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