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State-of-the-art sustainability analysis methodologies
for efficient decision support in green productionoperationsShaofeng Liu
a, Mike Leat
a& Melanie Hudson Smith
a
aUniversity of Plymouth , Plymouth, UK
Published online: 15 Apr 2011.
To cite this article:Shaofeng Liu , Mike Leat & Melanie Hudson Smith (2011) State-of-the-art sustainability analysismethodologies for efficient decision support in green production operations, International Journal of Sustainable Engineeri
4:3, 236-250, DOI: 10.1080/19397038.2011.574744
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State-of-the-art sustainability analysis methodologies for efficient decision support in green
production operations
Shaofeng Liu*, Mike Leat1 and Melanie Hudson Smith2
University of Plymouth, Plymouth, UK
(Received 15 November 2010; final version received 16 March 2011)
Over the last three decades, new concepts, strategies, frameworks and systems have been developed to tackle the sustainabledevelopment issue. This paper reviews the challenges, perspectives and recent advances in support of sustainable productionoperations decision-making. The aim of this review is to provide a holistic understanding of advanced scientific analysismethodologies for the evaluation of sustainability, to provide efficient decision support. Over 100 publications have beenanalysed, and a characterisation of state-of-the-art sustainability analysis methodologies has been produced, which includeslife cycle assessment and multi-criteria decision analysis (MCDA), along with their applications to three key areas ofproduction operations: sustainable design, sustainable manufacture and sustainable supply chain management. Distributionof existing work is discussed and future research directions are elicited from the literature. The paper finds three trends insupporting sustainable production operations decisions: (a) sustainability analysis has moved to whole life cycle assessmentfrom 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. The paper
concludes that integrated sustainability analysis can provide more efficient and effective support to complex decision-making in sustainable production operations.
Keywords: sustainable production operations; holistic decision-making; sustainability analysis methodology; life cycleassessment; multi-criteria decision analysis
1. Introduction
Sustainability, or sustainable development, was first
defined by the World Commission on Environment and
Development as the development that meets the needs of
present generations while not compromising the ability of
future generations to meet their needs (WCED 1987). It is
believed that the first consideration of sustainability can be
traced back to practices of many ancient philosophers,
although the concept of sustainability only entered modern
literature in the 1970s (Linton et al. 2007). However, the
definition of sustainability published by WCED (1987)
elevated it from a set of technical concepts into the political,
and subsequently business, mainstream. Not surprisingly, it
has since been recognised as one of the greatest challenges
facing the world (Ulhoi 1995, Wilkinson et al. 2001,
Bateman 2005, Espinosaet al.2008).
Common values for achieving sustainability have been
articulated in a recent review (Lindsey 2011). For
development to be sustainable, it is essential to integrate
environmental, social and economic considerations intothe action of greening operations (i.e. the transformation
processes that produce usable goods and services)
(Handfield et al. 1997, Kelly 1998, Gauthier 2005, Lee
and Klassen 2008), because operations have the greatest
environmental impacts among all business functions of a
manufacturer (Rao 2004, Nunes and Bennett 2010). In the
context of sustainable development, operations have to be
understood from a network perspective, in which
operations include not only manufacturing, but also design
and supply chain management (SCM) activities across
products, processes and systems (Geldermannet al.2007,
Allesian et al. 2010). Without proper consideration of
inter-relationships and coherent integration between
different operations activities, sustainability objectives
cannot be achieved (Sarkis 2003, Zhu et al. 2005). In the
past, there was a lack of true integration between
environmental and operations management, because
environmental management was viewed simply as a
narrow corporate legal function, primarily concerned with
reacting to environmental legislation. Subsequently,
managerial actions focused on buffering the operations
function from external forces to improve efficiency, reduce
cost and increase quality (Hill 2001, Taylor and Taylor
2009). More recently, green operations management has
been investigated from a more integrative perspective,instead of a constraint perspective, in which environmental
management is viewed as an integral component of an
enterprises operations systems (Yang et al. 2010). This
means that the research foci have shifted to the exploration
of the coherent integration of environmental and operations
ISSN 1939-7038 print/ISSN 1939-7046 online
q 2011 Taylor & Francis
DOI: 10.1080/19397038.2011.574744
http://www.informaworld.com
*Corresponding author. Email: [email protected]
International Journal of Sustainable Engineering
Vol. 4, No. 3, September 2011, 236250
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MCDA (Thawesangskulthai and Tannock 2008). Over the
past three decades, different variants of MCDA have been
developed. This section compares four important MCDA
methods: analytical hierarchy process (AHP), analytic
network process (ANP), fuzzy set theory and fuzzy
AHP/ANP.
