A Review of Decision-Support Tools and Performance Measurement for Sustainable Supply Chain Management
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A Review of Decision-Support Tools and Performance Measurement for
Sustainable Supply Chain Management
Abstract:
In recent years, interest on sustainable supply chain management has risen significantly in both the academic
and business communities. This is confirmed by the growing number of conferences, journal publications,
special issues and websites dedicated to the topic. Within this context, this paper reviews the existing
literature related to decision-support tools and performance measurement for sustainable supply chain
management. A narrative literature review is carried out to capture qualitative evidence, while a systematic
literature review is performed using classic bibliometric techniques to analyse the relevant body of
knowledge identified in 384 papers published from 2000 to 2013. The key conclusions include: the evidence
of a research field that is growing, the call for establishing the scope of current research, i.e. the need for
integrated performance frameworks with new generation decision support tools incorporating triple bottom
line (TBL) approach for managing sustainable supply chains. There is a need to identify a wide range of
specific industry-related TBL metrics and indexes, and assess their usefulness through empirical research
and case-base analysis. We need mixed methods to thoroughly analyse and investigate sustainable aspects of
the product life cycle across the supply chains, through empirical evidence, building and/or testing theory
from and in practice.
Keywords: sustainability; supply chain; performance management; decision-support tool
1. Introduction
Over the last decade, there has been an increase in awareness amongst consumers and society of green or
sustainable products (Hitchcock 2012). The resulting pressure from various stakeholders to commit to
sustainable practices and performance management (Dey and Cheffi 2012), has rapidly increased the interest
shown in sustainable supply chains and their management on the part of government regulators, NGOs,
academics and industrial players. There has been particular focus on the areas of for green supply chain
management and reverse logistics as a basis of sound practice.
The earliest work relating to the green supply chain management literature can be linked to Ayres and
Kneese (1969). Since then the research was mainly focused on understanding the operational issues related
to collecting, testing, sorting, and remanufacturing of returned products. In the 90’s, research started focusing
on investigating the quantitative models of reverse logistics related to distribution planning, inventory
control, and production planning (Fleischmann et al. 1997). Later research was focused on environmentally
conscious manufacturing and product recovery Gungor and Gupta (1999), recycling and remanufacturing
were (Guide, Jayaraman and Srivastava 1999; Guide and Van Wassenhove 2002). Researchers mainly
focused on the environmental aspects of supply chains, looking at intra-organisational aspects of
manufacturing firms. Corbett and Kleindorfer (2003) argued that as sustainability for supply chain became
important, a new wave of research emerged trying to capture the systemic nature of sustainability.
Kleindorfer, Singhal, and Van Wassenhove (2005) inaugurated a broader focus, incorporating various
sustainability concepts including environmental management, closed-loop supply chains (CLSC) and the
triple-bottom-line approach. Since then, operations management researchers started integrating sustainability
issues within their traditional domain of expertise. As a result, some key contributions have emerged across a
wide range of areas including strategy, finance, environmental operations and policy-making, product
design, supplier relationship management and after sale customer service.
Sustainable Supply Chain Management
Abstract:
In recent years, interest on sustainable supply chain management has risen significantly in both the academic
and business communities. This is confirmed by the growing number of conferences, journal publications,
special issues and websites dedicated to the topic. Within this context, this paper reviews the existing
literature related to decision-support tools and performance measurement for sustainable supply chain
management. A narrative literature review is carried out to capture qualitative evidence, while a systematic
literature review is performed using classic bibliometric techniques to analyse the relevant body of
knowledge identified in 384 papers published from 2000 to 2013. The key conclusions include: the evidence
of a research field that is growing, the call for establishing the scope of current research, i.e. the need for
integrated performance frameworks with new generation decision support tools incorporating triple bottom
line (TBL) approach for managing sustainable supply chains. There is a need to identify a wide range of
specific industry-related TBL metrics and indexes, and assess their usefulness through empirical research
and case-base analysis. We need mixed methods to thoroughly analyse and investigate sustainable aspects of
the product life cycle across the supply chains, through empirical evidence, building and/or testing theory
from and in practice.
Keywords: sustainability; supply chain; performance management; decision-support tool
1. Introduction
Over the last decade, there has been an increase in awareness amongst consumers and society of green or
sustainable products (Hitchcock 2012). The resulting pressure from various stakeholders to commit to
sustainable practices and performance management (Dey and Cheffi 2012), has rapidly increased the interest
shown in sustainable supply chains and their management on the part of government regulators, NGOs,
academics and industrial players. There has been particular focus on the areas of for green supply chain
management and reverse logistics as a basis of sound practice.
The earliest work relating to the green supply chain management literature can be linked to Ayres and
Kneese (1969). Since then the research was mainly focused on understanding the operational issues related
to collecting, testing, sorting, and remanufacturing of returned products. In the 90’s, research started focusing
on investigating the quantitative models of reverse logistics related to distribution planning, inventory
control, and production planning (Fleischmann et al. 1997). Later research was focused on environmentally
conscious manufacturing and product recovery Gungor and Gupta (1999), recycling and remanufacturing
were (Guide, Jayaraman and Srivastava 1999; Guide and Van Wassenhove 2002). Researchers mainly
focused on the environmental aspects of supply chains, looking at intra-organisational aspects of
manufacturing firms. Corbett and Kleindorfer (2003) argued that as sustainability for supply chain became
important, a new wave of research emerged trying to capture the systemic nature of sustainability.
