a scale of 1 to 9 is used, where 1 means equal importance, and 9 means that performance is entirely superior to other performance indicators over the least important performance indicator. The results of this comparison are vectorized asAW = (a1W,a2W, ...,anW), in which ajW is the preference index of the performance indicator j to the least important perfor- mance indicator.
6. The importance weight of each of the performance indicators is presented as(w*1,w*2, ...,w*n). This vector must be specified such that for each performance indicator of j, wB/wj=aBj and wj/wW=ajW
relations are valid. Therefore, to get close to the conditions mentioned, ⃒
⃒⃒wwBj− aBj
⃒⃒
⃒ and ⃒
⃒⃒wwWj− ajW
⃒⃒
⃒ must take a minimum. Also taking into account the assumption that non-negative weights of importance weight and the sum of the indicators equals 1, we solve the following mathematical programming problem (Rezaei, 2016):
minmax
j
{⃒⃒
⃒⃒ wB
wj
− aBj
⃒⃒
⃒⃒,
⃒⃒
⃒⃒ wj
wW
− ajW
⃒⃒
⃒⃒ }
(1)
s.t.
∑n
j=1
wj=1 (2)
wj⩾0∀j (3)
This problem can be formulated as the following mathematical programming model (Rezaei, 2016):
minε (4)
s.t.
ε⩾
⃒⃒
⃒⃒ wB
wj
− aBj
⃒⃒
⃒⃒∀j (5)
ε⩾
⃒⃒
⃒⃒wj
wW
− ajW
⃒⃒
⃒⃒∀j (6)
∑n
j=1
wj=1 (7)
wj⩾0∀j (8)
By solving the above mathematical programming model, the optimal value of ε* is determined. However, it should be noted that when there are more than three criteria, the above model might have multiple optimal solutions (Rezaei, 2016). Hence, we use the following two models to identify each of the weights’ upper and lower limits. It is also notable that in the below models, Eqs. (5) and (6) have been substituted with modified terms without absolute operators.
minwj (9)
s.t.
− ε*.wj⩽wB− aBj.wj⩽ε*.wj∀j (10)
− ε*.wW⩽wj− ajW.wW⩽ε*.wW∀j (11)
∑n
j=1
wj=1 (12)
wj⩾0∀j (13)
maxwj (14)
s.t.
− ε*.wj⩽wB− aBj.wj⩽ε*.wj∀j (15)
− ε*.wW⩽wj− ajW.wW⩽ε*.wW∀j (16)
∑n
j=1
wj=1 (17)
wj⩾0∀j (18)
Solving the two models given in Eqs. (4)–(18) ultimately yields the weight of the importance of performance indicators using Eq. (19) (Rezaei, 2016):
w*j =minwj+maxwj
2 ∀j (19)
It is important to note that if the BWM method has more than one decision-maker, we use the geometric mean of their views in the decision-making process. For example, if we show the decision-makers with k = 1,2,.., K, denote akBj and akjW as the announced score of decision-maker k for the preference of the criterion j over the best and worst criteria, respectively, and also present the importance of the decision-maker k with λk such that∑K
k=1λk =1; then, Eqs. (20) and (21) will be used to bring expert opinion together:
aBj=∏K
k=1
(akBj)λk (20)
ajW=∏K
k=1
(akjW)λk (21)
Also, the consistency of judgments in the BWM should be confirmed.
According to (Rezaei, 2016), the BWM method is perfectly consistent when the relation of abest,j×aj,worst=abest,worst holds for all j. The incon- sistency rate decreases when the definition of consistency is not valid, according to Eq. (22) (Rezaei, 2016):
abest,j×aj,worst∕=abest,worst (22)
To convert the inequality into an equation, the new variable δ is defined to represent deviations of the equality as Eq. (23) (Rezaei, 2016):
(abest,j− δ) × (aj,worst− δ) =abest,worst+δ (23)
When abest,j=aj,worst=abest,worst the above equation turns into Eq. (24) (Rezaei, 2016):
δ2− (1+2abest,worst)δ+ (a2best,worst− abest,worst) =0 (24)
Now, by solving Equation Eq. (24) for different values ofabest,worst, i.e., 1 to 9, the consistency indicators are obtained as Table 1.