The AHP was introduced by Saaty (1980) for solving
unstructured problems. Since its introduction, AHP has
become one of the most widely used analysis methods for
MCDA. AHP requires the decision maker to providejudgements about the relative importance of each criterion
and specify a preference for each decision alternative using
each criterion. The output of AHP is a prioritised ranking of
the decision alternatives based on the overall performance
expressed by the decision maker (Lee 2009). The key
techniques to successfully implement AHP include
developing a goal-criteria-alternatives hierarchy, pairwise
comparisons of the importance of each criterion and
preference for each decision alterative, and mathematical
synthesisation to provide an overall ranking of the decision
alternatives. The strength of AHP is that it can handle
situations in which the unique subjective judgements of the
individual decision maker constitute an important part ofthe decision-making process (Anderson et al. 2009).
However, its key drawback is that it does not take into
account the relationships between the decision factors.
The ANP is an evolution of AHP (Saaty and Vargas
2006). Given the limitations of AHP such as sole
consideration of one-way hierarchical relationships
among decision factors, failure to consider interaction
between the various factors and rank reversal, ANP has
been developed as a more realistic decision method. Many
decision problems cannot be built within the hierarchical
constraints of AHP because of dependencies (inner/outer)
and influences between and within clusters (goals, criteria
and alternatives). ANP provides a more comprehensive
framework to deal with decisions without making
assumptions about the independence of elements between
different levels and within the same level. In fact, ANP
uses a network without the need to specify hierarchical
levels (Sarkis 2003, Dou and Sarkis 2010) and allows both
interaction and feedback within clusters of elements (innerdependence) and between clusters (outer dependence).
Such interaction and feedback best captures the complex
effects of interplay in sustainable production operations
decision-making (Gencer and Gurpinar 2007). Both ANP
and AHP derive ratio scale priorities for elements and
clusters of elements by making paired comparisons on a
common property or criterion. The disadvantages of ANP
arise when the number of decision factors and respective
inter-relationships increase, requiring increasing effort by
decision makers. Saaty and Vargas (2006) suggested the
usage of AHP to solve the problem of independence
between decision alternatives or criteria, and the usage
of ANP to solve the problem of dependence amongalternatives or criteria.
Both AHP and ANP share the same drawbacks: (a)
with numerous pairwise comparisons, perfect consistency
is difficult to achieve. In fact, some degree of
inconsistency can be expected to exist in almost any set
of pairwise comparisons. (b) They can only deal with
definite scales in reality, i.e. decision makers are able to
give fixed value judgements to the relative importance of
the pairwise attributes. In fact, decision makers are usually
LCA: Life cycle thinking and principles
PLC analysis
Sustainable production operations decision making
Transformationprocess
(resources:materials,
energy, etc.)
(products,emissions, residue,
noise, etc.)
Inputs Outputs
Closed loop
OLC analysis
Development
Introduction
Growth
Maturity
Decline
Procurement
Production
Packaging
Distribution
Use
End-of-life &reverse logistics
Interrelationshipsbetween
PLC and OLC
Figure 2. Life cycle methods to support sustainable production operations decision making.
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more confident, giving interval judgements rather than
fixed value judgements (Kahraman et al. 2010). Further-
more, on some occasions, decision makers may not be able
to compare two attributes at all due to the lack of adequate
information. In these cases, a typical AHP/ANP method
will become unsuitable because of the existence of fuzzy
or incomplete comparisons. It is believed that if
uncertainty (or fuzziness) of human decision-making isnot taken into account, the results can be misleading.
To deal quantitatively with such imprecision or
uncertainty, fuzzy set theory is appropriate (Huang et al.
2009a, 2009b, Kahraman et al. 2010). Fuzzy set theory
was designed specifically to mathematically represent
uncertainty and vagueness and to provide formalised tools
for dealing with the imprecision intrinsic to multi-criteria
decision problems (Beskese et al. 2004, Mehrabad and
Anvari 2010). The main benefit of extending crisp analysis
methods to fuzzy techniques is that it can solve real-world
problems, which have imprecision in the variables and
parameters measured and processed for the application
(Lee 2009).