Kleindorfer, Singhal, and Van Wassenhove (2005) inaugurated a broader focus, incorporating various
sustainability concepts including environmental management, closed-loop supply chains (CLSC) and the
triple-bottom-line approach. Since then, operations management researchers started integrating sustainability
issues within their traditional domain of expertise. As a result, some key contributions have emerged across a
wide range of areas including strategy, finance, environmental operations and policy-making, product
design, supplier relationship management and after sale customer service.
Linton et al. (2007) Argued that it is important to address the systemic issues of sustainability and
environmental aspects of supply chains (Previous studies in this area have seen the incorporation of
sustainability in legislation and the modification of the competitive environment in which businesses operate
and perform (Webster and Mitra 2007; Kocabasoglu et al. 2007; Ackali et al. 2007; Mazhar et al. 2007). The
work of Jovane et al. (2009) on Competitive Sustainable Manufacturing (CSM) initiated a discussion on
sustainable practices as a possible source of competitive advantage in an industrial context at a strategic
operations level.
Tonelli et al. (2013) argued that Sustainable Supply Chains (SSC) are a key component of sustainable
development in promoting industrial sustainability. In order to maintain competitiveness, supply chain
members should consider not only economic aspects but also environmental and social aspects (TBL) in
fulfilling stakeholder requirements. As a consequence, companies practising SSC Management (SSCM) have
(according to traditional notions of goal trade-offs) to satisfy multiple and conflicting objectives such as
increasing returns while reducing costs, minimizing the environmental impact and increasing the social well-
being. Previous operations research models have similarly focussed on the trade-offs between three goal
dimensions of sustainability (i.e. economic, environment and social). However, Seuring (2013) argues that
further research might usefully explore the consequences of win-win (rather than trade-off) and/or minimum
achievement requirement on the three goal dimensions. In order to achieve these goals, decision-makers need
innovative decision-support tools capable of dealing with global supply chain management as well as
sustainability issues and opportunities (Dey and Cheffi 2012) that could overcome the disadvantages of
traditional trade-off approaches. These tools have to support performance management in a multi-stakeholder
environment assessing environmental impact and social benefits in a multi-party supply chain based on an
inter-organizational approach (Taticchi et al. 2013; Ates et al. 2013). Decision-support tools are still
insufficiently robust to deal with design, operational, economic, environmental, societal and technological
aspects of systematic implementation of SSCM while contributing to competitive advantage (Bjorklund et al.
2012; Bhattacharya et al. 2013).
Hence the overall aim of this paper is to explore decision-support tools (DST) for performance management
in the SSCM domain. Following Hassini et al. (2012, p.70), we define SSCM as: “...the management of
supply chain operations, resources, information, and funds in order to maximize the supply chain
profitability while at the same time minimizing the environmental impact and maximizing the social well-
being”. The focus of this paper will be on the sourcing, manufacturing and distribution side rather than
design side of the supply chain. Product-design aspects have already been discussed and presented in
numerous papers (see Roy 2000; Ehrenfeld 2001; Mont 2001; Manzini and Vezzoli 2003; McAloone and
Andreasen 2004; Aurich et al. 2006; Ramirez 2007; An et al. 2008; Sakao et al. 2009; Morelli 2009). An
initial scan of the background literature suggests that decision-support tools and performance measurement
(PM) in SSCM need to address three main aspects: (1) reduction of negative environmental and social
impacts within policy-making context (2) inclusion of all stages across the value chain of each product (3)
adoption of a multi-disciplinary perspective throughout the product life-cycle (Taticchi et al. 2013). As
already articulated, for each of these three aspects, goals should encompass minimum performance
(respecting environmental legislation), trade-offs (balancing TBL aspects), and win-win configurations
(improving value recovery).
Unfortunately, given the major influence of sustainability on firms’ supply-chains, competitiveness and
strategy, SSCM and DST remain isolated from one another. Some attention has been given to measuring
performance (Taticchi et al. 2013) and qualitative and quantitative modelling (Seuring 2013) within the
context of SSCM, yet no holistic approaches integrating SSCM, DST, and PM have been found in the
literature (Dey and Cheffi 2012; Bhattacharya et al. 2013) even if they share strongly related concepts in
practice. Thus, the specific objective of this study is to investigate the nature of existing literature and its
spread across publications to identify the potential development of DST in SSCM domain from a PM
environmental aspects of supply chains (Previous studies in this area have seen the incorporation of
sustainability in legislation and the modification of the competitive environment in which businesses operate
and perform (Webster and Mitra 2007; Kocabasoglu et al. 2007; Ackali et al. 2007; Mazhar et al. 2007). The
work of Jovane et al. (2009) on Competitive Sustainable Manufacturing (CSM) initiated a discussion on
sustainable practices as a possible source of competitive advantage in an industrial context at a strategic
operations level.