Now, by considering the optimal valueε*, the inconsistency rate is measured by Eq. (25):
consistency ratio=ε*
CI (25)
According to (Rezaei, 2016), the lower the consistency ratio, the better the results of the consistency of the BWM method. If the rate of inconsistency is less than 0.1, the consistency of judgments is acceptable (Rezaei, 2016).
4. Numerical results
As shown in Table A2, the e-waste sustainability criteria are arranged in two levels of performance attributes and performance measures. The BWM method must be applied at each level to determine the importance weight of the criteria. Hence, the results of this method are presented separately for each level so that, by combining the results of the two levels, we can gain the weight of the utmost importance of performance criteria. In this case illustration, we used the geometric mean of these experts’ opinions in paired comparisons, as explained in the BWM method.
It should be noted that the mathematical programming models in section 3. 3 for different levels of Table A2 have been solved in GAMS 23.0 software. The software has the ability to solve linear as well as non-
linear optimization problems.
4.1. Performance attributes weights
Among the performance attributes shown in Table A2, the majority of experts selected “Costs” as the best and “Social” as the least important attribute. The geometric mean of expert opinions in pairwise compari- sons of all attributes toward the best and worst ones are according to Tables 2 and 3, respectively.
By solving models in Eqs. (4)–(8) for the attributes, ε* will be equal to 0.23 and other results of the BWM method for the performance weight of criteria under study is according to Table 4.
Given that the CI index for abest,worst=6.6 is greater than 3 according to Table 1 and taking into account the optimal value of ε* at this level and Eq. (25), the consistency index of BWM method is less than 0.23/3
=0.076. Since the consistency index is also less than 0.1, expert results are consistent.
4.2. Performance measures weights
The second level of research hierarchy is performance evaluation measures. At this point, using the BWM method, the importance weights of measures related to the attributes of Agility (C3), Costs (C4), Asset management, Efficiency (C5), Quality (C6), and GreenScor (C7) should be recognized. First, based on the expert’s knowledge, each attribute’s best and worst measures are identified in Table 5.
Again, the BWM method is used for identifying the importance weight of measures. In Tables A4 through A9 in Appendix, respectively, the results of the BWM method for identifying the importance weight of measures related to the attributes of Agility (C3), Costs (C4), Asset Management Efficiency (C5), Quality (C6), GreenScor (C7) and Social (C8) are shown. Given that in all tables, the consistency ratio is less than 0.1, it can be concluded that all judgments are consistent.
Finally, regarding the weights of attributes and measures, the score of these criteria on a scale from 0 to 1000 points is specified. This is done by multiplying the final weight of an attribute or a measure by 1000 and then rounding it. The results in this regard have been described in Table 6.
According to the results of Table 6, the “Costs” attribute has the highest scores among performance attributes of the proposed PMS. It is followed by “Quality” “GreenScor,” as two other attributes with high scores. Moreover, the results indicate that social criteria are less important in the research community because the country under study is a developed country with a high level of job satisfaction ratio, gender equality, and other measures of social dimensions. But in developing and less developed countries, where these issues are challenging, the results of this study should be used with more caution, and the score of such indicators should be modified to put more emphasis on social responsibilities.
The method of measuring each of the performance attributes and performance measures is listed in the SCOR standard. In the following, to complete the proposed performance appraisal framework, in Table A10 in Appendix, the methods of calculating performance
appraisal indicators borrowed from SCOR are presented (APICS, 2015).
For other indicators that are not included in SCOR, the mentioned methods are the suggestion of the current paper.
