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A decision-making model for waste management in the footwear industry

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The footwear industry, over the last years, has placed significant effort in improving energy and material efficiency, but in comparison little effort has been directed at the recovery and recycling of shoes at the end of their functional life. In reality, most worn and discarded (end-of-life) shoes are disposed of in landfills. Producer responsibility issues and forthcoming legislation as well as increasing environmental consumer demands are expected to challenge the way the global footwear industry deals with its end-of-life waste. This paper presents an investigation into the steps required to consider the end-of-life implication of shoes and promote post-consumer recycling practices in the footwear industry. The paper describes the design and specification of a decision-making model to identify the most appropriate reuse, recovery and recycling option for post-consumer shoes. Such a tool in addition to supporting design and material selection processes could also provide benchmark information for the selection of a best end-of-life practice for a selected range of shoe types. The paper concludes by providing a case study for shoe waste management to demonstrate the practicality of this decision-making model.
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A decision-making model for waste management
in the footwear industry
T. STAIKOS* and S. RAHIMIFARD
Centre for Sustainable Manufacturing and Reuse/Recycling Technologies (SMART),
Wolfson School of Mechanical and Manufacturing Engineering,
Loughborough University, UK
The footwear industry, over the last years, has placed significant effort in
improving energy and material efficiency, but in comparison little effort has been
directed at the recovery and recycling of shoes at the end of their functional life.
In reality, most worn and discarded (end-of-life) shoes are disposed of in landfills.
Producer responsibility issues and forthcoming legislation as well as increasing
environmental consumer demands are expected to challenge the way the global
footwear industry deals with its end-of-life waste. This paper presents an
investigation into the steps required to consider the end-of-life implication of
shoes and promote post-consumer recycling practices in the footwear industry.
The paper describes the design and specification of a decision-making model to
identify the most appropriate reuse, recovery and recycling option for post-
consumer shoes. Such a tool in addition to supporting design and material
selection processes could also provide benchmark information for the selection of
a best end-of-life practice for a selected range of shoe types. The paper concludes
by providing a case study for shoe waste management to demonstrate the
practicality of this decision-making model.
Keywords: Footwear industry; End-of-life management; Decision-making model
1.
Introduction
The footwear industry is a diverse manufacturing sector which employs a wide
variety of materials to make products ranging from different types and styles of
shoes to more specialized footwear. Leather, synthetic materials, rubber and textile
materials are amongst the basic materials most commonly used in shoe manufacture;
each material having its own specific characteristics. These differ not only in their
appearance but also in their physical qualities, their service life, the different
treatment needs as well as their recycling and recovery options at the end of their
useful life. Design and material selection activities significantly influence not only the
life of the footwear, but also its end-of-life treatment.
In the recent past, a consumer would own a few pairs of shoes, some for
exercising and others for work or fashion. But today’s consumer demands a larger
*Corresponding author. Email: T.Staikos@lboro.ac.uk
variety of shoes including options for specialized footwear. To meet the needs of
customers and to be competitive, footwear companies face two key challenges: to be
responsive to market changes and to establish efficient product development in order
to identify or establish new consumer trends. Responsiveness to customer demands
leads to a shorter life cycle of shoes, and an increasingly shorter product
development cycle. A shorter life cycle means that more shoes will be produced
over the years, so leading to a higher level of post-consumer waste. From 1990 to
2004, worldwide footwear production has increased by 70% to around 17 billion
pairs of shoes while by 2010 experts in the sector expect the global footwear output
to reach 20 billion pairs (World Footwear 2005). In the European Union, footwear
consumption has increased by a staggering 22% from 2002 to 2005 to reach 2.3
billion pairs of shoes (EC 2006). Additionally, the footwear per capita consumption
has increased considerably, from one pair of shoes for every person in the world in
1950 to almost 2.6 pairs of shoes in 2005. However, footwear consumption differs
significantly per country. Although China, due to its large population, has the
highest footwear consumption in the world, the United States has the highest per
capita shoe consumption with each inhabitant purchasing an average of 6.9 pairs
of shoes every year (AAfA 2006). In Europe (including new Member States), the
yearly per capita shoe consumption in 2003 was 4.5 pairs of shoes, while in the
United Kingdom the average is slightly higher at 5.3 pairs of shoes/person/year
(CBI 2004). At the other extreme, in less developed countries the per capita shoe
consumption is only 0.6 pairs for India and 0.5 pairs of shoes for Vietnam (all types
of shoes included) (SATRA 2003).
In any product recovery application, there are a number of possible end-of-life
treatment options with different environmental impacts, economic values and
technical requirements. Hence, there is a need for a decision-making process to
evaluate these factors and provide support to decision making. This paper has
proposed such a decision-making model for end-of-life waste management in
footwear industry. Section 2 provides a review of relevant literature on product
recovery and section 3 investigates current reuse and recycling practices for post-
consumer shoes. Section 4 describes the design and specification of a decision-
making model for shoe waste management. In section 5 a case study of a selected
shoe type is presented and some conclusions are presented in section 6.
