master's degree in technology and engineering management
TRANSCRIPT
Titulació:
Master's degree in Technology and Engineering Management
Alumne:
Roberto Filisetti
Enunciat TFG / TFM:
Study: DSS Decision Support System for the Circular Economy
Director/a del TFG / TFM:
Romeral Martínez, Jose Luis
Convocatòria de lliurament del TFG / TFM:
15/01/2020
I declare that,
the work in this Master Thesis is completely my own work,
no part of this Master Thesis is taken from other people’s work without giving
them credit,
all references have been clearly cited,
I understand that an infringement of this declaration leaves me subject to the
foreseen disciplinary actions by The Universitat Politècnica de Catalunya -
BarcelonaTECH.
Roberto Filisetti 15/01/2020
Title of the Thesis: Study: DSS Decision Support for the Circular Economy
1
TABLE OF CONTENTS
1. ACKNOWLEDGEMENTS ................................................................................. 3
2. FIGURES AND TABLES INDEX ...................................................................... 4
3. ABSTRACT .......................................................................................................... 8
4. INTRODUCTION ................................................................................................ 9
4.1 THE LINEAR ECONOMIC MODEL AND THE CAUSES THAT BROUGHT TO THINK
TO AN ALTERNATIVE MODEL ................................................................................................ 9
4.2 THE CIRCULAR ECONOMY: THE CONTEXT WHERE THE IDEA TO ADOPT THE
MODEL AROSE ......................................................................................................................... 13
4.3 RELATED CONCEPTS ABOUT CIRCULAR ECONOMY: THE DIFFERENT SCHOOL
OF THOUGHTS .......................................................................................................................... 16
4.3.1 Cradle-to-cradle (C2C) ............................................................................................................. 16
4.3.2 Blue economy ........................................................................................................................... 17
4.3.3 Regenerative design ................................................................................................................. 18
4.3.4 Closed supply chains (CSCs) ................................................................................................... 18
4.3.5 Natural capitalism .................................................................................................................... 19
4.3.6 Industrial Ecology .................................................................................................................... 19
4.3.7 Performance economy .............................................................................................................. 20
4.3.8 Biomimicry .............................................................................................................................. 22
4.3.9 Reverse logistics ....................................................................................................................... 23
4.4 CIRCULAR ECONOMY: TODAY’S DEFINITION ......................................................... 23
4.5 CIRCULAR ECONOMY PROCESSES ............................................................................. 26
4.6 APPLICATION OF CIRCULAR PROCESSES TO INDUSTRIAL SECTORS AND
EXAMPLES .............................................................................................................................. 29
4.7 APPLICATION OF CIRCULAR ECONOMY IN PRACTICE AND POLICY ................ 32
4.8 BENEFITS OF CIRCULAR ECONOMY AT EUROPEAN AND NATIONAL LEVEL . 33
4.8.1 Economic impacts .................................................................................................................... 33
4.8.2 Environmental impacts ............................................................................................................. 34
4.8.3 Social impacts .......................................................................................................................... 35
4.9 CRITICISM ABOUT CIRCULAR ECONOMY ................................................................ 35
4.10 SWOT ANALYSIS ........................................................................................................... 36
5. TOOLS FOR THE CIRCULAR ECONOMY ................................................. 38
6. THE DECISION SUPPORT SYSTEM (DSS) ................................................. 40
6.1 PRACTICAL EXAMPLES OF DSS APPLIED IN OTHER FIELDS THAN CIRCULAR
ECONOMY ............................................................................................................................... 48
6.1.1 Company level ......................................................................................................................... 49
6.1.2 Bank ......................................................................................................................................... 49
6.1.3 Clinical sector ........................................................................................................................... 52
2
6.1.4 Agricultural production ............................................................................................................ 57
6.1.5 University ................................................................................................................................. 60
7. DSS IN CIRCULAR ECONOMY .................................................................... 64
7.1 EXAMPLE OF DSS SUPPORTING ECO-DESIGN ......................................................... 65
7.2 EXAMPLE OF DSS SUPPORTING INDUSTRIAL SYMBIOSIS ................................... 66
7.3 EXAMPLES OF MODELS AND METHODS WHICH COULD BE USED TO BUILD A
DSS FOR REVERSE SUPPLY CHAIN MANAGEMENT ..................................................... 69
7.3.1 A mathematical programming model to solve reverse distribution problems .......................... 70
7.3.2 A hybrid multi-objective metaheuristic (HMM) algorithm to find the most suitable disassembly
process for an end-of-life product ..................................................................................................... 75
7.4 EXAMPLES OF MODELS AND METHODS WHICH COULD BE IMPLEMENTED ON
A DSS FOR THE SELECTION OF END-OF-LIFE PRODUCT RECOVERY STRATEGIES
................................................................................................................................................... 89
7.4.1 Optimization methods .............................................................................................................. 90
7.4.2 Multi-criteria decision methods ................................................................................................ 93
7.4.3 Empirical methods .................................................................................................................... 97
8. CONCRETE EXAMPLE OF DSS FOR THE CIRCULAR ECONOMY .. 103
8.1 DSS ARCHITECTURE .................................................................................................... 107
8.2 DSS COMPONENTS AND MODULES .......................................................................... 110
8.2.1 Knowledge base ..................................................................................................................... 110
8.2.2 Inference engine ..................................................................................................................... 111
8.2.3 Model base ............................................................................................................................. 114
9. CONCLUSIONS ............................................................................................... 132
10. REFERENCES ............................................................................................... 134
10.1 BIBLIOGRAPHY ........................................................................................................... 134
10.2 WEBPAGES ................................................................................................................... 140
11. ANNEX ............................................................................................................ 142
3
1. ACKNOWLEDGEMENTS
Foremost, I would like to express my sincere gratitude to my supervisor Professor Luis
Romeral Martínez, for his complete availability and constant support in the development
of my master thesis.
I would like to acknowledge the PhD student Konstantinos Kampouropoulos for helping
me in a part of the work by providing insightful clarifications.
I wish to express my deepest gratitude to my family: my dad Renato, my mum Daniela
and my sister Arianna. You are the most important thing in my life, I want to thank you
for everything you did, you are doing, and you will do for me. Without you, none of this
would have been possible. Words don’t exist to express my gratitude for the constant
sacrifices you made and the constant support you provided me not only during my
University career but also, and above all, during my life. Moreover, I take the opportunity
to make a special wish to my sister Arianna who is following my same steps but in a
different field, that is, as a University law student: good luck for everything and exploit
all the opportunities the world of University proposes you, I wish you all the best !
I wish to thank all my friends from Italy: the group of friends from Dalmine, the group of
friends from Bolgare and the group of friends from the University. Despite the distance,
I always felt you close to me. Especially, I wish to express my gratitude to my friends
Davide, Matteo R. and Matteo V., you are special people for me. Thank you for being
always present in my life.
My sincere thanks also go to all the people with whom I share my astonishing experience
in Barcelona: the Italians, the Germans, the Spanish and all the others coming from all
over the world. Thank you for making this experience something unbelievable which I
always bring in my heart. Especially, I want to reserve a particular thank to the group of
friends from my Master course in Bergamo who lived the experience in Barcelona with
me: Jacopo, Marika, Simone and Lorenzo T. Among them, there is also my flatmate
Lorenzo C. that I want to deeply thank for sharing with me most of the time of this
experience.
Last but not the least, I would like to thank all my relatives who, despite the distance, they
always made me feel their affection.
4
2. FIGURES AND TABLES INDEX
Fig. 1: graph showing the relationship between the level of output and the production
costs. 10
Fig. 2: graph showing the global resources extraction from 1970 to 2017. 11
Fig. 3: (a) increase of global middle class of consumers; (b) increase in the expenditure
of global middle class of consumers. 13
Fig. 4: this figure represents the distinction between the technical cycle and the biological
cycle. 24
Tab. 1: table which shows the different sectors where each circular process could be
implemented. 30
Fig. 5: schematic view of a DSS with more specific focus on model-driven and
knowledge-driven DSSs. 45
Fig. 6: this figure shows a scheme which summarizes the functioning of the IDSSBALM
including the pre-working user tasks. Dashed and continuous line arrows represent
the pre-program tasks, while, bold arrows stand for program execution. 52
Fig. 7: figure which shows the steps of the data pre-processing procedure. 55
Fig. 8: scheme which describes how data, after the pre-processing phase, are firstly
manipulated and successively used to create the models. 56
Fig. 9: product life cycle scheme. 64
Fig. 10: scheme of the stages with some of the sub-factors which characterize the
methodology used in the DSS. 65
Fig. 11: schematic representation of the EoL options. 70
Fig. 12: figure showing a possible solution of the mathematical programming model built
to solve the reverse distribution problem. The figures in blue means that the site
is open, the figures in white means that the site is closed. 74
Fig. 13: section of a ballpoint pen. 76
Fig. 14: DAOG of the ballpoint pen. 76
5
Fig. 15: scheme of the iterative process regarding the artificial bee colony (ABC)
algorithm. 84
Fig. 16: example of a Paretian graph. 85
Fig. 17: code of the VND algorithm. 86
Fig. 18: (a) exchange. (b) insertion. (c) 2-opt. 87
Fig. 19: scheme which gives an overview of the HMM algorithm used to solve the
PEDSP. 88
Fig. 20: figure showing the table related to the EoL alternatives evaluation. Each row
corresponds to an EoL alternative. Each column identifies an indicator. 95
Fig. 21: structure used to conduct the cost-benefit analysis in the second step of the second
stage of the intelligent evaluation approach. 100
Fig. 22: figure showing the flowchart of the proposed CBR-based approach. 102
Fig. 23: figure showing the circular economy's concept of closing the loop: the
reintroduction of the product, at its end-of-life, in the different stages of the
cycle according to its current state. 104
Fig. 24: example of repairing strategy to be evaluated by the DSS. Each scenario has a
different sequence of actions to be performed on the product and represents a
sub-strategy. 106
Fig. 25: figure showing the architecture of the DSS. 109
Fig. 26: table "location" containing the list of the facilities which can process the products
to be recovered with the related main information. 111
Fig. 27: table "process" containing the list of processes and the main information
associated to them which can be carried out on the products to be recovered.
111
Fig. 28: table of the information of the product selected by the user for the evaluation of
the recovery strategies. 112
Fig. 29: figure showing the structure array "productCords" which stores the location of
the user defined by the latitude and the longitude. 112
6
Fig. 30: figure reporting the 1x1 structure array "dssConfig" along with the numeric fields
"circularStrategies" and "optimOptions" associated to it. 113
Fig. 31: figure showing an example the structure array "dssActions". 114
Fig. 32: (a) example of the structure array “Distancias” which is one of the input of the
function; (b) example of the structure array “rlResults” which is the output of
the function. 116
Fig. 33: (a) example of “tempCostValues” structure array which is input for the function;
(b) example of “realValuesMatrix” column matrix which is the output of the
function. 117
Fig. 34: (a) example of "tempRealValuesCosts" matrix which is input for the function;
(b) example of "minMatrix" (on the left) and "maxMatrix" (on the right) which
are input for the function. 118
Fig. 35: example of a sequence of actions with the correspondent lists of facilities where
each action can be performed. The red and the green paths are two examples of
possible paths which can be followed to carry out the correspondent sequence of
actions. 119
Fig. 36: (a) example of “detailedTripMatrix” structure which is input for the function; (b)
first part of the code of the "fitness_fcn" function; (c) second part of the code of
the "fitness_fcn" function. 120
Fig. 37: problem state after the first branching. 122
Fig. 38: problem state after the second branching. 122
Fig. 39: problem state after the third branching. 122
Fig. 40: problem state after exploring solutions F and G. The green circle represents the
root node where the process started, the blue circles stands for the solutions
which have been branched because not feasible and the yellow circles are the
feasible solutions. 123
Fig. 41: problem state after exploring solutions F, G and E. 123
Fig. 42: final tree representing the problem. 123
7
Fig. 43: example of "totalCost" row vector. The first cell refers to the repairing case, the
second one to the refurbishing case, the third one to the reuse strategy and the
last one to the disposal option. 124
Fig. 44: hierarchical graph representing the structure "detailedCost". 126
Fig. 45: example of "tempLocationList" structure. 128
Fig. 46: example of "distanceMatrix" structure. 128
Fig. 47: example of a tree generated from "distanceMatrix" shown in figure 36. The red
circles with a white number represent the levels of the tree. 129
Fig. 48: figure showing an example of "costMatrix" structure. Notice that htis structure
is equal to "distanceMatrix" but with one more field called "impacts". 129
Fig. 49: example of “impactCostsMatrix” matrix. 131
Tab. 2: table reporting some today's definitions about Circular Economy. 144
Tab. 3: table which reports the main digital solutions for Circular Economy whose main
aim is to enhance relationships and information sharing. 148
Tab. 4: table which reports the main digital solutions for Circular Economy whose main
aim is to make products, processes and services more circular. 152
Tab. 5: table which reports digital solutions for Circular Economy whose main aim is to
affect and empower consumers and citizens. 154
Tab. 6: table showing the definitions of the most important product recovery strategies.
155
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3. ABSTRACT
Among the events which are characterizing the world and humanity nowadays, there are
two phenomena that stand out particularly: Circular Economy and the technological
revolution. The first one is more recent, not in the theoretical concept but in the practical
realization of the idea. In the last years, the realities which are implementing Circular
Economy’s principles are growing a lot but the philosophy behind this concept has its
root from 1960s. Circular Economy represents the change of mentality which nowadays’
world is living. Specifically, global economy is moving from the traditional economic
model, that is, the linear economic model based on the paradigm “take-make-dispose”
which is causing many negative effects, above all in environmental terms, towards a
circular pattern which, mainly, aims at optimizing waste management (by trying to
completely eliminate waste) and at resource efficiency. As far as the technological
revolution is concerned, it’s strongly and constantly characterizing the world for many
years and is still exponentially increasing. Therefore, by combining these two big
phenomena, specifically, by exploiting the technological revolution to foster the
implementation of Circular Economy’s principles, it’s possible to bring many crucial
innovations that would allow humanity to progress and improve in environmental,
economic and social aspects.
The aim of this study is basically to understand how Circular Economy is already
combined with technology and how this integration can continue and can be improved.
Specifically, this work focuses on a determined technology tool, that is, the Decision
Support System (DSS). Through this research, it is possible to understand how DSS is
used or could be exploited in the Circular Economy. In this regard, the report is organized
in the following way: after a full-bodied introduction about Circular Economy concept, a
rundown of digital tools supporting Circular Economy is provided; successively, after a
detailed description of the Decision Support System, some examples of this tool in other
fields than Circular Economy are reported; the study continues with the presentation of
some examples of application of this tool in Circular Economy and with the explanation
of some methods and models supporting some branches of Circular Economy which
could be implemented in a DSS; finally, a practical example of DSS for Circular Economy
developed in MATLAB software is described.
9
4. INTRODUCTION
4.1 THE LINEAR ECONOMIC MODEL AND THE CAUSES THAT
BROUGHT TO THINK TO AN ALTERNATIVE MODEL
An economy is generally defined as a set of interrelated activities concerning the
production and consumption of goods, which aid in determining how scarce resources are
allocated. The scope of the economy is to satisfy the needs of the actors involved
(suppliers, producers, consumers and all the other stakeholders) in the economic system
and a model has to be adopted to achieve this objective. There are different kinds of
economies (market economy, command-based economy, etc.) but all of them have
adopted the linear model for working. The reasons of this are historical and regard the
initial uneven distribution of wealth by geographic region. Since most of the consumers
of goods were concentrated in the most developed nations (western regions) and
producers could procure raw materials worldwide, industrialized countries lived years of
abundance of energy and material resources. In this scenario, materials were cheaper than
human labour. Therefore, manufacturers were pushed to adopt business models, such as
the linear one, which leveraged on considerable use of materials and saved on human
capital. The companies which got the highest competitive advantage were those which
were able to procure most of the material inputs to support the human labour. In this
context, characterized by large availability of raw materials and expensive human capital,
the focus was on the high exploitation of material resources by obscuring the attention to
the natural environment and to all the sustainability actions linked to it such as recycling,
reusing of end-of-life products and reduction of waste. Furthermore, accounting, fiscal
and regulatory norms didn’t help to limit this business model since they didn’t charge
taxes or fees for externalities, so producers were not fostered to change their plans because
of the costs of external operations. It is from this economic terrain that the linear economic
model arose.
The linear economic model is funded on the following concept: take-make-dispose, which
means that companies, organizations, institutions acquire the resources they need to make
products or provide services to the consumers in order to make profits. Finally, the
customers dispose the products when they are at the end of their lifecycle, that is, are not
able anymore to carry out their functions because are obsolescent. The limits of this model
10
are mainly two: it considers inexhaustible finite resources and doesn’t consider the
environment. Basically, for these two reasons, humanity cannot continue exploiting this
model and needs to adopt another one to survive. This pattern has been successful up to
20th century, especially, it increased the material wealth in the developed countries, but,
starting from the new millennium it has entered a crisis that foresees the definitive
collapse in the near future. The linear economic model has reached its limits and the
natural environment seems to be not able to sustain the current exploitation of resources.
Nowadays numbers support the theory that the linear economic model is going to failure
by causing a huge amount of costs. According to the report of the Sustainable Europe
Research Institute (SERI), 21 billion tons of material exploited in production are not
included in the final product since during the transition phase from input to output are lost
due to efficiency troubles such as storage problems. In 2011, Eurostat’s report indicated
that 2.7 billion tons out of 65 billion tons of raw material were disposed as waste and only
the 40 % of them was reused in other forms through composting, recycling or reusing.
This meant that the remaining 60 % of waste was lost not only as part of the functions of
a product but also as source of energy.
Together with the progressive breakdown of the linear economic model, there are also
some events which are making harder the economic environment for the linear model: in
1999 commodity prices have reached a tipping point and in the meanwhile the costs of
raw materials, after a declining moment, have been subject to a volatile upward period.
Fig. 1: graph showing the relationship between the level of output and the production costs.
11
This is probably due to the increase of the demand which led the production to reach a
point in the costs curve where incremental production costs are high, and to the exhaustion
of the extraction sites with easy access by exposing mining to take technological risks for
bringing to light new sites. As it is possible to notice in Fig. 2, the increase in the demand
has also caused the exponential growth in global resources extraction which is leading to
a raise in the generation of waste and to a faster exhaustion of resources. In particular,
starting from 2002, there was a sudden rise which brought in 2017 the global resources
extraction to increase by about 83%. As regards Fig. 1, it shows the trend of costs in
relation to production volumes: 𝑄1 represents the level of output which minimizes the
average variable costs (AVC), while 𝑄2 is the amount of units produced which allows to
optimize the average total costs (ATC). It is possible to notice that for a certain quantity,
the level of marginal cost (MC) is so high that both AVC and ATC start to increase. This
is what is happening in the process which is conducting the failure of the linear model
where the level of output is increasing due to the raise of the demand and, consequently,
production costs are growing at a level that is counterproductive for the companies.
Circular economy, which allows to extend products life cycle as it will be explained later,
represents a good solution to adapt to this phenomenon.
Fig. 2: graph showing the global resources extraction from 1970 to 2017.
0
1E+10
2E+10
3E+10
4E+10
5E+10
6E+10
7E+10
8E+10
9E+10
1E+11
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
20
10
20
12
20
14
20
16
Ton
ne
s
Year
Global Resources Extraction
Biomass Fossil fuels
Metal ores Non-metallic minerals
12
Furthermore, the increased competition brought to reduce profits, prevent companies to
charge raising prices to customers and reduce the total economic production.