To compare the study results with other research, it should be pointed out that the SCOR model rarely is used in the context of the e- waste supply chain. However, the proposed model utilized some mea- sures that are common with the previous studies. For example, the measures of “Managing e-waste,” “proper waste disposal system,” “Eco- friendly materials,” “Environmentally friendly process,” “Job opportu- nity,” “Harmonious relationship,” and “Community Satisfaction” were recognized as influential and significant factors in the research of (Maheswari et al., 2020), they are also selected in the proposed PMS by the experts. A study that can be used to compare the resulting weight of some measures in the present paper is the one carried out by (Liu, Xu, &
Xu, 2018). They evaluated the performance of some supply chains associated with five green building projects. For this purpose, they considered the viewpoints of the balanced scorecard (BSC), i.e., “sus- tainability,” “financial,” “customer,” and “business process,” and then assigned to each of these viewpoints some measures from the SCOR model. Then, they attempted to specify the weights of these measures using the order relationship analysis (G1) and the entropy weight method. In Table 7, the weights of those measures for the PMS of (Liu,
Xu, & Xu, 2018) and the proposed PMS has been shown.
As the results in Table 7 show, the weights of the attributes and Table 1
Consistency indicators in BWM method (Rezaei, 2016).
abest,worst 1 2 3 4 5 6 7 8 9
CI(maxδ) 0.00 0.44 1.00 1.63 2.30 3.00 3.73 4.47 5.23
Table 2
Results of pairwise comparisons of best criteria (C3) with other criteria.
j C1 C2 C3 C4 C5 C6 C7 C8
C4 2.34 2.43 2.11 1 2.06 1.71 2.08 6.6
Table 3
Results of pairwise comparisons of other criteria with the worst criteria (C8).
j C1 C2 C3 C4 C5 C6 C7 C8
C8 2.5 2.8 3 6.6 3.3 3.8 3.5 1
Table 4
Weight of importance of performance attributes according to BWM method.
Main criteria minwj maxwj maxwj+minwj
2 Normalized weight
C1 0.098 0.102 0.1 0.1
C2 0.1 0.109 0.104 0.104
C3 0.109 0.12 0.115 0.115
C4 0.243 0.252 0.248 0.248
C5 0.119 0.13 0.124 0.124
C6 0.138 0.15 0.144 0.144
C7 0.125 0.13 0.127 0.127
C8 0.037 0.039 0.038 0.038
Table 5
Best and worst measures for performance attributes.
Attributes Worst Measure Best Measure
Agility (C3) Upside Supply Chain
Adaptability (C32) Downside Supply Chain Adaptability (C33) Costs (C4) Total Cost to Serve (C41) System efficiency (C43) Asset Management
Efficiency (C5) Cash-to-Cash Cycle Time
(C51) Return on Supply Chain
Fixed Assets (C52) Quality (C6) Extent of mutual assistance
leading in problem-solving efforts (C63)
Compliance with quality management system (C65)
GreenScor (C7) Landfill reduction (C79) Recycling utilization rate (C77)
Social (C8) Opportunity for professional
development (C89) Customer satisfaction (C88)
measures in the proposed PMS are significantly different from that of (Liu, Xu, & Xu, 2018). The first difference may be stemmed from the difference in the fields of the supply chains, one related to the building industry and the other to e-waste. But the main reason is due to the Table 6
The score of the proposed performance management system based on the BWM method.
Performance
Attribute Score Performance
Measures References of
the selected Measures and Attributes
Scores based on the BWM method Reliability (C1) 100 Perfect Order
Fulfilment (C11) APICS (2015) 100 Responsiveness
(C2) 104 Order Fulfilment
Cycle Time (C21) APICS (2015) 104 Agility (C3) 115 Upside Supply Chain
Flexibility (C31) APICS (2015) 34 Upside Supply Chain
Adaptability(C32) APICS (2015) 19 Downside Supply
Chain Adaptability (C33)
APICS (2015) 62
Costs (C4) 248 Total Cost to Serve
(C41) APICS (2015) 26
System feasibility
(C42) De Souza et al.