2.
Overview of relevant research
The increasing significance of product recovery has brought a corresponding growth
in research covering the various stages of the product life cycle (Gupta and Gungor
1999). Therefore, many definitions and categorizations exist in the literature
regarding product recovery activities. Thierry et al. (1995) present a categorization
of product recovery options into repair, refurbish, remanufacture, cannibalization
and recycling while Johnson et al. (1995) define the product recovery process simply
as a combination of remanufacture, reuse and recycling. On the other hand,
Moyer and Gupta (1997), Brennan et al. (1994), and US EPA (1997), simply classify
the recovery of end-of-life products into material recovery (recycling) and product
recovery (remanufacturing).
Additionally, a number of studies have investigated a range of different factors
such as economic, environmental, technical and social criteria, which influence
product recovery options (Krikke et al. 1998, Goggin et al. 2000, Erdos et al. 2001,
Lee et al. 2001 and Bufardi et al. 2003). Different methodologies have also been
developed to find a balance between the time and money invested in product
recovery operations and value gained from the recovered products and materials.
Johnson et al. (1995) suggest a methodology which aims to identify a preferred
sequence of disassembly steps whilst maximizing the value gained from recovery
products, while Hentschel et al. (1995) present an approach to recycling system
planning for used products at their end-of-life phase. Also, Rahimifard et al. (2004)
suggested a novel systematic five-stage methodology, called PRIME, to support
product end-of-life management in different manufacturing applications based on an
integrated view of a product supply and recovery chain.
3.
Current reuse and recycling practices in the footwear industry
In the UK, more than 330 million pairs of shoes are consumed every year (SATRA
2003). It is estimated that the amount of waste generated from post-consumer shoes
in the UK could reach 200 000 tonnes per year, with most of it ending up in landfills.
The figure for the European Union is almost 1.5 million tonnes per year. The
footwear industry’s response to this increasing problem of post-consumer shoe waste
has been negligible. In fact, only one major shoe manufacturer, Nike
®
, has taken
measures to manage its waste. Nike’s recycling programme ‘NikeGO-Places
®
(formerly ‘Reuse-A-Shoe
®
’) is the only product take-back and
recycling scheme currently established by a shoe manufacturer. This programme
has been operating for over a decade in the United States and has just started
operating in the UK, Australia and Japan Nike (2006). Their reuse and recycling
programme involves a series of collection points in retail centres where people can
deposit their worn-out and discarded athletic shoes. The shoes are then collected and
taken to a central recycling facility where they are shredded, producing a material
called ‘Nike-Grid
®
’, which can be used in the surfacing of tennis and basketball
courts, playgrounds and running tracks. According to Nike (2006), since its
inception in 1993, the ‘Reuse-A-Shoe’ programme has recycled more than 16 million
pairs of worn-out and defective athletic shoes in total.
Another form of reuse activity in the footwear sector is the collection and
distribution of worn or unwanted shoes to developing countries. Reuse schemes are
mainly supported by charity organizations, local authorities and municipalities such
as the Salvation Army Trading Company Ltd. (SATCOL
®
), Oxfam and others.
In the UK, SATCOL alone, with its 2300 banks and door-to-door collections and
donations, have managed to collect around 971 tonnes of worn or unwanted shoes
during the years 2000–2001 (Woolridge et al. 2006). However, there is a strong
debate about such reuse activities in terms of their overall environmental impact and
the economic consequences for the local communities. It has been argued that
collection and distribution of worn or unwanted shoes in developing countries
diverts post-consumer waste from the developed world to poor countries with no
infrastructure to deal with it. According to Wicks et al. (1996), re-distribution
of second-hand products into developing countries may also lead to net economic
damage to the local economies due to ‘dumping’ of cheap used footwear. In the case
of Uganda, the import of large volume of second hand shoes in recent years has
significantly reduced the size of the local footwear industry. About 7 million pairs
of second-hand shoes are imported into Uganda annually while only 240 000 pairs
of shoes are produced by the local footwear industry (Temsch et al. 2002). However,
as the cost of producing new shoes reduces and the markets are flooded with lower
quality shoes, it is expected that the price difference between new shoes and second-
hand shoes will shrink in less-developed countries. The demand for second-hand
shoes might then drop in these countries, leading to an increase in post-consumer
shoe recycling and disposal in the developed world.
However, not all materials used in footwear manufacturing can be recycled or
reused. Once post-consumer waste is collected, separated and converted into a form
that can be used by either the footwear industry or other industrial sectors, it must
compete with virgin materials both on price and performance. Although in the case
of other industrial sectors (i.e. metal and glass industry) established recycling
markets already exist, in other material markets such as leather, textiles and plastics
the situation is more complex.