Recent demographic changes represent another aspect which is contributing to reduce the
effect of the linear model. In particular, the middle class of consumers is increasing due
to the shift of the population from developed countries to emerging markets, and due to
the quick economic development of China and India. It is estimated a grow of 3 billion
with corresponding consumption, which is predicted to cost 3 trillion USD per annum in
infrastructural investment. Since businesses are not able to sustain this amount of
investment yearly, economies are unavoidably increasing pressed by the great need to
cover the offer. Fig. 3(a), below, shows well the phenomenon of the rapid growth of the
middle class of consumers. In 2030 the 61% of the world population is estimated to
belong to the middle class and a big contribution is given by China and India which would
represent almost the half of the class. Moreover, as it is possible to observe in Fig. 3(b),
it is supposed that also the expenditure of the global middle class will increase, which
means that people from that class will be more willing to spend money by raising
consumption. Notice that the increase in the expenditure is measured in USD and under
PPP regime, which stands for Purchasing Power Parity and means that the prices at
different locations are determined using a common good or goods so that the real
purchasing power between different currencies is normalized.
(a)
13
(b)
Fig. 3: (a) increase of global middle class of consumers; (b) increase in the expenditure of global middle class of consumers1.
So, summarizing, it is possible to declare that all these phenomena are contributing to the
expansion of the economy and, consequently, of the exploitation of resources which are
destined to finish since they are not inexhaustible. Therefore, it’s necessary to follow a
direction whose goals are mainly the reduction of waste (through recycling, reusing,
refurbishing, remanufacturing, etc.) and the resource efficiency. These are two of the
pillars of the circular economy and this is why the world is going towards this concept.
4.2 THE CIRCULAR ECONOMY: THE CONTEXT WHERE THE IDEA
TO ADOPT THE MODEL AROSE
It is possible to state that the necessity to embrace an alternative model to the linear one
starts to manifest around 1983. In this year, the Prime Minister of Norway Gro Harlem
Brundtland was asked, in the role of Chair of the World Commission on Environment and
Development (WCED), to think about long-term environmental strategies for achieving
sustainability by the year 2000 and beyond. The Commission developed a report which
was published in 1987. During those years, however, most of the governments decided to
adopt neo-liberal economic policies which meant deregulation in banking, globalisation
of capital markets, improvements in IT, offshoring production, etc. The result of this
1 SOURCE: https://www.caixabankresearch.com/en/emergence-middle-class-emerging-country-
phenomenon
14
phenomenon was the opposite of the WCED’s aim, that is, the increase in the
consumption in many sectors such as consumer goods, electronics and clothing.
Moreover, that moment became the longest period of unstopped growth with low inflation
since the Great Crash in the 1920s. In response to this build-up, companies should have
increased production, and this translated into over-use of resources and, consequently,
ineffectual responses to global warming and lack of focus on social justice. Businesses,
then, started to increase the volume of corporate responsibility and sustainability
information in their corporate reports but, no improvements arrived since concrete
changes in the production model didn’t be implemented. With the passage of time it was
evident that no recommendation by Brundtland would be implemented and the idea that
the right time for change was escaping was increasingly becoming apparent. It is in this
context that the idea to adopt a circular economic model started to arise. They realized
that it was necessary to overcome the linear economic model if they wanted to take care
about environment and achieve the goals for the economic survival stated in the WCED’s
report. Nevertheless, the linear economic model is currently the prevailing one because
the transition requires great changes, above all in terms of mentality.
However, before that the idea of adopting the circular economic model arose, the topic
had already been discussed from a conceptual and philosophic perspective, but it
remained something theoretical without real case studies nor practical application.
The conception of Circular Economy concept does not have shared and defined pioneers
and roots. There is still an open debate concerning the founders of the concept. It is
possible to state that the “Circular Economy” concept is the fruit of years of ideas, studies,
thoughts, in the fields of ecological and environmental economics and industrial ecology,
which have contributed to further refine the topic until today’s definition. However, most
of the scholars agree that the circular economic system was primarily proposed by the
environmental economists Pearce and Turner (1989) who relied on the previous studies
conducted by the ecological economist Kenneth Boulding (1966). In his paper The
economics of the coming Spaceship Earth2, Boulding asserts that the maintenance and
sustainability of human life on Earth strictly depends on the implementation of a circular
economic system. Boulding criticizes the basic assumption of the linear economic system
2 Boulding, E. K. (1966). The economics of the coming Spaceship Earth. New York: RFF Press (pp. 3-14).
15
(called “cowboy system”) according to which the resources in the natural environment
would be no limits and every process would be supplied by the needed energy and
material flows. He declares that this model is funded on a wrong understanding of the
physical possibility in the long run. Moreover, Boulding criticizes the “cowboy economy”
because of its both environmental impacts, such as pollution, and social impacts, such as
exploitative and violent behaviours. The ecological economist proposes the idea of the
“spaceman economy” in opposition to the linear economy. This concept is founded on
the thought of the Earth as a closed system where, through a circular relationship between
economy and environment, everything is input into everything else.
Inspired by Boulding’s studies, Pearce and Turner, in their book Economics of natural
resources and the environment (1989)3, describes the shift from the linear economic
model towards the circular one. Their theoretical framework is based on the hypothesis
that there is an extensive interdependence between the economy and the environment.
According to this assumption, then, the environment is characterized by four economic
functions: amenity values (e.g. the beauty of landscapes), provision of resources, sink for
waste and emissions, life-support system.
However, as declared by the Ellen MacArthur Foundation, “the circular economy, as a
generic notion, draws on several more specific approaches that gravitate around a set of
basic principles”4. This means that there is not a common and universal definition of
“Circular Economy” because the concept derives from studies, reflections and researches
of many different schools of thought which have in common some basic ideas. In the
following section, these different philosophies are analysed separately, trying to point out
the main principles and the main communal and differentiating characteristics from
today’s definitions of circular economy.
3 Pearce, D. W., Turner, R. K. (1990). Economics of natural resources and the environment. International
Monetary Fund Joint Library, The Johns Hopkins University Press Baltimore.
4 https://www.ellenmacarthurfoundation.org/circular-economy/concept/schools-of-thought
16
4.3 RELATED CONCEPTS ABOUT CIRCULAR ECONOMY: THE
DIFFERENT SCHOOL OF THOUGHTS
4.3.1 Cradle-to-cradle (C2C)
Cradle-to-cradle’s concept focuses on socially responsible products and improving
production processes, distribution and disposal practices in order to minimize the
environmental damage of products. Sometimes, it is also defined as a closed-loop supply
chain, where a product, at the end of its lifecycle is destined to the recycling process. The
concept, not only includes the production and recycling systems but, gives also
importance to the design stage. There is the double aim of redesigning existing products,
in order to increase the efficiency and minimize the negative effects, and designing
components for circular recovery and reutilization. C2C goes beyond the notion of eco-
efficiency which is only concentrated on minimizing the negative effects of the human
activity on the environment. It embraces rather the idea of eco-effectiveness where the
focus is shifted from the reduction of the quantity for negative impact to the increase of
the quality for positive impact. Furthermore, eco-effectiveness means “working on the
right things instead of making the wrong things less bad”5, that is, addressing directly to
the source of the problem with the objective to rebuild a positive interaction between the
environment and human activity. McDonough and Braungart6 set out three design
principles, inspired by nature, which help to implement the process for shifting from eco-
efficiency to eco-effectiveness.
• Waste equals food: in nature waste doesn’t exist. Starting from this statement,
this principle is based on the idea that materials have to be as nutrients in the
nature’s biological metabolism, that is, they should be designed in such a way
to enable the perpetual flow within two distinct cycles: the biological
metabolism and the technical metabolism. Moreover, C2C distinguishes two
types of product according to their use: products of consumption and products
of service. The former, as the word itself says, are consumed during their life
cycle, that is, they are subject, for example, to physical degradation and
abrasion. At the end of their function, they are destined to return to the natural
5 McDonough, W., Braungart, M. (2002). Cradle to Cradle: remaking the way we make things. North Point
Press (p. 76).
6 See note 5.
17
environment, therefore, they should be designed in such a way that their
environmental impact upon return is minimized. This kind of product belongs
to the biological cycle. Products of service, on the other hand, are made by
humans and should circulate within a closed-loop system of manufacture,
recovery and reuse (the technical metabolism), without contaminating the
biosphere. “Products for service” means that it is sold not the physical product
but its functions, in this way, at the end of the performance, the manufacturer
can take back the materials and put them back in the technical cycle;
• Use of renewable material and energy: differently from eco-efficiency
perspective which aims at reducing the energy consumption of products and
processes, eco-effectiveness focuses on the energy quality by supplying
energy through renewable sources;
• Embracing the ideal of diversity: this means that companies and
communities should live symbiotically in healthy ecosystems in order to foster
local social responsibility.
C2C is a holistic framework which is usually applied at micro-level but can also go
beyond manufacturing and design processes. It can be implemented to architecture and
construction, urban environments, and infrastructure design. Moreover, it includes social
criteria.
C2C is featured by many principles which overlap to CE ideas. Especially, the distinction
between biological and technical cycle and the strong reliance on renewable sources of
energy.
4.3.2 Blue economy
Blue economy philosophy is based on the main concept that the sustainable solutions are
present and should be find in the local environment with its ecological characteristics.
According to the most advocate of Blue Economy Gunter Pauli7, this model comes from
two other economic patterns: Red Economy, the prevailing economic model, and Green
Economy, which is the emerging one. Red Economy is concentrated on one core business
and one niche product. Accordingly, it is characterized by economies of scale which allow
to reduce total production costs and achieve a minimum margin, delocalization to reduce
7 Pauli, G. (2009). The Blue Economy: A Report to the Club of Rome. Paradigm Publications.
18
labour costs and robotization of human labour to produce more and faster at a lower cost.
Pauli criticizes Red Economy for its improper extensive use of resources with a negative
impact on the natural and social environment. Green Economy is the emerging economic
model which is based on the exploitation of innovative green technologies, renewable
energies and biomaterials. Pauli denounces this outlook for its high costs and,
specifically, for its lack of systemic approach. Green Economy, in fact, doesn’t take into
account the global perspective and the potential harmful effects.
The concept of Blue Economy is born from the global analysis of Red Economy and
Green Economy perspectives. Differently from them, Blue Economy leans on innovative
business models with the objective to restore the environment, enhance skills, produce
high quality and cheap products, and, provide many jobs. It aims, therefore, at adopting
a holistic approach taking care even of social issues.
4.3.3 Regenerative design
Regenerative design is related to systems theory and is focus on helping the design phase
of products and services. “Regenerative” because the idea is that materials and energy
used in the design stage could be revitalized and renewed. To be waste-free is one of the
main goals of this concept. Such purpose should be achieved through the conception that
all materials or waste should be reintroduced into the system or should become new
valuable resources at the end of the product’s lifecycle. In terms of research, Regenerative
design has been overcome by C2C which, by covering more aspects than the concept just
described, is able to attract more researchers.
4.3.4 Closed supply chains (CSCs)
CSCs highlight the importance of the concept about circularity. Closed supply chains are
supply chain networks that "include the returns processes and the manufacturer has the
intent of capturing additional value and further integrating all supply chain activities"8.
The two main strategies to close the loop are: the product reuse and the product recycling.
There are some details that are not shared by the concept of CE, despite its basic notion
8 Guide, V. Daniel R., Harrison, T. P., Van Wassenhove, L. N. (2003). The Challenge of Closed-Loop
Supply Chains. Interfaces (vol. 33, pp. 3-6).
19
is very close to the core idea of CSCs. For example, CSCs is more profit-oriented while
the concept of CE is more focus on taking care of the social and natural environment.
4.3.5 Natural capitalism
Natural capital refers to the world’s natural assets such as air, water, soil and other
organisms9. Natural capitalism is based on the following four principles:
• The purpose of natural capitalism is to extend the usable life of resources;
• Production models should be inspired to biological environment; therefore, they
should be based on closed-loop production systems in order to reduce or even
eliminate waste;
• With the aim of offering value to customers and increasing resource productivity
contemporary, a “service-and-flow” business model should be adopted as
alternative to the “sale-of-goods” model;
• The costs saved through appropriate strategies should be invested in natural
capital in order to obtain a higher index of regeneration in natural resources.
4.3.6 Industrial Ecology
The main feature in common between the Industrial Ecology and the Circular Economy
is the systemic approach. The core idea of the Industrial Ecology is to create an “industrial
ecosystem” at a global level in order to implement an integrated and environmentally
sustainable model for industrial activities. The industrial ecosystem focuses on resource
and energy efficiency, minimizing waste and pollution and considering the environmental
impact of every (secondary) product in every manufacturing process. Industrial ecology,
therefore, goes against the concept of competitiveness by fostering rather the cooperation
between different entities in the same system in order to encourage an efficient resource
management. According to Suren Erkman10, one of the most important advocates of this
philosophy, there are four key principles at the basis of the industrial ecosystem:
• Giving value to waste and by-products. The industrial ecosystem should work
following the concept of industrial symbiosis: companies should create a sort of
9 Costanza, R., Daly, H. E. (1992). Natural Capital and Sustainable Development. Conservation Biology
(Vol. 6, No. 1, pp. 37-46).
10 Erkman, S. (2001). Industrial ecology: a new perspective on the future of the industrial system. Swiss
medical weekly (vol. 131, pp. 533-534).
20
eco-industrial networks of resources and waste, where remains of a firm become
inputs of another industrial process;
• Minimizing loss generated by dispersion. Products should be designed in such
a way that they don’t have impact on the environment or at least their harmful
effect on it is mitigated;
• Dematerialization of the economy. The best way to achieve this purpose is to
shift from a product-oriented economy to a service-oriented economy where the
priority is given to the use of the products instead of their selling. In this way, it’s
also possible to minimize the material flows and, consequently, reduce the
environmental impact;
• Using less fossil hydrocarbon as energy.
Circular Economy not only uses IE’s principles to study and optimize industrial systems
at a micro-level, but it adapts also them to an economy-wide system where products and
processes are redesigned in order to maximize the efficiency in the use of resources.
Besides, as Circular Economy, IE focuses on strategies whose aim is not to maximize
profit but to minimize the environmental impact.
4.3.7 Performance economy
Performance economy is an approach developed by Walter Stahel11 who defines the
model as a utilization-focused economy which is concentrated to resource efficiency and
product-life extension. Moreover, Stahel highlights how the environment can benefit
from this approach and how it can create job opportunities. The reuse, repair and
remanufacturing activities favoured in a Performance Economy, in fact, are low-energy
and labour intensive which fosters job creation. Performance economy strongly relies on
circularity since it aims at maximizing the use value of products and minimizing input
and energy used for service. The adoption of Performance economy as main model
requires a change of focus, shifting from throughput flow management and value added,
the one associated to most of the businesses present in the current industrial economy, to
stock optimization and value preservation and maintenance. This change of focus gives
11 Stahel, W., The utilization-focused service economy: Resource efficiency and product-life extension. In
D. J. Richards & B. R. Allenby (Eds.) (1994). The greening of industrial ecosystems (pp. 178–190).
Washington, DC: National Academies Press.
21
the chance to exploit three cycles with different features and different geographical
impacts.
• Reuse loop: it includes those activities related to product remarketing which can
be carried out locally. For example, second-hand markets or commercial and
private reuse of goods;
• Remanufacturing loop: they are part of this loop those activities linked to the
product-life extension such as repair, remanufacturing and upgrading, which can
be performed both locally and regionally;
• Recycling loop: this loop is characterized by product reprocessing activities
needed to recover secondary materials which will be used for the manufacturing
of new products. These activities can be conducted at a regional level as part of a
global supply system.
There are some principles which Performance Economy is founded on:
• Resource sufficiency is better than material efficiency. These are the two
different kind of resource efficiency present in Performance Economy. The first
one characterizes the “Remanufacturing loop” where the main activities are reuse
and service-life extension, while the second one is associated to the “Recycling
loop” and embeds the activities of recycling of materials. By being part of the
“Remanufacturing loop” which is not inserted in a global context as the
“Recycling loop”, resource sufficiency saves money in terms of packaging and
transport costs, therefore, it is preferred to material efficiency;
• Loops don’t have neither a beginning nor an end. In the linear economic
model, the value-added approach prevails. It implies that the responsibility of
goods ends once reached the point of sale, after that, the item starts to depreciate.
In Performance economy, these notions don’t exist anymore, and they are
replaced by the ideas of maintaining value, quality and performance of goods
through stock management;
• The flow speed affects the efficiency of managing stocks. The efficiency
decreases with the increase of the flow speed. For example, a product with a short
life leads to a fast-circular flow (after the first cycle, the 50 % of material is
recycled, after the second one, the 25 %, and so on) and, therefore, to a rapid loss
22
of material stock. In these cases, it’s preferable a reuse strategy than a recycling
one;
• Maintaining the ownership is cost-efficient. Continued ownership allows to
internalize some activities, such as maintenance, so that transaction costs are
saved and, consequently, profit increase. In general, keeping the ownership
throughout the product lifecycle enables to save double transaction costs.
According to this principle, therefore, service-oriented approach (selling the
performance of the product) is preferable than a product-oriented approach
(selling the physical product);
• Performance economy needs functioning markets to work well. As the
industrial economy, to work at best, it needs marketplaces where supply and
demand can meet.
Circular Economy and Performance Economy have in common the adoption of business
models selling performance instead of physical products but, according to Stahel, the
main difference lays in the focus. CE aims at maintaining the stock, whereas Performance
economy is concentrated on maximizing the value obtained from using the stock. In
Circular Economy, businesses selling performance focus on maintaining the stock
without taking really care about the value preservation of their products (through
maintenance, repair, etc.) which can be carried out by external independent companies.
From this perspective, Performance economy goes a step further because, according to
its vision, the ownership is kept throughout all the service-life of products allowing to
internalize the costs of risk and waste and promoting, in this way, waste and loss
prevention.
4.3.8 Biomimicry
Biomimicry is based on the idea of imitating nature, how it works and related patterns, in
order to find proper options that could help to develop sustainable solutions. Nature has
already solved many of human problems so, biomimicry aims at copying some natural
mechanism which have found the solution to that troubles. The main goal of biomimicry
is to develop products and processes which work as natural components in an ecosystem,
that is, they don’t have negative effect on the environment. Therefore, biomimicry relies
on the importance of system thinking and it needs to understand how each product is
23
related to the other elements of the ecosystem. Moreover, biomimicry focuses on using
ecological indicators to evaluate sustainable innovations and technologies. In this regard,
it is possible to view CE as an application of biomimicry at an ecosystem level. As in CE,
also in biomimicry the concept of waste doesn’t exist. Biomimicry, in fact, is inspired by
nature which has no waste since each organism “waste” is food for another organism and
materials go through a cycle without polluting the environment.
4.3.9 Reverse logistics
The main (and maybe the only one) common point between reverse logistics and Circular
Economy is represented by the explanation of the details about how to establish a reverse
supply chain. Reverse logistics is defined as “the process of planning, implementing and
controlling backward flows of raw materials, in process inventory, packaging and
finished goods, from a manufacturing, distribution or use point, to a point of recovery or
point of proper disposal”12. Furthermore, reverse logistics is connected to the reuse of
materials and products, remanufacturing and refurbishing.
4.4 CIRCULAR ECONOMY: TODAY’S DEFINITION
After the description of the different philosophies related to Circular Economy, it’s
possible now to give a definition of the term by including also the different nuances
surrounding the concept nowadays. Because of many years of studies, researches and
debates, today, there isn’t still a formal definition of Circular Economy. However,
scholars agree that Circular Economy is a systemic approach which was born in contrast
to the linear economic model. Moreover, they agree that Circular Economy is founded on
the following three main principles which are characterized by a relationship of
interdependence between each other:
• Try to eliminate waste and pollution;
• Extend products and material’s life;
• Restore natural systems.