Tonelli et al. (2013) argued that Sustainable Supply Chains (SSC) are a key component of sustainable
development in promoting industrial sustainability. In order to maintain competitiveness, supply chain
members should consider not only economic aspects but also environmental and social aspects (TBL) in
fulfilling stakeholder requirements. As a consequence, companies practising SSC Management (SSCM) have
(according to traditional notions of goal trade-offs) to satisfy multiple and conflicting objectives such as
increasing returns while reducing costs, minimizing the environmental impact and increasing the social well-
being. Previous operations research models have similarly focussed on the trade-offs between three goal
dimensions of sustainability (i.e. economic, environment and social). However, Seuring (2013) argues that
further research might usefully explore the consequences of win-win (rather than trade-off) and/or minimum
achievement requirement on the three goal dimensions. In order to achieve these goals, decision-makers need
innovative decision-support tools capable of dealing with global supply chain management as well as
sustainability issues and opportunities (Dey and Cheffi 2012) that could overcome the disadvantages of
traditional trade-off approaches. These tools have to support performance management in a multi-stakeholder
environment assessing environmental impact and social benefits in a multi-party supply chain based on an
inter-organizational approach (Taticchi et al. 2013; Ates et al. 2013). Decision-support tools are still
insufficiently robust to deal with design, operational, economic, environmental, societal and technological
aspects of systematic implementation of SSCM while contributing to competitive advantage (Bjorklund et al.
2012; Bhattacharya et al. 2013).
Hence the overall aim of this paper is to explore decision-support tools (DST) for performance management
in the SSCM domain. Following Hassini et al. (2012, p.70), we define SSCM as: “...the management of
supply chain operations, resources, information, and funds in order to maximize the supply chain
profitability while at the same time minimizing the environmental impact and maximizing the social well-
being”. The focus of this paper will be on the sourcing, manufacturing and distribution side rather than
design side of the supply chain. Product-design aspects have already been discussed and presented in
numerous papers (see Roy 2000; Ehrenfeld 2001; Mont 2001; Manzini and Vezzoli 2003; McAloone and
Andreasen 2004; Aurich et al. 2006; Ramirez 2007; An et al. 2008; Sakao et al. 2009; Morelli 2009). An
initial scan of the background literature suggests that decision-support tools and performance measurement
(PM) in SSCM need to address three main aspects: (1) reduction of negative environmental and social
impacts within policy-making context (2) inclusion of all stages across the value chain of each product (3)
adoption of a multi-disciplinary perspective throughout the product life-cycle (Taticchi et al. 2013). As
already articulated, for each of these three aspects, goals should encompass minimum performance
(respecting environmental legislation), trade-offs (balancing TBL aspects), and win-win configurations
(improving value recovery).
Unfortunately, given the major influence of sustainability on firms’ supply-chains, competitiveness and
strategy, SSCM and DST remain isolated from one another. Some attention has been given to measuring
performance (Taticchi et al. 2013) and qualitative and quantitative modelling (Seuring 2013) within the
context of SSCM, yet no holistic approaches integrating SSCM, DST, and PM have been found in the
literature (Dey and Cheffi 2012; Bhattacharya et al. 2013) even if they share strongly related concepts in
practice. Thus, the specific objective of this study is to investigate the nature of existing literature and its
spread across publications to identify the potential development of DST in SSCM domain from a PM
perspective. In this research, the authors reviewed the existing literature assessing developments in SSCM,
PM, DST, aiming to derive implications and guidelines for a research agenda.
For this purpose, the authors performed both a narrative and a systematic literature review (Tranfield et al.
2003), in order to capture qualitative evidence from literature and rigorous facts. The next section presents
the findings of the narrative literature review, while section 3 introduces the methodology adopted to review
the literature. Section 4 incorporates presentation on systematic review with bibliographic analysis
demonstrating the trends in the literature. This will be followed by section 5 with a discussion on the key
findings from this research and the setting of an agenda for further work. Finally, conclusions are drawn in
section 6.
2. Narrative Literature Review
The aim of this review is to analyse the existing body of literature on performance measurement (PM) and
decision-support tools (DST) in the context of sustainable supply chain management (SSCM) so as to
identify major works, and thereafter, to classify them in order to identify relevant areas for further insight
based on systematic literature review as provided in the second part of this research. Initially, and for this
purpose, an approach based on narrative literature review is suitable since it can contribute to structuring the
research field and provide a reference for further research to be developed (Easterby-Smith et al. 2002). A
strong understanding of evidence from the literature is in fact necessary towards theory development (Weick
1995). The narrative review, provides basic definitions and key concepts of both PM and DST, and describes
the evolution of research in these fields and current challenges. Particular attention is given to the recent
application of these theories in the context of SSCM.
2.1 Performance Measurement of Sustainable Supply Chains management
Performance measurement and management (PMM) has increased predominantly in the last three decades
(Garengo et al. 2005; Taticchi et al. 2010; Nudurupati et al. 2011). Neely et al. (1995) describes
performance measurement as the process of quantifying efficiency and effectiveness of action, which
according to Sharma et al. (2005) is an important element in improving business performance. Bititci et al.