4.
Decision-making model for shoe waste management
This study presents a decision-making model for end-of-life shoe waste management.
This model has been developed to simultaneously consider quantitative and
qualitative waste management factors. For this reason, the analytic hierarchy
process (AHP), which is a multi-criteria decision-making (MCDM) method, has
been applied to construct the basic framework for analysing these factors.
Additionally, a number of other decision-making techniques have been utilized to
calculate economic and environmental criteria such as cost-benefit analysis (CBA)
and life cycle assessment (LCA), as described in figure 1.
Multi-criteria decision making is a scientific field which has seen a considerable
development during the last decades. As its name indicates, multi-criteria decision-
making aims to give decision makers tools to enable them to advance in solving
problems where several, often contradictory, criteria must be taken into account
(Vincke 1992). The AHP method, developed by Saaty (1980), is one of the most
widely used MCDM methods and has been applied in a variety of applications in
different fields such as planning, selecting the best alternative option, resources
allocation, and optimization (Vaidya et al. 2006). In addition, a number of
researchers have investigated the combined application of AHP and LCA in various
industrial case studies (Daniel et al. 2004, Huang et al. 2004 and Hermann et al.
2006). According to Henson et al. (2002) the AHP is consistent with the LCA
concept because the environmental factors can be hierarchically structured into
impacts and improvement options.
The proposed decision-making model for end-of-life shoe waste management, as
depicted in figure 1, outlines the main steps, and the decision aid method that has
been applied in each step. Economic criteria are calculated using CBA to identify
Prioritise Alternatives
Calculate Qualitative
Factors
(Techni cal)
Calculate Quantitative
Factors
(Economic)
Calculate Quantitative
Factors
(Environmental)
Identify Waste Management Factors
Formulate Life Cycle
Model
Calculate Life Cycle
Inventory
Interpret Results
Normalise Results
Catalogue Potential
Cost-Benefit Impacts
Attach Monetary
Values to all Impacts
Calculate Results
Normalise Results
Hierarchical
Classification of
Criteria
Calculate Criteria
Weights
Determine the
Performance of
the Alternatives
Synthesize to
Composite Weight
Design Shoe Waste Management Model
Post-Consumer Sho e
Optimal Shoe Waste Management Option
Figure 1. Decision making model for shoe waste management.
cost and benefits for each end-of-life management scenario, while environmental
impacts are calculated by a streamlined LCA.
4.1 Design shoe waste management model
A waste management model for post-consumer shoes determines the different end-
of-life management options, giving priority to recycling and reuse and minimizing
cost and environmental impacts. The output of such a model would identify
potential treatments for post-consumer shoes depending on the type of shoe.
However, a shoe waste management model does not optimize the waste management
treatments for each type of shoe; it simply lists the options available for treating the
post-consumer waste as well as identifying potential applications for recycled
materials.
In general, a shoe waste management model consists of the following end-of-life
management options (Staikos et al. 2006):
(a)
Reuse
(b)
Recycling
Analytic Hierarchy Process (AHP)
Life Cycle Assessment (LCA)
Cost Benefit Analysis (CBA)
Analytic Hierarchy Process (AHP)
(c)
Energy recovery
(d)
Disposal
Reuse of post-consumer shoes is a possible option but there are variables that
need to be considered such as the condition of the shoe at the end of its functional
life, the collection and distribution system as well as the purpose of its reuse
(see section 3). Recycling involves the reprocessing of post-consumer shoes, parts or
materials, either into the same product system (closed loop) or into different ones
(open loop). The waste is, therefore, re-introduced back into the market through a
series of destructive and non-destructive recycling processes. Energy recovery is
another possible waste management option for post-consumer shoes and includes a
number of established and emerging technologies such as incineration, gasification
and pyrolysis. Finally, disposal of waste to landfills is currently the most common
waste management option for post-consumer shoes.
4.2
Identify waste management factors
This decision-making model takes into consideration both quantitative (environ-
mental and economic criteria) and qualitative (technical criteria) factors.
Environmental criteria include a number of well-recognized environmental impact
category indicators (i.e. global warming potential, human eco-toxicity etc).
Economic criteria are simply divided into costs and benefits for each end-of-life
management scenario (i.e. resale price of reused shoe, cost of land filling, etc).
The list of technical criteria is almost endless and could be easily changed by the user
depending on the requirements of the analysis and the type of shoe under
consideration.