Basically, these three principles are connected in this way: by restoring natural systems,
it’s highlighted the need to completely avoid waste and pollution (in nature the concept
12 De Brito, M. P., Dekker, R. (2004). A framework for reverse logistics. In Reverse logistics (pp. 3-27).
Berlin, Germany: Springer Berlin Heidelberg.
24
of waste does not exist because everything is food for something else) and the main
solution to achieve this purpose is represented by keeping products and materials in use.
The vision of the Circular Economy approach is synthetized in the diagram below (Fig.
4) which is inspired by Cradle-to-cradle’s concept. The presence of two cycles means that
there are two kinds of material, biological materials and technical materials, which never
exit the loop by constituting waste and, in this way, the principle of regenerating natural
systems and eliminating waste and pollution becomes real. The principle of keeping
products and material in use is represented by the light blue loop which constitutes the
flow of technical materials. Technical materials cannot re-enter the environment because
they would represent waste, so they continue to cycle in the system in such a way that
their life is extended and their value is captured and recaptured (in this way waste is not
completely avoided but is greatly reduced). On the other hand, biological materials follow
the green loop and can safely re-enter the natural world after running across more times
the use cycle; there, they biodegrade and become nutrients for the natural environment.
Fig. 4: this figure represents the distinction between the technical cycle and the biological cycle13.
13 SOURCE: Ellen MacArthur Foundation, https://www.ellenmacarthurfoundation.org/circular-
economy/concept/infographic.
25
Circular Economy is a systemic approach, which means that is applied at a global level
with the aim of creating a huge ecosystem composed by many different sectors, industries
and companies. The idea is to allow flows of material from a sector to another one, for
instance waste of a company belonging to an industry sector can become raw material of
a firm which is part of a different industrial branch. This concept is taken from Industrial
Ecology’s vision but there are many other characteristics of CE which come from the
philosophies described in the previous chapter. One of the most important features is the
idea of closing the loop inspired to Closed-loop supply chain’s theory. Closing the loop
means avoiding waste by re-entering the material in the system; it means also extending
products’ life through reuse activity. The main difference between CE and CSCs is that
Circular Economy is not profit-oriented as CSCs but is more focus on social aspects and
natural environment. The purpose of taking care about social and environmental aspects
derives from Industrial Ecology philosophy.
Another important aspect of the Circular Economy is the shift from a product-oriented
model towards a service-oriented model where what is sold is not the physical product
but its functions. This idea is shared with the Performance Economy philosophy with the
difference that CE is focus on maintaining the stock while Performance Economy aims
at maximizing the value obtained from using the stock.
Furthermore, the idea to regenerate natural systems comes directly from Biomimicry
which is based on the intention of imitating nature to find possible sustainable solutions.
These are the main pillars on which Circular Economy is founded on. The different
definitions that nowadays researchers give depends upon which principles are more
highlighted. For example, Sauvé et al. (2016), Preston (2012), EEA (2014) and Mitchell
(2015) defines Circular Economy giving much more emphasis on the reduction of
resource consumption, pollution and waste in each step of the life cycle of the product.
Heck (2006) leverages on the importance of using sustainable energy. Ghisellini et al. and
ADEME (2014) explicitly report the objective of the Circular Economy to take care of
social aspects. In the Annex (Tab. 2), there is a table with all the definitions given by the
authors.
26
4.5 CIRCULAR ECONOMY PROCESSES
Now, the main processes, which help businesses to move from a linear economic model
to a circular one, are identified. These processes can have an economic, environmental
and social impact, and can be applied at all levels (micro, meso and macro). In particular,
at business level, they help to understand better how companies can implement Circular
Economy in practice. Eight processes are identified and classified in three categories in
this way14:
1. Use fewer primary resources: recycling, efficient use of resources, utilisation of
renewable energy sources;
2. Maintain the highest value of materials and products: Remanufacturing,
refurbishment and re-use of products and components, product life extension;
3. Change utilisation patterns: product as service, sharing models, shift in
consumption patterns.
These categories are not mutually exclusive and, moreover, a company can adopt multiple
circular processes. Now, we are going to describe briefly each process.
Recycling
Basically, the definition of recycling is connected to “the re-introduction of residual
materials into production processes so that they may be reformulated into new
products”15. This activity has a positive impact from both an environmental and an
economic perspective. It is important to consider also the concept of quality when we deal
with recycling because according to this process the residual materials are reintroduced
into the process and redirected towards the next lifecycle. Therefore, high quality
recycling is an essential condition for materials to be reintroduced into the production
process. Recycling helps also to build networks of companies applying the concept of
industrial symbiosis: the waste of a production process can become the inputs of another
industrial process.
14 The identification and the classification of these processes have been adopted from: Rizos, V., Tuokko,
K., Behrens, A. (2017). The Circular Economy: A review of definitions, processes and impacts, CEPS
Research report 2017/08.
15 United Nations, European Commission, International Monetary Fund, Organisation for Economic
Cooperation and Development, World Bank (2003). Accounting Integrated Environmental and Economic
Accounting 2003. Handbook of National Accounting (p. 79).
27
Efficient use of resources
This process is linked to the concept of cleaner production which is in turn connected to
improvements to both industrial production processes and products. Improvements to
industrial production processes include reduced material inputs, reductions in
consumption of energy and water, raw material conservation, avoidance of toxic
substances in processes and reduction of toxic emissions and waste. As regards the
products, the improvements are related to the reduction of impacts along the whole life
chain.
Utilisation of renewable energy sources
This process is crucial in the transition from the linear model to the circular one.
Specifically, the aim is to substitute the combustion of fossil fuel as source of energy
because is not restorative. Furthermore, among all the renewable energy sources, it’s very
important to consider waste in this role because is undervalued by many people but is
effectively a renewable source of energy.
Remanufacturing, refurbishment and reuse of products and components
These processes have the common goal to recover the products at the end of their function
in order to give them another lifecycle. In particular, refurbishment and remanufacturing
focus on maintaining the value added of materials by restoring the core parts of a product.
Remanufacturing is usually connected to eco-design where remanufacturing conditions
are already considered in the design phase of a product in order to make easier the
disassembly actions at the end of the lifecycle. As regards the benefits, remanufacturing
can reduce costs for manufacturers and helps to reduce environmental impacts since
resources are less used. On the other hand, remanufacturing is not so easy to implement
because it requires a change of mentality by consumers who have to return the product to
the manufacturer. Furthermore, investments into takeback systems and post-use phase of
the product are needed since this process entails that companies have to keep control over
their products and materials. Finally, remanufacturing contributes to job creation since it
is labour-intensive, and it demands knowledge and skills on combining design and
remanufacturing.
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Product life extension
As regards this process, the design stage has a crucial importance and could be translated
into standardisation of components. In this case the design stage is more focus on
designing products with components that can increase the durability rather than planning
items with the purpose of remanufacturing and refurbishing the materials at the end of the
lifecycle. The main benefits of this process are the less use of resources and the reduction
of waste. However, there are people who sustain that this strategy could have negative
effects such as the postponement of the market penetration of new technologically
advanced products.
Product as service
This process refers to the sale of product features instead of the physical item. In
particular, the manufacturer sells the product's performance while maintaining ownership.
This strategy can be implemented by adopting some different options such as pay-per-
use or performance-based business models, leasing, renting and so on. Specifically, A.
Tukker has identified eight categories of product-as-service models16: product related
service, advice and consultancy, product lease, product renting and sharing, product
pooling, activity management, pay per unit use and functional result. The benefits related
to “product as service” process are connected to the need of the companies to increase the
durability of their products so that the costs are cut. In this way, the use of resources and
waste are reduced.
Sharing models
Sharing models are strictly correlated to Circular Economy since they aim at avoiding the
under-use of products and, therefore, are oriented towards the efficient use of resources.
These models, furthermore, foster the creation of genuine social capital and help to build
a sense of community for example through sharing of knowledge or sharing of
technologies and infrastructure.
16 Tukker, A. (2004). Eight types of product-service system: eight ways to sustainability? Experiences from
Suspronet. Business Strategy and the Environment.
29
Shift in consumption patterns
Technological improvements and advanced information are contributing to change the
demand patterns of consumers. In particular, now, customers are more attracted by
products which increase their utility virtually rather than materially (some examples are
represented by digital books, smart phones, music and online stores). This shift could
bring improvements in productivity and resource savings because businesses may be led
to offer their products virtually through virtual channels and to build relationship with
customers virtually using web advertisements, e-mails and social media. In this way, the
materials flow is reduced and, consequently, there is less generation of waste.
4.6 APPLICATION OF CIRCULAR PROCESSES TO INDUSTRIAL
SECTORS AND EXAMPLES
According to Vasileios Rizos et al.17, these circular processes can be applied to the
different industrial sectors in the way shown in the table below.
Category Circular process Examples of sectors where the circular
process can be applied
USE OF LESS
PRIMARY
RESOURCES
Recycling
Automobile industry, Textile industry, Building
sector, Packaging sector, Critical Raw materials,
Forest sector, Chemical industry
Efficient use of
resources
Building sector, Plastics industry, Mining and
metals industry, Food sector
Utilisation of
renewable energy
sources
Chemical industry, Food industry, Forest sector
MAINTAIN THE
HIGHEST VALUE
OF MATERIALS
AND PRODUCTS
Remanufacturing,
refurbishment, and
reuse of products
and components
Automobile industry, Manufacture of computer,
electronic and optical products, Building sector,
Furniture sector, Transport
Product life
extension
Manufacture of computer, electronic and optical
products, Automobile industry, Household
appliances, Building sector, Food industry,
Textile industry, Defence industry
17 See note 14.
30
CHANGE
UTILISATION
PATTERNS
Product as service Household appliances, Transport, Building
sector, Printing industry
Sharing models Automobile industry, Transport,
Accommodation, Clothing
Shift in
consumption
patterns
Food sector, Publishing sector, E-commerce
sector
Tab. 1: table which shows the different sectors where each circular process could be implemented.
Now, those which are, or which could be the impacts and the challenges of the application
of these processes are shown through three examples in three different sectors and using
three different processes.
Recycling of Critical Raw Materials
According to European Commission’s definition, Critical Raw Materials (CRM) are raw
materials with a high economic importance as well as high risk of supply shortage. CRM
are crucial because are exploited by multiple industrial sectors, therefore, the difficulties
which involve them in the recycling process represent an important problem to be solved.
These difficulties are mainly due to some barriers such as absence of recycling standards,
lack of data, limited information exchange between manufacturers and recyclers and low-
quality recycling. However, on one hand, by finding an answer to the recycling issue
would bring multiple benefits, from the reduced dependency on imports from third
countries to the creation of a secondary raw material market which would contribute to
decrease material costs. On the other hand, there could be some challenges due mainly to
the fact that CRMs are spread around a great variety of consumer products, therefore, it
would be needed the establishment of sophisticated take-back systems which would
require high levels of investments. Furthermore, companies would need to establish new
partnerships, logistic chains and cooperation with partners across the value chain in order
to redirect, after recycling, the material to the sector where it can reach the highest
possible level.
Product lifecycle extension and remanufacturing in the building sector
The building sector is considered the industrial branch with the highest potential of costs
saving through the exploitment of circular processes. In this sector, product life extension
basically refers to the prolongation of life of assets, such as buildings. To achieve this
31
objective, it’s important the design stage where buildings are planned in such a way to
last longer. Specifically, in this phase, it is important to anticipate which could be the re-
use and re-purposing parts of the buildings. These sections could be made by using
materials which make easier the disassembling process. The main overall benefit is the
reduction of maintenance costs.
Moreover, companies in the building sector leverage on industrial symbiosis in order to
recover construction material; in this way, they can save costs and have positive
environmental impacts in terms of reduced waste.
As regards remanufacturing, the main requirements to permit the re-use of building
components are the standardization of products and the elimination of dangerous
materials. Even if to reach these aims substantial investments are needed, the overall
result is economically profitable because it would allow to recover the material or to sell
it for another sector. Furthermore, despite remanufacturing implies also other types of
cost such as on-site training of staff, storage space for materials and also an increase in
management time, there are studies which asses that this option is less costly than disposal
of construction waste and recycling the material without the purpose of reuse.
Together with product life extension and remanufacturing processes, businesses in the
building sector exploit sharing models (one of the most known is Airbnb). The main
advantage coming from this kind of strategy is the increase of the utilisation rate of assets.
Using biological resources in the forest sector
The production process in the forest sector is characterized by a large amount of side
streams which are not used at their fullest potential. The application of circular practises
may help to exploit these side streams at their best possibilities. For example, most of
forest side streams are used for energy recovery, but also other options could be taken
into account such as the production of other products. In Domínguez de María opinion18,
using side streams in production of materials can lead to improve employment benefits
and to a higher added value than the production of energy. This is mainly due to the
creation of longer supply chains compared to those that would be present with the
adoption of energy recovery strategies. In this regard, industrial symbiosis and new
partnerships between businesses and the traditional forest industry are required in order
18 Domínguez de María, P. (2016). Industrial Biorenewables: A Practical Viewpoint. Wiley.
32
to sustain the initial investments and the increase in the R&D expenses needed for the
creation of new products. Finally, even if they are not attractive to large companies,
smaller side streams could be exploited by SMEs since they are not focused on achieving
large production volumes.
4.7 APPLICATION OF CIRCULAR ECONOMY IN PRACTICE AND
POLICY
China could be considered as the main and maybe also the first example of adoption of
Circular Economy. For sure, it should be assessed as the most important instance of
application of CE in practice and policy since it performed Circular Economy at all levels
(micro, meso and macro); whereas regarding the fact of being considered as the first to
implement this model there are still some debates.
China decided officially to adopt Circular Economy in its economic model in 2002, when
the 16th National Congress of the Communist Party of China proposed an aspiring plan
called “circular economy”. In general, it aimed at economic growth, social equality and
environmental protection; while, specifically, it referred to the activities of reducing,
reusing and recycling conducted in production, consumption and circulation processes.
This endeavour has been extended at all levels: at the micro level or company level China
encouraged eco-design and cleaner production approaches, at the meso level it pushed for
the creation of eco-industrial park to promote regional development and natural
environment, and at the macro or national level it sponsored a “recycling-oriented
society” through the creation of eco-industrial cities and sustainable production and
consumption.
The responsible in charge to monitor the development of the process was the National
Development and Reform Commission (NDRC). It decided to involve academic and
policy experts with the purpose of drawing up a set of indicators basically related to
reduction, reuse and recycling activities. The result are two separate sets of indicators,
one focused on macro or regional and national level, and one oriented to meso or
industrial park level. Both aimed at measuring resource output, consumption, and
utilization, as well as waste, pollution and emissions.
33
4.8 BENEFITS OF CIRCULAR ECONOMY AT EUROPEAN AND
NATIONAL LEVEL
Now, the positive effects of Circular Economy at EU and national level are reported.
About this topic, some studies have been developed showing the benefits of CE in terms
of economy, environment and sociality.
4.8.1 Economic impacts
According to Cambridge Econometrics and BIO Intelligence Service19, an improvement
by 2 % in EU’s resource productivity could create two million additional jobs in 2030.
Moreover, it assessed that an increase by 2-2.5 % in resource productivity would generate
a small but positive impact on EU GDP.
As regards reuse and recycling processes, they considered that, through their exploitment,
it’s possible to create around 635,000-750,000 additional jobs by 2025 and about
710,000-870,000 by 2030. These results come from the calculation of the number of jobs
created per thousand tonnes of reused or recycled waste.
Ellen MacArthur Foundation and McKinsey Center for Business and Environment20
conducted 150 interviews with experts in the mobility, food systems and built
environment sectors. It concluded that through technological improvements and
organisational innovations, there’s the possibility to increase resource productivity by 3%
by 2030, generating a total annual benefit of 1.8 trillion. Furthermore, the GDP would
raise by 7 %.
The European Commission21 estimated that through a review of waste management
legislation, more specifically policy options on recycling targets and landfill disposal
restrictions, by 2025, there’s the potential to create between 136,000 and 178,000 full-
time jobs.
19 Cambridge Econometrics, BIO Intelligence Service (2014). Study on modelling of the economic and
environmental impacts of raw material consumption. Final Report prepared for the European Commission.
20 Ellen MacArthur Foundation, McKinsey Center for Business and Environment (2015). Growth within a
Circular Economy vision for a competitive Europe. https://tinyurl.com/jec5ykg.
21 European Commission (2015). Commission Staff Working Document: Additional analysis to complement
the impact assessment SWD (2014) 208 supporting the review of EU waste management targets.
34
At country level, Wijkman and Skånberg22 developed an input/output model based on the
following steps towards Circular Economy: enhancing energy efficiency, increasing the
percentage of renewable energy in the energy mix and organising manufacturing along
the lines of a material-efficient and performance-based economy. For each step they
developed distinctive scenarios and then they evaluated their combined effects. The result
of the three following strategies was the creation of 75,000 additional jobs in Finland,
100,000 in Sweden, 200,000 in the Netherlands, 400,000 in Spain and around 500,000 in
France.
4.8.2 Environmental impacts
According to studies conducted by Cambridge Econometrics and BIO Intelligence
Service23, an improvement by 3 % in EU’s resource productivity could reduce GHG
emission by 25 % by 2030.
Ökopol24 developed models able to estimate GHG emissions and impacts on climate
protection depending on different recycling rates. One scenario reveals that it’s possible
to improve the reduction of CO2eq from 247 to 330 million tonnes.
EEB modelling25 shows the potentialities deriving from applying circularity for water-
use savings. It states that the amount of water saved could pass from 26.1 Ml to 52.2 Ml
by 2025 and 34.8 Ml to 60.9 Ml by 2030.
According to the study above mentioned and conducted by EMF26, Circular Economy
could reduce the consumption of raw materials up to 32 % within 2030 and 53 % within
2050 in land use, agricultural water use and fertilizer use, primary-material consumption.
22 Wijkman, A., Skånberg, K. (2015). The Circular Economy and Benefits for Society: Jobs and Climate
Clear Winners in an Economy Based on Renewable Energy and Resource Efficiency. Study requested by
the Club of Rome with support from the MAVA Foundation.
23 See note 19.
24 Ökopol - Institute for Environmental Strategies (2008). Climate protection potential of EU recycling
targets. https://tinyurl.com/jn2qsot.
25 European Environmental Bureau (2014). Advancing Resource Efficiency in Europe: Indicators and waste
policy scenarios to deliver a resource efficient and sustainable Europe.
26 See note 20.
35
At national level, the work of Wijkman and Skånberg27, already cited before, shows the
possibility to reduce CO2 emissions by 66% in Sweden, by 68% in Finland, by 67% in
the Netherlands, by 66% in France and by 69% in Spain.
4.8.3 Social impacts
Since the capacity of Circular Economy of creating additional jobs has been already
analysed in the previous section (“Economic impacts”), this paragraph will be more focus
on other social aspects such as gender, skills, occupational and welfare effects, poverty
and inequalities.
In this regard, Morgan and Mitchell28 estimates that in UK some job losses with medium
level qualifications caused by industrial changes could be compensated by CE. Or, the
study provided by Cambridge Econometrics and BIO Intelligence Service29 shows that
by improving the EU’s resource productivity by 2%, the distributional impacts across
different income groups would be almost even.
4.9 CRITICISM ABOUT CIRCULAR ECONOMY
CE has reached a broad appeal, but its interpretation and application have been very
diverse. In particular, there are some divergent perspectives regarding the impact of the
circular economy transition. It has been explained before, in fact, that there are different
current definitions about Circular Economy, with different focus, different measures,
different nuances. This is probably due to the fact that CE is still in its early phase, so
quantitative models are based on simplifications and assumptions.
