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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

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*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.

International Journal of Sustainable Engineering 239


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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.

S. Liuet al.240


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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

S. Liuet al.242


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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

References

Optimalproductrecoverydecision

BestEOLtreatmentsalternati

ves

Networkcosts,energyuse,waste

volume

LCA,PLC

andOLC

Netoetal.

(2010)

OutsourcingERPvendorselection

Vendoroverallperformance

Marketleadership,functionality,quality,price,

implementationspeed,interfacew

ithothersystems,

internationalorientation

FuzzyAHP

Kahramanetal.

(2010)

Pre-selectionofsupplier

Tomaximisethesupplychain

performance

Cost,quality,serviceandreliabili

ty

FuzzyAHP

Senetal.

(2010)

Productionoutsourcingdecisio

n

Countryalternatives

Totalcost(production,shipping,inventorycarrying);

totalrisks(economic,social,publ

icsafety)

Fuzzyset

theory

Kumaretal.

(2010)

Inventorydecisionsoptimal

system

policy

Sensitivityanalysis

Just-in-timedelivery,cleaninganddisassemblycost,

remanufacturingcost,convertibili

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)

S. Liuet al.244


11/16


12/16

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.

S. Liuet al.246


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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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