4.3 Prioritize alternatives
Although many multi-criteria decision-making methods can be applied to prioritize
alternatives, AHP is considered as one of the most comprehensive MCDM methods
(Triantaphyllou 2000). In general, the AHP method decomposes a complex decision
problem into a hierarchy and allows the consideration of both quantitative and
qualitative (objective and subjective) factors in selecting the best alternative option
(Saaty 1980). It also provides a methodology to calibrate the numeric scale for the
measurement of quantitative and qualitative performances. Application of the AHP
method requires the following steps: structuring of the problem into a hierarchy,
making pairwise comparisons, calculating criteria weights, and synthesizing the
priorities (Saaty and Vargas 2001).
4.3.1
Structuring the problem into a decision hierarchy. In applying the AHP
method, the first step is decomposition or the structuring of the problem into
a hierarchy. Decomposition requires that the decision problem be decomposed into
a hierarchy that captures the essential variables (factors, criteria, sub-criteria) of the
problem. The decision hierarchy is structured so that the top level represents
the overall objective or goal of the problem. Factors, criteria and sub-criteria
upon which this goal is dependent are assigned to the lower levels of the hierarchy.
X
: : : :
5
The lower level contains the alternatives or options though which the goal may be
achieved.
4.3.2
Making pairwise comparisons. The next step is to make pairwise comparisons
of any two decision variables belonging to the same hierarchical level. The pairwise
comparison of the decision variables is performed using the fundamental Saaty scale
shown in table 1.
The relative weights or priorities of decision criteria and alternatives need to be
identified. From the set of pairwise comparison of the variables, a judgment matrix
A is generated with n rows and n columns, where n is the number of variables being
considered.
2
A11
: :
A1n
3
A
¼
6
4
: : : :
A1n
: :
Ann
7
4.3.3
Calculate waste management criteria weights. Based on the developed
judgement matrix A, a series of calculations are then performed in order to identify
the relative weight of each waste management criterion.
4.3.3.1
Environmental criteria. Environmental criteria for end-of-life management
scenarios are calculated using the LCA methodology. The environmental impact (EI)
score of each scenario is expressed in eco-indicator points (mPt) and computed as
follows:
EI
j
¼
n
i¼1
ICIi ð1Þ
where IC
i
is the impact category indicator I; n the number of impact category
indicators; and j the number of waste management scenarios
The life cycle inventory (LCI) data is derived from a streamlined LCA study of
average shoes, which was based on generalized manufacturing data found in
commercial databases. The LCI calculations and the life cycle impact assessment
(LCIA) phase are conducted in SimaPro 7 LCA software using recognized impact
assessment methods.
Table 1. The AHP pairwise comparison scale (Saaty 1980).
Numerical rating Definition
1 Both criteria equally important
3 Very slight importance of one criterion over the other
5 Moderate importance of one criterion over the other
7 Demonstrated importance of one criterion over the other
9 Extreme or absolute importance of one criterion over the other
2, 4, 6, 8 Intermediate values between two adjacent judgements
s
P
¼ ¼
C
RE
Finally, for the sake of consistency, the environmental impact score (EI
j
) of each
scenario requires normalizing and expressing in unit-free numbers. The normalized
environmental impact score (NEI
j
) for each scenario is calculated as follows:
(i)
calculate the reciprocal of each environmental impact score (REI
j
);
(ii)
divide the reciprocal of each environmental impact score (REI
j
) by the sum of
all reciprocal scores.
where
REIj
NEI
j
¼
P
REIj
ð2Þ
1
REI
j
¼ EIj
and EI
j
is the environmental impact score of each scenario(s).