Another challenge is represented by the historic difficulty of human being in facing
change. In this perspective, the shift from linear economic model, which exists since
decades, to the circular one results harder.
Another topic at the centre of debates is the social dimension often missed in the
definitions of Circular Economy. Social dimension means gender, equality of social
opportunities, inter- and intra-generational equity and racial and financial equality.
27 See note 22.
28 Morgan, J., Mitchell, P. Employment and the circular economy Job creation in a more resource efficient
Britain. Green Alliance, WRAP.
29 See note 19.
36
Finally, some people criticize the net environmental impact of some circular processes
sustaining that sometimes there are some aspects which are undervalued. For instance,
the breakdown and recycling or reusing of products designed for a long life could need
more energy than products configured for a shorter lifetime. This could be due to the fact
that these products are manufactured with technical materials and are more difficult to
recycle.
4.10 SWOT ANALYSIS
After analysing the benefits and some challenges regarding the concept of Circular
Economy, we can develop a SWOT analysis to have a final overview of the topic.
Strengths
• Relevant know-how in the reverse material flow cycle leads to get competitive
advantage;
• The inclusion of Circular Economy features in the R&D phase gives the chance
to improve materials science and to develop components of higher quality and
longer life;
• Since externalities are dependent on the use of material, the reduce consumption
of material decreases, consequently, the externalities;
• The elimination of waste from the value chain allows to reduce systemic and direct
costs of materials and the dependency on resources;
• Businesses are less exposed to price fluctuations of materials and the cost curve
become almost flat translating into more efficient use of resources in terms of both
value and volume. These facts are due to the adoption of closed-loop processes.
Weaknesses
• Circular Economy is still in its early stages so there’s a sort of inefficiency in
spreading the concept and a lack of effective marketing campaign to access to
sectoral people;
• For the same reason explained in the previous point, in some sectors, investments
to introduce the system are not enough;
• There are no specific guidelines to sectors as regards how to practically implement
Circular Economy; moreover, there is still no internationally recognized standards
37
institution to regulate the sector and no special legal regulation about circular
economy and its application30;
• Circular economy still requires combination of the entire product life cycle from
raw material procurement to disposal31.
Opportunities
• The set-up of the Circular Economy in the design phase enables to have access to
better and cheaper materials;
• The development of knowledge in circular options in legal, mechanical,
operational, sectoral or cross sectoral challenges allows enablers to find business
opportunities;
• Through Circular Economy it’s possible to save billions of dollars because it
enables to considerably reduce the level of needed material input.
Threats
• The management of the entire product life cycle and strong cooperation could
foster the birth of cartel structures;
• By managing whole product life cycle, businesses could easily finance different
activities, and this could cause elevated prices and incapable products;
• A financing interruption can lead to troublesome outcomes to the interdependent
sector due to its complexity and interconnections32;
• In case producers could control their own product waste, it could be harder to
obtain benefits from waste management for those which are in scale economy.
30 Circular Academy (2017). Circular economy: critics and challenges – How can we bridge the circularity
gap?. http://www.circular.academy/circular-economy-critics-and-challenges/.
31 Van Ewjik, S. (2014). Three Challenges to the Circular Economy. UCL Institute for Sustainable
Resources.
32 See note 31.
38
5. TOOLS FOR THE CIRCULAR ECONOMY
Europe is living an era characterized by two big phenomena: the transition towards a
Circular Economy and the digital revolution. The ability of human being in carrying on
both in the same time could contribute to start a new period of economic prosperity,
together with environmental and social benefits, which could last for years. The reasons
lie in the fact that the implementation of CE could allow to solve some big problems of
nowadays humanity, mainly, the exhaustion of resources and waste management; and,
the exponential growth of digital technologies could be exploited to improve the
potentialities of Circular Economy. Data, digitally enabled solutions, IoT (Internet of
Things), software, platforms, AI (Artificial Intelligence) and all the other tools can
increase connectivity and the sharing of information; bring circularity in processes,
products and services; make citizens and consumers aware so that they could take
sustainable decisions. Digital technologies can improve many circular processes such as
the use of natural resources, design, production, consumption, reuse, repair,
remanufacturing, recycling and the overall waste management. However, the integration
of digital solutions within Circular Economy does not automatically lead to sustainability;
in fact, it’s very important to guide this process well, otherwise the risk of rebound effects
such as an overdrive of a linear “take-make-dispose” economy, and increase in
greenhouse gas emissions raises.
Digital technology solutions for Circular Economy can be grouped in three different
categories according to the objectives for which they have been created:
• Enhance relationships and information sharing. From the digital revolution,
more and more digital data are created and should be turned into valuable
information. To do that, digital data should be collected and managed, that is, they
should be mined, systematised, processed and shared;
• Make products, processes and services more circular. A transition towards a
CE requires making products, processes and services more circular, which means
that it’s necessary to change the way some operations such as design, use, reuse,
produce and recycle are implemented. In this regard, it’s important to change
business models, and digitalization, in these terms, plays a fundamental role;
39
• Affect and empower consumers and citizens. In the transition towards a circular
economy, consumers and citizens play an active role since their ways of living,
consuming, reusing and recycling materials and products, influence this process.
Moreover, when they are asking for sustainable, convenient, safe and reliable
solutions, not only they are influencing the market, but they are also contributing
to the transition towards a CE. For this reason, it’s important to increase their
awareness about the importance of this kind of economy. With this scope, there
are already some tools which leverage on some aspects, such as the sharing
information about the products’ environmental footprint; others try to encourage
citizens to use and collect data by becoming co-creators of knowledge that other
consumers, decision-makers, businesses, and investors can use.
In the Annex (Tab. 3, Tab. 4, Tab. 5), for each category is reported a table listing some
already existing or emerging digital technology solutions.
Another tool which could help Circular Economy deployment is the DSS (Decision
Support System).
The DSS could be very useful for circular economy because there are already tools in this
field which can be integrated in a decision support system by providing further benefits.
These tools are mainly databases which DSSs use to draw information, and algorithms
which transform information into valuable outcomes that help users in their decision-
making process.
40
6. THE DECISION SUPPORT SYSTEM (DSS)
A DSS (Decision Support System) is a computerized program which supports users in
the decision-making process. Decision-making is the subjective process of choosing one
action-plan among various possibilities. It is said to be a mental build since the choosing
process is not visible. Just the consequences of the chosen option can be seen. DSS is
helpful for solving unstructured problems, that is, problems new or unusual characterized
by a high level of uncertainty due to the presence of ambiguous or incomplete
information. Basically, a DSS collects a huge amount of information, synthetizes,
analyses and examines it, and, finally, it produces a comprehensive information report
which, combined with human judgement, allows to solve problems. This is the main
difference between a DSS and ordinary operations applications whose purpose is just to
collect information without processing it.
A DSS can be either completely computerized or built on human power. Sometimes, it
can be the combination of both alternatives.
The main characteristic of a DSS is represented by its ability to handle large amounts of
data and to present results to the user in an easy-to-understand way. From this perspective
the DSS is very flexible because is able to show reports and presentation in different
formats, both graphical and textual such as charts, trend lines, tables, and so on.
Moreover, it’s possible to talk about flexibility at operational level since a DSS allows to
solve many kinds of problems involving decision making, from the simplest ones to the
most complex ones thanks to its ability to support advanced software packages. Finally,
a DSS is flexible also in terms of usability since it’s essentially an application and can be
implemented on most computer systems. Some DSS are even available on mobile
devices. Other important features are the presence of an interactive environment, the
responsiveness, that is, the ability to respond quickly to the changing of needs of the
decision maker, the use of effective models in data analysis.
There are many types of DSS that differ in objectives, types of inputs, types of outputs
and the type of modules that make them up. However, all of them have in common the
following elements:
41
• DSS Database: this unit collects information needed to the user and integrate
them with other data coming from outside for instance Internet or other different
applications. This means that the structure of the DSS depends on the problem to
be solved and on the user, who is interacting with the DSS. DSS Database is
managed by DBMS (DataBase Management System), a software which enables
to schematically organize data, modify and manage them in order to allow an easy
extraction of data from the databases. DSS Database could be a small database or
a standalone system or a huge data warehouse. Generally, in case of a
manufacturing company for example, this component includes a copy of the
production database so that there are no interferences with the functioning of
operational systems;
• DSS Software System: this unit is managed by MBMS (Model Based
Management System). It is a software similar to DBMS but instead of managing
databases, it controls procedures. Specifically, it is focused on memorizing and
modifying the use of models. In this section, therefore, there are all the algorithms
and mathematical and analytical models used to process information that are
transformed into actionable knowledge. These patterns elaborate data and produce
different outputs according to information in inputs and to external conditions.
MBMS includes a lot of models, each one with a specific function. It’s a task of
the user selecting the proper models according to his requirements. Moreover, this
section of the DSS already comprises predefined patterns which can be exploited
by the user to build other models more specific for some types of decisions. The
most common used mathematical models are the following: statistical models,
sensitivity analysis models, optimization analysis models, forecasting models and
backward analysis sensitivity models. Statistical models are used to find out
correlations between the occurrences of events and the factors affecting that
events. For instance, it is possible to establish relationships between product sales
and income, season or other factors. Together with statistical models, there are
software able to analyse series of data and to predict future outcomes according
to those series. Sensitivity analysis models are needed to study how some
organizational variables are affected by the occurrence of an event. The procedure
consists of changing repeatedly a variable and observing the correspondent
42
behaviour of other variables. Sensitivity models, for example, can be applied to
analyse the sensitivity of product sales: this parameter is influenced by many
factors such as price, expenses on ads, production and so on; by changing the
price, together with some other factors, the number of products sales change as
well (it increases or decreases) and the change in the amount of product sales in
proportion to the modification of the amount of the price represents the sensitivity.
Optimization analysis models allows to find out optimum values for target
variables under some circumstances. They are commonly used to find out the
optimum values for resources utilization. These models exploit linear
programming techniques: some variables are changed recurrently, by respecting
given constraints, until an optimum value for a target variable is found. For
instance, these patterns can be used to discover the combination of job
assignments which allows to perform the highest possible amount of production
under the constraint that some workers are skilled and cannot change their job
assignment. Forecasting models exploit tools such as time series analysis, market
research methods, regression models and so on, to make reports about the future
or to predict some data, information or events in advance. For example, these
patterns are usually used to forecast product sales. Backward analysis sensitivity
models (known also as goal seeking analysis), as suggested by the name, use
techniques which are the opposite of those ones exploited in classical sensitivity
analysis. From a point of view, they are similar to optimization models, but the
main difference is that, here, the scope is to find out the combination of values of
some variables which gives, not the optimum, but a target value for a specific
variable. For instance, by knowing that the goal of the company is to increase the
production level by 40 %, it’s possible to set a target value for the production and
then, by performing backward sensitivity analysis, to find the values of the
variables affecting the production which allow to achieve that target level.
• DSS User Interface: this unit is managed by DGMS (Dialogue Generation and
Management System), the software responsible for the creation of the user
interface which should be developed in a way that facilitates the interaction
between the DSS and its users. This section is fundamental because users give
much importance to the communication capability of the system. In this unit, the
43
results of the various analysis are shown and can be viewed in different ways,
such as table, text, graphics, charts, according to the needs and requirements of
the user.
In DSS literature, there are various ways to classify this device. Firstly, according to the
frequency of the decision making, it’s necessary to distinguish among institutional DSS
and ad hoc DSS. Institutional DSSs are used to support users in decisions which have a
recurring nature, while ad hoc DSSs usually enable to solve specific problems
characterized by one-of-a-kind decisions.
By establishing the relationship with the user as criterion of the taxonomy,
Haettenschwiler33 distinguishes between passive, active and cooperative DSS. Passive
DSS means that the tool helps the user without giving to him an explicit solution to the
problem. This is done, instead, by an active DSS. Finally, cooperative DSS consists of an
iterative process between the user and the device which leads towards a consolidated
solution. Basically, the DSS provides a solution to the user who can refine, modify and
improve it. Afterwards, the user sends back the answer to the device for validation which
in turn refines, modifies and improves it and sends back again the solution to the user.
This process is repeated until a satisfying solution for the user is found.
Daniel Power34, instead, classifies DSSs according to the mode of assistance and he
differentiates between communication-driven DSS, data-driven DSS, document-driven
DSS, knowledge-driven DSS and model-driven DSS. Communication-driven DSS
emphasizes collaboration, communications and shared decision-making support.
Through this kind of device two or more people can communicate with each other, share
information and co-ordinate their activities. In this way, decision making is faster and
more efficient. It is based on network technologies. The main platforms where
communication-driven DSS can be implemented are client/server systems and web
(nowadays, all kind of application can be executed on the web). Chats software, document
33 Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung.
Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich: vdf Hochschulverlag AG.
34 Power, Daniel J. (2002). Decision Support Systems: Concepts and Resources for Managers. Faculty
Book Gallery, 67. https://scholarworks.uni.edu/facbook/67.
44
sharing, online collaboration, net-meeting systems are some examples of communication-
driven DSS.
Data-driven DSS (DD-DSS) enables to access to and manipulate time series of internal
company data and, sometimes, external data, to provide, after their analysis, new
information. It is mainly addressed to managers, staff and product/service suppliers who
can query a database or a data warehouse to search for specific answers to specific aims.
Its functioning is based on data warehouse and online analytical processing (OLAP). The
platforms where data-driven DSS can be implemented are mainframe system,
client/server systems and web. Examples of this kind of DSS are represented, in general,
by all types of computer-based databases such as Executive Information Systems (EIS)
and Geographic Information Systems (GIS).
Document-driven DSS includes a variety of storage and processing technologies with the
purpose to retrieve and analyse documents. These documents are unstructured
information collected in multiple electronic formats. The purpose of this DSS is typically
to find documents or web pages according to a specific set of keywords or search terms.
It is deployed via client/server systems or the web. Search engines are a typical example
of document-driven DSS.
Knowledge-driven DSS (KD-DSS) provides problem-solving expertise in form of rules,
facts and procedures which can help the user to find the solution of his problem.
Generally, this DSS is used with the goal of providing management advice or choosing
products/services. The usual technology used to set up such DSS are stand-alone PCs,
client/server systems and the web. To build up a knowledge-driven DSS many methods
can be exploited, specifically, intelligent decision support methods, data mining, artificial
intelligence methods, knowledge discovery methods and heuristic methods. This kind of
DSS is featured by many fields of application; among the others, it’s correct to report
medical diagnosis, equipment repair, investment analysis, financial planning, vehicle
routing, production control and training.
Finally, model-driven DSS (MD-DSS) facilitates the access and the manipulation of
financial, simulation, optimization and statistical models in order to assist in decision-
making. It is generally used by members of a business or people who interact with an
organization with many aims (from scheduling to decision analyses and so on) depending
45
on how the model is set up. Model-driven DSS can be developed through stand-alone
PCs, client/server systems and the web.
Another kind of taxonomy is that one made by Daniel J. Power which uses the scope as
criterion35. According to Power, DSSs can be distinguished between enterprise-wide DSS
and desktop-wide DSS. The first works with large data warehouses and supports many
managers of a company; the second one is smaller and runs on an individual manager’s
PC.
In general, the most common used types of DSS are model-driven DSS and knowledge-
driven DSS. In the following section, they are defined. Before showing the main
differences existing between each other, it’s better to have a look to their structure and
components.
Fig. 5: schematic view of a DSS with more specific focus on model-driven and knowledge-driven DSSs36.
In the figure above (Fig. 5), it’s possible to notice that there are five elements:
35 Power, D. J. (2006). Free Decision Support Systems Glossary. DSSResources.COM, World Wide Web.
http://DSSResources.COM/glossary/.
36 Reworking of a figure extracted from the following scientific paper: Nižetić, I., Fertalj, K., Milašinović
B. (2016). An Overview of Decision Support System Concepts. Zagreb: Faculty of Electrical Engineering
and Computing.
46
• User: the person who uses the DSS;
• User Interface (UI): it’s the part of the DSS which allows the user to communicate
with the system; it is composed by two parts, one responsible for the entrance of
the data in the system as a text or as other options which can be chosen by the
user, another one which returns to the user the results;
• Model Base: this unit receives a set of analytical and optimization tools needed to
perform the decision-making process;
• Inference Engine: it’s the part of the DSS which develops conclusions according
to the calculations carried out in the model base and sends them to the UI which
will show to the user the results;
• Knowledge base: it’s the component which stores information, data of different
nature and rules in a database.
These elements of a DSS exchange with each other data and information which can be
distinguished in the following way:
• Requests: they are the input data sent by the user to the system through the user
interface which can adapt them; these data are successively sent to the inference
engine which processes them;
• Results: they are the solutions of the user input data which flow from the inference
engine to the user interface which has the task to show them to the user;
• Data: they refer to the data stored in the database present in the knowledge base
unit, and to all the requests for new data or information generated by the inference
engine.
The main feature of the model-driven DSSs which distinguishes them from knowledge-
driven DSSs is the role of the knowledge base which has not to be necessarily present in
model-driven DSSs. This because data either are received by the user through the user
interface either are previously collected. Generally, the quantity of data processed by
model-driven DSSs is small. As regards knowledge-driven DSSs, instead, the knowledge
base plays a central role. Not only it stores large amount of information, but it is useful
also to highlight important relationships between the data. In case a problem requests for
both a mathematical model and a large database, there’s also the option to use the hybrid
solution between knowledge-driven and model-driven DSS.
47
There are different methodologies which can be suitable for the development of a DSS.
Basically, they differ in paradigm, models and objectives. A paradigm refers to a specific
way of thinking about problems, while a model is related to the subjective way of
representing reality. In general, however, all the methodologies have in common four
stages for the development of a DSS:
• Intelligent phase: it is the phase where internal and external data and information
are collected in to order to well identify the problem to be solved;
• Design phase: this stage consists of building the models and generating the
possible solutions;
• Choice phase: in this phase optimal solution are identified and chosen, and testing
is performed;
• Implementation phase: this is the final stage where the DSS is built by
implementing the solution chosen.
DSSs present many advantages. First at all, they allow users to find solutions that only
through operative research would not be possible. Moreover, DSSs help users to choose
the best option faster than other strategies. DSSs improve efficiency in operations,
planning and even management. For this reason, such tool is primarily exploited by mid-
to-upper-level management. Furthermore, let’s consider a company, DSSs enable to
improve communication within different groups since they collect data and information
from many different departments. Finally, these devices, thanks to manipulate and
integrate many models of different nature, allow to observe the problem from different
perspectives and, last but not least, they permit to automatize many business processes.
As every existing tool, DSSs present also some drawbacks. There is the risk to base too
much the decisions on the machine, by reducing, in this way, skills in staff. Moreover,
relying too much on computers contributes to reduce the sense of responsibility in
managers whit the risk to bring them to blame the machine in case of mistake. Another
possible consequence of giving less responsibility to workers is that they can become
demotivated because they risk seeing in decrease their contribution. Finally, if, from one
perspective, managing huge amounts of data allows to improve efficiency by enabling to
get solutions faster, from another point of view, the management of large quantities of
information creates the risk of overload which affects negatively efficiency.
48
Now, some application examples of DSS are shown. This tool is used in several domains.
At company level, it is used to foster decision making, to identify negative trends and to
better allocate business resources. An example is the planning of company’s revenues
based on some assumptions made about product sales. Since in this case many variables
are involved, the calculation would be difficult, so the DSS makes this process easier to
the managers. It collects past data about past product sales and by correlating them with
current variables, it produces a valuable outcome for the user. Another example could be
an engineering firm which has some bids on several projects and wants to know if it can
be competitive with its costs.
