State-of-the-art sustainability analysis methodologies for efficient decision support in green production operations

Abstract

Over the last three decades, new concepts, strategies, frameworks and systems have been developed to tackle the sustainable development issue. This paper reviews the challenges, perspectives and recent advances in support of sustainable production operations decision-making. The aim of this review is to provide a holistic understanding of advanced scientific analysis methodologies for the evaluation of sustainability, to provide efficient decision support. Over 100 publications have been analysed, and a characterisation of state-of-the-art sustainability analysis methodologies has been produced, which includes life cycle assessment and multi-criteria decision analysis (MCDA), along with their applications to three key areas of production operations: sustainable design, sustainable manufacture and sustainable supply chain management. Distribution of existing work is discussed and future research directions are elicited from the literature. The paper finds three trends in supporting sustainable production operations decisions: (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. 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 cycle assessment
• 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, Espinosa et 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 into the 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 (Geldermann et 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 enterprise's 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 management through business managers' proactive decision-making (Liu et al. 2010).

Many decisions have to be made for green production operations in relation to sustainable design, manufacture and SCM (Sarkis 2003, Galasso et al. 2009). Slack et al. (2010) classify strategic operations decisions into two main categories: structural and infrastructural. Structural decisions are those which primarily influence design activities, such as those relating to new product design and supply network design, whereas infrastructural decisions are those which influence manufacturing planning and control, and management improvements, such as inventory management and supplier development. Miltenburg (2005) defines six production decision areas: production planning and control, organisation structure and control, human resource, facilities, sourcing and process technology. This classification scheme was also adopted by Choudhari et al. (2010), and 54 decision types were further identified under the 6 decision areas with 113 decision choices suggested for the decision types. However, the papers did not provide discussion on how the optimal decision choice could be reached for each decision type.

To allow the optimal decision choice to be made, decision makers have to be able to define appropriate decision criterion or criteria for decision problems (Anderson et al. 2009). On some occasions, a single decision criterion can be used. For example, an economic objective (minimum cost or maximum profit) has been widely used as the sole criterion in traditional production operations decision-making. However, when environmental regulations are getting tougher and public awareness of the green issues is getting stronger, companies have to adopt other decision criteria aside from economic objective. The concept of the triple bottom line (3BL), which encompasses economic, environmental and social sustainability, has been widely recognised as offering general guidelines to sustainable development decision-making (Lu et al. 2007).

Multi-criteria decision-making is challenging. There is a broad consensus that decision evaluation holds the key to reaching an optional decision choice, especially under complex decision situations (Mehrabad and Anvari 2010). Decision evaluation enables decision makers to perform scientific analysis; to weigh, score and rank the alternatives against decision criteria and to assess the consequences of each decision alternative, so that optimal decision choices become more transparent to decision makers (Karsak and Ahiska 2008, Chituc et al. 2009). In recent years, sustainability analysis has emerged as an important decision aid to provide efficient and effective decision evaluation in all aspects of business (Hodgman 2006). Sustainability analysis is important to production research because decision-making in sustainable operations often has high complexity, cost and risk. Getting the decisions right will generate not only considerable economic value, but also great environmental and social impacts, which can sharpen a company's competitive edge (Noran 2010).

To the authors' best knowledge, there has, so far, been no comprehensive review that addresses sustainability analysis for complex decision-making in the sustainable operations context, although reviews on general green operations management have been available (Bloemhof-Ruwaard et al. 1995, Angell and Klassen 1999, Kleindorfer et al. 2005, Craighead and Meredith 2008). Therefore, this paper examines, for the first time, existing work in the area from a holistic point of view, discussing the sustainable operations decisions from both life cycle and multi-criteria analysis perspectives. Major contributions include: (a) producing a classification scheme to systematically link disparate work in sustainability analysis for green production operations decision-making, which will help researchers to better understand the current status of research in this area and (b) eliciting potential research trends that provide direction for future research. The paper focuses solely on production operations decision-making (Pun 2006, Bayraktar et al. 2007) and so does not cover service operations such as banking, education or medical services. The boundary of this work is life cycle assessment support for multi-criteria decision-making problems within production operations. Therefore, other eco-efficiency technologies such as environmental accounting, value analysis, eco-indicators, lifetime design strategies (LiDS) wheel and materials, energy, toxicity (MET) matrix are outside the scope of this paper. Decision-making situations where only a single decision criterion is required are also not within the scope of this review. The next section of the paper reviews advanced sustainability analysis methodologies. Section 3 examines how the sustainability analysis methodologies have been explored to support sustainable production operations decision-making. Distribution of existing work and future research directions are discussed in Sections 4 and 5, respectively, before conclusions are drawn in Section 6.