4.3.3.2
Economic criteria. Economic values for each end-of-life management
scenario are calculated using the benefit-to-cost ratio (BCR) approach. The BCR
ratio must be greater than or equal to 1, i.e. B/C41, where B is the benefit and C is
the cost of each alternative. The end-of-life economic value and benefit/cost ratio are
calculated based on the following methods:
(i)
Reuse benefit/cost ratio (BCR
RE
)
The revenue of the reuse scenario (B
RE
) derived from the resale value of the shoe
(B
resale
) while the costs (C
RE
) arise from collection costs (C
collection
), transportation
costs (C
trans
) and refurbishing costs (C
refurb
). Therefore, the reuse benefit/cost ratio
(BCR
RE
) can be obtained as follows:
BCR
B
RE
B
resale
¼
P
¼
ð3Þ
(ii)
Recycling benefit/cost ratio (BCR
RC
)
The revenues of the recycling scenario (B
RC
) is a function of the weight of the
recovered material (B
weight
) and the market value of the material (B
value
). The costs
(C
RC
) arise from collection costs (C
collection
), transportation costs (C
trans
), separation
costs (C
separation
) and shredding costs (C
shred
). Therefore, the recycling benefit/cost
ratio (BCR
RC
) can be obtained as follows:
BCR
B
RC
B
weight
* B
value
ð4Þ
(iii)
Energy recovery benefit/cost ratio (BCR
ER
)
The revenues of the energy recovery scenario (B
ER
) are a function of the net energy
produced (B
energy
) and the unit price of the produced energy (B
price
). The costs (C
ER
)
C
RC
RE
C
collection
þ
C
trans
þ
C
refurb
RC
C
collection
þ C
trans
þ C
separaton
þ C
rshred
P
¼ ¼
C
DS
C
trans
þ
ðW
shoe
* C
al
Þ
j
CR
j
arise from collection costs (C
collection
) and transportation costs (C
trans
). Therefore,
the energy recovery benefit/cost ratio (BCR
ER
) can be obtained as follows:
BCR
B
ER
B
energy
* B
price
ð5Þ
(iv)
Disposal benefit/cost ratio (BCR
DS
)
There are no projected revenues in the disposal scenario (B
DS
). The costs (C
DS
) arise
from transportation costs (C
trans
) and landfilling costs (C
land
). Landfilling cost
(C
land
) is a function of the weight of the shoe (W
shoe
) and the actual cost of landfilling
per tonne of material (C
al
). Therefore, the disposal benefit/cost ratio (BCR
ER
), which
is always zero, can be obtained by the following formula:
BCR
B
DS
0
¼
P
¼ ¼ 0
ð6Þ
The benefit-to-cost ratio (BCR
j
) for each shoe waste management scenario is then
normalized for consistency purposes. The normalized benefit/cost ratio (NBCR
j
) is
calculated by dividing each benefit/cost ratio by the sum of all benefit/cost ratios as
given in equation (3):
B
CR
j
NBCR
j
¼
P
B
ð7Þ
where NBCR
s
is the normalized benefit/cost ratio for each scenario; BCR
s
the
benefit/cost ratio for each scenario; and j the number of waste management
scenarios.
4.3.3.3
Technical criteria. The technical criteria are calculated by using the AHP
method. In fact, a micro-AHP analysis is performed to calculate these criteria
weights as part of a macro-AHP method for the overall analysis. In this respect, the
same AHP steps are performed as described before (see section 4.3): structuring the
problem into a hierarchy, making pairwise comparisons, calculating criteria weights
and synthesizing the priorities.
It should be mentioned that the weight value of the technical criteria relies less on
numbers and statistics but more on interviews, questionnaires, subjective reports and
case studies. In this respect, the technical criteria and their weights can be easily
changed by the user depending on the requirements of the analysis.
4.3.4
Synthesize the priorities. The final step in applying the AHP method is to
calculate the composite weight factor (W
j
) of each alternative shoe waste manage-
ment scenario. A simple additive method is utilized to synthesize the AHP priorities
(P
i
) and the weights of the alternatives with respect to each decision variable (K
ij
).
W
j
¼
X
P
i
x
K
jjI
ð8Þ
where i ¼ 1,
...
, n is the decision variables (factors, criteria, sub-criteria); W
j
the
composite weight of alternative option j; P
i
the relative weight of variable i with
C
ER
ER
C
collection
þ
C
trans
DS
respect to the overall goal; and K
ij
the relative weight of alternative j with respect to
variable i.
5.
Illustrative example of decision-making model
The proposed decision-making model is applied to evaluate a real shoe waste
management problem. The selected shoe is a men’s casual shoe (MCS), as depicted in
picture 1, with the following characteristics:
MCS upper: Leather
MCS lining: Leather
MCS sole: Rubber
MCS weight: 350gr
This type of shoe has been selected because it is made of leather and rubber, two
of the most common materials used in shoes. This illustrative example demonstrates
the practicality of the decision-making model for shoe waste management.
5.1
MCS waste management model
Figure 2 presents the MCS waste management model. Five end-of-life management
scenarios have been selected for this type of shoe:
1.
Reuse scenario: reuse of shoe to less-developed countries.
2.
Recycling scenario 1: shredding of shoe as a whole.
3.
Recycling scenario 2: disassembly of shoe to isolate materials and then
shredding of separated materials.
4.
Incineration scenario: incineration of shoe in municipal solid waste
incinerators to generate heat and electricity.
5. Disposal scenario: land filling of shoe.
Picture 1. Men casual shoe (MCS).
5.2
MCS waste management factors
Quantitative (environmental and economic) and qualitative factors are being
considered in this case study. Environmental criteria include a number of well-
recognized impact category indicators such as global warming potential, human eco-
toxicity, ozone depletion, etc. Economic criteria are simply divided into costs and
benefits for each end-of-life management scenario. Finally, the technical factors
comprise of technical feasibility, compliance with legislation, market pressures and
public opinion.
5.3 Prioritize alternatives
As previously described (see section 4.3), the application of the AHP method requires
the following steps: structuring of the problem into a decision hierarchy, making
pairwise comparisons, calculating criteria weights and, finally, synthesizing the
priorities.