(2012) describes performance management as the process of using measurement information for supporting
managers in decision-making processes aiming to link strategy to operations. Nudurupati et al. (2011)
reported that performance management is an organisation-wide shared vision that surrounds performance
measurement activity. Today, PMM practices have become common in all sectors of industry to compete in
complex and continuously changing environments (Bititci et al. 2012). As articulated by Nudurupati et al.
(2011) and Taticchi et al. (2009) firms have to measure, monitor and manage performance in multiple
dimensions using balanced and dynamic set of measures that facilitates support of decision-making
processes. The word “balanced” implies the necessity of using different metrics, (i.e. financial vs non-
financial; quantitative vs qualitative; internal vs external; etc.) that provide a holistic view of the organisation
(Kaplan and Norton 1996; Burgess et al. 2007). The word “dynamic” implies the need of developing a
system that constantly monitors the internal and external context and reviews objectives and priorities up to
date (Garengo et al. 2005).
Although there is existing literature on designing and implementing PMM in supply chains, it needs a
significant shift due to the changing nature of competitive environment, i.e. shift of competition from
individual organisations to supply chains competing against each other (Bai et al. 2012; Taticchi et al. 2012;
Cagnazzo et al. 2009). Consequently, there are several calls from researchers to develop performance
PM, DST, aiming to derive implications and guidelines for a research agenda.
For this purpose, the authors performed both a narrative and a systematic literature review (Tranfield et al.
2003), in order to capture qualitative evidence from literature and rigorous facts. The next section presents
the findings of the narrative literature review, while section 3 introduces the methodology adopted to review
the literature. Section 4 incorporates presentation on systematic review with bibliographic analysis
demonstrating the trends in the literature. This will be followed by section 5 with a discussion on the key
findings from this research and the setting of an agenda for further work. Finally, conclusions are drawn in
section 6.
2. Narrative Literature Review
The aim of this review is to analyse the existing body of literature on performance measurement (PM) and
decision-support tools (DST) in the context of sustainable supply chain management (SSCM) so as to
identify major works, and thereafter, to classify them in order to identify relevant areas for further insight
based on systematic literature review as provided in the second part of this research. Initially, and for this
purpose, an approach based on narrative literature review is suitable since it can contribute to structuring the
research field and provide a reference for further research to be developed (Easterby-Smith et al. 2002). A
strong understanding of evidence from the literature is in fact necessary towards theory development (Weick
1995). The narrative review, provides basic definitions and key concepts of both PM and DST, and describes
the evolution of research in these fields and current challenges. Particular attention is given to the recent
application of these theories in the context of SSCM.
2.1 Performance Measurement of Sustainable Supply Chains management
Performance measurement and management (PMM) has increased predominantly in the last three decades
(Garengo et al. 2005; Taticchi et al. 2010; Nudurupati et al. 2011). Neely et al. (1995) describes
performance measurement as the process of quantifying efficiency and effectiveness of action, which
according to Sharma et al. (2005) is an important element in improving business performance. Bititci et al.
(2012) describes performance management as the process of using measurement information for supporting
managers in decision-making processes aiming to link strategy to operations. Nudurupati et al. (2011)
reported that performance management is an organisation-wide shared vision that surrounds performance
measurement activity. Today, PMM practices have become common in all sectors of industry to compete in
complex and continuously changing environments (Bititci et al. 2012). As articulated by Nudurupati et al.
(2011) and Taticchi et al. (2009) firms have to measure, monitor and manage performance in multiple
dimensions using balanced and dynamic set of measures that facilitates support of decision-making
processes. The word “balanced” implies the necessity of using different metrics, (i.e. financial vs non-
financial; quantitative vs qualitative; internal vs external; etc.) that provide a holistic view of the organisation
(Kaplan and Norton 1996; Burgess et al. 2007). The word “dynamic” implies the need of developing a
system that constantly monitors the internal and external context and reviews objectives and priorities up to
date (Garengo et al. 2005).
Although there is existing literature on designing and implementing PMM in supply chains, it needs a
significant shift due to the changing nature of competitive environment, i.e. shift of competition from
individual organisations to supply chains competing against each other (Bai et al. 2012; Taticchi et al. 2012;
Cagnazzo et al. 2009). Consequently, there are several calls from researchers to develop performance
measures for supply chains (Chan and Qi 2003; Gunasekaran et al. 2004). According to Shepherd and
Gunter (2005) as reported in Taticchi et al. (2013), several metrics were developed and classified, as follows:
Whether they are qualitative or quantitative;
What they measure (i.e. cost vs non-cost; quality, delivery and flexibility, resource utilization,
visibility, trust and innovativeness);
Their operational, tactical or strategic focus;
The process in the supply chain they relate to.