(2016) 46
System efficiency
(C43) De Souza et al.
(2016) 101
Effectiveness of the E- Waste Collectors (C44)
Baidya et al.
(2020) 75
Asset Management Efficiency (C5)
124 Cash-to-Cash Cycle
Time (C51) APICS (2015) 16
Return on Supply Chain Fixed Assets (C52)
APICS (2015) 60
Return on Working
Capital (C53) APICS (2015) 48 Quality (C6) 144 Partnership Level
(C61) Bai et al
(2012), Bai and Sarkis (2014)
36
Extent of mutual planning cooperation leading to improved quality (C62)
Bai et al.
(2012), Bai and Sarkis (2014)
19
Extent of mutual assistance leading in problem-solving efforts (C63)
Bai et al.
(2012), Tseng et al. (2015), Bai and Sarkis (2014)
9
Quality and frequency of exchange of logistics information between partners (C64)
Bai et al.
(2012), Tseng et al. (2015), Bai and Sarkis (2014)
16
Compliance with quality management system (C65)
Bai et al.
(2012), Kiris¸
et al. (2019), Bai and Sarkis (2014)
52
Quality of perspective taking in supply networks (C66)
Bai et al.
(2012), Bai and Sarkis (2014)
12
GreenScor (C7) 127 Source Reduction
(C71) Stohler et al.
(2018) 9
Managing e-waste
(C72) (Maheswari
et al., 2020) 8 Proper waste disposal
system (C73) (Maheswari et al., 2020) 3 Formal and informal
e-waste collection (C74)
(Baidya et al.,
2020) 6
Energy (C75) Stohler et al.
(2018), ( APICS, 2015)
4
Emissions (C76) APICS (2015), (Maheswari et al., 2020)
7
Water (C77) 5
Table 6 (continued) Performance
Attribute Score Performance
Measures References of
the selected Measures and Attributes
Scores based on the BWM method APICS (2015),
(Maheswari et al., 2020) Effluents and Waste
Generated (C78) APICS (2015), (Maheswari et al., 2020)
12
Recycling utilization
rate (C79) APICS (2015), (Baidya et al., 2020)
20
Eco-friendly materials
(C710) (Maheswari
et al., 2020), ( APICS, 2015)
6
Environmentally friendly process (C711)
(Maheswari et al., 2020) 5 Remanufacturing and
Reclaiming rate (C712)
(Baidya et al., 2020), (APICS, 2015)
9
ISO certificate (C713) Kiris¸ et al.
(2019) 4
Landfill reduction
(C714) Yeh and Xu
(2013) 3
Green Technology
innovation (C715) Yeh and Xu
(2013) 7
Strategic planning for the environmental management (C716)
Tseng et al
(2015), 4
Life cycle assessment
(C717) Tseng et al
(2015), De Souza et al (2016)
15
Social (C8) 38 Job Satisfaction Ratio
(C81) Tseng et al
(2015), Stohler et al (2018), ( Maheswari et al., 2020)
4
Labour diversity
(C82) Kiris¸ et al.
(2019) 1
Job opportunity
(C83) (Maheswari
et al., 2020), ( Yang et al., 2021)
4
Harmonious
relationship (C84) (Maheswari et al., 2020) 2 Gender Equality
(C85) Kiris¸ et al.
(2019) 2
Competency of EM, OHS, BSM systems (C86)
Kiris¸ et al.
(2019) 3
Innovativeness (C87) Kiris¸ et al.
(2019) 4
Research and Development investment (C88)
Tseng et al.
(2015) 3
Health and safety at
workplace (C89) Yeh and Xu
(2013) 2
Corporate reputation and acceptability (C810)
Yeh and Xu
(2013) 2
Customer satisfaction
(C811) Yeh and Xu
(2013) 8
Community
Satisfaction (C812) Maheswari et al., 2020) 2 Opportunity for
professional development (C813)
De Souza et al.
(2016) 1
