A Review of Decision-Support Tools and Performance Measurement for Sustainable Supply Chain Management

This paper reviews the existing literature related to decision-support tools and performance measurement for sustainable supply chain management. It discusses the need for integrated performance frameworks with new generation decision support tools incorporating triple bottom line (TBL) approach for managing sustainable supply chains.

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.

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

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

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).

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

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.

 A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of
electronic formats.
 A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules,
procedures, or in similar structures.
 A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or
simulation model. Model-driven DSS use data and parameters provided by users to assist decision
makers in analyzing a situation even if they are not necessarily data-intensive.
Dey and Cheffi (2013) developed a framework for green supply chain performance measurement consisting
of two higher order constructs based on environmental practices and sustainable performances across the
supply chain using AHP. Bhattacharya et al. (2013) identified five constructs and developed a green supply
chain performance measurement framework and tested with collaborative decision-making approach using
fuzzy analytical network process based on green balance scorecard. The study identified and tested the green
causal relationships between the constructs, i.e. organisational commitment, eco-design, green supply chain
process, social performance and sustainable performance.
Bradenburg et al. (2013) argue that several studies are emerging on SSCM using case study or model-based
approaches within food, apparel and automotive industries while other sectors being neglected. Hence it
could be argued that more sophisticated modeling approaches such as dynamic programming, agent based
modeling, system dynamics, evolutionary algorithms, could be developed to offer large-scale optimization,
while considering different dimensions of SSCM.
The narrative literature review of the decision support systems for sustainable supply chain management
highlights the need for further research on the interaction of sustainability and supply-chain management, the
insufficient investigation on decision support tools related to performance measurement for SSC
Management, and the prevailing tendency to analyze sustainability issues at isolated stages of the supply
chain, rather than to use integrated approaches.
The narrative literature review summarized above highlights relevant areas with regard to both performance
measurement and decision support tools in the context of SSCM. In order to investigate further, a systematic
literature review was carried out to complement our initial findings and support in achieving the overall aim
of this research. We believed that a systematic literature review with bibliometric analysis would prove
useful in addressing the following objectives:
 To understand and assess the size of the research and trends in this field,
 To identify the leading journals and authors contributing to the field and in which areas,
 To explore the existing methods and approaches used in the field in an attempt to explore the need for
more methods .
We developed dimensions that were used to investigate the above objectives and synthesized in Table 1 and
described in the next section.
3. Review Methodology
The systematic literature review was performed by using classic bibliometric techniques such as the analysis
of publications, citations and adopted research methods. These techniques were used several times for
performing literature reviews in different management fields in the past (Pilkington and Leston-Heyes 1999;
Neely 2005; Burgess et al 2006; Taticchi et al 2010; Hassini et al. 2012). While Taticchi et al. (2013)

interrogated PM and SSCM in the past, this research extends and revises their work by incorporating
decision support tools with PM and SSCM.
In order to perform this study, authors have used “ISI Web of Science”, a dataset which is one of the most
consistent source for articles in business and management fields (Shepherd and Gunter, 2005). In September
2013, the dataset was interrogated twice (so as to build two separate databases) searching for papers’ titles,
abstracts and keywords:
 [“Sustainable” OR “Sustainability”] AND [“Supply Chain Performance”] AND [“Measurement” OR
“Management” OR “Metrics” OR “Indicators”];
 [“Sustainable” OR “Sustainability”] AND [“Supply Chain”] AND [“Decision”] AND [“Making”
OR “Support” OR “Tool” OR “System” OR “Application”];
The interrogation allowed the construction of a database1 related to PM in SSCM with 274 papers, and
another database related to DST in SSCM with 110 papers. Works included in the search/databases were not
limited in time, but restricted to peer-reviewed publications in the areas of business, management,
engineering, economic, environmental and social sciences. To perform the bibliometric analysis, the dataset
was uploaded to the Sitkis software (Schildt et al. 2006). Two authors independently performed the
reviewing process by verifying every record in the databases to ensure accuracy of the process, and errors
identified were consequently amended. This accorded with current best practices for bibliometric analysis
(Schildt 2002). In this process, fifty-one publications were found to be common to both datasets, thus
highlighting a strong overlap among the two research areas.
Two classification frameworks were developed with the goal of clarifying the review methodology adopted.
The first framework, summarised in Table 1, presents 12 dimensions that were used for analysing the articles
related to two groupings: analysis of publication data and analysis of citation data. The dimensions identified
were designed to provide a comprehensive characterisation of available literature. The second framework,
summarised in Figure 1, identifies 16 criteria that were used for classifying research methods. The latter is a
modification of the classification proposed by Wacker (1998), who identified two broad macro-categories of
research methods (analytical and empirical) further divided into six categories (analytical: conceptual,
mathematical and statistical; empirical: experimental design, statistical sampling and case studies). The
classification developed by Wacker has already been used by Burgess et al. (2006) in a systematic review of
SCM literature. Wacker’s classification was modified by identifying additional sub-categories for the
“mathematical” category from the findings of Seuring (2013) who reviewed modelling approaches for SSCs
and from empirical evidence during the review process performed by the authors. The “conceptual” category
and “empirical” macro-category were adopted from the works of: Coughlan and Coghlan (2002), Weick
(1995) and Tranfield et al. (2003).
With the purpose of ensuring consistency in the classification process of research methods, an independent
classification was undertaken by two of the authors. Then, all authors discussed discrepancies together when
necessary.
Papers may of course be based on more than one research method. For example, it is common to find papers
using literature review and theory building at the same time, or statistical sampling and case studies
simultaneously. In the presentation of findings, results are presented both in terms of total numbers (here the
total of research methods is higher than the total of papers examined) and percentages.
(Insert Table 1 here)
1 A full list of papers is available to public online at: http://sscm.esy.es/IJPR Dataset Web.htm