When DSS is applied in clinical field, it’s called CDSS, that stands for Clinical Decision
Support System. This tool is exploited to support clinical decision-making tasks, such as
medical diagnosis. Robert Hayward of the Centre for Health Evidence defines CDSS as
a device which “links health observations with health knowledge to influence health
choices by clinicians for improved health care".
DSS is even acquiring importance in agricultural production. The main example is
represented by DSSAT (Decision Support System for Agrotechnology Transfer), a set of
computer programs which allow to simulate agricultural growth crop. This tool has
already enabled to asses many agricultural production systems around the world by
facilitating decision-making both at farm and policy level.
Another domain where DSS is used is forest management which needs specific
requirements due to the properties of this field characterized by long-term planning and
particular spatial dimensions of planning problems.
DSSs are used also in universities in the management of students and places. For example,
they can forecast the number of students who will enrol to a specific course.
Finally, other fields where DSS has been applied are the integration of weather conditions
with air traffic management, financial planning for small size companies and planning of
goods transportation networks.
6.1 PRACTICAL EXAMPLES OF DSS APPLIED IN OTHER FIELDS
THAN CIRCULAR ECONOMY
Next step is to see more in details some applications of DSS in some of these fields.
49
6.1.1 Company level
The DSS is going to be shown is a supply chain decision support system based on a novel
hierarchical forecasting approach37. The scope of this tool is to overcome traditional time
series forecasting models in the management of supply chain. The new pattern, included
in the DSS object of study, takes into account an aspect overlooked by traditional models,
that is, the complex hierarchies with different levels of aggregation which characterize
the sales of many products within an organization. In this way the optimization of the
supply chain increases. Previous models provide solutions on hierarchical forecasting
problems based on forecasts produced by independent models for each time series
involved in the hierarchy. The step forward made by the new model consists of providing
a solution which is optimal not only in terms of hierarchy but also in terms of time. This
latter is missing in the previous approach which offers optimal reconciled forecasts but
the link along time that is implied by the dynamics of the models is completely ignored.
The approach adopted by the DSS is based on SS (StateSpace) modelling and Kalman
Filter algorithm which give the advantage to offer a clean and elegant solution to the
problem. This approach allows also to unify the treatment of other forecasting models
reported in literature, such as top-down, bottom-up, middle-out and reconciled.
Moreover, this fact of converting the hierarchical forecasting problem into a standard SS
system permits to make the assumptions which the approach relies on easier. Finally, the
DSS has been tested using real data of a Spanish grocery retail and the results have proved
that the new approach provided significantly better results than existing approaches.
6.1.2 Bank
Next example is represented by a DSS for bank asset liability management
(IDSSBALM)38. Since banking world is characterized by a lot of uncertain situations, it’s
not enough to say that a solution is the best but, the most correct explanation is to say that
this is the best solution given the predicted future scenario, the chosen parameters and the
set and importance of the chosen objectives. Mainly for this reason, it consists of an
interactive DSS which is very responsive and can easily react on all required problem
37 Villegas, M. A., Pedregal, D. J. (2018). Supply chain decision support systems based on a novel
hierarchical forecasting approach. Decision Support Systems (vol. 114, pp. 29-36).
38 Langen, D. (1989). An (Interactive) Decision Support System for Bank Asset Liability Management,
Decision Support Systems (vol. 5, pp. 389-401).
50
changes. The object of the study is a German universal bank operating nationally and
internationally which is focus on many conflicting targets and has to deal with legal
(principles I-III of the German Banking Law ("KWG"), reserve requirements, etc.) and
bank policy or market constraints (e.g. financial, accounting and management constraints,
lower and upper growth limits through market forecasts). The bank aims at achieving five
major objectives:
• Maximization of bank’s gains and returns, where gain refers to return to the
internet business;
• Maximization of bank’s balance or business volume, which is important for the
bank’s standing since it is, among other multiple reasons, an indicator for
management performance, ability to place corporate bonds, market power and
customer popularity;
• Minimization of credit losses: even if it could be included, this objective is
distinguished from maximization of bank’s gain objective because in addition to
national credit risks, the bank has to face international and country credit risks and
has to regularly take safeguards against faulty credits. Faulty credits that need to
be kept necessarily low in order to avoid uncomfortable side-effects, such as
higher refinancing interest rates, lower credit and stock ratings, drop of the bank’s
stock price and customer money withdrawals;
• Minimization of interest rate risk;
• Minimization of currency or exchange rate risk.
Since only quantitative models are used, qualitative objectives (e.g. the customer service
or enlarging the number of branches) has not been taken into account.
The pattern used is a Multi-Criteria-Decision-Making (MCDM) model where uncertain
parameters are considered by scenarios given by the user and using risk evaluation
through risk measures. For this reason, the DSS can be defined as an interactive decision
support system, because the decision-maker (DM) can choice among a selection of
alternatives based on his preferences and can modify the problem specifications.
IDSSBALM can be defined as a traditional DSS which helps users to take and implement
strategic decisions through the exploitment of databases and models, mainly
mathematical. Before using the DSS, the DM should perform the following tasks:
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• Collect the necessary data;
• Take decisions regarding the model components, that is, objectives, constraints
and so on;
• Store raw problem data (general and specific problem specifications) in proper
datafiles (SPECS.DAT and MODEL.DAT).
After that, the DSS is normally used in this way: the user inserts data into STATISTICS
and makes his decisions about objectives and constraints. Then, he should write a
FORTRAN program MODEL and identify the two input files SPECS.DAT and
MODEL.DAT. The user can communicate with the system through several kinds of
commands:
• SPECS and MODEL which enables to change problem parameters or sets;
• PRINT which allows to specify output media and the desire for intermediate
results;
• OBJECT which is used to change reference points or scaling factors;
• RESULT which displays different types of results;
• START which allows to manipulate starting points;
• OTHER which enables to get help and show parameters;
• RUN which is used to compute solution or utopia points.
Finally, after processing internal data, the user should choose the model and specify the
problem structure. In Fig. 6, it’s possible to understand better the functioning of the
IDSSBALM with its pre-working user tasks.
52
Fig. 6: this figure shows a scheme which summarizes the functioning of the IDSSBALM including the pre-working user tasks. Dashed and continuous line arrows represent the pre-program tasks, while, bold arrows stand for program
execution39.
It is possible to conclude that IDSSBALM is a valuable decision support system which
allows the user to choose his own approach, his opinion about the major objectives and
his attitude towards risk. Considering the uncertainty of the banking environment where
the device is applied, this freedom of acting left to the user enables him to get the best
solution according to his requirements and preferences.
6.1.3 Clinical sector
Another field where DSS finds common application is the clinical sector. In the following
example is reported the development of a clinical decision support system (CDSS) which
is based on data analytics approach for diabetic retinopathy diagnosis40.
39 Reworking of a figure extracted from the following scientific paper: see note 38.
40 Piri, S., Delen, D., Liu, T., Zolbanin, H. M. (2017). A data analytics approach to building a clinical
decision support system for diabetic retinopathy: Developing and deploying a model ensemble. Decision
Support Systems (vol. 101, pp. 12-27).
53
Through this CDSS they want to improve the accuracy of the annual diabetic retinopathy
evaluation which presents one of the lowest rates of compliance patients for multiple
reasons. First at all, this disease is asymptomatic at the early stages; then, there is low
availability or even completely absence of ophthalmologists in many areas, above all in
rural communities; finally, many patients considers the necessary cure, that is the eye
dilatation, so unpleasant that they don’t want to undergo that treatment.
This CDSS show a lot of advantages compared to existing diagnostic tools. First, instead
of using images processing on images of retina which is an expensive procedure, it
requires only the results of a simple blood test and demographic data to provide the
accurate prediction of the diabetic retinopathy risk. Moreover, in this way, the need to
have access to specialists, which is critical for people living in remote areas, is eliminated.
Second, this CDSS is based on large databases which include data covering many years
and many regions and, compared to existing tools which use data coming from only few
hundred people, it can provide more valuable and accurate results. Finally, this clinical
decision support system incorporates many risk factors to predict the outcome.
In general, it is possible to state that the development of such device contributes to the
improvement of both medical and informatic literatures from three main perspectives:
methodology, data management and application. From the methodological point of view,
the CDSS is based on a novel approach in building ensemble models which consists of
calculating a weighted confidence margin across all the models which is used as a base
to aggregate the predictions of individual models. For each observation, these weights are
calculated as the distance between the estimated probabilities of records and the decision
cut off point. In the data management aspect, the observations at patient level have been
aggregated with a large transactional database of clinical encounters. This enables the
CDSS to consider many risk factors and a more realistic depiction of the coexistence of
chronic diseases. Finally, from the application perspective, this CDSS represents an
accessible, not expensive and easy-to-implement solution for the large portion of
diabetics with retinopathy who have not been diagnosed with the disease.
This is not the first tool developed for predicting the risk of contracting retinopathy. Most
of the existing devices, however, present some different limits. For example, most of them
are based on image processing algorithm which, by requiring the presence of a specialist,
54
are expensive and not accessible to all patients. Another category is represented by those
tools including lenses or an ophthalmologist which can be used on a smartphone.
Basically, they capture images (fundus images in case of the ophthalmologist and retinal
images in case of the lenses) which will be processed by an algorithm installed on the
smartphone (usually a neural network). Despite the algorithm implemented on the
smartphone is good and shows good results, this procedure still requires equipment and
can make this solution cost-prohibitive and not affordable to some patients. There are also
some models which exploit lab results, as the pattern used by the CDSS object of study,
but they still require retinal imaging. Another example is represented by the work of
Balakrishnan41 who used two models, decision tree and case-based reasoning (CBR), to
find out all the possible outcomes, and, a voting mechanism to select the best option. Even
if it does not require retinal imaging procedure, it presents some other limits: it is based
on small samples and considers a limit number of risk factors. The limits of the existing
tools represent another reason, together with the high rate of not diagnosed diabetics with
retinopathy, for developing a CDSS which can overcome these weaknesses.
Now, the structure of the CDSS is shown. Before talking about the model, it’s necessary
to briefly describe the work of data pre-processing which, above all in data analytics,
requires the highest portion of work time. The main actions performed in this phase have
been data extraction and cleaning, together with data integration, aggregation and
representation. The resulting table included data from over 300,000 unique patients. In
Fig. 7, the steps to reach the final table are summarized.
41 Balakrishnan, V., Shakouri, M. R., Hoodeh, H. (2013). Developing a hybrid predictive system for
retinopathy. J. Intell. Fuzzy Syst. (vol. 25, pp. 191-199).
55
Fig. 7: figure which shows the steps of the data pre-processing procedure42.
After that, before the construction of the models, some data manipulation steps have been
performed, such as replacing and filtering extreme data points, transforming variables to
approximate a normal distribution and applying different imputation methods. Then, the
following models have been developed: logistic regression (to write an optimal regression
equation, the stepwise method has been adopted as selection method), decision tree,
random forest and artificial neural networks (ANN). In Fig. 8, the data manipulation steps
conducted before the construction of the models are summarized.
42 Reworking of a figure extracted from the following scientific paper: see note 40.
56
Fig. 8: scheme which describes how data, after the pre-processing phase, are firstly manipulated and successively used to create the models43.
As regards the predictive models, four different sets have been created: Models based on
Lab and Demographic Data (Basic Data), Models based on basic and Comorbidity Data,
Models based on Over-sampled data and Ensemble models.
Finally, according to the analyses, diabetic neuropathy is the most relevant factor in
detecting diabetic retinopathy and is followed by the creatinine serum, the blood urea
nitrogen, the glucose serum plasma, and the haematocrit. This CDSS, in addition to the
advantages explained before, allows also to detect this disease at early stages by
improving the accuracy of the diagnoses and by providing great value to all the people
who are suffering from diabetes.
43 Reworking of a figure extracted from the following scientific paper: see note 40.
57
6.1.4 Agricultural production
Agricultural production is another field where DSSs are usually implemented. The case
is going to be present, concerns the development of a DSS based on a new approach,
suited above all to medium-large farms, about farm planning44. Many tools have already
been built regarding this subject but, most of them revealed not useful since they are too
simple and have not understood and managed the complexity of the topic. The complexity
of farm planning is due to the presence of many aspects, decision-making process and
situations that should be taken into account, such as investment analysis, scheduling of
field tasks, machinery selection, cost/benefit analysis and other aspects of the agricultural
production process. Basically, the problem which leads to the need to develop this tool is
represented by the economic and environmental pressures which farmers should face
nowadays. Specifically, the fall of the production prices is forcing farmers to find solution
to decrease their production costs and evaluate new production alternatives and crops, so
that farming becomes profitable again. Crop production is a complex business affected
by many factors some of which are inherent to the farm and cannot be controlled while
others, such as the current structure of the machinery stock and personnel, the irrigation
infrastructure in place, etc., can be managed by the owner with the purpose of maximizing
profitability. Decision support systems are built and implemented by leveraging on these
controllable factors with the aim of helping farmers to optimise their resources according
to business prospects and manage the production risk in the best possible way according
to their interests. These kinds of DSS are called Decision Support Systems for Planning
Field Operations.
After this premise, here following the reasons which led to build the model adopted by
the DSS are reported. First at all, a lot of models coming from operational research,
together with heuristic search and other AI techniques, have been developed for solving
planning field operations problems. However, linear programming approach, dynamic
programming approach and simulation approach are the alternatives which have shown
the best solutions. Then, even if planning models are characterized by probabilistic
elements, the adoption of probabilistic models shows several disadvantages (the structure
of the probabilistic model has a great impact on its behaviour; moreover, models, mainly
44 Recio, B., Rubio, F., Criado, J. A. (2003). A decision support system for farm planning using AgriSupport
II. Decision Support Systems (vol. 36, pp. 189-203).
58
linear programming and simulation, are based on a deterministic orientation and most of
the models applied in field operation planning are of deterministic nature), this is why it’s
more common to apply deterministic models by setting probabilistic elements with fixed
values based on statistic methods. That said though, it's possible to consider probabilistic
factors without restrictions but, they cannot be included in a DSS since other methods
different from linear programming, which would be too heavy for this kind of tool, should
be used.
The general aim of planning field operations is to optimise the work (minimum
requirements in terms of machinery, labour, etc.) on each field which has a specific goal.
Each goal is achieved by performing activities (tasks) which are grouped in a specific
operation. Operations have multiple ways to reach a goal. This means that an operation
can have different sequences of tasks (technical path) with the common aim of achieving
the fixed field goal of which the operation is part. In turn, there are possible variants for
performing a task which are called modes. Each mode differs in time and cost. Moreover,
tasks are characterized by relationships of precedence that should be respected. In
addition to this, resources should be considered. They are either material, including
machinery, implements, etc., or human, including permanent or temporary workers
employed on the farm. Clearly, each type of resource has associated a cost (both variable
and fixed) for the use. The data required for building the optimization model can be
summarized as follows:
Technical path through which are implicitly known:
• Tasks to be performed
• Precedence among tasks
About each task
• Precedence with other tasks (established by the technical path which includes the
task)
• Time window
• Modes for performance
About each mode:
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• Resources used (information given by the definition of the mode)
• Length
• Cost
About each resource:
• Variable cost (needed to compute the cost of the mode using the resource)
• Fixed cost
After the explanation of the data, it’s possible to define the model. It consists of a mixed-
integer linear programming problem for decision-making on annual planning, that is,
when to perform each task and using what resources, or, alternatively, by what mode. The
goal consists in minimizing the cost calculated as the sum of the costs of the chosen modes
and the fixed cost of the resources used. Clearly, everything should be achieved by
respecting all the associated constraints. This is the core of the model which has been
adjusted to solve some technical troubles related to that specific kind of problem (e.g. the
management of time and the possibility to generate a huge number of nodes).
Finally, the DSS which includes this model has been developed for ITAP, a public sector
company set up by Albacete Provincial Council (Spain) whose activities include the use
and management of the land owned by the provincial council, the provision of technical
advice for arable and livestock farmers in the province, and agricultural research,
especially about farming techniques for the most important species of provincial interest.
One of the most important activities performed by the ITAP is planning field operations
as a research and consultancy activity. The DSS has been built for this purpose, that is, to
help ITAP in its consultancy activity with farmers who want to find the most suitable
operations for a crop according to the characteristics of their farms, the resources required
for alternative crops (analysing the programmed tasks), and the costs and benefits
associated with these alternatives. The DSS works in this way:
1. Data are collected. Some of them are supplied by the farmer, others by the expert
engineer according to the characteristics of the farm. These are data about the
plots, general data about the farm and a crop plan;
2. The model processes the data and elaborates some results. For each alternative,
the time and the costs to perform each operation are calculated by taking into
60
account also the farm structure. The goal is to output the resource allocation for
each field operation on each plot with the starting and finishing date for each
operation, ensuring that the production plan is viable and by providing the lowest
overall cost;
3. The results are shown to the user.
The best feature of this DSS is the graphical interface which has an easy procedure for
entering the data, an easy to interpret presentation of the results and a fast system
execution time.
6.1.5 University
Next DSS is applied to the university world for the enrolment management, a crucial
process for many universities that rely on tuition for a significant portion of their
operating budgets45. Specifically, the problems this DSS addresses are the accurate
prediction of the enrolment yield, defined as the percentage of admitted students who will
enrol, and the optimal allocation of financial aid to admitted students, translated into a
sort of discount rate on the total tuition which is offered to the enrolled class. A good
quality prediction of the enrolment yield is crucial for an effective fiscal planning. On one
hand, if the yield is underestimated, it means that more admitted students than expected
will enrol and this probably causes the exceeding of the fixed capacity of the school which
results in significant incremental costs for additional housing, faculty, and other
resources. Moreover, in the worst case, this situation can impact also the quality of
instruction, since classrooms become overcrowded and the ratios of students over
professors risk to overcome suitable levels to optimal learning. On the other hand, if the
yield is overestimated, fewer students than expected will enrol and university’s revenue
will decrease. While, as regards the financial aid, its importance lays in the fact that is a
powerful lever for admissions and, more importantly, has major fiscal implications.
Furthermore, the idea of developing an internal tool for the enrolment management comes
from the want to increase knowledge sharing and to speed up the transition from a tacit
knowledge to an explicit knowledge of the admissions staff. The achievement of this goal,
as it will be discussed successively, had dramatic positive impacts on organisational
45 Maltz, E. N., Murphy, K. E., Hand, M. L. (2007). Decision support for university enrollment
management: Implementation and experience. Decision Support Systems (vol. 44, pp. 106-123).
61
performance, especially, it avoided the university to give in outsourcing the processes of
prediction of the enrolment yield and of definition of the strategy regarding the financial
aid. The outsourcing, probably, was the main drawback of the traditional admissions
process because:
• Admissions staff gained limited explicit knowledge about the relevant factors
affecting enrolment since the consultant’s work used its own models for the
prediction;
• The model built by the consultant is based on assumptions used also in other
models created for a huge variety of clients;
• Admission personnel are limited in their decision making by the timing and scope
of the information provided by the consultant.
After introducing the issue to be addressed, it's time to define the structure of the DSS. It
is characterized by two sections: a predictive model to estimate the likelihood of
enrolment of individual applicants along with the enrolment yield and the discount rate;
and, a user-friendly interface to allow managers to have a better understanding of the
problem and to take the best possible decisions.