To solve decision problems with uncertainty and vague
information in which decision makers cannot give fixed
value judgements, while also taking advantage of the
systematic weighting system presented by AHP/ANP,
many researchers have explored the integration of
AHP/ANP and fuzzy set theory to perform more robust
decision analysis. The result is the emergence of an
advanced analytical method fuzzy AHP/ANP (Huang
et al.2009a, 2009b, Sen et al.2010). Fuzzy AHP/ANP is
considered as an important extension of conventional
AHP/ANP (Kahraman et al. 2010). A key advantage of
fuzzy AHP/ANP is that it allows decision makers toflexibly use a large evaluation pool including linguistic
terms, fuzzy numbers, precise numerical values and ranges
of numerical values. Hence, it offers the ability to supply
more comprehensive evaluations to provide more effective
decision support (Bozbura et al. 2007).
The relationship between (or evolution of) the MCDA
methods is diagrammatically shown in Figure 3. The two
axes indicate the levels of interactions and uncertainty
which the MCDA methods can deal with. Details of the
key features, strengths and weaknesses of different MCDAmethods are compared in Table 1.
3. Application of sustainability analysis
methodologies in green production operations
This section examines how sustainability analysis
methodologies have been explored to support integrated
decision-making in three key areas of the sustainable
production operations: sustainable design, sustainable
manufacturing and sustainable SCM.
3.1 Sustainability analysis to support sustainable
design decisions
The growing interest in sustainable development has led
many companies to examine the ways in which they deal
with environmental issues during their design of products,
processes, systems and supply chains. Design for
environment (DfE) has, therefore, become an increasingly
important topic for academic research. DfE has been
defined as the systematic consideration of design
performance with respect to environment, health and
safety objectives over the full PLC and OLC (Ray and
Guzzo 1993, Dinget al.2009, Kimet al.2010). The aim ofDfE is the reduction of a products environmental impact
without creating a negative trade-off with other design
High level ofinteractions
betweendecision
factors
High level of uncertaintyand vagueness
Fuzzy set theory
AHP
ANP
Fuzzy ANP
&
Fuzzy AHP
Figure 3. Relationships between the different types of MCDA method.
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criteria, such as costs and functionality (Grote et al.2007).
In earlier years, DfE was very technically focused, but it
has gradually evolved to affect every aspect of business
and the entire supply chain. The evolution has taken three
major phases (Johansson et al. 2007):
. Phase 1 start-up period in the early 1990s, during
which DfE was introduced into companies throughprojects with specific focus on environmental issues.. Phase 2 consolidation period in mid 1990s, during
which environmental science and methodology
formed the basis of the DfE activities. Even though
technical activities were still at the core, an initial
understanding was gained that the drivers for
environmental concern should be analysed from a
business perspective.. Phase 3 business-integrated DfE. This phase
acknowledges that management of DfE is the real
key to success, i.e. the DfE efforts should be
embedded into all business activities.
The benefits of integrating environmental impacts into
the design of products, processes, systems and supply
chainsat early development stage have been well identified.
Such an approach helps in reducing emissions and waste,
avoids excessive use of energy or non-renewable energy
sources, offers proof of a sense of responsibility towards the
consumer and improves the market position of the firm
(Rahimifard and Clegg 2007). However, it is perceived that
DfE principles are without value unless considered within
a specific context.
Most literature on sustainability analysis to support
DfE decision-making discusses product design. MCDA
techniques such as AHP and ANP have been widely usedto support decisions at early product design stage such as
product screening (Calantone et al.1999) and preliminary
design (Lee et al. 2010). Studies on the application of
environmental principles and directives to sustainable
design have been targeted at complex products such as
automobiles, electrical and electronic equipment, and
energy-using products (Grote et al.2007, Johansson et al.
2007). Techniques such as LCA, PLC and OLC analyses
have also been extensively used to assist in determining
how to design a product to minimise environmental impact
over its useable life and afterwards (Pennington et al. 2004,
Linton et al. 2007). Gao et al. (2010) explored the
utilisation of function, cost and environmental perform-ance as primary decision-making factors for scheme
selection in green design. References are available for
prescribing the process of designing environmentally
friendly products, e.g. ISO/TR 14062 (2001). However, it
appears that there has been little discussion on integrating
both MCDA and LCA methods for sustainability analysis
in product design (Bevilacqua et al. 2007).