(Insert Figure 1 here)
4 Systematic Literature Review
4.1 Analysis of Publication Data
Figures 2 and 3 present the distribution of publications in both domains over time, highlighting a research
field that is growing very fast in the last few years.
(Insert Figure 2 here) (Insert Figure 3 here)
Table 2 presents the list of the top ten journals where the research on PM-SSCM and DST-SSCM were
published respectively. Journal of Cleaner Production, International Journal of Production Economics,
International Journal of Production Research seems to be amongst the top three in both the databases.
(Insert here Table 2)
Table 3 presents the ranking of most prolific scholars in both the databases. Sarkis J., Klassen, R.D., Seuring
S., and Vachon S. lead in the field of PM-SSCM with at least 6 publications. These authors have similar
disciplinary backgrounds in operations and supply chain management. The list in the field of DST-SSCM is
led by Govindan, K. (with 4 publications) who is followed by a number of other authors with three
publications. The academic background of the leading author (Govindan) is management accounting. The
backgrounds of the authors following him in the list include business, computer science and manufacturing.
Here it is also interesting to note that the academic backgrounds of scholars working in DST-SSCM appears
to embrace more fields than those of scholars researching in PM-SSCM, with system sciences, business and
management being particularly prominent. Sarkis and Govindan are present in both lists (PM and DST).
(Insert Table 3 here)
Table 4 presents the geographic breakdown of scholars. North American and European academic institutions
have made substantial contributions to the research field’s development, in approximately equal measure, in
both PM-SSCM and DST-SSCM. In addition, Table 4 highlights the emerging contribution of scholars from
China and Taiwan, suggesting the relevance of this topic to Asian countries. Studies from Asian countries
show a preference for practical and quantitative approaches.
(Insert Table 4 here)
4.2 Analysis of Citation Data
The frequency of citations was explored in a greater detail for individual publications. The 274 papers
included in the PM-SSCM dataset provided 3,149 citations. The 110 papers included in the DST-SSCM
dataset provided 686 citations. As is often observed in such analyses, the trend of citations over time is
consistent with the trend of publications as demonstrated in Figures 4 and 5.

(Insert Figure 4 here) (Insert here Figure 5 here)
As demonstrated in Table 5, the most frequently cited journals in PM-SSCM research are: International
Journal of Operations & Production Management (491 citations), Journal of Cleaner Production (381
citations), International Journal of Production Economics (300 citations) and the International Journal of
Production Research (253 citations). The most frequently cited journals in DST-SSCM research are: Journal
of Cleaner Production (175 citations), International Journal of Production Research (117 citations),
International Journal of Production Economics (97 citations) and the International Journal of Physical
Distribution & Logistics Management (37 citations). The most cited journals are in the field of operations
and production management, demonstrating its strong link (typically in relation to tactical and operational
decisions) with supply chain management in the sustainability and decision-making domains.
(Insert Table 5 here)
The most frequently cited authors in the field of PM-SSCM are presented in Table 6: Rao and Holt (168
citations), Linton et al (133 citations), Kleindorfer et al (128 citations), Guide et al (114 citations) and
Vachon et al (111 citations). The authors identified here have a diverse disciplinary background: Rao –
environmental science, Linton and Guide – supply chain management, Kleindorfer – technology and
operations management, and Vachon – supply chain management.
(Insert Table 6 here)
The most frequently cited authors in the field of DST-SSCM are presented in Table 7: Lu et al (49 citations),
Hutchins and Sutherland (43 citations), Bai & Sarkis (40 citations), Tsai and Hung (34 citations) as well as
Ukidve and Bakshi (34 citations). These researchers have different disciplinary backgrounds: Lu -
mathematics and science computing, Hutchins - political science and economics, Bai - industrial
engineering, Tsai - mathematics and Ukidwe - environmental science. These findings, clearly demonstrates
diversity in the disciplines.
(Insert Table 7 here)
4.3 Analysis of Keywords
Wasserman and Faust (1994) developed an analytical technique for studying the social network of keywords
for the most frequently cited works in literature. The authors followed this technique and downloaded the
keywords from the dataset with the help of the Sitkis software (research works with over 20 citations were
included) and using the UCINET software (Borgatti et al. 2002) to carry out the social network analysis.
Figure 6 presents the visual network obtained from PM-SSCM dataset highlighting the centrality of eight
keywords: “sustainable development”, “sustainability”, “green”, “supply chain management”, “industry”,
“impact”, “performance” and “competitive advantage”. The social network analysis was repeated for DST-
SSCM dataset and the keywords for the most frequently cited works in literature are presented in Figure 7.
The visual network obtained highlights the centrality of five keywords: “sustainability”, “performance”,
“design”, “management” and “framework”. Interestingly the keyword “design” shows multiple connections
(more than management), suggesting that the DST should mainly be addressed to the design phase of SSCs,
at least at this transition stage towards sustainability (reinforcing the notion of orienting the development and
application of DST towards the tactical decision-making process). The social network analysis shows clearly
that the DST-SSCM domain is still under strong development, and so deserving greater attention from
scholars with quantitative decision science background.

(Insert Figure 6 here) (Insert Figure 7 here)
4.4 Analysis of Research Methodologies
By using the framework presented in Figure 1, PM-SSCM papers identified were classified based on the
research methodology employed (Table 8). The findings highlight a balance between analytical and
empirical papers. In the context of analytical papers, conceptual works based on theory building were
significantly the most numerous, followed by works based on statistical methods. In the context of empirical
papers, survey and case study based works dominate the table. A similarly classification was undertaken for
DST-SSCM papers (Table 9). Findings highlight a majority of analytical papers where works based on
theory building were significantly the most prevalent, followed by works based on equilibrium models,
MCDM and LCA. In the context of empirical papers, case-study based works dominate the table, followed
by papers based on statistical sampling.
(Insert Table 8 here)
(Insert Table 9 here)
5 Discussion and Research Agenda
This paper employs a systematic and methodologically rigorous process to review the existing and potential
linkages between and practices within DST and PM for SSC design and management, using content analysis
to assess a large sample of related papers and identifying current gaps and future perspectives. Literature
reported from PM-SSCM dataset remained as analytical with 194 papers and empirical with 192 papers.
Similarly, the literature reported from DST-SSCM dataset remained as analytical with 109 papers and
empirical with 55 papers. Although not as significant as PM-SSCM, DST-SSCM is a growing area of its
application, concepts, principles, techniques, and tools in industrial sustainability. There are however only 51
papers that considered aspects of both PM and DST in the SSCM domain, indicating the need for more
research on exploring DSTs for measuring performance of SSCs
The rate of growth in these areas of research over the last few years is quite relevant. The top three journals
identified are the same that were identified for PM-SSCM research showing an interesting similarity between
PM and DST in the SSCM domain. The lead authors of PM-SSCM papers tend to have a background in
management accounting, and we suggest that there is scope for supply chain management scholars to engage
more in this aspect of research. The academic background of scholars working in DST-SSCM appears to
embrace a wider range of fields, with roots particularly in system science, business and operations
management. From the analysis, although there is evidence of some diversity in disciplines, there is a need
for more focused research in the area of policy development. We urge the journal editors to stimulate more
interest in these areas and in cross-disciplinary research.
As was demonstrated during the narrative and structured literature review, DST-SSCM research has
concentrated on the resolution of isolated issues within the supply chain (Cannon et al. 2005; Lejeune 2006;
Lu et al. 2007; Srivastava 2008; Dou and Sarkis 2010; Bai and Sarkis 2010). Existing research
predominantly focuses on either the environmental or the social aspects of supply chains along with
economic aspects, with only a few studies incorporating all three dimensions. The TBL approach, however,
represents an emerging strand within the burgeoning literature on PM–SSCM and DST-SSCM. Relevant
metrics can be classified along three dimensions: economic, environmental and social.