2. Sustainability analysis methodologies

Decision analysis would be theoretically straightforward if the key interacting variables and boundaries of responsibilities were well understood by decision makers (Mihelcic et al. 2003). Unfortunately, such situations are rare, whereas benefits from sustainability efforts have been elusive. Practitioners continue to grapple with how sustainability analysis should be undertaken, due to the complexities and uncertainties of the environmental systems involved and the imperfections of human reasoning (Allenby 2000, Hertwich et al. 2000). According to Hall and Vredenburg (2005), innovating for sustainable development is usually ambiguous. When it is not possible to identify key parameters or when conflicting pressures are difficult to reconcile, such ambiguities make traditional risk assessment techniques unsuitable. Researchers further argue that sustainability analysis frequently involves a wide range of stakeholders, many of whom are not directly involved with the company's production operations activities. Decision makers are thus likely to have significant difficulties in reaching the right decisions if efficient support is not available. Powerful systematic analysis methodologies have great potential to guide decision makers to see through the complexities and ambiguities (Matos and Hall 2007). This section discusses two types of the most influential analysis methodologies that could facilitate efficient and effective sustainable production operations decision-making: life cycle analysis (LCA) and multi-criteria decision analysis (MCDA).

2.1 Life cycle analysis

Life cycle assessment or analysis (LCA) is regarded as a ‘cradle to grave’ technique that can support environmentally friendly product design, manufacture and management (Hunkeler et al. 2003, Verghese and Lewis 2007, Jose and Jabbour 2010). It can be used to assess the environmental aspects and potential impacts associated with a product, process or system (Matos and Hall 2007, Kim et al. 2010). It also allows decision makers to evaluate the type and quantity of inputs (energy, raw materials, etc.) and outputs (emissions, residues, other environmental impacts, etc.) of production operations to completely understand the context involving product design, production and final disposal (Fuller and Ottman 2004, Jose and Jabbour 2010). Standards for the application of LCA have been set up by International Standards Organisation (ISO). A four-stage LCA process has been defined (ISO 14040 1997), as illustrated in Figure 1.

Figure 1 Four key stages of LCA process.

Life cycle analysis can be conducted along two different dimensions: product life cycle (PLC) and operational life cycle (OLC) analysis. A new product progresses through a sequence of stages from development and introduction to growth, maturity and decline. This sequence is known as the PLC (Sarkis 2003, Bevilacqua et al. 2007, Gunendran and Young 2010). On the other hand, OLC includes stages of procurement, production, packaging, distribution, use, end-of-life (EOL) disposal and reverse logistics (Nunes and Bennett 2010, Yuan and Gao 2010). The nature of both of these analytical tools can generate important insights into environmentally conscious practices in organisations, and there are certainly interdependencies between PLC and OLC. For example, in the PLC introduction phase, procurement is more influential than production for sustainable practices, whereas in the maturity and decline stages of the PLC, efficient EOL and reverse logistics are more influential than distribution operations. It is also not difficult to understand that distribution decisions, such as facility locations and modes of transportation, will influence both the forward and the reverse logistics networks (Bayazit and Karpak 2007, Chan et al. 2010). However, it is widely acknowledged that environmental methods (LCA, PLC and OLC analysis) should be ‘connected’ with social and economic dimensions to help address the 3BL and that this is only meaningful if they are applied to support the decision-making process and are not just a ‘disintegrated’ aggregation of facts (Matos and Hall 2007). It is advantageous that both PLC and OLC analyses are collectively conducted to obtain a more holistic picture of the economic and ecological impacts of production operations (Neto et al. 2010). Figure 2 shows the relationships between LCA, PLC and OLC, and how they can be used together to support sustainable production operations decision making.

Figure 2 Life cycle methods to support sustainable production operations decision making.