There is relatively little existing work addressing the
sustainable design decision-making issue in the design ofTable1.ComparisonbetweentheMCDAmethods.
Analysis
methods
Keyelements
Strengths
Weaknesses
Sele
ctedreferences
AHP
Multi-criteriaan
dmulti-attributes
hierarchy;pairw
isecomparison;
graphicalrepresentation
Canhandlesitu
ationsinwhichdecision
makerssubjectivejudgementsconstitute
akeypartofth
edecision-makingprocess
Relationshipsbetweendecisionfactorsare
notconsidered;inconsistencyofthepairwise
judgements;cannotdealwithuncertainty
andvagueness
Saaty(1980)and
And
ersonetal.
(2009)
ANP
Controlnetwork
withsub-networksof
influence
Allowsinteractionandfeedback
Inconsistencyofthepairwisejudgements;
cannothand
lesituationsinwhichdecision
makerscan
onlygiveintervalvalue
judgements
orcannotgivevaluesatall
SaatyandVargas(2006)
and
DouandSarkis(2010)
Fuzzyset
theory
Mathematicalrepresentation;handle
uncertainty,vaguenessandimprecision;
groupingdatawithlooselydefined
boundaries
Cansolvereal-
worlddecisionproblems
withimprecisio
nvariables
Lackofasystematicweightingsystem
Beskeseetal.
(2004)and
MehrabadandAnvari
(2010)
Fuzzy
AHP/ANP
Fuzzymembershipfunctionstogether
withpriorityweightsofattributes
Combinedstrengthsoffuzzysettheory
andAHP/ANP
Timeconsu
ming;complexity
Kah
ramanetal.
(2010)
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supply chains, as most often the assumption is that product
quality and environmental sustainability are not directly
affected by supply chain designs. However, in some
industries such as the consumer goods and food industry,
especially in the context of shorter life cycle products,
supply chain design is a key factor in product quality and
environmental sustainability (Matthewset al.2006). Thus,
effective supply chain design decision-making is intrinsicto sustainable design success (van der Vorst et al. 2009).
A key decision issue in supply chain design is supply
chain configuration, concerning the number of echelons
required, the number of facilities per echelon, re-order
policy to be adopted by the echelons, assignment of a
market region to the locations and the selection of
suppliers for materials, components and sub-assemblies.
Recently, a network optimisation modelling framework
and an MCDA algorithm have provided to compute
solutions to sustainable supply networks design (Nagurney
and Nagurney 2010). Analysis is centred on the evaluation
of the effects of different configurations on the resulting
total supply chain performance. Decision criteria that have
been used include supply chains costs, the bullwhip effect,
quality improvement initiatives, lead time reduction and
environmental impacts (Bottani and Montannari 2010).
MCDA is the predominant method used to support the
green supply chain design decision-making, but there are
some pilot investigations integrating MCDA and LCA for
a more robust decision analysis. For example, a fuzzy AHP
analysis has been successfully integrated with LCA by
Lu et al. (2007) to evaluate supply chain configuration
alternatives.
3.2 Sustainability analysis to support sustainable
manufacturing decisions
Rapid technology advancement in product development,
coupled with consumer desire for newer product models,
has resulted in shorter PLCs and premature disposal of
products. As a result, many manufacturers are being forced
to take back their products at the end of their useful life,
driven by government legislation and increasing public
awareness of environmental issues. This has led to
sustainable, or environmentally conscious, manufacturing
methods that have been receiving considerable research
interest in recent years (Rawabdeh 2005). According to
Gupta and Lambert (2008), sustainable manufacturing isconcerned with the development of manufacturing
methods and technologies that comply with environmental
legislation and requirements considering all phases in a
products life cycle. Jose and Jabbour (2010) summarised
the fundamental aims of sustainable manufacturing as
4Rs reduce, reuse, recycle and remanufacture. These
aims have been addressed to some extent through various
manufacturing strategies, such as manufacturing processes
and product disassembly.
Manufacturing processes were in the centre of the
earliest sustainable manufacturing initiatives and pro-
grammes. Since the 1980s, governments and industries
have tended to focus their environmental policies and
programmes towards addressing process-related environ-
mental impacts resulting from, for example, cutting,
welding and painting processes (Kim et al. 2010).
Decision making focuses on the optimisation of manu-facturing processes to reduce waste (solid/liquid), energy
use (electricity and water), air emissions and noise. As a
result, environmentally questionable processes can be
substituted (Rao 2004).