 Economic: While there is abundant literature focusing on this perspective, DST in SSCM domain
have to advance towards the inter-organizational aspects of SCM and extend this to the industry
system level (Tonelli et al. 2013; Kannegiesser and Günther 2013). Economic contributions of
vertical coordination in the SSCM context can be assessed quantitatively using a set of performance
metrics. As argued by Brandenburg et al. (2013), quantitative models could be employed to
elaborate on the interplay of regulatory decisions made by legal authorities and managerial decision
making in firms, supply chains, or industries, with the results being incorporated and implemented in
a new generation of DSTs. Some of the criteria or metrics used to assess these aspects are:
microeconomic factors such as cost, profitability, or revenue (Lovric et al. 2013); macroeconomic
metrics including gross domestic product or growth rate (Agrell et al. 2004) as well as labor
productivity, market concentration, or import dependency (Yakovleva et al. 2011) or overall macro-
economic development (Feng et al. 2007). At tactical and operational level, a great deal of attention
has been paid to reverse logistics and related activities such as collecting, testing, sorting, and
remanufacturing of returned products (according to the CLSC and end-of-life concepts). At a more
strategic level, the economic aspects of industrial sustainability have been neglected (it has been
mainly analysed in pure financial terms). Hence understanding and measuring the costs of
implementing sustainability practices versus costs for mitigating activities and outcomes derived by
non-sustainable practices in supply-chains should, we suggest, be high on the future research agenda.
These tactical and strategic considerations require a multi-stakeholder based PM framework
(Taticchi et al. 2013) alongside new generation DSTs.
 Environmental: There is significant potential in addressing environmental and risk management
aspects through a new generation DST incorporating a good knowledge of environmental
performance metrics. As reported in Brandenburg et al. (2013), some of the metrics needed for this
aspect include: input oriented factors including renewable energy sources (Georgopoulou et al. 1998;
Munda 2009), natural resources (Liu et al. 2011), water and energy consumption (Yakovleva et al.
2011), or water quality (Feng et al. 2007); output-oriented environmental factors focus on waste
(Yakovleva et al. 2011) and pollution (Georgopoulou et al. 1998). As discussed earlier, there is a
need to understand the relative costs of implementing sustainable practices versus mitigating their
non-implementation, and also to augment the hitherto limited research on how environmental
considerations might eventually affect economic aspects of the businesses and social well-being. As
suggested by Taticchi et al. (2013) and Tonelli et al. (2013) there is a need for an innovative holistic
sustainable business model to tackle the above issues. Specific industry-oriented environmental
performance evaluation systems and DSTs (i.e. capable of supporting decision-making through the
best available techniques for each industrial sector), should be studied and developed.
 Social aspects: accordingly to the SSCM definition provided in the narrative review, social aspects
need to be incorporated in the decision-making process of the new generation DSTs in order to
maximize the social well-being. The existing literature contains some measures for the evaluation of
the social dimension which include (Brandenburg et al. 2013): internal factors such as wages,
employees, or employment gender ratios (Yakovleva et al. 2011) and external factors such as
individual customer needs and requirements (Lovric et al. 2013), social acceptance and contribution
to employment (Georgopoulou et al. 1998), and population growth (Feng et al. 2007). However,
contributions relating to the social dimension of SSCM have been limited, and mainly related to the
competitive aspects (Jovane et al., 2009) of a specific industrial area/cluster (thus involving
employees and communities).
The foregoing represents an assessment of the detailed usage within the literature of the three sustainability
dimensions, e.g. which metrics have been found suitable to represent sustainability factors in formal SSCM
models and which perspectives are taken in holistic SSCM models (Brandenburg et al. 2013). It, strongly
suggests a need to further integrate holistic TBL measures and the resulting performance impacts within

SSCM modeling. This would bridge the gap between the ‘win-win’ approaches to the three aspects of
sustainability prevalent in SSCM research and the trade-off-based modeling approaches that dominate formal
modeling research (Brandenburg et al. 2013; Seuring and Müller 2008; Seuring 2013). There is also a need
to correct the sectorial imbalance that comes from papers on SSCM metrics tending to deal with
manufacturing sectors such as automotive or electronics industries (Hassini et al. 2012). We still need to
identify a wider range of specific industry-related TBL metrics and indexes, and assess their usefulness
through empirical research and case-base analysis.
The PM & DST for SSCM data sets were separately analyzed because the two research fields emerged as
largely isolated from one another. We suggest that there is a need for an integrated approach and a holistic
framework. Our interpretation of quantitative DSS-DST research leads us to believe that these tools can
benefit greatly from an appropriate link with SSCM-PM. This belief is also echoed by other researchers
(Seuring and Müller 2008; Carter and Easton 2011; Carter and Rogers 2008; Gold et al. 2010a/b; Golicic and
Smith 2013; Tang and Zhou 2012). On the other, hand most of the papers reported in the DST-SSCM
dataset, include implementation of individual DST tools or techniques to resolve an issue within supply
chain (such as supplier selection, scheduling, facility location, etc.) rather than consideration of sustainability
aspects of the entire supply-chain. As articulated by Bai and Sarkis (2010), effective DST could allow rapid
assessment of several SSC configurations (so supporting the design phase) around the world to facilitate
decision-making at the firm and policy levels. Within the most frequently cited authors, a disciplinary
diversity can be observed, ranging from mathematics and computing science to political, economic and
environmental sciences. With the DST-SSCM domain still developing strongly, it represents a worthy object
of attention for scholars with quantitative decision science and industrial engineering backgrounds. There is
more work required in the development of integrated performance frameworks with new generation of DSTs
in managing SSCs.
Much of the research reported in this paper focus on developing the frameworks for integrating sustainability
to supply chain practices. Few researchers (Dey and Cheffi 2013; Bhattacharya et al. 2013) have worked in
this field in the development of integrated framework (PM & DST) but however did not focus on
implementation issues. For instance, Walker and Jones (2012) argue that most of the organisations provide
lip service when it comes to sustainable aspects of their supply chain operations, suggesting a wide gap
between theory and practice. This has implications on policy development and consumer awareness, as
identified earlier. In respect of the limitations, based on the findings of this work, there is a strong need for
research that focus on identifying issues (such as barriers and drivers) in implementing integrated (PM &
DST) framework in sustainable supply chains considering all three aspects of sustainability. More
importantly, we need research to identify impact of such integrated frameworks.
The visual network analysis in Figure 6 highlights the centrality of eight keywords: “sustainable
development”, “industry”, “supply chain management”, “impact”, “green”, “performance”, “competitive
advantage” and “sustainability”. This suggests that measuring performance on the sustainable aspects of
SCM could lead businesses to competitive advantage and thus impact industry as a whole. This proposition
is supported by the work of Rao and Holt (2005), which received the highest number of citations as
demonstrated in Table 6. Similarly, the visual network analysis in Figure 7 highlights the centrality of five
keywords: “sustainability”, “performance”, “design”, “management” and “framework”. This supports our
earlier claim regarding the desirability an integrated performance framework for managing sustainable
aspects of SCs that have design implications.
The classification by research methodology shows a majority of analytical papers (theory building) followed
by works based on equilibrium models, MCDM, fuzzy-logic and LCA. In the context of empirical papers,
case-study based work dominates, followed by papers based on statistical sampling. Formal models such as
fuzzy-logic and MCDM seem to be the natural connection between PM-SSCM and DST-SSCM, even if the

