fusing decision support systems into the fabric of the...

An Integrated Sustainability Analysis

Approach to Support Holistic Decision

Making in Sustainable Supply Chain

Management

Shaofeng LIUa,1, Zhihong WANG

b and Li LIUa a

University of Plymouth, Plymouth, UK b Donghua University, Shanghai, China

Abstract. Sustainable Supply Chain Management (SSCM) has recently emerged to address environmental and social concerns in product and service operations. Existing research has investigated sustainability issues at isolated stages of the supply chains. This paper aims to address Triple Bottom Line sustainability in a coherent manner. The paper proposes an integrated sustainability analysis (ISA) approach which seamlessly integrates Life Cycle Assessment into Multi Criteria Decision Making process. Application of the integrated approach to an industrial case demonstrates that the proposed approach can provide concerted scientific backup to efficiently and effectively support holistic decision making in SSCM. The ISA approach has the potential to enable true integration of environmental management and social responsibility with supply chain management.

Keywords. Sustainable supply chain management, integrated sustainability analysis, life cycle assessment, multi criteria decision making

Introduction

Since the concept of “Sustainable Development” was introduced by the World

Commission on Environment and Development, researchers in supply chain

management (SCM) started to bind environmental sustainability to SCM [1]. It was

noted that corporate environmental management becomes potentially fallacious without

the contribution of SCM to accomplish superior performance [2]. To address the

Triple Bottom Line (3BL) sustainability objective for SCM [3], decision making

requires better support from advanced decision techniques, so that the sustainability

analysis is sufficiently undertaken in order for supply chain managers to make better

informed decisions.

This paper proposes an integrated sustainability analysis (ISA) approach to provide

holistic evaluation and support for sustainable supply chain management (SSCM)

decision making. There are two key objectives to explore the ISA approach: (a) to

understand the SSCM decision support requirements from a through-life perspective;

(b) to address the SSCM decision issue taking multiple decision criteria into account.

Based on a case study in plastic manufacturing area, the paper concludes that the ISA

1 Shaofeng Liu, School of Management, University of Plymouth, UK, [email protected]

Fusing Decision Support Systems into the Fabric of the ContextA. Respício and F. Burstein (Eds.)IOS Press, 2012© 2012 The authors and IOS Press. All rights reserved.doi:10.3233/978-1-61499-073-4-391

391

approach can provide more efficient and effective support to complex decision making

in SSCM. The paper is organized as follows. Next section reviews related work on

methods and tools that have been developed to address SSCM decision issues, and

identifies the gap in literature. Section 2 proposes an integrated approach for systematic

analysis of sustainability in SSCM decision making. Application of the ISA approach

to real supply chain situations is discussed in Section 3. Section 4 reflects on the

strengths and limitations of the proposed ISA approach, and draws conclusions.

1. Related work

Many researchers have undertaken both theoretical and empirical studies to explore the

concepts, models and frameworks for SSCM [2,4]. Because it lacked a clear definition,

SSCM has gone through many different names over time, such as environmental SCM

and green SCM [5]. Consensus on the definition of SSCM was finally reached through

the influential work in the field by Carter and Rogers, in which they stated “SSCM is

the strategic, transparent integration and achievement of an organization’s

environmental, social, and economic goals in the systematic co-ordination of key inter-

organizational business processes for improving the long-term economic performance

of the individual company and its chains” [6]. 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 noted, “almost all environmental improvements possibly undertaken

by a company depend on the contribution of SCM for their execution (implementation

of decisions)” [7].

SSCM is sometimes referred to as closed-loop SCM. Closed-loop supply chains

are those supply chains where 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 [2]. 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 [8]. Increasing legislation in the field of

producer responsibility, take-back obligations and setting up collection and recycling

systems has 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, while 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 [9]. Nevertheless, to maximize 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 decentralized and centralized decision making, which requires efficient support

from advanced sustainability analysis.

Sustainability analysis would be theoretically straightforward if the key interacting

variables and boundaries of responsibilities were well understood by decision makers.

Unfortunately, such situations are rare, while 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 environmental systems

involved and imperfections of human reasoning. According to Hall and Vredenburg,

S. Liu et al. / An ISA Approach to Support Holistic Decision Making in SSCM392

innovating for sustainable development is usually ambiguous, i.e. 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 for

SSCM decision making [10]. Researchers further argue that sustainability analysis

frequently involves a wide range of stakeholders, many of which are not directly

involved with the company’s supply chain 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 in guiding

the decision makers to see through the complexities and ambiguities along the supply

chains [9]. Life cycle assessment and multi-criteria decision analysis are two of the

most widely used analysis methodologies in supporting supply chain decisions.