As regards the model construction, CRISP methodology has been followed which is a
paradigm composed by six steps to develop successful data mining models. In the first
phase, interviews to enrolment managers have been conducted to gain a better
understanding of the institutional setting. Successively, after that data have been prepared
to form the initial analysis database, the model building stage commenced. Neural
networks, decision trees and logistic regressions were the main data mining models which
have been tested. Initially, because of its good accuracy to make prediction in complex
problems and, since it demonstrated its effectiveness in previous works related to this
topic, neural network has been the first model to be adopted. However, this model showed
some problems of transparency (difficulties to determine the exact relationships among
the variables) which pushed to move towards decision trees and logistic regressions
models. Decision tree does not rely upon assumptions about the linearity relationship
between the response and selected predictor variables, by producing predictions more
accurate. However, it has a crucial drawback for this kind of problem: they output only
discrete breakpoints to describe the influence of financial aid on applicant propensity to
62
enrol. While, one of the purposes of the DSS is to allow managers to evaluate the impacts
of small adjustments on financial aid policies. Moreover, a decision tree would have had
some troubles to be implemented in a software package available to managers.
Consequently, for these reasons, logistic regression has been the definitive model adopted
for the deployment of the DSS. This choice allowed to maximize the ease of
understanding and implementation by enrolment managers. There was only a little limit
represented by the fact that the model predicted with more accuracy those who would not
enrol than those who would enrol. After some iterations, refinements and modifications,
the problem has been solved and they arrived at the final version of the model.
A similar path has been followed for the creation of the user interface. The first attempt
has been developed in Microsoft Access and has given good results in terms of
transparency of the process for managers but not in terms of transparency of the system
due to their limit understanding of the database. As it happened for the construction of
the model, some refinements and modifications have been applied (e.g. the DSS has been
re-deployed in Microsoft Excel) by arriving at the final version of the user interface which
gives to the managers a complete overall understanding, and the possibility to exploit the
DSS according to their specific needs.
The development and implementation of the DSS had multiple positive impacts both at
organizational and operational level. First at all, it allows to improve the enrolment
management process. Specifically, the admissions staff, by leveraging on the knowledge
acquired through the project and the resulting system, adjusted the sequence,
effectiveness and expediency of enrolment decision making. Moreover, admissions staff
is now able to create an initial financial aid allocation grid which decreases the time
needed to its construction and assessment by positively increasing the time available to
manage individual cases (after the introduction of the DSS, 50 % of financial aid
allocations conducted at grid level are adjusted through individual modifications). Along
with this, managers can make better prediction about environmental yield and discount
rate thanks to their acquired understanding of process goals and of how financial aid
choices influence the discount rate. Then, improved predictions translate themselves into
better estimates of incoming class characteristics. Furthermore, the lead time needed
when working with the outside consultant has been reduced. With the introduction of the
DSS, decision makers can implement adjustments to unexpected market conditions more
63
rapidly and, consequently, they can manage the waiting list (those applicants who have
not been admitted and are waiting for one of the admitted who renounces to enrol) more
strategically. As far as the operational results are concerned, the variance related to the
prediction of the enrolment yield improved a lot by passing from 17 – 21 % to 5 % above
or below the desired value. Variance from the discount rate has been reduced as well,
from 2–3.5% to less than 1%. It’s very important to underline how these great
improvements have been achieved without any decline in the academic quality.
Clearly, the implementation of the DSS has some costs. During the development period,
in this case, the total monetary disbursements were less than 50,000 €. Thus, by
considering that a 1 % reduction in the discount rate yields hundreds of thousands of
dollars in additional revenue to the university and the discount rate decreased 10% over
the three-year implementation period, from a cost-benefit analysis perspective, the
implementation was profusely profitable. The education of project participants in
business procedures, the provision of input for the implementation of the interface to
achieve both maximum functionality and ease of use, and the assimilation of the DSS in
business processes represent some other costs which are, however, difficult to quantify.
64
7. DSS IN CIRCULAR ECONOMY
After explaining the broad concept of Circular Economy and describing in detail what is
a DSS and its main features along with some examples of application in different contexts,
it’s time to show some instances of DSS applied to industrial situations where the
principles of Circular Economy are implemented. In Fig. 9, it is reported the life cycle of
a manufactured product which is characterized by the following stages: design,
manufacturing, distribution, customer use and end-of-life (EoL).
Fig. 9: product life cycle scheme.
Main CE’s principles are designing out waste and pollution, keeping products and
materials in use, regenerating natural systems (use of renewable sources of energy instead
of fossil fuels). These principles can be applied at each product life cycle levels. For
example, at the design phase, it’s possible to implement eco-design in order to make
products more durable and easier to disassemble so that at their EoL it will be easier to
decide the best recovery strategy. In this way, the principles of completely avoiding waste
and pollution and of retaining the value of products and materials are respected. In the
manufacturing stage, through industrial symbiosis, it’s possible to eliminate waste
because, according to IS’s concept, waste of a production process becomes an input or a
resource for the process of another company. Finally, at the end-of-life phase, by
leveraging on closed loop supply chains, products can be disassembled and different
recovery strategies among recycling, refurbishing, reconditioning, repairing and
remanufacturing, are chosen for each component so that its market value is exploited as
much as possible.
In this regard, there are some existing DSSs which help the application of these principles.
Design
Manufacturing
DistributionCustomer use
EoL
65
7.1 EXAMPLE OF DSS SUPPORTING ECO-DESIGN
For example, as regards eco-design, there is a decision support system which contains
Life Cycle Assessment information to provide time and expertise to designers and is able
to compare the different alternatives in a quantitative manner in each phase of the eco
design46. This DSS is composed by 8 stages (which come from ISO 14062 guidelines),
from the project planning to the product launch on the market plus the final follow up
activities. Each stage is characterized by sub-factors which can lead to potential
sustainability decisions to be taken. In the stages where comparisons need to be carried
out, it’s used a pair-wise decision-making tool based on Analytic Hieratical Process
(AHP). AHP is a process which allows to select the best options among a group of two
alternatives according to a set of weighted criteria. In Fig. 10, the stages with the
correspondent sub-factors are reported. For each phase only the most important sub-
factors are shown.
Fig. 10: scheme of the stages with some of the sub-factors which characterize the methodology used in the DSS47.
46 Kulatunga, A. K., Karunatilake, N., Weerasinghe, N., Ihalawatta, R. K. (2015). Sustainable
Manufacturing based Decision Support model for Product Design and Development Process. Procedia
CIRP (vol. 26, pp. 87-92).
47 Reworking of a figure extracted from the following scientific paper: see note 46.
66
The first stage is the project planning. In this phase, the DSS shows to the user a window
where for each sub-factor multiple options can be chosen according to user’s relevancy
and importance. In the product analysis stage, the functional units, which refer to the main
functions, and the ways to measure them are considered along with a market and
environmental analysis to compare two distinct alternatives. Moreover, legal aspects with
eco benchmarking are taken into account. As regards the eco design strategies stage, there
are seven strategies which are characterized by a set of criteria to sub-divide them in other
sub-alternatives: select lower impact materials has nine main criteria, four criteria should
be considered in reducing the use of materials, five in reducing the environmental impact
of production, nine for promoting environmentally friendly packaging and logistics, six
for reducing the environmental impact in use phase, increase product durability is
characterized by eight criteria and finally nine categories are taken into account in
optimizing the end of life system. Obviously, in this section comparisons need to be
carried out and, for this purpose, the DSS provides a link to MS Excel where the
calculations related to the AHP can be made. Generally, to develop a new product
concept, creative thinking paths are followed. This DSS provides the steps to be pursued
of two main ways of creative thinking: scheduled thinking and lateral thinking. The
product detailing phase include information related to the technical aspects, quality and
safety aspects, environmental aspects, economic aspects and legal aspects, along with the
prototyping process. Since it contains specific information about the product, it is
considered the most important module. As far as the production and the market launch
stage is concerned, there are a lot of strategies but only few of them are considered by the
DSS: internal promotion, market launch, communication, green marketing and eco
labelling. The reasons at the basis of the choice of these strategies concern the ease of
reaching environmentally friendly consumers by adopting these plans. The last stage
consists of the evaluation against the benchmark product and the assessment of the eco
design project according to the factors identified in the project planning phase.
7.2 EXAMPLE OF DSS SUPPORTING INDUSTRIAL SYMBIOSIS
Next step consists of describing a tool called SymbioSyS which help companies to find
sustainable solutions by fostering Industrial Symbiosis, a mean to implement CE’s
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principles48. This tool has the objective to allow users to have access to the application
through a web service in order to store large amounts of information regarding both
implicit and explicit knowledge. This information, including also transportation and
geographical data, are processed in order to find the best possible synergies between
companies (companies could belong to different industrial sectors and have different
volume or size). Basically, companies can establish two types of synergies: the exchange
of resources and joint waste management. This tool can be considered as a decision
support system because of its structure which includes a database, a graphical module
where results are shown and finally a user interface which enables to have access to the
platform and work on the database. Because of its scopes, this tool implies the
involvement of multiple private and public stakeholders and can be exploited by both
large and small-medium enterprises. This DSS is very simple to use since companies have
just to upload their business info by filling a predefined form; then, this information is
stored into the system and processed to detect possible relationships. The outputs are
provided in written and graphic format through different map servers included in the tool.
Now, let’s see in detail how the DSS has been built and how the modules which compose
it are designed. Before starting to explain, it’s important to define the objectives of the
DSS, that are the ability to store large amounts of information and process it efficiently
and quickly, the possibility to access the system anywhere by any user and the ability to
visualize results in an easy-to-understand way, affordable to each user, regardless his
knowledge. The database is characterized both by explicit knowledge, represented by the
information coming from the participating companies, and tacit knowledge which refers
to partnerships, know-how of IS experts and case studies from scientific literature. The
database is built upon 5 tables. The table Company contains all the information related to
the company (name, industrial park which it belongs to, country, city, email) and it’s
important to notice that two fields are represented by the latitude and the longitude which
convert the database into a spatial database. In this way, geographical information can be
shown on a map. The table User is connected to the table Company with the purpose of
including the user profiles of each company in the database. Users can have access to the
48 Álvarez, R., Ruiz-Puente, C. (2016). Development of the Tool SymbioSyS to Support the Transition
Towards a Circular Economy Based on Industrial Symbiosis Strategies. Waste and Biomass Valorization
(vol. 8, pp. 1521-1530).
68
database through two different profiles: “entrepreneur” who has the possibility to edit
data and his final goal is the decision-making, and “worker” who can use data without
modifying them with the scope of generating reports and studies. The table RawMaterial
comprises more than 3000 products classified by activity according to the hierarchical
classification CPA, the European version of the Central Product Classification (CPC)49.
From this table the user can select the inputs necessary to his production process and can
also identify new products or raw materials as by-products. The table Waste incorporates
more than 1000 types of waste arranged according to the hierarchical classification of
waste from European Waste Catalogue (EWC)50. From this table the user can select the
types of waste which characterize his production activity. By joining Waste table with
RawMaterial table, we obtain the table Symbiosis which contains the matchings between
raw materials and waste types that can be exchanged. The information related to the
feasibility of the matchings is extracted from scientific literature references. By showing
which raw materials can be substituted by which types of waste, the table Symbiosis is
the most important because allows to detect all the possible symbiotic industrial situations
by saving costs and improving waste management plans of the companies involved.
As regards the user interface, it is made through a web-application where different users
can have access, edit and delete information simultaneously. In the final module of the
DSS, where graphical results are shown, the solution provided by the table Symbiosis,
which shows the matchings between raw materials and types of waste, is projected on the
map server included in the module. In this regard, a second analysis is conducted in order
to verify the convenience of the solution at geographical level. The result of this second
analysis is the presentation of the geographical arrangement of the companies of the
symbiotic solution along with some geometric information such as distances, optimal
routes or areas of influence. As it has been already explained, IS takes place not only
49 “The Central Product Classification (CPC) is a product classification for goods and services promulgated
by the United Nations Statistical Commission. It is intended to be an international standard for organizing
and analysing data on industrial production, national accounts, trade, prices and so on.” Definition taken
from: https://en.wikipedia.org/wiki/Central_Product_Classification.
50 The European Waste Catalogue (EWC) is a hierarchical list of waste descriptions established by
Commission Decision 2000/532/EC. It is divided into twenty main chapters which are identified by a two-
digit code between 01 and 20. Most of the chapters refers to industries but some of them are related to
materials and processes. Every chapter is composed by a list of waste each one defined by a six-figure code.
The codes and the descriptions reported in the EWC are suitable so as to comply with your duty of care.
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when waste materials of a company become inputs for other companies but also when
two firms share the waste management. SymbioSyS provides a solution also to this case.
The tool analyses the residual flows of a company and shows, in case there are, those
companies which generate the same waste. This kind of use of the platform is useful for
governmental agents or local development agencies for the planning of common transport
and waste collection or treatment services.
7.3 EXAMPLES OF MODELS AND METHODS WHICH COULD BE
USED TO BUILD A DSS FOR REVERSE SUPPLY CHAIN
MANAGEMENT
Here following are presented some examples of models for Reverse Supply Chain
Management (RSCM) which could be implemented in a DSS. Reverse Supply Chain
(RSC) is a good example of business applying CE’s concepts. RSC consists of collecting
and working on used manufactured products in order to recover their remaining market
value. This business, therefore, realises the CE’s principle of keeping materials and
products in use. Let’s see how DSSs can be useful in such business. First at all, it’s
important to understand how RSC works. It is composed by a series of activities for
collecting and processing used items. Specifically, at the end of life, a product can be
disposed or recovered by performing the following tasks: repairing, reconditioning,
remanufacturing and recycling. Repairing consists of identifying the parts of an item
which don’t work well to repair and return them to normal functioning. Reconditioning
encompasses the disassembly of the product in order to test and eventually replace parts
not functioning so that the returned product can be resold again. Remanufacturing is a
complex process which involves the complete disassembly of the product, the
replacement of absent modules and the restore of malfunctioning parts with the aim of
returning the product to its original initial functioning. Recycling, as it has already been
explained in the introduction, is the process of converting waste material into reusable
material and objects. If none of these operations can be implemented on some parts of a
product, the last unique option is the traditional dispose which involves process of
landfilling or incinerating. In Fig. 11, EoL options in RSC process are summarized.
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Fig. 11: schematic representation of the EoL options.
In the field of RSC there are some studies which have developed tools and frameworks
with the purpose of support decision making processes. Some of them have been later
included in a DSS.
7.3.1 A mathematical programming model to solve reverse distribution problems
First example can be represented by reverse distribution problems which consist of
finding out the network composed by origination site, collection site and refurbishing site,
which minimizes the total costs. The origination site is where used or damaged products
are brought back by the consumers, the collection site is the place where these products
are retrieved, and the refurbishing site is the area where the most suitable recovery
strategy is applied to each product. The total costs mainly refer to transportation, opening
of the sites and flows of material. In this regard, it is reported the study conducted by V.
Jayaraman, R. A. Patterson and E. Rolland51 who proposes a mathematical programming
model characterized by the following assumptions:
51 Jayaraman, V., Patterson, R. A., Rolland, E. (2001). The design of reverse distribution networks: Models
and solution procedures. European Journal of Operational Research (vol. 150, pp. 128-149).
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• The model decides which collection sites and refurbishing sites should be open
by taking into account that, among the other costs, the opening of the sites has a
fixed cost to be sustained; moreover, there is a limit number of collection sites and
refurbishing sites which can be open;
• Normally, the network follows this sequence: the used product is released by the
customer at the origination site, then, it’s sent to the collection site and, finally, it
reaches the refurbishing site. However, the product can go directly from the
origination to the refurbishing area, but this implies a higher cost because small
lots of size don’t allow to reduce ship costs as large lots size;
• The origination sites are represented by retailers or wholesalers where customers
can release the damaged or used product by receiving a refund or purchasing
another product.
After these assumptions, the model is reported in the following section.
As it has already said before, the types of product considered in the model could be end-
of-life items which can be recycled, reused, remanufactured or refurbished, damaged
articles which are hazardous, products recalled or items to which no recovery strategy can
be applied, so they should be disposed. Here following all the variables needed to build
the model are listed.
• 𝐶𝑖𝑗𝑘: Total variable cost to transport a product at end of life from the site of origin
𝑖 to the recycling site 𝑘 via the intermediate collection site 𝑗; this cost comprises
the costs to process the products at the origination site in addition to the total
transportation cost;
• 𝐼𝑗: it’s the cost to open a collection site 𝑗;
• 𝑅𝑘: it’s the cost to open a refurbishing site 𝑘;
• 𝑞𝑖: number of end-of-life products at the origination site 𝑖;
• 𝐷𝑗: maximum number of products that the collection site 𝑗 can accept;
• 𝐸𝑘: maximum number of products that the refurbishing facility 𝑘 can accept;
• 𝑆𝑚𝑖𝑛: minimum number of collection sites should be open;
• 𝑆𝑚𝑎𝑥: maximum number of collection sites should be open;
• 𝑇𝑚𝑖𝑛: minimum number of refurbishing facilities should be open;
• 𝑇𝑚𝑎𝑥: maximum number of refurbishing facilities should be open;
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• 𝑋𝑖𝑗𝑘: fraction of elements at the origination site 𝑖 which is transported to the
refurbishing site 𝑘 by passing through collection site 𝑗; in case 𝑗 = 0 it means that
those units go directly from the origination site to the refurbishing site without
passing through the collection site; 𝑖 and 𝑘 cannot take value zero; this variable,
as it is a fraction, can take values between 0 and 1;
There are two binary variables:
• 𝑆𝑗 = {1, 𝑖𝑓 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛 𝑠𝑖𝑡𝑒 𝑗 𝑖𝑠 𝑜𝑝𝑒𝑛 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
• 𝑇𝑘 = {1, 𝑖𝑓 𝑟𝑒𝑓𝑢𝑟𝑏𝑖𝑠ℎ𝑖𝑛𝑔 𝑠𝑖𝑡𝑒 𝑘 𝑖𝑠 𝑜𝑝𝑒𝑛 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Now, it’s possible to write the formulation of the problem.
The first thing to be defined is the objective function. The goal is to minimize the costs
of transportation and opening of the sites. Therefore, the function is written in this way:
𝑀𝑖𝑛 𝑌 = ∑∑∑𝐶𝑖𝑗𝑘𝑞𝑖𝑋𝑖𝑗𝑘 + ∑𝐼𝑗𝑆𝑗 + ∑𝑅𝑘𝑇𝑘
𝑘𝑗𝑘𝑗𝑖
Where ∑ ∑ ∑ 𝐶𝑖𝑗𝑘𝑞𝑖𝑋𝑖𝑗𝑘𝑘𝑗𝑖 refers to the costs of transportation, ∑ 𝐼𝑗𝑆𝑗𝑗 represents the costs
for opening the collection sites, finally, ∑ 𝑅𝑘𝑇𝑘𝑘 deals with the costs for opening the
refurbishing sites. Afterwards, the constraints should be defined.
all_p: this constraint states that from the origination site all the products are transported
to the refurbishing facility either passing through the collection site or directly.
∑∑𝑋𝑖𝑗𝑘 = 1
𝑘𝑗
max_D: this constraint assures that the maximum capacity of each collection site is not
exceeded.
∑ ∑ 𝑞𝑖𝑋𝑖𝑗𝑘 ≤ 𝐷𝑗𝑘𝑖 for all 𝑗
Note that when 𝑗 = 0 means that the products go directly from the origination site to the
refurbishing facility and the capacity constraint related to the collection site should be
deactivated. To do that, it’s necessary to put 𝐷0 = 𝑀 where 𝑀 is a very big number such
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as 100,000. By doing this, the constraint works but doesn’t make sense. It’s always
respected since, according to the data related to 𝑞𝑖 and 𝑋𝑖𝑗𝑘, it’s impossible to exceed a
quantity of 100,000 units.
max_E: this constraint assures that the maximum capacity of each refurbishing facility is
not exceeded.