popularity of statistical sampling has only started to grow in the last three years. In terms of modeling results,
the findings do confirm the relevance of AHP/ANP, LCA, and MCDM for SSCM models (Dey and Cheffi
2013; Bhattacharya et al. 2013), but discrete event simulation (DES) and genetic algorithms (GA), system
dynamics (SD) and agent-based modeling (ABM) could be further explored to offer large scale optimization
and solve complex problems, while considering different dimensions of SSCM (Seuring 2013). So a further
research stream is to identify and feature descriptive or normative (deterministic vs. stochastic) approaches
to be developed inside DST as well as extending proposed approaches (e.g. evaluating recent advances) in
using Agent Based simulation for sustainability formal modeling. For instance, we should identify and adapt
the use of existing methods available in other disciplines such as conjoint analysis using the technique of
discrete choice analysis (DCA) in an innovative way to this research field (Verma and Pullman, 1998). In
summary, DCA is an econometric method used to quantify the relative weights of attributes and criteria
assessed by the decision-maker in a choice experiment. We have no doubt that similar models will prove
beneficial to research in the areas of policy development and implementation and consumer research within
the domain of SSCM that we identified earlier as a future research direction.
As reported earlier, the majority of studies, whether analytical or empirical, involve developing a framework
conceptually and then testing it in an organizational context through empirical study. In majority of these
studies, the constructs, models or algorithms were theoretically developed with little input from practice.
Thus, we need mixed methods to thoroughly analyse and investigate sustainable aspects of the product life
cycle across the SCs. This implies designing, implementing and testing the integrated frameworks through
empirical evidence, building and/or testing theory from and in practice.
6 Conclusion
This paper reports on an extensive literature review on DST and PM associated with SSCM. After an initial
presentation of narrative literature review, it is evident that PM-SSCM and DST-SSCM are two isolated
fields emerging in literature with only a handful of studies on the integrated approach. On one hand, it is
clearly evident from this literature that sustainability include all three dimensions, namely economic, social
and environmental dimensions. Hence there is a need for PM framework incorporating all three dimensions.
On the other hand, literature provides evidence of using DSTs such as LCA, equilibrium, MCDM,
AHP/ANP, etc. to enable decision making in SSCM. However most of these studies are limited to solve
isolated or specific problems within the supply chains. Hence more studies need to be considered to use
DSTs for inter-organisational aspects of SSCM and extend it to industry system level. It also identifies the
gap for more DSTs based on system dynamics and agent based simulation-modeling methods for enabling
decision making in complex situations.
While researchers have predominantly focused on developing integrated framework to tackle sustainability
issues, there is dearth of literature relating to issues in the implementation of such frameworks: practical
difficulties, ease and usefulness of implementing integrated frameworks, resistance from people or
businesses in implementing such frameworks, etc. Some researchers (for example Walker and Jones, 2012)
argue that many organisations provide only lip service while doing very little, highlighting a gap between
literature and practice. Exploring and addressing this gap has major implications for policy research, for
policy makers need to identify alternative ways of making businesses implement sustainable aspects, part of
which can be raising consumer awareness. SCs could also take advantage in exploring consumer behaviour
aspects that has implications on reverse SCs flows and policies as well as product (re)design aspects.
In summary the literature analysis and debate uncovers some interesting issues, including the need for:

 A holistic framework for integrating DST with PM and SCM considering TBL approach
 More research on sector specific measures and indicators for all sustainability dimensions of SCM
 More DSTs for incorporating inter-organizational aspects of SSCM and enabling decision making in
solving complex problems
 Exploration of issues related to the implementation of the integrated approach for SSCM, e.g.
difficulties, its usefulness and the implications for policy research
 More research focusing on implementing holistic frameworks and learning from industry/practice
through inductive, exploratory and longitudinal studies
 More research on methods and approaches that could be adopted from other disciplines such as conjoint
analysis and discrete choice experiments
The points listed above constitute a base for addressing future research on DST and PM for SSCM.
However, there is a need to develop a detailed research agenda for each of these topics. As can be seen, the
SSCM field has grown fast with increasing recognition from various stakeholders (including governments,
citizens, consumers) and hence more sector specific measures and indicators as well as practical decision-
making approaches need to be implemented to fully aid decision-making in more complex and dynamic
situations within sustainable supply chains.
The work presented in this paper analysed the nature and spread of available publications, citation, keywords
and research methods. Although we analysed the majority of the papers, of which the authors had prior
knowledge, for the narrative literature review and citation tracking, our study has certain limitations. Firstly,
the database (of 384 papers) was retrieved by using specific combinations of keywords might hence have
omitted some relevant papers, which did not use those specific keywords. Secondly, we were not able to
access all the papers listed in the database. This study could therefore be extended by conducting a content
analysis of the papers listed in our database, which is now publicly available. We contend that these
limitations do not compromise the value of our work to researchers.