Life cycle assessment (LCA) is regarded as a “cradle to grave” technique that can

support environmentally friendly product design, manufacture and management [9]. It

has been used to assess the environmental aspects and potential impacts associated with

products, processes and systems. It also allows decision makers to evaluate the type

and quantity of inputs (energy, raw materials, etc.) and outputs (emissions, residues,

and other environmental impacts, etc.) of supply chain operations in order to

completely understand the context involving product design, production, and final

disposal [5]. LCA can be conducted along two different dimensions: Product Life

Cycle (PLC) and Operational Life Cycle (OLC). A new product progresses through a

sequence of stages from development, introduction to growth, maturity, and decline.

This sequence is known as the PLC. On the other hand, OLC includes stages of

procurement, production, packaging, distribution, use, end-of-life disposal and reverse

logistics [2]. However, it is widely acknowledged that environmental methods (both

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

decision making process throughout the supply chain and are not just a “disintegrated”

aggregation of facts.

Multi-Criteria Decision Analysis (MCDA) is a well-developed method that can

simultaneously address multiple objectives of complex decision making. The method

can empower decision makers to consider all decision criteria and decision factors,

resolve the conflicts between them, and arrive at justified choice. Over the past three

decades, different variants of MCDA have been developed, including Analytic

Hierarchy Process (AHP) and Analytic Network Process (ANP). AHP was introduced

by Saaty for solving unstructured problems [11]. Since then, AHP has become one of

the most widely used analysis methods for multi-criteria decision making. AHP

requires the decision maker to provide judgments about the relative importance of each

criterion and specify a preference for each decision alternative using each criterion. The

output of AHP is a prioritized ranking of the decision alternatives based on the overall

performance expressed by the decision maker. The strength of AHP is that it can

handle situations in which the unique subjective judgments of the individual decision

maker constitute an important part of the decision making process [12]. However, its

key drawback is that it does not take account of the relationships between the decision

factors.

ANP is the evolution of the AHP. Given the limitations of AHP, ANP has been

developed as a more realistic decision method. Many decision problems cannot be built

as hierarchical as in AHP because of dependencies 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

S. Liu et al. / An ISA Approach to Support Holistic Decision Making in SSCM 393

of elements between different levels and within the same level. In fact, ANP uses a

network without the need to specify levels as in a hierarchy 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 SSCM decision making [13]. Both ANP and the AHP derive ratio scale

priorities for elements and clusters of elements by making paired comparisons of

elements on a common property or criterion. ANP disadvantages may arise when the

number of decision factors and respective inter-relationships increase, requiring

increasing effort by decision makers. Saaty and Vargas 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 [11].

Separately, both LCA and MCDA are popular analysis technologies for decision

making and sustainable development. The reason why LCA stands out from other eco-

efficiency technologies such as Environmental Accounting and Value Analysis is in its

capability of highlighting environmental issues from a holistic (“cradle to grave”)

perspective. By breaking down the environmental problems into specific issues at

different life cycle stages that can be articulated by supply chain managers, it helps the

managers, as decision makers, explicitly define, capture and implement corresponding

green objectives in their decision making process. MCDA’s main merit is in its

competence in handling complex decision situations by incorporating multiple decision

criteria to resolve conflicting interests and preferences.

SSCM decisions need to address the 3BL which undoubtedly require MCDA

methods. In the meantime, it is critical that the environmental and social concerns are

addressed right from the early stages, so that their adverse impact can be minimized or

mitigated. Therefore, SSCM decision making requires MCDA and LCA to be explored

coherently. By considering both LCA and MCDA technologies, it could provide

decision makers with the vital analysis tool for improved judgments. It therefore allows

supply chain managers to take concerted decisions, not only to limit, but also to reverse

any long term environmental damage, and thus ensuring that supply chain activities are

undertaken in a sustainable manner. Despite the urgent requirements from SSCM for

powerful analysis support, there is little report in the literature discussing the successful

integration of both LCA and MCDA technologies in support of SSCM decision making.

Therefore, there is a need for an integration approach to fill the gap.