In spite of substantial research on supply chain metrics, research focussing on the development of integrated
tools and frameworks for measuring the performance of supply chains was limited (Gunasekaran et al. 2001;
Bagchi et al. 2005). The Supply Chain Council (SCC) has developed an integrated framework (SCOR model
ver. 11) for describing the entire processes of the supply chain and extending the performance metrics for
individual organisations and supply chain over the entire network (Gunasekaran and Kobu 2007). Although
SCOR model is widely implemented across the industry, it receives limited attention from researchers and
academia (Taticchi et al. 2013). Similarly there are other frameworks proposed by academics (Beamon 1999;
Chan and Qi 2003; Chan et al. 2003; Gunasekaran et al. 2004; Berrah and Clivillé 2007) that has received
little attention from industry creating a gap for researchers. Shepherd and Gunter (2005) has reported a
number of limitations on the current available performance measurement frameworks for supply chains.
They argued that the existing frameworks, lack connection with strategy, has no focus on balanced
approaches (as articulated earlier), has insufficient focus on customers, lack holistic focus thus aiming at
local optimizations, etc. Ahi and Searcy (2013) conducted an extended review and summarised the key
characteristics of SCM as focusing on flow, co-ordination, stakeholders, relationships, value, efficiency, and
performance aspects, useful to integrate current understanding about the complex nature of the SCM.
Recently, several researchers have argued that there are inconsistencies between the known principles of
performance measures and supply chain dynamics (Lehtinen and Ahola 2010; Bititci et al. 2012). In parallel
to the development of PM for SCM, there were also calls to develop performance measures for
environmental and social aspects of SCM (McIntyre et al. 1998; Keating et al. 2008). Since then several
studies have emerged in literature reporting on performance measurement and green supply-chains, with
their focus on environmental aspects (Lee and Klassen 2008; Gavronski et al. 2011; Kim et al. 2011).
Bjorklund et al. (2012) identified five dimensions of performance measurement for green SCM, namely
stakeholder perspective, purpose of measuring, managerial levels of measuring, measuring across the supply
chain and combination of measurements. Shi et al. (2012) identified causal links between institutional
drivers, intra-organisational and inter-organisational environmental practices that would affect green SCM
performance. There are also similar studies reported on corporate social responsibility (CSR) and SCM, with
their focus however on social aspects (Dahlsrud 2008).
While the call for sustainability measurement is certainly not new (Milne 1996), many organisations are still
reluctant to implement performance measures, unless legally obliged to do so. The implementation of
sustainable measures appears however to have been boosted in recent times (Eccles and Krzus 2010). For
instance, many organisations have started measuring the sustainability with three main goals: transparency
and communication to stakeholders, improvement of operations and strategy alignment. As reported in
Taticchi et al. (2013), a number of metrics and frameworks have been proposed by practitioner bodies such
as the the Carbon Disclosure Project (CDP 2013), Global Reporting Initiative (GRI 2013) and the
International Federation of Accountants (IFAC 2013). Academia has produced both revised versions of
traditional frameworks such as the Responsive Business Scorecards (Van der Woerd and Van den Brink
2004) and more innovative models such as the Corporate Sustainability Model (Epstein 2008), the
Sustainability Evaluation and Reporting System (Perrini and Tencati 2006), the Sustainability DartBoards
(Bonacchi and Rinaldi 2007) and the Sustainability Assessment Model (Bebbington et al. 2007).
Gunter (2005) as reported in Taticchi et al. (2013), several metrics were developed and classified, as follows:
Whether they are qualitative or quantitative;
What they measure (i.e. cost vs non-cost; quality, delivery and flexibility, resource utilization,
visibility, trust and innovativeness);
Their operational, tactical or strategic focus;
The process in the supply chain they relate to.
In spite of substantial research on supply chain metrics, research focussing on the development of integrated
tools and frameworks for measuring the performance of supply chains was limited (Gunasekaran et al. 2001;
Bagchi et al. 2005). The Supply Chain Council (SCC) has developed an integrated framework (SCOR model
ver. 11) for describing the entire processes of the supply chain and extending the performance metrics for
individual organisations and supply chain over the entire network (Gunasekaran and Kobu 2007). Although
SCOR model is widely implemented across the industry, it receives limited attention from researchers and
academia (Taticchi et al. 2013). Similarly there are other frameworks proposed by academics (Beamon 1999;
Chan and Qi 2003; Chan et al. 2003; Gunasekaran et al. 2004; Berrah and Clivillé 2007) that has received
little attention from industry creating a gap for researchers. Shepherd and Gunter (2005) has reported a
number of limitations on the current available performance measurement frameworks for supply chains.
They argued that the existing frameworks, lack connection with strategy, has no focus on balanced
approaches (as articulated earlier), has insufficient focus on customers, lack holistic focus thus aiming at
local optimizations, etc. Ahi and Searcy (2013) conducted an extended review and summarised the key
characteristics of SCM as focusing on flow, co-ordination, stakeholders, relationships, value, efficiency, and
performance aspects, useful to integrate current understanding about the complex nature of the SCM.