7 References
1. Ackali, E., Karakayali, I. and Emir-Farinas, H., 2007. An analysis of decentralized collection and
processing of end-of-life products. Journal of Operations Management, 25 (6), 1161–1183.
2. Agrell, P. J., Stam, A., and Fischer, G. W., 2004. Interactive multi-objective agro-ecological land use
planning: The Bungoma region in Kenya. European Journal of Operational Research, 158, 194-217.
3. Ahi, P. and Searcy, C., 2013. A Comparative Literature Analysis of Definitions of Green and Sustainable
Supply Chain Management. Journal of Cleaner Production, 52, 329-341.
4. Akkerman, R., Farahani, P. and Grunow, M., 2010. Quality safety and sustainability in food distribution:
a review of quantitative operations management approaches and challenges. Or Spectrum, 32 (4), 863-
904.
5. An, H. J., Yang, H. S., Liu, Z. D., and Zhang, Z. Z., 2008. Effects of heating modes and sources on
nanostructure of gelatinized starch molecules using atomic force microscopy. LWT-Food Science
Technology, 41 (8), 1466–1471.
6. Ates, A., Garengo, P., Cocca, P., Bititci, U. 2013. The development of SME managerial practice for
effective performance management, Journal of Small Business and Enterprise Development, 20 (1), pp.
28-54
7. Aurich, J.C., Fuchs, C., Wagenknecht, C., 2006. Life cycle oriented design of technical Product-Service
Systems. Journal of Cleaner Production, 14 (17), 1480–1494.
8. Ayres, R.U. and Kneese, A.V., 1969. Production, consumption, and externalities. The American
Economic Review, 59 (3), 282–297.
9. Bagchi, P. K., Ha, B. C., Skjoett-Larsen, T. and Soerensen, L. B., 2005. Supply chain integration: a
European survey. The International Journal of Logistics Management, 16 (2), 275 – 294.
10. Bai, C., & Sarkis, J., 2010. Integrating sustainability into supplier selection with grey system and rough
set methodologies. International Journal of Production Economics, 124(1), 252–264.
11. Bai, C., Sarkis, J., Wei, X. and Koh, L., 2012. Evaluating ecological sustainable performance measures
for supply chain management. Supply Chain Management: An International Journal, 17 (1), 78-92.
12. Beamon, B.M., 1999. Designing the green supply chain. Logistics Information Management, 12 (4), 332-
342.
13. Bebbington, J., Brown, J. and Frame, B., 2007. Accounting Technologies and Sustainability Assessment
Models. Ecological Economics, 61 (2-3), 224-236.
14. Berrah, L. and Clivillé, V., 2007. Towards an aggregation performance measurement system model in a
supply chain context. Computers in Industry, 58 (7), 709-719.
15. Bhagwat, R., Sharma, M.K., 2007. Performance measurement of supply chain management: a balanced
scorecard approach. Computers & Industrial Engineering, 53 (1), 43-62.
16. Bhattacharya A, Mohapatra P., Kumar V., Dey P.K, Brady M, Tiwari M. K. and Nudurupati S., 2013.
Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a
collaborative decision-making approach. Production Planning & Control: SI - The Management of
Operations, DOI:10.1080/09537287.2013.798088.
17. Bititci U., Garengo P., Dörfler V. Nudurupati S., 2012. Performance Measurement: Challenges for
Tomorrow. International Journal of Management Reviews, 14 (3), 305.
18. Bjorklund, M., Martinsen, U. and Abrahamsson, M., 2012. Performance measurements in the greening
of supply chains. Supply Chain Management: An International Journal, 17 (1), 29–39.
19. Bonacchi, M. and Rinaldi, L., 2007. DartBoards and Clovers as New Tools in Sustainability Planning
and Control. Business Strategy and the Environment, 16 (7), 461-473.
20. Borgatti, S.P., Everett, M.G. and Freeman, L.C., 2002. Ucinet for Windows: Software for Social Network
Analysis. MA: Analytic Technologies, Harvard.

21. Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., 2013. Quantitative models for sustainable supply
chain management: developments and directions. European Journal of Operational Research,
doi:http://dx.doi.org/10.1016/j.ejor.2013.09.032.
22. Bruzzone, A.A.G., Anghinolfi, D., Paolucci, M. and Tonelli, F., 2012. Energy-aware scheduling for
improving manufacturing process sustainability: A mathematical model for flexible flow shops. CIRP
Annals - Manufacturing Technology, 61 (1), 459-462.
23. Burgess, K., Singh, P.J., Koroglu, R., 2006. Supply chain management: a structured literature review and
implications for future research. International Journal of Operations & Production Management, 26 (7),
703-729.
24. Burgess, T., Ong, T.S. and Shaw, N., 2007. Traditional or Contemporary? The Prevalence of
Performance Measurement System Types. International Journal of Productivity and Performance
Management, 56 (7), 583-602.
25. Cagnazzo, L., Taticchi, P., Brun, A., 2009. The Role of Performance Measurement Systems to Support
Quality Improvement Initiatives at Supply Chain Level. The International Journal of Productivity and
Performance Management, 59 (2), 163-185.
26. Cannon, M., Kouvaritakis, B. and Huang, G., 2005. Modelling and optimisation for sustainable
development policy assessment. European Journal of Operational Research, 164 (2), 475–490.
27. Carbon Disclosure Project (CDP), 2013. Collaboration delivers targets and mutually beneficial energy
savings. available at: https://www.cdproject.net/en-US/WhatWeDo/Pages/Case-Study-Dell.aspx
(accessed 4 March 2013).
28. Carter, C.R. and Easton, P.L., 2011. Sustainable supply chain management: evolution and future
directions. International Journal of Physical Distribution & Logistics Management, 41 (1), 46-62.
29. Carter, C.R. and Rogers, D.S., 2008. A framework of sustainable supply chain management: moving
toward new theory. International Journal of Physical Distribution & Logistics Management, 38 (5), 360-
387.
30. Chaabane, A., Ramudhin, A. and Paquet, M., 2012. Design of sustainable supply chains under the
emission trading scheme. International Journal of Production Economics, 135 (1), 37-49.
31. Chan, F. T. S.,Kumar,N., Tiwari,M.K., Lau, H. C. W. and Choy,K. L., 2008. Global supplier selection:
A fuzzy-AHP approach. International Journal of Production Research, 46, 3825–3857.
32. Chan, F.T.S. and Qi, H.J., 2003. An innovative performance measurement method for supply chain
management. Supply Chain Management: An International Journal, 8 (3), 209-223.
33. Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W. and Ip, R.W.L., 2003. A conceptual model of
performance measurement for supply chains. Management Decision, 41 (7), 635-642.
34. Che, Z.H., 2010. Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced
and defective supply chain problems considering WEEE/RoHS directives. International Journal of
Production Research, 48 (11), 3355–3381.
35. Corbett, C.J. and Kleindorfer, P.R., 2003. Environmental management and operations management:
introduction to the third special issue. Production and Operations Management, 12 (3), 287–289.
36. Coughlan, P. and Coghlan D., 2002. Action research for operations management. International Journal
of Operations & Production Management, 22 (2), 220 – 240.
37. Cruz, J.M., 2008. Dynamics of supply chain networks with corporate social responsibility through
integrated environmental decision-making. European Journal of Operational Research, 184 (3), 1005–
1031.
38. Cucek, L., Klemes, Jiri, J. and Kravanja, Z., 2012. A review of footprint analysis tools for monitoring
impacts on sustainability. Journal of Cleaner Production, 34, 9-20.
39. Dahlsrud, A., 2008. How Corporate Social Responsibility Is Defined: An Analysis of 37 Definitions.
Corporate Social Responsibility and Environmental Management 15 (1), 1-13.