2. An integrated sustainability analysis approach

The perceived benefit of proposing an integrated approach is that, through integration,

LCA will enhance MCDA with life cycle stage specific information on green

objectives so that SSCM decisions can be made from a more holistic view (through-life

view). In the meantime, MCDA will enhance LCA with multi-criteria analysis to help

pin down stage-specific decision variables and correlations to decision goals and

alternatives. Based on the above understanding, this paper proposes an integrated

sustainability analysis approach for SSCM holistic decision making. The approach

integrates two key technologies: ANP and OLC analysis. Seamless integration of the

ANP and OLC analysis is enabled through the integration of information about

decision objectives and criteria.


2.1. Performing OLC analysis

During the OLC – procurement, production, distribution, use, end-of-life treatment and

reverse chain, different green issues need to be addressed at different stages. Therefore

environmental objectives may be defined in different forms for SSCM decision making.

For example, greener material selection at procurement stage, cutting down greenhouse

gas emission at production stage, reduces energy consumption at the use and

distribution stages, safe waste management for end-of-life treatment, and product

recovery through reverse logistics.

In sustainability analysis, decision objectives can be represented by using

appropriate indicators. An indicator expresses one or more characteristics that can be

empirically observed or calculated. An indicator aims at identifying those aspects of

phenomenon that are considered to be important as for monitoring and control.

Therefore, it is a piece of information that refers to an intrinsic attribute or to a set of

attributes of the phenomenon or associated to other phenomena closely related to the

former. In SSCM, indicators are usually described with reference to the three principal

sustainability dimensions. For example, greenhouse gas emissions and quantity of

wastes are common environmental indicators. Social indicators can be unemployment

rate or crime level etc. While economic indicators include GDP, inflation rate and so on.

Researchers have recognized that it is a system of indicators rather than individual

indicators that is more significant for SSCM [14]. Although made up of individual

indicators, the system of indicators can describe and give inter-correlated information

from a logical and functional view. The proposed ISA approach in this paper explores a

system of indicators.

2.2. Developing the decision hierarchical model with ANP

In order to understand SSCM holistic decision making, it is extremely important:

(1) To identify the relationships between the key components in a sustainable supply

chain: supply chain strategy, structural and infrastructural decisions,

environmental issues and social issues. The relationships should be based on the

understanding of the contents of each component. For example, how

environmental issues such as waste management, reduce-reuse-recycle, and

pollution control can be addressed by supply chain strategies and its structural and

infrastructural decisions. Similarly, how social issues such as staff and customer

safety, employment policy, workplace stress, price manipulation, honesty and

transparency of supplier relationships can be addressed by the supply chain

strategies and decisions;

(2) To define supply chain decision hierarchy/network, i.e. the dependency between

supply chain strategic decisions, structural decisions and infrastructural decisions.

Within the decision network, if decision on one network node changes, what are

the decision propagation paths along the network and consequences to other

decisions? What needs to be done to manage the decision changes?

To address the above issues and to make sure that multiple criteria including

environmental and social objectives from the OLC analysis are integrated in the

decision making process, ANP technology has been explored [11]. The result of the

process is an ANP model consisting of a control hierarchy, clusters and elements, as

well as interactions between the clusters and elements. Seven key steps have been

undertaken to develop the ANP model for SSCM decision making.


3. Application of the ISA approach to case study

This section discusses the application of the proposed integrated sustainability analysis

approach to a case example from the supply chain management in the plastic

manufacturing industry.

3.1. The case study

Manufacturing industry is, without a doubt, a major contributor to world’s economy. At

the same time, manufacturing has been in the centre of the root cause for environmental

issues. Along with the wave of business globalisation, more and more social problems

are being unfolded from the plastic manufacturing industry. It is a common

acknowledgement that the quicker to take effective means to tackle the environmental

and social problems caused by the plastic manufacturing industry, the better. This

paper looks at a case study from the Trinity Environmentally Friendly Plastic Products

Company in Hunan, China. Insightful interviews were undertaken with the company’s

production engineers and managers for two purposes: (a) to collect empirical data for

the development of the plastics shopping bag OLC and ANP models; (b) to gain

feedback and validate the ISA approach.

One of the most influential plastic products in the plastics manufacturing industry

is plastic shopping bags. Since its introduction to US supermarkets over 30 years ago, it

has entered every household. Today, 80 per cent of grocery bags are plastic [16].