Recently, several researchers have argued that there are inconsistencies between the known principles of
performance measures and supply chain dynamics (Lehtinen and Ahola 2010; Bititci et al. 2012). In parallel
to the development of PM for SCM, there were also calls to develop performance measures for
environmental and social aspects of SCM (McIntyre et al. 1998; Keating et al. 2008). Since then several
studies have emerged in literature reporting on performance measurement and green supply-chains, with
their focus on environmental aspects (Lee and Klassen 2008; Gavronski et al. 2011; Kim et al. 2011).
Bjorklund et al. (2012) identified five dimensions of performance measurement for green SCM, namely
stakeholder perspective, purpose of measuring, managerial levels of measuring, measuring across the supply
chain and combination of measurements. Shi et al. (2012) identified causal links between institutional
drivers, intra-organisational and inter-organisational environmental practices that would affect green SCM
performance. There are also similar studies reported on corporate social responsibility (CSR) and SCM, with
their focus however on social aspects (Dahlsrud 2008).
While the call for sustainability measurement is certainly not new (Milne 1996), many organisations are still
reluctant to implement performance measures, unless legally obliged to do so. The implementation of
sustainable measures appears however to have been boosted in recent times (Eccles and Krzus 2010). For
instance, many organisations have started measuring the sustainability with three main goals: transparency
and communication to stakeholders, improvement of operations and strategy alignment. As reported in
Taticchi et al. (2013), a number of metrics and frameworks have been proposed by practitioner bodies such
as the the Carbon Disclosure Project (CDP 2013), Global Reporting Initiative (GRI 2013) and the
International Federation of Accountants (IFAC 2013). Academia has produced both revised versions of
traditional frameworks such as the Responsive Business Scorecards (Van der Woerd and Van den Brink
2004) and more innovative models such as the Corporate Sustainability Model (Epstein 2008), the
Sustainability Evaluation and Reporting System (Perrini and Tencati 2006), the Sustainability DartBoards
(Bonacchi and Rinaldi 2007) and the Sustainability Assessment Model (Bebbington et al. 2007).
Despite this effort, most of the frameworks mentioned above are based on individual elements of the triple
bottom line (TBL) concept identifying the need to approach sustainability with both generic and industry-
specific measures of performance. This is echoed by Ahi and Searcy (2013) who conducted an extended
review summarising the characteristics of business sustainability, focusing on economic, environmental,
social, stakeholder, volunteer, resilient and long-term aspects. Walker and Jones (2012) identified external as
well as internal barriers and enablers for SSCM and validated their findings in seven large companies in UK.
Hassini et al. (2012) have reviewed the literature on sustainable supply chains during the period 2000-2010
to develop an original framework for sustainable supply chain management and performance measurement.
The framework incorporates six elements, namely sourcing, transformation, delivery, value proposition,
customers and product use along with reuse, recycle and return, which provides a link to closed-loop supply
chains.
The above researchers highlight the need to develop performance measures for supply chain, the insufficient
development of integrated tools and frameworks for measuring the performance of supply chain. They call
for the development of performance measures for environmental and social aspects of SCM, particularly in
the two areas relating to performance measurement for SSCM. Firstly, much of the literature is fragmented
into silo fields concentrating on either economic, environmental or social performance of SCM with few
studies recently emphasizing on addressing all three aspects. Secondly, according to Walker and Jones
(2012) there is a wide gap between what practitioners say and do about SSCM in reality because they only
provide lip service to SSCM. Although these needs are initially raised through conceptual work, there is still
a need to establish empirical work in this field, particularly using quantitative models (Seuring, 2013), also
demonstrating the utility of PM in SSCM.
2.2 Decision-Support Tools for Sustainable Supply Chains Management
Seuring (2013) suggests that the intersection of sustainability and supply-chain management needs to be
further researched, especially on the quantitative side, so as better to support decision-making. Decision-
making can be effectively supported by a computer or knowledge based information system supporting
organizational automatic, manual or hybrid decision-making activities associated with management,
operations, and planning levels of an organization (usually middle and higher management), usually called
Decision Support Systems (DSS). According to Keen (1980), academics usually perceive DSS as a tool to
support decision-making process while typical DSS users see DSS as a tool to facilitate organizational
processes. Sprague (1980) defines DSS by its characteristics, i.e. targeting underspecified problems,
combining use of models or analytical techniques, enabling features that for ease of use as well as providing
flexibility and adaptability to change.
Decision support tools (DSTs), being part of a more extensive decision support system (DSS), can play a key
role in improving the ability of decision-makers to assess and decide how good different configurations
might be with respect to set criteria or goals. In other words, DSS-DST could be defined as an approach that
would identify and assess multiple control variables (constructs or criteria) that would impact performance of
supply chains in general and SSCM in particular. As reported in Taticchi et al. (2013), several studies
focused on analytical models to implement sustainability: scheduling (Lejeune 2006) with energy aware
considerations (Bruzzone et al. 2012), facility location (Srivastava 2008; Dou and Sarkis 2010), supplier
selection, policy assessment, optimization (Cannon et al. 2005), analytical hierarchy process (Che 2010),
fuzzy decision making (Tsai and Hung 2009), heuristics such as genetic algorithm (Wang and Hsu 2010),
simulation (Van Der Vorst et al. 2009; Vlachos et al. 2007), “exergoeconomics” (Ji 2008), Life Cycle
Costing (LCC) and Life Cycle Assessment (LCA) (Matos and Hall 2007; Frota Neto et al. 2010). There are
also several studies using MCDM methodology that employ integrated analytical hierarchy process or
analytical network process, most are focused on either performance measurement (Lee et al. 2008; Wu et al.