40. Dey, P. K. and Cheffi, W., 2012. Green Supply Chain Performance Measurement using the Analytic
Hierarchy Process: A Comparative Analysis of Manufacturing Organisations. Production Planning &
Control: The Management of Operations, 24 (8-9), 702-720, doi: 10.1080/09537287.2013.798088.
41. Dey, P.K. and Cheffi W., 2013. Managing supply chain integration: contemporary approaches and scope
for further research. Production Planning & Control: The Management of Operations, 24 (8-9).
42. Dou, Y. and Sarkis, J., 2010. A joint location and outsourcing sustainability analysis for a strategic
offshoring decision. International Journal of Production Research, 48 (2), 567–592.
43. Easterby-Smith, M., Thorpe, R. and Lowe, A., 2002. Management Research: an Introduction. London:
Sage Publications.
44. Eccles, R.G. and Krzus, M.P., 2010. One Report: Integrated Reporting for a Sustainable Strategy. John
Wiley & Sons, Inc., Hoboken, New Jersey.
45. Epstein, M.J., 2008. Making Sustainability Work, Greenleaf Publishing Limited. Sheffield, UK.
46. Ehrenfeld, J.R., 2001. Sustainability by Design: A Subversive Strategy for Transforming Our Consumer
Culture, Yale University Press, New Haven.
47. Feng, S., Li, L. X., Duan, Z. G., and Zhang, J. L., 2007. Assessing the impacts of South-to-North water
transfer project with decision support systems. Decision Support Systems, 42, 1989-2003.
48. Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., Van Der Laan, E., Van Nunen, J.A.E.E., Van
Wassenhove, L.N., 1997. Quantitative models for reverse logistics: a review. European Journal of
Operational Research, 103 (1), 1–17.
49. Frota Neto, J.Q., Walther, G., Bloemhof, J., Van Nunen, J. and Spengler, T., 2010. From closed-loop to
sustainable supply chains: the WEEE case. International Journal of Production Research, 48 (15),
4463–4481.
50. Garengo, P., Biazzo, S. and Bititci, U.S., 2005. Performance measurement systems in SMEs: a review
for a research agenda. International Journal of Management Reviews, 7 (1), 25–47.
51. Gavronski, I., Klassen, R.D., Vachon, S. and do Nascimento, L.F.M., 2011. A resource-based view of
green supply management. Transportation Research Part E: Logistics and Transportation Review, 47
(6), 872-885
52. Georgopoulou, E., Sarafidis, Y., and Diakoulaki, D., 1998. Design and implementation of a group DSS
for sustaining renewable exploitation. European Journal of Operational Research, 109, 483-500.
53. Global Reporting Initiative (GRI), 2013. Reporting Framework Overview. available at:
www.globalreporting.org/reporting/reporting-framework-overview/Pages/default.aspx (accessed 23
February 2013).
54. Gold, S., Seuring, S., and Beske, P., 2010a. The constructs of sustainable supply chain management – a
content analysis based on published case studies. Progress in Industrial Ecology – An International
Journal, 7 (2), 114-137.
55. Gold, S., Seuring, S., and Beske, P., 2010b. Sustainable supply chain management and
interorganizational resources: a literature review. Corporate Social Responsibility and Environmental
Management, 17, 230-245.
56. Golicic, S. L., and Smith, C. D., 2013. A meta-analysis of environmentally sustainable supply chain
management practices and firm performance. Journal of Supply Chain Management, 49 (2), 78-95.
57. Guide Jr., V.D.R., Jayaraman, V. and Srivastava, R., 1999. Production planning and control for
remanufacturing: A state-of-the-art-survey. Robotics and Computer-Integrated Manufacturing, 15 (3),
221-230.
58. Guide Jr., V.D.R., Jayaraman, V., Srivastava, R. and Benton, W.C., 2000. Supply Chain Management for
recoverable manufacturing systems. Interfaces, 30 (3), 125-142.
59. Guide, V. D. R., Jr. and Van Wassenhove, L. N., 2002. The reverse supply chain. Harvard Business
Review, 80 (2), 25-26.
60. Gunasekaran, A., Patel, C. and Tirtiroglu, E., 2001. Performance measures and metrics in a supply chain
environment. International Journal of Operations & Production Management, 21 (1-2), 71.

61. Gunasekaran, A. and Kobu, B., 2007. Performance measures and metrics in logistics and supply chain
management: a review of recent literature (1995-2004) for research and applications. International
Journal of Production Research, 45 (12), 2819-40.
62. Gunasekaran, A., Parel, C. and Mc Gaughey, R.E., 2004. A framework for supply chain performance
measurement. International Journal of Production Economics, 87 (3), 333-347.
63. Gungor, A. and Gupta, S., 1999. Issues in environmentally conscious manufacturing and product
recovery: a survey. Computers and Industrial Engineering, 36 (4), 811-853.
64. Hadiguna, R.A., 2012. Decision support framework for risk assessment of sustainable supply chain.
International Journal of Logistics Economics and Globalisation, 4 (1–2), 35-54.
65. Hassini, E., Surti, C. and Searcy, C., 2012. A literature review and a case study of sustainable supply
chains with a focus on metrics. International Journal of Production Economics, 140 (1), 69-82.
66. Hitchcock, T., 2012. Low carbon and green supply chains: the legal drivers and commercial pressures.
Supply Chain Management: An International Journal, 17 (1), 98 – 101.
67. International Federation of Accountants (IFAC), 2013. IFAC Sustainability Framework 2.0. available at:
http://www.ifac.org/publications-resources/ifac-sustainability-framework-20 (Accessed: 23 February
2013).
68. Ji, G., 2008. Performance evaluation of complaint management and virtual enterprise in closed-loop
supply chains by using exergoeconomics and extenics: International Journal of Services and Operations
Management, 4 (3), 368–397.
69. Jovane, F., Westkamper, E., Williams, D., 2009. The ManuFuture Road: Towards Competitive and
Sustainable High-Adding-Value Manufacturing. Springer, Berlin.
70. Kannegiesser, M., and Gü nther, H.O., 2013. Sustainable development of global supply chains—part 1:
sustainability optimization framework. Flexible Services and Manufacturing Journal, doi:
10.1007/s10696-013-9176-5.
71. Kaplan, R. and Norton, D., 1996. Using the Balanced Scorecard as a strategic management system.
Harvard Business Review, January - February, 75–85.
72. Keating, B., Quazi, A., Kriz, A. and Coltman, T., 2008. In pursuit of a sustainable supply chain: insights
from Westpac Banking Corporation. Supply Chain Management: An International Journal, 13 (3), 175-
179.
73. Keen, P., 1980. Decision support systems: a research perspective. Cambridge, Mass.: Center for
Information Systems Research, Afred P. Sloan School of Management.
74. Kim, H., Kim, S. and Dale, B.E., 2009. Biofuels, land use change and greenhouse gas emissions: some
unexplored variables. Environmental Science & Technology, 43 (3), 961-967.
75. Kim, J., Realff., M.J. and Lee, J.H., 2011. Optimal design and global sensitivity analysis of biomass
supply chain networks for biofuels under uncertainty. Computers & Chemical Engineering, 35 (9), 1738-
1751.
76. Kleindorfer, P.R., Singhal, K. and Van Wassenhove, L.N., 2005. Sustainable operations management.
Production and Operations Management, 14 (4), 482–492.
77. Kocabasoglu, C., Prahinski, C. and Klassen, R., 2007. Linking forward and reverse supply chain
investments: the role of business uncertainty. Journal of Operations Management, 25 (6), 1141–1160.
78. Lee, C-K., Tan, B. and Chiu, J-Z., 2008. The impact of organisational culture and learning on innovation
performance. International Journal of Innovation and Learning, 5 (4), 413-428.
79. Lee S.Y., Klassen R.D., 2008. Drivers and enablers that foster environmental management capabilities in
small- and medium-sized suppliers in supply chains. Production and Operations Management, 17 (6),
573–586.
80. Lehtinen, J. and Ahola, T., 2010. Is performance measurement suitable for an extended enterprise?.
International Journal of Operations and Production Management, 30 (2), 181–204.
81. Lejeune, M.A., 2006. A variable neighborhood decomposition search method for supply chain
management planning problems. European Journal of Operational Research, 175 (2), 959–976.