5.3.1
Structuring the problem into a decision hierarchy. Figure 3 presents the AHP
hierarchy of the MCS waste management problem.
The hierarchy is structured into four levels:
1.
At the first (or top) level is the overall goal of the decision-making problem.
In our case, the goal is to identify optimal end-of-life management option
for MCS.
Figure 2. MCS waste management model.
Men’s Casual Shoe Waste
Management Model
Reuse Scenario
Recycling
Incineration
Scenario
Disposal Scenario
Recycling Scenario1 Recycl ing Scenario2
-Inspection
-Repair
-Refurbishing
Inspection
Shreddi ng
Incineration
Disassembly
Reprocessing
Reprocessing
Footwear Product System
(Closed-Loop)
Other Product System
(Open-Loop)
Landfill
Figure 3. AHP hierarchy of men’s casual shoe waste management problem.
2.
At the second level, the goal is broken down using quantitative and
qualitative factors.
3.
At the third level, the quantitative and qualitative factors are divided into
criteria and sub-criteria.
4.
At the fourth (or bottom) level are the five MCS end-of-life management
options that are to be evaluated in terms of the criteria and sub-criteria of the
third level.
5.3.2
Making pairwise comparisons. To estimate the significance of each end-of-life
management scenario (level 4) in achieving the overall goal (level 1), pairwise
comparisons of the decision variables within a lower level of the hierarchical
structure with respect to the variables in the next higher level are performed.
1
st
Level
Goal
Factors
Criteria
Sub-Criteria
4
th
Level
Alternatives
Public
Opinion
Market
Pressures
Compliance
with
Legislation
Technical
Feasability
Technical
3
rd
Level
Qualitative
2
nd
Level
etc
Hazardous
Waste
Human Toxicity
Eco-toxicity
Acidification
Ozone
Depletion
Global Warming
Potential
Environmental
Benefits
Costs
Economic
Disposal
Scenario
Incineration
Scenario
Recycling
Scenario 2
Recycling
Scenario 1
Reuse
Scenario
Quantitative
MCS Optimal End-of-Life Management Option
Table 2. Pairwise comparison matrix for Level 2.
Economic
Environmental
Technical
Priority
weight
Economic
1
1/2
3
0.3196
Environmental
2
1
4
0.5584
Technical
1/3
1/4
1
0.1220
These decision variable weights could be determined by using questionnaires to
obtain stakeholders (governmental, experts, public, business, etc.) opinions.
The judgement matrix of pairwise comparisons of the factors in the upper level of
the hierarchy is shown in table 2, along with the resulting weight of priorities. This
weight gives the relative priority of the factors measured on a ratio scale. In our case,
environmental factors have the highest priority, with 0.559.
Note, for example, that in comparing the economic factors row with the
environmental factors column, a value of 1/2 is assigned. However, when comparing
it with technical factors it is preferred, and a value of 3 is entered in the first row. At
the same time, the reciprocal value 1/3 is automatically entered in the third row
under technical factors.
In the same way, analysis can be done at the lower level. Pairwise comparisons of
each end-of-life management scenario are performed with respect to each of the
decision variables. For example, the five end-of-life management scenarios are
compared with one another; first relative to economic criteria, then relative to
environmental criteria and, finally, relative to technical criteria.
5.3.3
Calculate criteria weights
5.3.3.1
Environmental criteria. Sixteen (16) potential impact category indicators
(ICI
i
) have been selected (i.e. global warming potential, acidification potential, eco-
toxicity factors in water etc.), as depicted in table 3. The LCI data is derived from a
streamlined LCA of a men’s casual shoe, which was based on generalized
manufacturing data. The total environmental impact (EI
j
) of each MCS waste
management scenario and the score of each impact category indicator (ICI
i
), are
calculated by using equation (1), and presented in table 3.
The disposal scenario has the highest environmental impact score with
282.59 mPt while recycling scenario 1 (shredding of the shoe as a whole) has the
lowest impact score of 23.16 mPt. The LCA calculations were conducted in SimaPro
7 using the EDIP (Environmental Design of Industrial Products) impact assessment
method (Wenzel et al. 1997). The total environmental impact (EI
j
) score for each
scenario is, then, normalized, by using equation (2), and expressed in unit-free
numbers as shown in table 4.
5.3.3.2
Economic criteria. An experimental data set based on average values for
costs and benefits has been used to calculate the benefit/cost ratio for each MCS end-
of-life management scenario. Each scenario is calculated as follows:
(i)
Reuse MCS scenario
Table 3. Total environmental impact (EI
j
) of each scenario.