Recently strengthened environmental regulations, such
as those relating to energy-using products and waste
electrical and electronic equipment, have driven manu-
facturing companies to strive for improved environmen-
tally conscious treatment of EOL products. Disassembly
has been the focus of EOL discussion for some time. From
a business perspective, environmentally conscious treat-
ment induces additional cost, hence EOL treatment and
disassembly decisions have to balance the criteria between
environmental value and economic performance (Kang
et al.2010). To assess economic and environmental value,
an EOL decision maker needs to find out the optimal,
feasible processes by which a product can be disassembled
and remanufactured.
To help decision makers evaluate the environmental
consequences of alternative manufacturing processes,
disassembly and remanufacturing choices, the effective
sustainability analysis methods reviewed in Section 2,
LCA and MCDA, have been explored. A quantitative PLC
model for strategic decision-making in the remanufactur-
ing sector has been presented in Hu and Bidanda (2009).Kimet al. (2010) used the LCA methodology to evaluate
painting process alternatives for sustainable manufactur-
ing decision-making, through a case study in forklift
production. Similarly, Cabrera et al. (2010) discussed
decision support for processes in the automotive industry
with a case study of rubber extrusion. In the case studies,
the entire life cycle of the products, from raw material
extraction to EOL disposition, was considered.
Fuzzy set theory has been widely used to support
sustainable manufacturing decision-making because of
the complex, uncertain and dynamic nature of the decision
situations. Kulak and Kahraman (2005) applied fuzzy set
theory to the decisions related to the acquisition ofautomated manufacturing systems. In Mehrabad and
Anvari (2010), provident measures are presented, which
enable decision makers to consider not just the effect of
current changes but also the effect of future changes in the
decision-making process. An integrated methodology
proposed by Rao (2009) collectively used fuzzy AHP
and LCA to address the environmental impact of the
interrelated decisions that are made at various stages of
product life.The integrated multi attribute decision-making
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methodology enabled the effective evaluation of sustain-
able manufacturing programmes for producing a given
product. A wide range of decision criteria have been used
in the above discussions: cost reduction (financial and
ecological costs), energy and water conservation, and
minimisation of overall output of waste. Most literature
addressed the green production issues from the regulatory
compliance point of view, but did not include employeehealth and safety as key decision factors.
3.3 Sustainability analysis to support sustainable SCM
decisions
As the concept of Sustainable Development was
introduced by the WCED, researchers in SCM started to
bind environmental sustainability to SCM (Leeet al.1997,
Mentzeret al. 2007, Chan et al. 2010). It was noted that
corporate environmental management becomes poten-
tially fallacious without the contribution of SCM to
accomplish superior performance (Wu and Dunn 1995).
Many researchers have undertaken both theoretical and
empirical studies to explore the concepts, models and
frameworks for sustainable SCM (SSCM) (Jayaramanet al.
2007, Svensson 2007). Over time, SSCM has gone through
many different names because it lacked a clear definition,
such as environmental SCM and green SCM (Geoffrey
et al. 2001, Tsoulfas and Pappis 2006). Consensus on the
definition of SSCM was finally reached through the most
influential work in the field by Carter and Rogers (2008), in
which they stated SSCM is the strategic, transparent
integration and achievement of an organisations environ-
mental, social, and economic goals in the systematic co-
ordination of key inter-organisational business processesfor improving the long-term economic performance of the
individual company and its chains. Researchers and
industrial practitioners have learnt that the challenge of
SSCM is to integrate the environmental dimension into the
context of supply chain managers decision-making; as
Gonzalez-Benito and Gonzalez-Benito (2006) noted,
almost all environmental improvements possibly under-
taken by a company depend on the contribution of SCM to
their execution (implementation of decisions).
SSCM is sometimes referred to as closed-loop SCM.