∑ ∑ 𝑞𝑖𝑋𝑖𝑗𝑘 ≤ 𝐸𝑘𝑗𝑖 for all 𝑘
coll_S: this constraint assures that the fraction of units which is transported from the
origination site to the refurbishing facility passes also through the collection site only if
this one is open.
𝑋𝑖𝑗𝑘 ≤ 𝑆𝑗 for all 𝑖, 𝑗, 𝑘
ref_T: this constraint assures that the fraction of units is transported from the origination
site to the refurbishing facility only if this latter is open.
𝑋𝑖𝑗𝑘 ≤ 𝑇𝑘 for all 𝑖, 𝑗, 𝑘
num_S: this constraint guarantees that the minimum number of collection sites are open
without exceeding the maximum number allowed.
𝑆𝑚𝑖𝑛 ≤ ∑𝑆𝑗
𝑗≠0
≤ 𝑆𝑚𝑎𝑥
num_T: this constraint guarantees that the minimum number of refurbishing facilities are
open without exceeding the maximum number allowed.
𝑇𝑚𝑖𝑛 ≤ ∑ 𝑇𝑗
𝑘≠0
≤ 𝑇𝑚𝑎𝑥
Finally, the constraints related to the domains of existence of the variable are set.
0 ≤ 𝑋𝑖𝑗𝑘 ≤ 1
The formulation above states that the variable 𝑋𝑖𝑗𝑘 should be continuous by assuming
values between 0 and 1.
𝑆𝑗 ∈ {0,1}
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𝑇𝑘 ∈ {0,1}
The last two constraints refer to the nature of the variables 𝑆𝑗 and 𝑇𝑘 which are binary and
can take only the values 0 or 1.
As an example, let’s consider a problem with 11 origination sites, 7 collection sites and 5
refurnishing facilities. A possible solution of the problem could be the one depicted in the
figure below (Fig. 12).
Fig. 12: figure showing a possible solution of the mathematical programming model built to solve the reverse distribution problem. The figures in blue means that the site is open, the figures in white means that the site is
closed52.
From the solution represented in the picture we can see that all the eleven origination sites
are blue which means that they are open by respecting the constraints that all the products
laying at each origination site should be transported to the refurbishing facility. As regards
the collection sites, 3 out of 7 are open while, as far as the refurbishing facilities are
concerned, 2 out of 5 are open.
This is a zero–one mixed integer-linear programming (MIP) problem. The use of
traditional MIP tools to solve it would generate a limited solution because of the
complexity of the problem which is characterized by many variables to be manged (the
example in the figure above is not realistic since it has few sites and facilities). This is the
reason why, first, it is suggested the use of a heuristic method to find a subset of potential
sites. In this way, the number of variables to deal with is reduced and the MIP tool can
find a better solution.
52 Reworking of a figure extracted from the following scientific paper: see note 51.
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It is possible to notice that the model proposed by V. Jayaraman, R. A. Patterson and E.
Rolland can be used to build a DSS. The database module would be used to collect
information regarding the costs associated to transportation, opening of the sites and
whatever, the module for the calculations and analyses would be composed by the
mathematical programming model needed to solve the reverse distribution problem and,
finally, a user interface would be built to allow mainly to have access to the database and
change the data according to the different circumstances. According to the kind of
problem (it’s a sort of specific case of the vehicle routing problem) and the path to follow
to solve it (a zero-one mixed integer-linear programming model), specifically, it is
suggested a knowledge-driven DSS.
7.3.2 A hybrid multi-objective metaheuristic (HMM) algorithm to find the most
suitable disassembly process for an end-of-life product
Along with reverse distribution problems, in the world of the closed-loop supply chain
management, there are also some DSSs, or models which could be implemented in such
tools, which support the disassembly process of products. In this work, it’s reported a
hybrid multi-objective metaheuristic (HMM) algorithm which allows to find the most
suitable disassembly process by paying attention not only to profit maximization but also
to energy conservation53. The adoption of a metaheuristic algorithm is due to the too much
high computational time of exact algorithms which doesn’t allow to proceed with them.
This choice is also supported by the literature in this field which presents many other
recent studies which used (meta)heuristics algorithms instead of exploratory tools
because of their limitations not only in terms of computational time but also in terms of
poor quality of the solutions provided for nonlinear problems.
The construction of the model to be solved with the metaheuristic algorithm follows this
path: firstly, a disassembly AND/OR graph (DAOG) to represent the disassembly of a
product, specifically a ballpoint pen, is built. After that, three relationship matrices are set
up to define the profit-oriented and energy-efficient disassembly sequencing problem
(PEDSP). The DAOG is useful to show the connections, the precedence relationships
among the components and the activities to be processed in order to disassemble a
53 Lu, Q., Ren, Y., Jin, H., Meng, L., Li, L., Zhang, C., Sutherland, J. W. (2019). A hybrid metaheuristic
algorithm for a profit-oriented and energy-efficient disassembly sequencing problem. Robotics and
Computer Integrated Manufacturing (vol. 61).
76
product. In this graph, the parts which cannot be disassembled anymore are called sub-
assemblies. As it has already said before, the example is going to be shown deals with a
ballpoint pen and Fig. 13 shows in detail its structure along with all the parts which
compose it. In Fig. 14, instead, the DAOG is represented.
Fig. 13: section of a ballpoint pen54.
Fig. 14: DAOG of the ballpoint pen55.
By looking to Fig. 14, the hyperarcs represent the disassembly tasks and are defined by
integer numbers from 1 to M, where M stands for the total number of possible tasks. Each
node constitutes a sub-assembly and is identified by an index which can vary from (1) to
(N) where N is the total number of sub-assemblies that product can have. Then, for
example, from Fig. 14 we can notice that sub-assembly (2)ABCDE derives from sub-
assembly (1)ABCDEF, therefore (1) is called the parent and (2) is called the child.
Between children and parents subsist a AND relation which assures that the child sub-
54 SOURCE: see note 53.
55 SOURCE: see note 53.
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assembly can be obtained only after completing the disassembly task of its parent sub-
assembly. Whereas, the OR relation is established between two disassembly tasks which
can be performed on the same sub-assembly. Specifically, it consists of a mutual
exclusive relation (EOR), for instance, by looking to sub-assembly (4)ABCD, the
disassembly tasks 13 and 5 cannot be processed simultaneously. This means that there a
lot of alternatives regarding the disassembly sequence. From the DAOG it’s possible to
derive three relationship matrices needed to define the mathematical model for the
PEDSP. The matrix R is built to guarantee the respect of the precedence constraints
among sub-assemblies (that is, child sub-assembly cannot be created before the
completion of its parent). This means for example that task 13 cannot be performed before
task 4. Before making up the matrix it’s necessary to define the following binary variable.
𝑟𝑗𝑘 = {1, 𝑖𝑓 𝑡𝑎𝑠𝑘 𝑗 𝑚𝑢𝑠𝑡 𝑏𝑒 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑 𝑖𝑚𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑙𝑦 𝑝𝑟𝑖𝑜𝑟 𝑡𝑜 𝑡𝑎𝑠𝑘 𝑘 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Afterwards, the precedence matrix is built as follows.
𝑅 =
[ 0 0 1 0 0 0 0 0 0 0 0 1 00 0 0 1 0 0 0 0 0 0 1 0 00 0 0 0 1 0 0 0 0 0 0 0 10 0 0 0 1 0 0 0 0 0 0 0 10 0 0 0 0 0 1 0 0 0 0 0 00 0 0 0 0 0 0 1 0 0 0 0 00 0 0 0 0 0 0 0 0 1 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 1 0 0 0 1 0 0 00 0 0 0 0 0 1 0 1 0 0 0 00 0 0 0 0 0 0 1 0 1 0 0 0]
Each row (𝑗) and each column (𝑘) correspond to a task. So, row/column 1 refers to task
1, row/column 2 is linked to task 2 and so on. To understand better the matrix, let’s see
an example. In row 5, which indicates task 5, there is value 1 in correspondence with
column 7, which is related to task 7, because, according to DAOG, task 7 cannot be
executed before task 5.
Next matrix concerns the EOR relations between two tasks which cannot be performed
simultaneously.
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𝑚𝑗𝑘 = {−1, 𝑖𝑓 𝑡𝑎𝑠𝑘 𝑗 𝑎𝑛𝑑 𝑘 𝑎𝑟𝑒 𝑚𝑢𝑡𝑢𝑎𝑙𝑙𝑦 𝑒𝑥𝑐𝑙𝑢𝑠𝑖𝑣𝑒 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑀 =
[
0 −1 0 −1 0 −1 0 0 0 0 −1 0 0−1 0 −1 1 0 0 0 0 −1 0 0 −1 00 −1 0 −1 0 −1 0 0 −1 0 −1 −1 0
−1 0 −1 0 0 −1 0 0 −1 0 −1 −1 00 0 0 0 0 −1 0 −1 −1 0 −1 −1 −1
−1 0 −1 −1 −1 0 −1 0 −1 0 0 −1 −10 0 0 0 0 −1 0 −1 0 0 −1 0 −10 0 0 0 −1 0 −1 0 −1 0 0 −1 00 −1 −1 −1 −1 −1 0 −1 0 0 −1 0 −10 0 0 0 0 0 0 0 0 0 0 0 0
−1 0 −1 −1 −1 0 −1 0 −1 0 0 −1 −10 −1 −1 −1 −1 −1 0 −1 0 0 −1 0 −10 0 0 0 0 0 0 1 0 1 0 0 0 ]
Also in this case, each row (𝑗) and each column (𝑘) refer to a task. Row/column 1
corresponds to task 1, row/column 2 stands for task 2 and so on. Let’s see an example to
understand better: in correspondence of row 2 and column 1 there’s a -1 because task 2
(row 2) and task 1 (column 1) cannot be performed simultaneously. The execution of one
of them excludes the processing of the other one. Whilst, at the intersection of line 7 (task
7) and column 3 (task 3), for instance, value 0 is reported because the correspondent tasks
can be carried out at the same time.
After that, an incidence matrix which manages the relationships between sub-assemblies
and tasks is built in order to make the profit calculation easier.
𝑠𝑎𝑗 = {1, 𝑖𝑓 𝑠𝑢𝑏 − 𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑦 𝑎 𝑑𝑒𝑟𝑖𝑣𝑒𝑠 𝑓𝑟𝑜𝑚 𝑡𝑎𝑠𝑘 𝑗
−1, 𝑖𝑓 𝑜𝑛 𝑠𝑢𝑏 − 𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑦 𝑎 𝑖𝑠 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑒𝑑 𝑡𝑎𝑠𝑘 𝑗 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
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𝑆 =
[ −1 −1 0 0 0 0 0 0 0 0 −1 0 01 0 −1 0 0 0 0 0 0 0 0 −1 00 1 0 −1 0 0 0 0 0 0 −1 0 00 0 1 1 −1 0 0 0 0 0 0 0 −10 0 0 0 0 −1 0 0 0 0 1 0 00 0 0 0 1 0 −1 0 0 0 0 1 00 0 0 0 0 1 0 −1 0 0 0 0 10 0 0 0 0 0 0 0 −1 0 0 1 00 0 0 0 0 0 1 0 0 −1 1 0 10 0 0 0 1 0 0 1 1 0 0 0 00 0 0 0 0 0 1 1 0 0 0 0 00 0 0 0 0 0 0 0 0 1 0 0 00 0 0 0 0 0 0 0 0 1 0 0 00 1 1 0 0 0 0 0 1 0 0 0 01 0 0 1 0 1 0 0 0 0 0 0 0 ]
In this matrix there are 15 rows (𝑎) which correspond to the 15 sub-assemblies and 13
columns (𝑗) which refer to the 13 tasks. Now, for example, let’s take the second row
which stands for sub-assembly (2)ABCDE: in correspondence with the first column,
which refers to task 1, there’s correctly the value 1 which indicates that sub-assembly
(2)ABCDE derives from task 1. Whilst, at columns 3 (task 3) and 12 (task 12), value -1
is reported correctly since the tasks referring to those columns are performed on sub-
assembly (2)ABCDE. Clearly, when none of the conditions just explained is met means
that among that sub-assembly and that task there are no relations therefore the value 0 is
reported (for example, at the intersection of line 2, which refers to sub-assembly 2, and
column 6, standing for task 6, the value 0 means that activity 6 is performed neither to
create sub-assembly 2 neither to disassemble it; this is correct according to the DAOG).
After the definition of the DAOG and the three relationship matrices, the moment of the
formalization of the mathematical model for the PEDSP is arrived. First at all, let’s have
a summary of the indexes just explained which will be used in the model.
𝑎: it’s the index related to the sub-assembly; 𝑎 = 1, 2, . . . , 𝑁 where 𝑁 represents the total
number of sub-assemblies.
𝑗, 𝑘,𝑚: they are the indexes used for the tasks; 𝑗, 𝑘,𝑚 = 0, 1, 2, . . . , 𝐽 where 𝐽 stands for
the total number of tasks. Notice that Task 0 is a dummy task necessary for the creation
of a product.
After that, the decision variables are defined as follows:
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𝑧𝑗 = {1, 𝑖𝑓 𝑡𝑎𝑠𝑘 𝑗 𝑖𝑠 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑒𝑑0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑥0 = 1 because refers to the dummy task which must be performed in order to have the
product for the disassembly sequencing problem.
𝑤𝑗𝑘 = {1, 𝑖𝑓 𝑡𝑎𝑠𝑘 𝑘 𝑖𝑠 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑒𝑑 𝑖𝑚𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑙𝑦 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑠𝑘 𝑗0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
This decision variable is needed to consider the intermediate operations between two
consecutive tasks such as transportation of sub-assemblies or changing of disassembly
tools which imply the consumption of time and energy.
Here following the constant variables are expressed.
• 𝑑𝑗: cost of completing task 𝑗;
• 𝑄𝑗𝑘: cost of intermediate operations for the state 𝑤𝑗𝑘;
• 𝑃𝑗𝑘: power of intermediate operations for the state 𝑤𝑗𝑘;
• 𝐸: the total amount of energy available for a disassembly process;
• 𝑉𝑎: recycling/reuse value of sub-assembly 𝑎;
• 𝑏𝑗: time of completing task 𝑗;
• 𝐵𝑗𝑘: time of intermediate operations for the state 𝑤𝑗𝑘;
• 𝑝𝑗: power of completing task 𝑗.
Now, let’s compute the revenues of a possible disassembly sequence. For instance, by
considering the sequence of tasks 2 → 4, revenues should be calculated as follows:
(𝑠12 ∙ 𝑉1 + 𝑠32 ∙ 𝑉3 + 𝑠14,2 ∙ 𝑉14) + (𝑠34 ∙ 𝑉3 + 𝑠44 ∙ 𝑉4 + 𝑠15,4 ∙ 𝑉15)
= −𝑉1 + 𝑉3 + 𝑉14 − 𝑉3 + 𝑉4 + 𝑉15 = −𝑉1 + 𝑉14 + 𝑉4 + 𝑉15
The recycling/reuse values of the components (variable 𝑉) connected to tasks 2 and 4 are
summed to get the total revenues coming from this disassembly sequence. These values
are multiplied by the variable 𝑠𝑎𝑘 (index 𝑎 refers to the sub-assembly, while index 𝑗
corresponds to the task performed on it or from which the component derives) which
makes the revenue negative, if the sub-assembly is disassembled by a task and does not
exist anymore therefore its recycling/reuse value is lost (it’s negative because represents
a cost since it’s an opportunity lost), positive if a component is generated by a task. In
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this case the recycling/reuse value of the component created represents a potential source
of income until it’s not disassembled. As it is possible to notice, the expression in the first
bracket refers to task 2 (the index 𝑗 of each variables 𝑠 is equal to 2) which is linked to
sub-assemblies 1, 2 and 14 (for which the recycling/reuse values are respectively
represented by 𝑉1, 𝑉2, 𝑉14). With the same explanation, the expression in the second
bracket is related to task 4. Clearly, the recycling/reuse value related to sub-assembly
(1)ABCDEF will be always represented by a negative revenue since it’s the starting point
of the disassembly process so it will be always disassembled by losing its potential source
of income. After that, the formulation of the PEDSP can be described.
First step is the definition of the objective function. In this case, there are two objective
functions because there are two goals: profit maximization and energy minimization.
𝑀𝑎𝑥 ∑ ∑ 𝑠𝑎𝑗𝑉𝑎𝑧𝑗
𝑁
𝑎=1
𝐽
𝑗=1
− ∑ ∑ 𝑄𝑗𝑘𝑤𝑗𝑘
𝐽
𝑘=1
𝐽
𝑗=1
− ∑𝑑𝑗𝑧𝑗
𝐽
𝑗=1
(1)
𝑀𝑖𝑛 ∑ ∑ 𝑃𝑗𝑘𝐵𝑗𝑘𝑤𝑗𝑘
𝐽
𝑘=1
𝐽
𝑗=1
+ ∑𝑝𝑗𝑏𝑗𝑧𝑗
𝐽
𝑗=1
(2)
By looking to the objective function (1), the first term (∑ ∑ 𝑠𝑎𝑗𝑉𝑎𝑧𝑗𝑁𝑎=1
𝐽𝑗=1 ) represents the
revenue, the second one (∑ ∑ 𝑄𝑗𝑘𝑤𝑗𝑘𝐽𝑘=1
𝐽𝑗=1 ) refers to the costs of intermediate operations
and the third one (∑ 𝑑𝑗𝑧𝑗𝐽𝑗=1 ) constitutes the costs of completing the tasks. As regards the
objective function (2), the first component stands for the cost of the power needed to the
processing of intermediate operations which depends on the time; the second element
refers to the cost of power to complete the activities which is function of time as well.
Next step consists of formulating the constraints.
∑𝑧𝑗 ≥ 1
𝐽
𝑗=1
(3)
𝑧𝑘 = ∑𝑤𝑗𝑘
𝐽
𝑗=0
, 𝑘 = 1, 2, . . . , 𝐽 (4)
82
𝑧𝑘 ≤ ∑𝑟𝑗𝑘𝑧𝑗
𝐽
𝑗=0
, 𝑘 = 1, 2, . . . , 𝐽 (5)
𝑚𝑗𝑘(𝑧𝑗 + 𝑧𝑘) ≥ −1, 𝑗, 𝑘 = 1, 2, . . . , 𝐽 (6)
∑ ∑ 𝑃𝑗𝑘𝐵𝑗𝑘𝑤𝑗𝑘
𝐽
𝑘=1
𝐽
𝑗=1
+ ∑𝑝𝑗𝑏𝑗𝑧𝑗
𝐽
𝑗=1
≤ 𝑄 (7)
∑𝑤𝑗𝑚
𝐽
𝑗=1
≥ ∑ 𝑤𝑚𝑘
𝐽
𝑘=1
, 𝑚 = 1, 2, . . . , 𝐽 (8)
Finally, there are the constraints related to the domains of existence of the decision
variables.
𝑧𝑗 , 𝑤𝑗𝑘 ∈ {0, 1}, 𝑗, 𝑘 = 1, 2, . . . , 𝐽
Let’s describe each constraint. Inequality (3) assures that at least one task, excluding task
0 which is performed by default, is carried out. The constraints (4) and (5) ensure that the
precedence relationships between the activities are respected. Specifically, equality (4)
guarantees that each task, excluding task 0, is always performed after the completion of
one and only one of the tasks which immediately precede it; constraint (5) assures that an
activity cannot be processed without one of the activities immediately preceding it having
been completed. Inequality (6) governs the relationship of mutual exclusivity existing
between the activities. Constraint (7) ensures that the energy used for intermediate
operations summed to the energy needed to perform the tasks of the disassembly process
don’t exceed the total amount of energy available. Inequality (8) refers to the equilibrium
relationship regarding each sub-assembly between in-degree and out-degree. According
to this relationship, the sum of the outgoing flows (∑ 𝑤𝑚𝑘𝐽𝑘=1 ) should not exceed that of
the incoming flows (∑ 𝑤𝑗𝑚𝐽𝑗=1 ). The disassembly process stops when at task 𝑚
∑ 𝑤𝑗𝑚𝐽𝑗=1 = 1 and ∑ 𝑤𝑚𝑘
𝐽𝑘=1 = 0.