82. Linton, J.D., Klassen, R. and Jayaraman, V., 2007. Sustainable supply chains: an introduction. Journal of
Operations Management, 25 (6), 1075–1082.
83. Liu, S., Leat, M.,Smith, M.H., 2011. State-of-the-art sustainability analysis methodologies for efficient
decision support in green production operations. International Journal of Sustainable Engineering, 4 (3),
236-250.
84. Liu, S., Wang, Z. and Liu, L., 2012. An integrated sustainability analysis approach to support holistic
decision making in sustainable supply chain management. Frontiers in Artificial Intelligence and
Applications, 238, 391-402.
85. Lovric, M., Li, T. and Vervest, P., 2013. Sustainable revenue management: A smart card enabled agent-
based modeling approach. Decision Support Systems, 54 (4), 1587-1601.
86. Lu, L.Y.Y., Wu, C.H., Kuo, T.C., 2007. Environmental principles applicable to green supplier evaluation
by using multi-objective decision analysis. International Journal of Production Research, 45 (18-19),
4317-4331.
87. Manzini, E.and Vezzoli, C., 2003. A strategic design approach to develop sustainable product service
systems: examples taken from the ‘environmentally friendly innovation’ Italian prize. Journal of Cleaner
Production, 11 (8), 851–857.
88. Matos, S. and Hall, J., 2007. Integrating sustainable development in the supply chain: the case of life
cycle assessment in oil & gas and agricultural biotechnology. Journal of Operations Management, 25
(6), 1083–1102.
89. Mazhar, M.I., Kara, S. and Kaebernick, H., 2007. Remaining life estimation of used components in
consumer products: life cycle data analysis by Weibull and artificial neural networks. Journal of
Operations Management, 25 (6), 1184–1193.
90. McAloone, T. C. and Andreasen, M. M., 2004. Design for utility, sustainability and societal virtues:
Developing Product Service Systems. International Design Conference - Design 2004, Dubrovnik.
91. McIntyre, K., Smith, H.A., Henham, A. and Pretlove, J., 1998. Environmental performance indicators for
integrated supply chains: the case of Xerox Ltd'. Supply Chain Management: An International Journal, 3
(3), 149-156.
92. Milne, M. J., 1996. On sustainability; the environment and management accounting. Management
Accounting Research, 7 (1), 135-161.
93. Mollenkopf, D., Stolze, H., Tate, W. and Ueltschy, M., 2010. Gree, lean, and global supply chains.
International Journal of Physical Distribution & Logistics Management, 40 (1-2), 14-41.
94. Mont, O., 2001. Introducing and developing a Product-Service System (PSS) concept in Sweden. IIIEE
Reports 2001:6, Lund University, Sweden.
95. Morelli, N., 2009. Service as value co-production: reframing the service design process. Journal of
Manufacturing Technology Management, 20 (5), 568-590.
96. Munda, G., 2009. A conflict analysis approach for illuminating distributional issues in sustainability
policy. European Journal of Operational Research, 194 (1), 307–322.
97. Neely, A., 2005. The evolution of performance measurement research: development in the last decade
and a research agenda for the next. International Journal of Operations & Production Management, 25
(2), 1264-1277.
98. Neely, A., Gregory, M., Platts, K., 1995. Performance measurement system design: A literature review
and research agenda. International Journal of Operations & Production Management, 15 (4), 80-116.
99. Nudurupati, S. S., Bititci U.S., Kumar V. and Chan F.T.S., 2011. State of the art literature review on
performance measurement. Computers & Industrial Engineering, 60 (2), 279-290.
100. Perrini, F. and Tencati, A., 2006. Sustainability and stakeholder management: the need for new
corporate performance evaluation and reporting systems. Business Strategy and the Environment, 15 (5),
296-308.
101. Pilkington, A. and Liston-Heyes, C., 1999. Is production and operations management a discipline? A
citation/co-citation study. International Journal of Operations & Production Management, 19 (1), 7-20.