Highly convenient, strong and inexpensive, plastics bags were appealing to business

and consumers as a reliable way to deliver goods from the stores to home. However,

many issues associated with the production, use and disposal of plastic bags along the

supply chain may not be initially apparent to most users, but now are recognised

extremely important and need to be addressed urgently. By exploring the ISA approach

to the case study, this paper aims to help decision makers achieve better understanding

of the full ecological footprint of the products along the supply chain, and to provide

efficient decision support in dealing with the associated negative impacts on

environment and social equity.

3.2. Eliciting decision criteria and indicators through OLC analysis

It was recognized that the Plastic Manufacturing company needs to understand plastic

bags life cycle impacts by undertaking streamlined OLC analysis to elicit

environmental indicators. The information can then be used to enlighten supply chain

managers and help them make informed decisions. From the manufacturer’ viewpoint,

planning ahead ensures that any potential risks to business are anticipated whenever

possible. A key benefit is that a proactive approach is likely to be more scientifically

sound than a reactive approach, which is merely responding to government legislation

or consumer concerns.

Figure 2 demonstrates the application of OLC analysis to plastic bags, with energy

inputs and emission outputs at each key stage of the life cycle. In terms of

sustainability objective in the supply chain operations, three options are available:

making recyclable, reusable or degradable bags. From the OLC analysis process, it is

clear that good practices of SSCM should address environmental and social concerns.

Along this line, there is a fourth option, i.e. do not make plastic bags at all and replace

them with paper bags. Specific indicators for environmentally friendly operations


Figure 3 ANP network control hierarchy for the Plastic Bags case

3.3.2. Pairwise comparisons for the Plastic Bags case

In complex decision making using ANP, pairwise comparisons are made to establish

the relative importance of the different clusters and elements with respect to a certain

component of the network. In the pairwise comparison process for the Plastic Bags

case, a ratio scoring system suggested by Saaty and Vargas has been employed [11]. A

judgment or comparison is the numerical representation of a relationship between two

elements. Each judgment reflects the answers to two questions: which of the two elements

is more important with respect to a higher level criterion, and how strongly, using the 1 to

9 scale. Scale 9 means that one element is extremely important compared with the other,

and scale 1 means that both elements have equal importance.

On the level of clusters comparison, there are four 4x4 matrices containing the

judgments made on pairwise comparisons established from the survey and discussion

with groups of experts of sustainability assessment in the Plastic Manufacturing

company. The pairwise comparison matrices at this level allow decision makers to

evaluate the relationships existing between the different sustainability aspects, i.e.

economic, environmental and social. Based on the comparison among the four clusters

from the point of view of socially responsible operations, the priority vectors are then

calculated. When the priority vectors for all four clusters are calculated and aggregated

in one table, it generates the Cluster Matrix for the Plastic Bags case. The Cluster

Matrix is used later to transform an unweighted supermatrix to a weighted supermatrix.

Once the clusters comparisons are done, it is essential to perform the pairwise

comparisons at more detailed level, i.e. comparisons between elements (of the clusters).

The element comparisons can be done in similar manner as for the cluster comparisons.

3.3.3. Supermatrix formation and global priorities for the Plastic Bags case

The result of all pairwise comparisons is then inputted for computation to formulate a

supermatrix. In the Plastic Bags case study, three different super-matrices have been

generated: an Un-weighted, a Weighted and a Limit Supermatrices. Supermatrices are

arranged with the clusters in alphabetical order across the top and down the left side,

and with the elements within each cluster in alphabetical order across the top and down

the left side. An Unweighted Supermatrix contains the local priorities derived from the

pairwise comparisons throughout the network.


The Unweighted Supermatrix has to be transformed into a Weighted Supermatrix.

The transformation process involves multiplying the Unweighted Supermatrix by the

Cluster Matrix, so that the priorities of the clusters can be taken into account in the

decision making process. Finally, the Weighted Supermatrix is transformed into a

Limit Supermatrix to make the distribution of the vector values meaningful to decision

makers. The Limit Supermatrix is obtained by raising the Weighted Supermatrix to

powers by multiplying itself. When the column of numbers is the same for every

column, the Limit Supermatrix has been reached and multiplication process is halted.