2.2 Multi-criteria analysis

There is no doubt that sustainable production operations decision-making has multiple criteria to meet simultaneously, i.e. environmental, social and economic performance objectives. The production operations decisions can envelop both quantitative and qualitative, and tangible and intangible factors. The methodology that can empower decision makers to consider all the decision criteria and decision factors, resolve the conflicts between them and arrive at justified trade-offs is the well-known 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 provide judgements 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 of the 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 (inner dependence) 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 among alternatives 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 more confident, giving interval judgements rather than fixed value judgements (Kahraman et al. 2010). Furthermore, 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 is not 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 to flexibly 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 MCDA methods are compared in Table 1.

Figure 3 Relationships between the different types of MCDA method.

Table 1 Comparison between the MCDA methods.

Analysis methods	Key elements	Strengths	Weaknesses	Selected references
AHP	Multi-criteria and multi-attributes hierarchy; pairwise comparison; graphical representation	Can handle situations in which decision maker's subjective judgements constitute a key part of the decision-making process	Relationships between decision factors are not considered; inconsistency of the pairwise judgements; cannot deal with uncertainty and vagueness	Saaty (1980) and Anderson et al. (2009)
ANP	Control network with sub-networks of influence	Allows interaction and feedback	Inconsistency of the pairwise judgements; cannot handle situations in which decision makers can only give interval value judgements or cannot give values at all	Saaty and Vargas (2006) and Dou and Sarkis (2010)
Fuzzy set theory	Mathematical representation; handle uncertainty, vagueness and imprecision; grouping data with loosely defined boundaries	Can solve real-world decision problems with imprecision variables	Lack of a systematic weighting system	Beskese et al. (2004) and Mehrabad and Anvari (2010)
Fuzzy AHP/ANP	Fuzzy membership functions together with priority weights of attributes	Combined strengths of fuzzy set theory and AHP/ANP	Time consuming; complexity	Kahraman et al. (2010)

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, Ding et al. 2009, Kim et al. 2010). The aim of DfE is the reduction of a product's environmental impact without creating a negative trade-off with other design 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 through projects 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 chains at 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 used to 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 performance 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 of 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 (Matthews et al. 2006). Thus, effective supply chain design decision-making is intrinsic to 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 is concerned with the development of manufacturing methods and technologies that comply with environmental legislation and requirements considering all phases in a product's 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 programmes. Since the 1980s, governments and industries have tended to focus their environmental policies and programmes towards addressing process-related environmental impacts resulting from, for example, cutting, welding and painting processes (Kim et al. 2010). Decision making focuses on the optimisation of manufacturing 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 manufacturing companies to strive for improved environmentally conscious treatment of EOL products. Disassembly has been the focus of EOL discussion for some time. From a business perspective, environmentally conscious treatment 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 remanufacturing sector has been presented in Hu and Bidanda (2009). Kim et al. (2010) used the LCA methodology to evaluate painting process alternatives for sustainable manufacturing 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 of automated 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 methodology enabled the effective evaluation of sustainable 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 employee health 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 (Lee et al. 1997, Mentzer et al. 2007, Chan et al. 2010). It was noted that ‘corporate environmental management becomes potentially 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) (Jayaraman et 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 organisation's environmental, social, and economic goals in the systematic co-ordination of key inter-organisational business processes for 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 manager's decision-making; as Gonzalez-Benito and Gonzalez-Benito (2006) noted, ‘almost all environmental improvements possibly undertaken 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 supply chain 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, Simpson et 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, environmental 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 (Huang et 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 downstream 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.

Table 2 Comparison of different MCDA methods used to support decision making in SSCM.