2009), supply chain management issues (Wang et al. 2004; Ravi et al. 2006; Chan et al. 2008) or both
bottom line (TBL) concept identifying the need to approach sustainability with both generic and industry-
specific measures of performance. This is echoed by Ahi and Searcy (2013) who conducted an extended
review summarising the characteristics of business sustainability, focusing on economic, environmental,
social, stakeholder, volunteer, resilient and long-term aspects. Walker and Jones (2012) identified external as
well as internal barriers and enablers for SSCM and validated their findings in seven large companies in UK.
Hassini et al. (2012) have reviewed the literature on sustainable supply chains during the period 2000-2010
to develop an original framework for sustainable supply chain management and performance measurement.
The framework incorporates six elements, namely sourcing, transformation, delivery, value proposition,
customers and product use along with reuse, recycle and return, which provides a link to closed-loop supply
chains.
The above researchers highlight the need to develop performance measures for supply chain, the insufficient
development of integrated tools and frameworks for measuring the performance of supply chain. They call
for the development of performance measures for environmental and social aspects of SCM, particularly in
the two areas relating to performance measurement for SSCM. Firstly, much of the literature is fragmented
into silo fields concentrating on either economic, environmental or social performance of SCM with few
studies recently emphasizing on addressing all three aspects. Secondly, according to Walker and Jones
(2012) there is a wide gap between what practitioners say and do about SSCM in reality because they only
provide lip service to SSCM. Although these needs are initially raised through conceptual work, there is still
a need to establish empirical work in this field, particularly using quantitative models (Seuring, 2013), also
demonstrating the utility of PM in SSCM.
2.2 Decision-Support Tools for Sustainable Supply Chains Management
Seuring (2013) suggests that the intersection of sustainability and supply-chain management needs to be
further researched, especially on the quantitative side, so as better to support decision-making. Decision-
making can be effectively supported by a computer or knowledge based information system supporting
organizational automatic, manual or hybrid decision-making activities associated with management,
operations, and planning levels of an organization (usually middle and higher management), usually called
Decision Support Systems (DSS). According to Keen (1980), academics usually perceive DSS as a tool to
support decision-making process while typical DSS users see DSS as a tool to facilitate organizational
processes. Sprague (1980) defines DSS by its characteristics, i.e. targeting underspecified problems,
combining use of models or analytical techniques, enabling features that for ease of use as well as providing
flexibility and adaptability to change.
Decision support tools (DSTs), being part of a more extensive decision support system (DSS), can play a key
role in improving the ability of decision-makers to assess and decide how good different configurations
might be with respect to set criteria or goals. In other words, DSS-DST could be defined as an approach that
would identify and assess multiple control variables (constructs or criteria) that would impact performance of
supply chains in general and SSCM in particular. As reported in Taticchi et al. (2013), several studies
focused on analytical models to implement sustainability: scheduling (Lejeune 2006) with energy aware
considerations (Bruzzone et al. 2012), facility location (Srivastava 2008; Dou and Sarkis 2010), supplier
selection, policy assessment, optimization (Cannon et al. 2005), analytical hierarchy process (Che 2010),
fuzzy decision making (Tsai and Hung 2009), heuristics such as genetic algorithm (Wang and Hsu 2010),
simulation (Van Der Vorst et al. 2009; Vlachos et al. 2007), “exergoeconomics” (Ji 2008), Life Cycle
Costing (LCC) and Life Cycle Assessment (LCA) (Matos and Hall 2007; Frota Neto et al. 2010). There are
also several studies using MCDM methodology that employ integrated analytical hierarchy process or
analytical network process, most are focused on either performance measurement (Lee et al. 2008; Wu et al.
2009), supply chain management issues (Wang et al. 2004; Ravi et al. 2006; Chan et al. 2008) or both
performance measurement and supply chain management (Bhagwat and Sharma 2007). However few studies
offer decision support tools strongly related to performance measurement for SSCM.
SSCM related complex decisions, in practice, often involve groups of inter-related players, require the
synthesis of lot of information that often have high risks. Examples might include deciding on where to
locate a new manufacturing facility or how to select suppliers with multi-stakeholder needs. Such complex
problems often involve decisions/techniques to break them down into manageable steps and overcome any
inherent biases and errors through traditional active decision-support tools and techniques:
Structured Decision Making (SDM) - involves defining a complex problem with stakeholder input and
breaking that into decision objectives. It then involves picking and evaluating different alternatives.
Finally trade-offs are made for picking the preferred alternative.
Multi Criteria Decision Analysis (MCDA) – involves establishing multiple decision criteria. It then
involves assessing the criteria against each of the alternatives. Finally a weighting is obtained, which is
fed into the software that calculates an overall score for each alternative.