102. Power, D. J., 2002. Decision support systems: concepts and resources for managers. Westport,
Conn., Quorum Books.
103. Ramirez, M., 2007. Sustainability Integration in Industrial Design Education: a world wide survey.
Connected 2007, International Conference on Design Education, University of New South Wales.
104. Rao, P. and Holt, D., 2005. Do green supply chains lead to competitiveness and economic
performance? International Journal of Operations & Production Management, 25 (9), 898-916.
105. Ravi, R., McStravick, P., Bingham, B., Rowan, L. and Loynd, L., 2006. Worldwide and US business
process outsourcing 2006-2010 forecast: market opportunities by horizontal business process. Available
at: www.idc.com
106. Roy, R., 2000. Sustainable product-service systems. Futures, 32 (3–4), 289–299.
107. Sakao, T., Sandström, G. Ö., Matzen, D., 2009. Framing research for service orientation of
manufacturers through PSS approaches. Journal of Manufacturing Technology Management, 20 (5), 754
– 778.
108. Schildt, H.A., 2002. SITKIS: Software for Bibliometric Data Management and Analysis, Helsinki
Institute of Strategy and International Business, available at http://users.tkk.fi/~hschildt/sitkis/.
109. Schildt, H.A., Zahra, S.A. and Sillanpää, A., 2006. Scholarly Communities in Entrepreneurship
Research: A Co-Citation Analysis, Entrepreneurship Theory and Practice, 30 (3), 399-416.
110. Seuring, S., 2013. A review of modeling approaches for sustainable supply chain management.
Decision Support Systems, 54 (4), 1513-1520.
111. Seuring, S. and Muller, M., 2008. From a literature review to a conceptual framework for sustainable
supply chain management. Journal of Cleaner Production, 16 (15), 1699–1710.
112. Sharma, M.K., Bhagwat, R. and Dangayach, G.S., 2005. Practice of performance measurement:
experience from Indian SMEs. International Journal of Globalisation and Small Business, 1 (2), 183-
213.
113. Shepherd, C. and Gunter, H., 2005. Measuring supply chain performance: current research and future
directions, International Journal of Productivity and Performance Management, 55 (3-4), 242-258.
114. Shi, Q., Zuo, J. and Zillante, G., 2012. Exploring the management of sustainable construction at the
programme level – a Chinese case study. Construction Management and Economics, 30 (6), 425–440
115. Sprague, R., 1980. A framework for the development of decision support systems. Journal MIS
Quarterly, 4 (4), 1-26.
116. Srivastava, S.K., 2008. Network design for reverse logistics. Omega, 36 (4), 535–548.
117. Tan, R.B.H., Khoo, H.H., 2005. An LCA study of primary aluminium supply chain. Journal of
Cleaner Production, 13, 607–618.
118. Tang, C. S., and Zhou, S., 2012. Research advances in environmentally and socially sustainable
operations. European Journal of Operational Research, 223, 585-594.
119. Taticchi, P., Cagnazzo, L. and Tonelli, F., 2010. Performance measurement and management: a
literature review and a research agenda. Measuring Business Excellence, 14 (1), 4-18.
120. Taticchi, P., Cagnazzo, L., Barber, K. and Beach, R., 2012. A Management Framework for
Organisational Networks: a Case Study. Journal of Manufacturing Technology Management, 23 (5),
593-614.
121. Taticchi, P., Tonelli, F. and Cagnazzo, L., 2009. A Decomposition and Hierarchical Approach for
Business Performance Measurement and Management. Measuring Business Excellence, 13 (4), 47-57.
122. Taticchi, P., Tonelli, F. and Pasqualino, R., 2013. Performance measurement of sustainable supply
chains. A literature review and a research agenda. International Journal of Productivity and
Performance Management, 62 (8), 782-804.
123. Tonelli, F., Evans, S. and Taticchi, P., 2013. Industrial sustainability: challenges, perspectives,
actions. International Journal of Business Innovation Research, 7 (2), 143-163.

124. Tranfield, D., Denyer, D. and Smart, P., 2003. Towards a Methodology for Developing Evidence-
Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14,
207–222.
125. Tsai, W.H. and Hung, S.J., 2009. A fuzzy goal programming approach for green supply chain
optimisation under activity-based costing and performance evaluation with a value-chain structure.
International Journal of Production Research, 47 (18), 4991–5017.
126. Tsoulfas, G.T., and Pappis. C. P, 2008. A model for supply chains environmental performance
analysis and decision making. Journal of Cleaner Production, 16 (15), 1647–1657.
127. Ukidwe, N.U. and Bakshi, B.R., 2004. Thermodynamic accounting of ecosystem contribution to
economic sectors with application to 1992 US economy. Environmental Science & Technology, 38 (18),
4810-4827.
128. Vachon, S. and Klassen, R.D., 2006. Extending green practices across the supply chain – The impact
of upstream and downstream integration. International Journal of Operations & Production
Management, 26 (7), 795-821.
129. Vachon, S. and Klassen, R.D., 2008. Environmental management and manufacturing performance:
The role of collaboration in the supply chain. International Journal of Production Economics, 111 (2),
299-315.
130. Van Der Vorst, J.G.A.J., Tromp, S.O. and Van Der Zee, D.J., 2009. Simulation modelling for food
supply chain redesign; integrated decision making on product quality, sustainability and logistics.
International Journal of Production Research, 47 (23), 6611-6631.
131. Van der Woerd, F. and Van den Brink, T., 2004. Feasibility of a Responsive Business Scorecard - a
Pilot Study. Journal of Business Ethics, 55 (2), 173-186.
132. Verma, R. and Pullman, M. E., 1998. An Analysis of the Supplier Selection Process. Omega
International Journal of Management Science, 26 (6), 739-750
133. Vlachos, D., Georgiadis, P. and Iakovou, E., 2007. A system dynamics model for dynamic capacity
planning of remanufacturing in closed-loop supply chains. Computers and Operations Research, 34 (2),
367–394.
134. Wacker, J.G., 1998. A definition of theory: research guidelines for different theory-building research
methods in operations management. Journal of Operations Management, 16, 361-85.
135. Walker, H. and Jones, N., 2012. Sustainable supply chain management across the UK private sector,
Supply Chain Management: An International Journal, 17 (1), 15 – 28.
136. Wang, F.K., Du, T.C. and Li, E.Y., 2004. Applying six-sigma to supplier development. Total Quality
Management & Business Excellence, 15 (9-10), 1217-29.
137. Wang, H.F. and Hsu, H.W., 2010. A closed-loop logistic model with a spanning-tree based genetic
algorithm. Computers and Operations Research, 37 (2), 376-389.
138. Wasserman, S. and Faust, K., 1994. Social Network Analysis, Cambridge University Press,
Cambridge.
139. Webster, S. and Mitra, S., 2007. Competitive strategy and the impact of take-back laws. Journal of
Operations Management, 25 (6), 1123– 1140.
140. Weick, K.E., 1995. What Theory Is Not, Theorizing Is. Administrative Science Quarterly, 40 (3),
385-390.
141. Wu, H. Y., Tzeng, G. H., & Chen, Y. H., 2009. A fuzzy MCDM approach for evaluating banking
performance based on balanced scorecard. Expert Systems with Applications, 36, 10135–10147.
142. Yakovleva, N., Sarkis, J., and Sloan, T., 2011. Sustainable benchmarking of supply chains: the case
of the food industry. International Journal of Production Research, doi:
10.1080/00207543.2011.571926.
143. Zhu, Q,H., Sarkis, J. and Geng, Y., 2005. Green supply chain management in China: Pressures,
practices and performance. International Journal of Operations & Production Management, 25 (5-6),
449-468.

144. Zhu, Q,H., Sarkis, J. and Lai, K., 2007. Green supply chain management pressures, practices and
performance within the Chinese automobile industry. Journal of Cleaner Production, 15 (11-12), 1041-
1052.