Decision problems	Evaluation parameters	Decision criteria	Analysis methods	References
Optimal product recovery decision	Best EOL treatments alternatives	Network costs, energy use, waste volume	LCA, PLC and OLC	Neto et al. (2010)
Outsourcing – ERP vendor selection	Vendor overall performance	Market leadership, functionality, quality, price, implementation speed, interface with other systems, international orientation	Fuzzy AHP	Kahraman et al. (2010)
Pre-selection of supplier	To maximise the supply chain performance	Cost, quality, service and reliability	Fuzzy AHP	Sen et al. (2010)
Production outsourcing decision	Country alternatives	Total cost (production, shipping, inventory carrying); total risks (economic, social, public safety)	Fuzzy set theory	Kumar et al. (2010)
Inventory decisions – optimal system policy	Sensitivity analysis	Just-in-time delivery, cleaning and disassembly cost, remanufacturing cost, convertibility cost	LCA, OLC analyses	Yuan and Gao (2010)
High-value components supplier decisions	Supplier overall performance	Cost, quality, IT, human resource, time	AHP	Wang et al. (2009)
Joint venture and strategic alliance decision	Buyer–supplier relationship	Cost, quality, delivery time, supply chain transparency	Fuzzy AHP	Lee (2009)
Supplier selection	Evaluate the supplier's overall performance	45 criteria under three clusters: (1) business structure, (2) manufacturing capability and (3) quality system of the supplier	ANP	Gencer and Gurpinar (2007)
Integration in oil and gas supply chain – innovation evaluating	Evaluation of complex and novel technologies for sustainable development	Economic, environmental and social sustainability	LCA, PLC	Matos and Hall (2007)
Selecting third-party reverse logistics providers	Evaluate the provider's overall capability	Recycling, reuse and remanufacturing capability; repair, testing and product servicing capability; return shipment capability	Fuzzy AHP	Bottani and Rizzi (2006)
Green supply chain decisions – partner, technology and organisational practice	Evaluate organisational performance	Cost, quality, time, flexibility, total quality environmental management standards	ANP, PLC and OLC	Sarkis (2003)
Transportation decisions	Evaluate alternative fuel vehicles	Cost, sustainability (renewable source)	AHP	Byrne and Polonsky (2001)

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 compare interests from different areas and to identify possible future research directions.

Figure 4 Classification of sustainability analysis methodologies and their applications (corresponding section numbers are in brackets).

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 both LCA and MCDA to support decision-making in all three areas has been actively carried out (with a significant number of publications in each area). However, LCA methodology is primarily explored for sustainable design (with 18 publications) and is rarely utilised for SSCM decision-making (with only two publications). MCDA is widely used in all three areas, especially in SSCM in which it has been extensively investigated as the main decision support methodology (39 publications using MCDA compared with 2 using LCA).

Figure 5 Application areas of LCA and MCDA methodologies.

Figure 6 illustrates the differentiation between the analysis methods within each category, i.e. LCA and MCDA. The majority of LCA work is on the OLC analysis. Among the 23 publications surveyed in relation to LCA, over half of them (13 out of 23) are on OLC. Only three publications (constituting 13%) collectively used both OLC and PLC analyses. Within the MCDA methodology (with 71 publications on the topic surveyed), ANP has received substantial attention with an impressive 25% (19 out of the 71) despite its relatively short history. AHP undoubtedly is the most widely used method, because of its long history and maturity as well as user friendliness (easy to implement). Fuzzy set theory and fuzzy AHP/ANP have also gained increasing research interests.

Figure 6 Proportional distributions of the sustainability analysis methods.

The second comparison is carried out between different journal categories that have actively published articles about green production operations. On the basis of the publications surveyed in this paper, the top three categories of journals with the most articles included are:

•	Forty-four publications from production research journals, represented by International Journal of Production Research, International Journal of Production Economics, Journal of Cleaner Production and International Journal of Sustainable Engineering.
•	Twenty-two publications from operations management journals, represented by International Journal of Operations and Production Management, Journal of Operations Management, Production and Operations Management and European Journal of Operational Research.
•	Ten papers from SCM journals, represented by SCM: an International Journal, International Journal of Logistics Management, and International Journal of Physical Distribution and Logistics Management.

The third comparison is carried out between different publication times. Figure 7 shows the distribution of the publications along the time line, including 104 publications 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.

Figure 7 Distribution of the publications over last decade.

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 requirements 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 methodologies to improve decision analysis for green production operations decision-making, with many research questions still 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 operations 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 manager's subjective preferences and judgements 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 maker's subjective influence and the scientific rigour of the methodologies, i.e. where the line should be drawn so that decision maker's 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 manufacturing 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 to consider 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 supplier's suppliers and customer's 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. Conclusions

Sustainability 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 straightforward issue, because decision makers can be easily swamped 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 in support 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.

Additional information

Notes on contributors

Mike Leat

1

Melanie Hudson Smith

2

Notes

1. Email: [email protected]

2. Email: [email protected]