From this preliminary analysis, it is clear that these studies do not simply relate to analytical models or tools
to analyse and optimize one, or a few, sustainability dimensions to the aim is rather to extend them in line
with a new holistic view, considering multiple stakeholders and goal relationships, and so related
performance measures. This new view moves from linear to non-linear thinking, from a sectorial to a multi-
sector and multi-dimensional approach, from short to long term, from local to global or ‘glocal’ analysis,
from excluding externalities and exogenous variables from the model to internalizing them within it. In line
with this view, a recent review of sustainability analysis methodologies for efficient decision support in
green production operations (Liu et al. 2011), identified three main: (a) sustainability analysis has moved to
whole life cycle assessment from single-stage assessment, (b) sustainability analysis has shifted away from
single criterion to MCDA and (c) sustainability analysis has evolved from stand-alone approaches to
integrated systematic methodologies.
It is generally agreed that sustainability analysis is most effective and efficient in its support for complex
decision making in a sustainable supply chain when it is integrated. However most available studies analyze
sustainability issues at isolated stages of the supply chains. Hadiguna (2012) introduced a new paradigm for
sustainable assessment in supply chain operations based on functional capabilities: modelling, data
management, and knowledge management to support all decision-making processes.
Adding to this approach, Liu et al. (2012) suggest an integrated sustainability analysis (ISA) framework,
which integrates life cycle assessment into a multi-criteria decision-making process to support integration of
environmental management and social responsibility with the economic aspects of supply chain
management. Tan and Khoo (2005) demonstrated the usefulness of the LCA method in quantitatively
measuring the environmental impacts of sustainable operations in supply chains.
Cruz (2008) developed a dynamic decision-making model for supply chain networks incorporating corporate
social responsibility in attaining an equilibrium between environmental and economic impacts. Tsoulfas and
Pappis (2008) proposed a MCDM model based on five sets of environmental performance indicators to
support decision making in supply chains. Power (2002) presented another taxonomy for DSS in using the
mode of assistance as the criterion:
A communication-driven DSS supports more than one person working on a shared task.
A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of
internal company data and, sometimes, external data.
offer decision support tools strongly related to performance measurement for SSCM.
SSCM related complex decisions, in practice, often involve groups of inter-related players, require the
synthesis of lot of information that often have high risks. Examples might include deciding on where to
locate a new manufacturing facility or how to select suppliers with multi-stakeholder needs. Such complex
problems often involve decisions/techniques to break them down into manageable steps and overcome any
inherent biases and errors through traditional active decision-support tools and techniques:
Structured Decision Making (SDM) - involves defining a complex problem with stakeholder input and
breaking that into decision objectives. It then involves picking and evaluating different alternatives.
Finally trade-offs are made for picking the preferred alternative.
Multi Criteria Decision Analysis (MCDA) – involves establishing multiple decision criteria. It then
involves assessing the criteria against each of the alternatives. Finally a weighting is obtained, which is
fed into the software that calculates an overall score for each alternative.
From this preliminary analysis, it is clear that these studies do not simply relate to analytical models or tools
to analyse and optimize one, or a few, sustainability dimensions to the aim is rather to extend them in line
with a new holistic view, considering multiple stakeholders and goal relationships, and so related
performance measures. This new view moves from linear to non-linear thinking, from a sectorial to a multi-
sector and multi-dimensional approach, from short to long term, from local to global or ‘glocal’ analysis,
from excluding externalities and exogenous variables from the model to internalizing them within it. In line
with this view, a recent review of sustainability analysis methodologies for efficient decision support in
green production operations (Liu et al. 2011), identified three main: (a) sustainability analysis has moved to
whole life cycle assessment from single-stage assessment, (b) sustainability analysis has shifted away from
single criterion to MCDA and (c) sustainability analysis has evolved from stand-alone approaches to
integrated systematic methodologies.
It is generally agreed that sustainability analysis is most effective and efficient in its support for complex
decision making in a sustainable supply chain when it is integrated. However most available studies analyze
sustainability issues at isolated stages of the supply chains. Hadiguna (2012) introduced a new paradigm for
sustainable assessment in supply chain operations based on functional capabilities: modelling, data
management, and knowledge management to support all decision-making processes.
Adding to this approach, Liu et al. (2012) suggest an integrated sustainability analysis (ISA) framework,
which integrates life cycle assessment into a multi-criteria decision-making process to support integration of
environmental management and social responsibility with the economic aspects of supply chain
management. Tan and Khoo (2005) demonstrated the usefulness of the LCA method in quantitatively
measuring the environmental impacts of sustainable operations in supply chains.
Cruz (2008) developed a dynamic decision-making model for supply chain networks incorporating corporate
social responsibility in attaining an equilibrium between environmental and economic impacts. Tsoulfas and
Pappis (2008) proposed a MCDM model based on five sets of environmental performance indicators to
support decision making in supply chains. Power (2002) presented another taxonomy for DSS in using the
mode of assistance as the criterion:
A communication-driven DSS supports more than one person working on a shared task.
A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of
internal company data and, sometimes, external data.
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