The Limit Supermatrix for the Plastic Bags case is shown is Figure 4. A graphical

overview of the Limit Supermatrix of the case is shown in Figure 5, in which the

consequence of all four decision alternatives is more visualized.

Based on the Limit Supermatrix results and their visual representation shown in

the Figures 4 and 5, recommendation of the decision alternatives can be drawn as

follows:

1) Paper bags as a replacement of plastic bags is the least ideal choice, based on

evidence from the supermatrix: a paper bag requires more energy than a plastic

bag (in the supermatrix, alternative 4 Paper bags contributes a lot less to objective

of minimum energy consumption); in the manufacturing process, paper bags

generate a lot more air and water pollutions than plastic bags (in the supermatrix,

alternative 4 paper bags contributes a lot less to objectives 2.2 and 2.4); paper

bags also take up more landfill space.

2) Plastic bag’s recycling is not a preferred choice in terms of achieving economic

objective (i.e. the cost). This is confirmed by other research that the economic

value of recycling a plastic bag is very little (FMI, 2008).

3) Making degradable bags is a relatively ideal choice (with an overall relative value

of 0.98 in the Figure 5).

4) Making reusable bags is the preferred choice because it has the highest overall

score based on the data collected from the case study.

Figure 4 The Limit Supermatrix for the Plastic Bags case


Figure 5 Visualized representations of the global priorities of the four alternatives

4. Discussion and conclusions

The focus of the paper is on an integrated sustainability analysis (ISA) for the holistic

decision making in SSCM. The approach integrates two core elements: an Operational

Life Cycle (OLC) analysis considering different stages along a supply chain, and an

Analytic Network Process (ANP) for multi-criteria decision analysis. At different

stages of OLC (procurement, production, distribution, use, and reverse logistics),

SSCM has different strategic focus. Understanding the OLC influence on supply chain

operations foci, the environmental and social sustainability issues can be better

addressed in the SSCM decision making process. The evaluation of the ISA approach

has been illustrated through a decision case from the plastic manufacturing industry.

The case study shows that the approach has great potential in providing scientific

evidence to support SSCM decision making under complex situations with multiple

decision criteria and from a through-life perspective.

The strengths of the integrated approach lies in that information about decision

objectives and indicators derived from the OLC analysis is directly fed into the ANP

model development, which allows decision makers to find the optimal solution to

decision problems to achieve the multiple sustainability criteria. At its highest level,

SSCM decision criteria include economic, environmental and social objectives. Within

each of the three areas, more specific sub-criteria and detailed criteria have been

generated from the OLC analysis. For example, economic criteria have been further

broken down to cost reduction, high margin, productivity improvement, maximum

profit etc. Environmental sustainability has included such criteria as waste

minimisation, reduce-reuse-recycle, and pollution control. Social criteria have been

based on labour, discrimination, mistreatment, health and safely, working hours,

minimum wages etc. For supply chain decision making across multi-stages of OLC,

there can be dozens of decision variables and decision alternatives. Under such

complex decision situations, ANP allows supply chain managers to rate the importance

of each criterion, to rate the importance and their preference of each decision

alternative, to calculate global priorities for decision choices, and to predict the

consequence of each decision alternative [11]. Therefore supply chain managers can

confidently and transparently perform “what-if” for each decision option, subsequently

improve their judgements and make informed decisions. The novelty of the ISA

approach is augmenting the ANP by OLC analysis, so that life cycle stage impact on

the SSCM decisions is coherently incorporated. The benefit of the ISA approach is that


it provides a formal, evidence-based justification for SSCM decisions that integrates

environmental, social and economic sustainability objectives into supply chain

manager’s proactive decision making process. Therefore, environmental and social

values are not just being talked (in words) but also enacted (in actions).

Limitations of the approach include that: (a) it is developed for and evaluated in

manufacturing SCM case. Its applicability to service SCM needs further investigation;

(b) it is a quite complicated process to implement the LCA and ANP, which requires

domain knowledge to elicit the environmental objectives and indicators from the OLC

analysis as well as technical skills of mathematics to create the supermatrices.

Future work needs to investigate the uncertainty of SSCM decision situations, and

to accommodate the fuzziness of decision maker’s judgment into the approach. It is

also the authors’ intention to explore the applicability of the ISA approach to service

SCM scenarios. For the plastics shopping bag case study with the Trinity

Environmentally Friendly Plastic Products Company, further work would be

conducting a sensitivity analysis.

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