Impact Category
Indicator (ICI)
Unit
Re-use
Scenario
Recycling
Scenario 1
Recycling
Scenario 2
Incineration
Scenario
Landfill
Scenario
Total (EI
j
)
mPt
29.33
23.16
23.26
221.88
282.59
Global warming (GWP 100)
mPt
1.10
0.61
0.61
0.81
0.70
Ozone depletion
mPt
0.04
0.03
0.03
0.03
0.03
Acidification
mPt
0.64
0.12
0.12
0.12
0.12
Eutrophication
mPt
0.05
0.04
0.04
0.05
0.11
Photochemical smog
mPt
0.71
0.21
0.21
0.21
0.22
Ecotoxicity water chronic
mPt
7.60
6.67
6.68
102.00
121.00
Ecotoxicity water acute
mPt
7.33
6.39
6.40
99.90
130.00
Ecotoxicity soil chronic
mPt
2.66
1.64
1.65
3.62
1.63
Human toxicity air
mPt
1.66
1.15
1.15
1.16
1.15
Human toxicity water
mPt
1.50
1.49
1.50
8.05
20.80
Human toxicity soil
mPt
2.86
2.20
2.20
2.54
3.22
Bulk waste
mPt
1.52
1.02
1.08
1.05
1.99
Hazardous waste
mPt
0.95
0.95
0.95
0.95
0.95
Radioactive waste
mPt
0.71
0.64
0.64
0.68
0.68
Slags/ashes
mPt
0.00
0.00
0.00
0.71
0.00
Resources (all)
mPt
0.00
0.00
0.00
0.00
0.00
SimaPro 7.0
Method: EDIP/UMIP 97 V2.03/EDIP World/Dk
4416
T. Staikos and S. Rahimifard
Table 4. Normalized environmental impact (NEI
j
) score.
Environmental
impact score (mPt)
Normalized
results
Reuse scenario
29.33
0.2656
Recycling scenario 1
23.16
0.3367
Recycling scenario 2
23.26
0.3351
Incineration scenario
221.88
0.0351
Disposal scenario
282.59
0.0273
Calculations are based on equation (3) taking into consideration the following
values:
B
RE
¼ B
resale
C
RE
¼ C
collection
þ
C
trans
þ
C
refurb
(ii)
Recycling MCS scenario 1
Calculations are based on equation (4) taking into consideration the following
values:
BRC1 ¼ ðBweightÞ* ðBvalueÞ
C
RC1
¼ C
collection
þ
C
trans
þ
C
shred
(iii)
Recycling MCS scenario 2
Calculations are based on equation (4) taking into consideration the following
values:
BRC2 ¼ ðBweightÞ* ðBvalueÞ
C
RC2
¼ C
collection
þ
C
trans
þ
C
separation
þ
C
shred
(iv)
Incineration MCS scenario
Calculations are based on equation (5) taking into consideration the following
values:
BER ¼ ðBenerg yÞ* ðBpric eÞ
C
ER
¼ C
collection
þ
C
trans
(v)
Disposal MCS scenario
Calculations are based on equation (6) taking into consideration the following
values:
B
DS
¼ nil
C
DS
¼
C
trans
þ
½
ð
W
shoe
Þ *
ð
C
land
Þ]
Benefit-to-cost ratio is then normalized, by using equation (7), in order to be used
by the AHP method. Table 5 presents the benefit to cost ratio and the normalized
results for each end-of-life management scenario.
5.3.3.3
Technical criteria. The technical criteria are calculated by applying the
AHP method in a local scale. Once again, a series of pairwise comparisons are
performed in order to identify the weight of each criterion. The MCS waste
management scenarios are then compared to each other with respect to each
criterion, again by making a series of pairwise comparisons. The final result is a score
(composite weight) for each alternative MCS waste management scenarios with
respect to technical criteria. The results of the pairwise comparison of alternative
scenarios with respect to each technical criterion as well as the final composite weight
of each scenario are presented in table 6.
5.3.4
Synthesize the priorities. Finally, the composite weight factor (W
j
) of each
MCS waste management option need to be calculated. The results of the pairwise
comparison of the five end-of-life management scenarios with respect to each
decision variable as well as the final composite weight of each scenario are presented
Table 5. Normalized MCS benefit/cost ratio.
Benefit/cost ratio*
Normalized results
Re-use scenario
2.00
0.3200
Recycling scenario 1
1.66
0.2667
Recycling scenario 2
1.75
0.2800
Incineration scenario
0.83
0.1333
Disposal scenario
0.00
0.0000
*An experimental data set has been used for these values.
Table 6. Synthesis of technical criteria weights.