Closed-loop supply chains are those supply chains in
which care is taken of items once they are no longer
desired or can no longer be used. A closed-loop supplychain consists of a forward chain and a reverse chain
(Yuan and Gao 2010). In the forward chain, raw materials
are transformed into new products, distributed to and used
by customers. In the reverse chain, used products are
recycled, reused, repaired or remanufactured (Hoek 1999,
Simpsonet al. 2007). Increasing legislation in the field of
producer responsibility and take-back obligations, and
setting up collection and recycling systems have led to a
strong focus on closed-loop SCM. The primary objective
of closed-loop supply chains is to improve the maximum
economic benefit from the end-of-use products, whereas
SSCM requires the co-ordination of the social, environ-
mental and economic dimensions. However, closed-loop
supply chains are regarded as environmentally friendly by
mitigating the undesirable environmental footprint of
supply chains. Therefore, closed-loop supply chains are
assumed to be sustainable almost by definition (Huanget al. 2009a, 2009b, Neto et al. 2010). Nevertheless, to
maximise the profit for a closed-loop supply chain and to
manage the co-ordination of the social, environmental and
economic performance objectives, decision-making in
SSCM has been further complicated for both decentralised
and centralised decision-making, which requires efficient
support from advanced sustainability analysis.
Table 2 summarises some of the most recent work,
investigating sustainability analysis methodologies for
SSCM decisions. From Table 2 we can see that a wide
range of SSCM decisions are addressed, including
decisions on partner selection (Crispim and Sousa 2009),
purchasing (insourcing and outsourcing) (Gunasekaran
and Irani 2010), packaging (Verghese and Lewis 2007),
transportation (Yang et al. 2005), upstream and down-
stream integration (Vachon and Klassen 2006) and reverse
logistics (Meade and Sarkis 2002, Bottani and Rizzi 2006,
Erol et al. 2010). Because of the various decision criteria
considered, SSCM decisions are among the most complex
multi-criteria decision issues that production operations
can encounter. Therefore, both life cycle tools (such as
LCA, PLC and OLC) and MCDA methods (e.g. AHP,
fuzzy set theory, fuzzy AHP and ANP) have been widely
explored to perform robust decision evaluation for
effective decision making.An under-addressed area in SSCM decision-making is
the inclusion of ethics values as decision criteria so that
principles of corporate actions and behaviour can be
integrated into management alternative actions (Cruz and
Matsypura 2009, Svensson 2009).
4. Discussion
This paper has surveyed and classified over 100
publications in sustainability analysis to support green
production operations decision-making. Figure 4 shows
the classification scheme that is used in this section
to discuss the distribution of existing work, to compareinterests from different areas and to identify possible
future research directions.
The first comparison has been undertaken through the
application of sustainability analysis methodologies (i.e.
LCA and MCDA) to the three areas (sustainable design,
sustainable manufacture and SSCM) of sustainable
production operations decision-making. The number of
the publications is represented by the height of the column
bars in Figure 5. The Figure shows that research on using
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Table2.Comparisonofdiffe
rentMCDAmethodsusedtosupportdecisionmakinginSSCM.
Decisionproblems
Evaluationparameters
Decisioncriteria
Analysis
methods
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BestEOLtreatmentsalternati
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Networkcosts,energyuse,waste
volume
LCA,PLC
andOLC
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(2010)
OutsourcingERPvendorselection
Vendoroverallperformance
Marketleadership,functionality,quality,price,
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tycost
LCA,OLC
analyses
YuanandGao(2010)
High-valuecomponentssupplier
decisions
Supplieroverallperformance
Cost,quality,IT,humanresource,time
AHP
Wangetal.
(2009)
Jointventureandstrategic
alliance
decision
Buyersupplierrelationship
Cost,quality,deliverytime,supplychaintransparency
FuzzyAHP
Lee(2009)
Supplierselection
Evaluatethesuppliersoverall
performance
45criteriaunderthreeclusters:(1
)businessstructure,
(2)manufacturingcapabilityand(3)qualitysystem
ofthesupplier
ANP
GencerandGurpinar
(2007)
Integrationinoilandgassupply
chaininnovationevaluating
Evaluationofcomplexandno
vel
technologiesforsustainable
development
Economic,environmentalandsoc
ialsustainability
LCA,PLC
MatosandHall(2007)
Selectingthird-partyreverse
logisticsproviders
Evaluatetheprovidersoverall
capability
Recycling,reuseandremanufactu
ringcapability;
repair,testingandproductservicingcapability;return
shipmentcapability
FuzzyAHP
BottaniandRizzi
(2006)
Greensupplychaindecisions
partner,
technologyandorganisational
practice
Evaluateorganisational
performance
Cost,quality,time,flexibility,totalquality
environmentalmanagementstandards
ANP,PLC
andOLC
Sarkis(2003)
Transportationdecisions
Evaluatealternativefuelvehicles
Cost,sustainability(renewablesource)
AHP
ByrneandPolonsky
(2001)
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publications along the time line, including 104 publi-
cations that were published over the last decade. The trend
in the figure clearly indicates that there has been increasing
research interests in the topic in more recent years.