Technical criteria
Technical
feasibility
Public
opinion
Market
pressures
Compliance
with legislation
Relative weight
of technical criteria 0.07 0.15 0.43 0.35
End-of-life
management scenarios
Relative weight of
scenarios with respect to criteria
Composite
weight
Re-use scenario
0.16
0.18
0.26
0.26
0.2415
Recycling scenario 1
0.44
0.33
0.33
0.32
0.3353
Recycling scenario 2
0.29
0.37
0.26
0.29
0.2882
Incineration scenario
0.08
0.09
0.11
0.09
0.0969
Disposal scenario
0.04
0.03
0.04
0.04
0.0381
in table 7. This table synthesizes the results of both the relative weights of decision
variables with respect to the overall goal (Pi), and the relative weights of alternative
with respect to each decision variable (Kij). To calculate the composite weight (Wj)
for each scenario the equation (1) has been utilized as described in section 4.3.4.
The composite weight (Wj) indicates the overall significance of each end-of-life
management option after considering the importance of the decision variables.
In fact, composite weight represents the cumulative weights of each alternative
option throughout the entire AHP hierarchy, as described in figure 3. For example,
the composite weight of reuse scenario after considering the entire hierarchy is
0.2800. This is the cumulative weight after considering the relative weight of reuse
scenario with respect to each decision variable and the relative weight of the decision
variables to overall goal. This is calculated in equation (8) as follows:
W
MCS reuse scenari o
¼
X
P
MCS reuse scenario
x
K
i MCS reuse scenario
¼ ð0
:
2656 x 0
:
5584Þþ ð0
:
3200 x 0
:
3196Þ
þ
ð0
:
2415
x
0
:
1220Þ
¼ 0:2800
The graphical representation of the aggregated results for each MCS end-of-life
management scenario is shown in figure 4.
Results indicate that recycling scenario 1 (shedding the shoe as a whole) is the
most preferable option for a men’s casual shoe, whereas disposal scenario (land
filling) is the least. However, the priority weight given to each decision variable
clearly influences the final results. If a waste management option received the least
weight with respect to most of the criteria, then it will most likely be the least
preferable option, as in the case of disposal. Based on the results, the priority weight
given to environmental criteria (0.5584) as the most important factor in evaluating
the end-of-life management options of a men’s casual shoe, corresponds to the high
composite weight given to MCS reuse and recycling scenarios.
Table 7. Synthesis of priorities.
Decision variables for men’s casual shoe
Environmental
Economic
Technical
Relative weight of
decision variables (Pi)
0.5584
0.3196
0.1220
End
-of-life Relative weight of scenarios
Composite
management
scenarios with respect to variables (Kij) weight (Wj
)
Reuse scenario
0.2656
0.3200
0.2415
0.2800
Recycling scenario 1
0.3367
0.2667
0.3353
0.3141
Recycling scenario 2
0.3351
0.2800
0.2882
0.3118
Incineration scenario
0.0351
0.1333
0.0969
0.0740
Disposal scenario
0.0273
0.0000
0.0381
0.0199
0.3500
0.3000
0.2500
0.2000
0.1500
0.1000
0.0500
0.0000
EoL Scenarios
Figure 4. Aggregated results for MCS end-of-life management scenarios.
6.
Conclusion
The growing number of post-consumer shoes and the wide range of materials and
construction methods used in shoe manufacturing, highlight the need for a
systematic approach to deal with end-of-life shoe waste. However, the viability of
recovery and recycling scenarios has always been subject to a number of factors
including economical, environmental and technical considerations. This paper has
presented a decision-making model to identify the most appropriate end-of-life
management option for a selected range of shoe types. The decision-making model
provides an integrated approach to evaluate a number of related factors which
influence the final decision for a shoe waste management option. Although this
decision-making model has been applied in the footwear industry, it can also be
utilized in other industrial sectors. However, a holistic approach to waste
management requires commitment by various actors within the supply chain,
including material suppliers, manufacturers, retailers and even consumers. The
authors’ future research will focus on a number of specific challenges in establishing
a sustainable shoe recovery and recycling chain which includes consideration on
sustainable reverse logistics, identifying new generation of recycling processes in
footwear industry and, finally, establishing value recovery chains for shoe recycled
materials.
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... Second, hybrid recovery is critical to balance the sustainability. Remanufacturing is possibly more profitable than dismantling (de-manufacturing and component reclaim) or recycling, (Bufardi et al., 2004); (Staikos and Rahimifard, 2007); (Dhouib and Elloumi, 2011); (Remery et al., 2012); ( Dhouib, 2014); (Ziout et al., 2014); (Ilgin et al., 2015); (Dehghanbaghi et (2002); (Kongar and Gupta, 2006); ( Inderfurth and Langella, 2006); (Langella, 2007); ( Kongar and Gupta, 2009); (Jun et al., 2012); (Kang and Hong, 2012); (Ondemir et al., 2012); (Wang and Huang, 2013); (Ondemir and Gupta, 2014); (Ilgin and Taşo� glu, 2016); (Inderfurth et al., 2017) ...
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