5. Future research directions
With the rapid development of green production operation
theories and practices, and with the importance of holistic,
integrated management decision-making, the require-
ments for decision support are changing. There is an
urgent need for realistic and effective sustainability
analysis methodologies to enable management decision
makers to address the changes and take advantage of the
opportunities presented by the changes. There has been
active research to explore the key issues and method-
ologies to improve decision analysis for green production
operations decision-making, with many research questionsstill open for discussion. Future research directions include
(but are not limited to):
(1) As it is now commonly accepted that environmental
concerns are global issues, green production oper-
ations should be understood from both network and
multi-stakeholder perspectives. The outputs of green
production operations should emphasise not only
delivering high-quality products and services to
customers and maximising profits for the owners and
investors, but also reducing the impacts from
discharge and used products to environment, and
increasing social benefits to other stakeholders(public, employees, communities, etc.). Therefore,
sustainability decision assessment should continue to
be undertaken with the whole life cycle instead of
within a single, isolated stage of the life cycle. The
majority of the existing work on LCA has focused on
either PLC or OLC analysis. Future research needs to
investigate the coherent integration of both PLC and
OLC analyses, as they are effectively two sides of
the same coin. A key area could be to study the
inter-relationships between the stages of the two life
cycles.
(2) It is a known fact that green operations decisions are
multi-criteria problems, so the exploration of MCDA
in sustainable design, manufacture and SSCM should
be expected to hold continued interest for future
research. As the environmental objectives and
performance measures have been diverse and elusive,future research in decision analysis needs to address
the vagueness and uncertainties of green production
operations situations, at the same time accommodating
business managers subjective preferences and judge-
ments in the decision-making process. Although
existing research has started to uncover the power of
fuzzy AHP/ANP in dealing with these issues, future
research needs to address the trade-offs between the
decision makers subjective influence and the scientific
rigour of the methodologies, i.e. where the line should
be drawn so that decision makers preferences and
values will not lead to unacceptably biased decisions.
(3) Future research on sustainability analysis should
abandon the traditional, stand-alone, individual
decision optimisation and pursue global integration
and optimisation. In the 1990s and early 2000s, many
firms turned inwards to focus on efficiency and
integration of their sustainable design and manufac-
turing processes. SSCM initially emphasised local
optimisation of supply chain activity. However,
because of the dysfunctional consequences that local
optimisation can have, there is a need for companies
to balance their entire supply chain through holistic
decision-making and strategic operations decisions.
Subsequently, decision criteria should aim toconsider net revenue maximisation, total emissions
minimisation and total risk minimisation, not just for
the focal operations within a company but also for
the whole supply chain, i.e. to include the suppliers
suppliers and customers customers (Liu and Young
2004, Stonebraker and Liao 2004). To manage the
complexity of global decision-making, it is suggested
that both MCDA and LCA methodologies be
explored coherently to improve the decision makers
scientific judgements.
6. ConclusionsSustainability analysis methodologies have been widely
explored to provide effective, insightful and powerful
support for complex decision-making in sustainable
production operations. It is essential that an integrated
approach is taken to tackle the sustainability issues from
both life cycle and multi-criteria perspectives. Applying the
LCA and MCDA methodologies to green production
operations, decision-making has never been a straightfor-
ward issue,because decision makerscan be easilyswamped
0
5
10
15
20
25
30
Pub
licationnumbers
Year
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 7. Distribution of the publications over last decade.
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by the complexity of the decision situations and the
minutiae of evaluation techniques for an accurate analysis.
This paper has reviewed over one hundred recent
publications in three key areas of sustainable production
operations: sustainable design, manufacture and SCM. The
key contribution of this review is that it provides a holistic
understanding of the key challenges, perspectives and
recent advances of sustainability analysis methodologies insupport of decision-making in green production operations.
In particular, the key features, strengths and weakness of
different sustainability analysis methodologies have been
compared and contrasted. A classification of the key issues
addressed by existing work using the LCA and MCDA
methodologies has been produced. On the basis of a critical
analysis of literature, future research directions have been
suggested. It is highlighted that sustainable development
principles and practices should be integrated into the
holistic decision-making of production operations based on
systematic sustainability analysis.
Notes
1. Email: [email protected]. Email: [email protected]
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