The solution proposed to this kind of problem is an algorithm-HMM which is more
efficient compared to other metaheuristic approaches since it offers a good variety of
feasible disassembly sequences with Pareto front of high quality. This algorithm is based
83
on the framework of an artificial bee colony algorithm (ABC) and integrates also a
double-phase heuristic, a non-dominated sorting (NS) approach and a variable
neighbourhood descent (VND) approach to reach the best possible solution. The double-
phase heuristic is used to decode the DAOG and find efficiently feasible disassembly
sequences. It consists of a double vector defined as 𝑥 = {𝑥1, 𝑥2} where 𝑥1 =
{𝑦1, 𝑦2, . . . , 𝑦𝐽} is a sequence of disassembly tasks, while 𝑥2 = {𝑤1, 𝑤2, . . . , 𝑤𝐽} is a binary
vector. If 𝑤𝑗, which refers to task 𝑦𝑗, is equal to 1 means that the task is performed,
otherwise not. The first phase of the heuristic method allows to eliminate the EOR tasks
from 𝑥. Through the second phase, instead, the respect of the precedence constraints is
ensured. In this case, the whole HMM algorithm is based on the ABC algorithm. It is an
optimization algorithm which uses as model the behaviour of bees to search for honey
and nectar. Basically, there are three kind of bees: employed bees, onlooker bees and
scout bees. Each employed bee is associated to a food source in the hive and their task is
to memorize the main characteristics of the source. This information is shared with the
onlooker bees with a probability which is proportional to the goodness of the source.
Therefore, the more the quantity of data regarding that source is shared with the onlooker
bees, the more is the probability for them to exploit that source. Scout bees, instead, are
in charge of finding out other food sources. Moreover, once a food source is finished, the
employed bee associated to it becomes a scout bee and starts to find other food sources
nearby. Translating this explanation into an optimization algorithm, the hive is identified
as the space containing all the possible solutions which represent the food sources. The
goodness of each solution depends on its fit with the problem to be optimized. Employed
bees together with onlooker bees represent the effort to improve known solutions by
searching other possible solutions nearby. A greedy selection, then, is performed and if a
new solution found is better, the old one is replaced. After n efforts to try to improve a
solution fail, there’s a new generation of feasible solutions. This process is repeated until
a solution which meets the requirements is found. In Fig. 15 is reported a scheme related
to the iterative process of the ABC.
84
Fig. 15: scheme of the iterative process regarding the artificial bee colony (ABC) algorithm56.
The non-dominated sorting algorithm (NS) is used to find the non-dominated Pareto
solutions according to two objective functions: maximization of the profit and
minimization of the energy consumption. A NS is generally defined as follows.
Definition of Non-dominated sorting algorithm57: a NS algorithm divides a set of 𝑆
solutions {𝑟1, 𝑟2, . . . , 𝑟𝑆} into different fronts {𝐿1, 𝐿2, . . . , 𝐿𝑍} which are arranged in
decreasing order of their dominance such that the two following conditions are satisfied:
• ∀𝑟𝑖, 𝑟𝑗 ∈ 𝐿𝑘: 𝑟𝑖 ⊀ 𝑟𝑗 𝑎𝑛𝑑 𝑟𝑗 ⊀ 𝑟𝑖 (1 ≤ 𝑘 ≤ 𝑍)
• ∀𝑟 ∈ 𝐿𝑘: ∃𝑟′ ∈ 𝐿𝑘−1: 𝑟′ ≺ 𝑎𝑛𝑑 𝑟 (2 ≤ 𝑘 ≤ 𝑍)
Front 𝐿1 has the highest dominance, front 𝐿2 has the second highest dominance and so
on. The last front 𝐿𝑍 has the lowest dominance. In the next figure (Fig. 16), three frontiers
are identified, and the best is that one represented by black squares since it is closer to
zero and both the objective functions are to minimize.
56 Reworking of a figure extracted from the following scientific paper: see note 53.
57 Mishra, S., Saha, S., Mondal, S., Coello Coello, C. A. (2019). A divide-and-conquer based efficient non-
dominated sorting approach. Swarm and Evolutionary Computation (vol. 44, pp. 748-773).
85
Fig. 16: example of a Paretian graph58.
In this case, the NS algorithm works in this way: a population of feasible disassembly
sequences is divided into several levels according to their dominated solution number
(level 1 contains solutions of the frontier 1, level 2 encompasses solutions of the frontier
2 and so on). Basically, each solution belongs to a frontier: the best frontier is that one
which dominates all the others; while, the more are the frontiers which dominate a
frontier, the worse is that frontier. An archive A is set to store the solutions of the current
population which cannot be dominated by others. Then, for each population a set of non-
dominated solutions is regarded to verify the possibility to update the archive A. In case
there were better solutions, A is updated by adding such solutions and by removing all
those which became dominated.
The variable neighbourhood descent (VND) approach is useful to improve the quality of
the non-dominated solutions. It explores the neighbourhood space of the current best
solution by continually updating the local optimum in order to get close to the global
optimum. Specifically, VND algorithm passes from a neighbourhood which is distant (in
order to get away from the local optimum) from the current solution to another one, only
if an improvement is made. Here following (Fig. 17), the code of VND is reported.
58 Reworking of a figure extracted from the following scientific paper: see note 57.
86
As it is possible to see, the algorithm ends when all the possible neighbourhoods of the
current solution 𝑥1 in a range of 𝑙𝑚𝑎𝑥 are explored. At each While cycle, the best solution
of the neighbourhood is found and then compared to the current solution: if it’s better, it
becomes the new solution, otherwise another neighbourhood is explored. Once arrived at
𝑙𝑚𝑎𝑥 iterations, it means that all the neighbourhoods have been explored and the algorithm
stops. The searching of the neighbourhoods of the current solution takes place through
some local search techniques such as exchange, insertion, and 2-opt.
(a)
(b)
Input: 𝑥1, 𝑙𝑚𝑎𝑥
Output: 𝑥1
1. 𝑙 = 1
2. While (𝒍 ≤ 𝒍𝒎𝒂𝒙) Do
3. Local search
4. 𝑥𝑙1 = best solution in the neighbourhood
𝐼𝑙(𝑥1)
5. If 𝒙𝒍𝟏 is better than 𝑥1 then
6. 𝑥1 = 𝑥𝑙1
7. 𝑙 = 1
8. Else
9. 𝑙 = 𝑙 + 1
10. End if
11. End While
Fig. 17: code of the VND algorithm.
87
(c)
Fig. 18: (a) exchange. (b) insertion. (c) 2-opt.59
The figure (Fig. 18) above shows one example for each local search technique adopted in
the VND algorithm. Image (a) refers to the exchange technique which consists of
identifying randomly Task 1 of a disassembly sequence, and then Task 2 is the one on its
left or right. Then, the two tasks identified are exchanged. As regards picture (b), the
insertion technique is represented: a task (Task 1) is randomly identified and with it a
random position different from that one where the task identified is currently placed. After
that, the task selected is moved to the position identified and before and after it the
sequence is not altered. Finally, figure (c) shows the 2-opt technique where two random
tasks (Task 1 and Task 2) are selected in the sequence and the subsequence between them
is reversed.
Finally, the three algorithms just described are combined to form the HMM algorithm
proposed to solve the PEDSP. By looking to Fig. 19, it’s possible to have an overview of
the HMM algorithm.
59 Reworking of a figure extracted from the following scientific paper: see note 53.
88
Fig. 19: scheme which gives an overview of the HMM algorithm used to solve the PEDSP60.
Also in this case, the HMM algorithm just described could be included in a decision
support system. Specifically, it would consist in a model-driven DSS where the input
database would be composed by the data related to the characteristics of the product to be
disassembled along with the data related to the costs, consumption of energy and revenues
associated to each recovery activity, transportation costs, and preferences of the user. The
algorithm used to solve the PEDSP, obviously, would be placed in the module where
calculations are made. Finally, a user-friendly interface would be created in order to allow
users to have access to data and manage them according to their needs and preferences.
60 SOURCE: see note 53.
89
7.4 EXAMPLES OF MODELS AND METHODS WHICH COULD BE
IMPLEMENTED ON A DSS FOR THE SELECTION OF END-OF-LIFE
PRODUCT RECOVERY STRATEGIES
In the field of end-of-life (EoL) product recovery strategies there are a lot of decision
methods which could be implemented on a DSS and whose common aim is to find the
most suitable option for the specific product. The research about this topic is increasing,
pushed by the legislation which extended the responsibility of OEMs (Original
Equipment Manufacturer) on the product by including the take-back process once the
product reaches its end-of-life stage. Specifically, manufacturers are responsible for the
entire life cycle of their products, from the design phase until the recovery or disposal
phase. Some differences among EoL product recovery decision methods come from the
diverse interpretation of the expression end-of-life: some researches identify the term as
the moment when the product does not satisfy the end user anymore, therefore it refers to
the last user of the product; others refer the definition to the first user who implements
reuse or minor repair strategies on the item. Other differences regard the amount and the
type of factors and criteria included in the decision approach. However, the literature
about this topic is overall univocal as regards the definition of Product Recovery Strategy
(PRS). Generally, a EoL option can be considered a product recovery strategy if implies
the collection of used products, the reprocessing phase and the stage of distribution of the
reprocessed item. In this regard, remanufacture, repair, recondition, repurpose,
cannibalization, refurbish and recycle are considered the most important EoL product
recovery strategies. In the Annex (Tab. 2), there’s a table containing the definitions of
each product recovery strategy.
It is possible to divide end-of-life decision methods in three main categories according to
many factors such as inputs and outputs, criteria considered, objectives to fulfil, degree
of involvement of the user and so on. The three groups are the following:
• Optimization methods;
• Multi-criteria decision methods;
• Empirical methods.
90
7.4.1 Optimization methods
Optimization methods represent the majority in the literature. Basically, they use
optimization problems such as mathematical models, mixed integer programming models
and numerical models to find the most suitable strategy for a typical product. The main
limit of these methods is the exclusive focus on quantitative objectives such as cost and
economic benefit without considering other unquantifiable factors such as the
environment, social and legislative aspects, and the preferences of the user. Another con
of such models is their complexity which not only do not allow to take into account
qualitative criteria along with quantitative factors, but they make also this approach
difficult to be used by the industry. An example of optimization method is a multi-
objective meta-heuristic algorithm which is used to determine the best joint decision-
making on automated disassembly system scheme selection and recovery route
assignment61. Specifically, the algorithm is divided in two phase: in the first stage the
optimal disassembly depth, disassembly sequence and recovery route in terms of
maximization of the profit and minimization of the consumption of energy are found for
any WEEE (Waste Electrical & Electronic Equipment) with a given automated
disassembly system (ADS) ; in the second phase the optimal ADS is selected from a set
of ADS schemes. For the purposes of this work, only the first phase is relevant, and the
related optimization model is reported here following. First at all, as it has been already
said, it is required to maximize the profit defined as the difference between the recovery
income and the disassembly cost (energy and equipment), and to maximize the energy
saved expressed as the difference between the material energy spared thanks to the
recovery and the energy consumed through the disassembly process. Let’s list the
variables and constants needed to define the mathematical model.
𝐺𝑖𝑘: it’s the revenue gained by assigning recovery option 𝑘 to component 𝑖
𝑘 = 1: renovating for reuse
𝑘 = 2: directly reuse
𝑘 = 3: material recycling
61 Tao, Y., Meng, K., Lou, P., Peng, X., Qian, X. (2019). Joint decision-making on automated disassembly
system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm.
International Journal of Production Research (vol. 57, pp. 124-142).
91
𝑟𝑖𝑘 = {1, 𝑖𝑓 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑜𝑝𝑡𝑖𝑜𝑛 𝑘 𝑖𝑠 𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑 𝑡𝑜 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑖0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝜑𝑖𝑗 = {1, 𝑖𝑓 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑗 𝑖𝑠 𝑑𝑖𝑠𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑒𝑑 𝑖𝑚𝑚𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑙𝑦 𝑎𝑓𝑡𝑒𝑟 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑖
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑃𝑀(𝑖, 𝑗): it’s the matrix of precedencies among components to be disassembled;
𝑃𝑀(𝑖, 𝑗) = {1, 𝑖𝑓 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑖 𝑚𝑢𝑠𝑡 𝑏𝑒 𝑑𝑖𝑠𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑒𝑑 𝑏𝑒𝑓𝑜𝑟𝑒 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑗
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
FE: total power consumption to disassemble a product
𝐸𝑐𝑢: cost of energy per unit
T: total time to disassemble a product
𝐷𝑒𝑢: depreciation cost of automated disassembly equipment per time unit
𝑀𝑖: material energy obtainable from part 𝑖
𝑆𝑖: mass of component 𝑖
𝐻: group of hazardous components
𝑅: minimum part recovery ratio
𝑥𝑖 = {1, 𝑖𝑓 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑖 𝑠ℎ𝑜𝑢𝑙𝑑 𝑏𝑒 𝑑𝑖𝑠𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑒𝑑0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝜇𝑖: average material recovery ratio of component 𝑖
𝑛: total number of components in a product
𝑀𝑎𝑥 ∑ ∑(𝐺𝑖𝑘 ∙ 𝑟𝑖𝑘) − 𝐹𝐸 ∙ 𝐸𝑐𝑢 − 𝑇 ∙ 𝐷𝑒𝑢
3
𝑘=1
𝑛
𝑖=1
(1)
𝑀𝑎𝑥 ∑(𝑀𝑖 ∙ 𝑥𝑖)
𝑛
𝑖=1
+ ∑𝜇𝑖𝑀𝑖 ∙ (1 − 𝑥𝑖) − 𝐹𝐸
𝑛
𝑖=1
(2)
In formula (1) the revenue is represented by (𝐺𝑖𝑘 ∙ 𝑟𝑖𝑘), 𝐹𝐸 ∙ 𝐸𝑐𝑢 and refers to the cost of
the energy consumed, while 𝑇 ∙ 𝐷𝑒𝑢 is the cost for the usage of the equipment. Formula
(2) can be divided in three parts: ∑ (𝑀𝑖 ∙ 𝑥𝑖)𝑛𝑖=1 , ∑ 𝜇𝑖𝑀𝑖 ∙ (1 − 𝑥𝑖)
𝑛𝑖=1 and 𝐹𝐸. This latter,
as it has been explained before, refers to the total power consumption to disassemble the
product. As regards the first two elements, they are activated or deactivated depending on
the value of the decision variable 𝑥𝑖. If it takes value 1, component 𝑖 should be
disassembled and the material energy associated to it is fully recovered. In mathematical
terms, ∑ (𝑀𝑖 ∙ 𝑥𝑖)𝑛𝑖=1 is active while ∑ 𝜇𝑖𝑀𝑖 ∙ (1 − 𝑥𝑖)
𝑛𝑖=1 becomes 0. If 𝑥𝑖 = 0,
component 𝑖 does not need to be disassembled and only a fraction of its material energy
92
can be recovered. In this case, ∑ (𝑀𝑖 ∙ 𝑥𝑖)𝑛𝑖=1 goes to zero while ∑ 𝜇𝑖𝑀𝑖 ∙ (1 − 𝑥𝑖)
𝑛𝑖=1 is
active and the fraction of material energy recovered is represented by 𝜇𝑖𝑀𝑖. The objective
functions are subject to the following constraints.
∑ 𝑟𝑖𝑘 = 1 ∀𝑖 = 1, 2, . . . , 𝑛
3
𝑘=1
(3)
∑𝑆𝑖 ∙𝑥𝑖
∑ 𝑆𝑖𝑛𝑖=1
≥ 𝑅
𝑛
𝑖=1
(4)
𝑟𝑖2 + 𝑟𝑖3 ≤ 𝑥𝑖 (5)
∑𝑥𝑖 = 𝐻
𝑖∈𝐻
(6)
𝑥𝑖 ∙ 𝑥𝑗 ≥ 𝜑𝑖𝑗 ∀𝑖, 𝑗 = 1, 2, . . . , 𝑛 (7)
𝜑𝑖𝑗 = 0 ∀𝑗 ∈ {𝑧|𝑃𝑀(𝑧, 𝑖) = 1, 𝑖 = 1, 2, . . . , 𝑛} (8)
𝑟𝑖𝑘, 𝑥𝑖 , 𝜑𝑖𝑗 ∈ {0,1} (9)
Constraint (3) assures that only one recovery option is assigned to all the components.
Constraint number (4) guarantees that the minimum component recovery ratio for an item
is respected. To better understand, for each product a minimum percentage in terms of
mass should be recovered. Constraints (5) and (6) state that the components for which
reuse or remanufacturing options are envisaged and the components composed by toxic
parts must be disassembled. Constraint (7) represents the basic assumption for the
existence of a disassembly sequence which goes from 𝑖 to 𝑗, that is, it can be formed if
and only if both the components must be disassembled. Constraint (8) makes sure that the
matrix of precedencies among components is respected. Finally, constraint (9) establishes
the domain of existence of the decision variables which, as such, can assume only the
values 1 or 0.
A meta-heuristic algorithm is proposed to solve this model because of its higher speed in
getting a feasible solution and because of the elevated complexity which the problem
would assume in case of sophisticated products. The same approach is adopted to solve
the second phase of the problem, that is, the research of the optimal ADS scheme. In this
93
study, however, this meta-heuristic algorithm is not reported. In this case concerning the
optimization methods, only the definition of the mathematical model is relevant for
research purposes.
7.4.2 Multi-criteria decision methods
Multi-criteria decision methods are useful to solve problems characterized by conflicting
factors. A good example is represented by the management of the investment portfolio
where, generally, the solution which would offer the highest return is also that one which
involves a high degree of risk. These techniques, normally, work in this way: they
compare a set of alternatives and they choose the best one according to a set of weighted
criteria. The main advantage of these methods is the opportunity to take into account
qualitative factors along with quantitative variables; moreover, they imply the
involvement of the preferences of the user since he has to decide the weight of the criteria.
On the other hand, however, there’s the risk to shift too much the problem towards a
qualitative sight by losing the objectiveness represented by data and numbers related to
quantitative factors. In the following section a multi-criteria decision-aid (MCDA)
approach is reported. It is focus on the selection of the best recovery alternative for a
product (recycling, reuse, refurbishing, remanufacturing or disposal) among a set of
feasible solutions by considering the preferences of the user, the characteristics of the
product and the impacts of such alternatives from an environmental, social and economic
point of view62. These impacts are measured through a set of indicators which refer to a
group of criteria selected by the user. Moreover, these criteria are weighted, and the user
is responsible also for the allocation of the weights. Therefore, there’s the possibility that,
for the same product, different users choose a different set of alternatives and a different
set of criteria. It’s even possible that users with the same set of alternatives and the same
group of criteria obtain different solutions because they allocate different weights to the
criteria. The MCDA approach is going to be described was developed in AEOLOS, a
project of the 5th Framework Programme of the European Union: Competitive and
Sustainable Growth. The scope of this project was to support users who deal with the
selection of EoL product recovery strategy in finding relevant EoL alternatives and
62 Bufardi, A., Gheorghe, R., Kiritsis, D., Xirouchakis, P. (2004). Multicriteria decision-aid approach for
product end-of-life alternative selection. International Journal of Production Research (vol. 42, pp. 3139-
3157).
94
comparing them according to selected economic, environmental and social factors and to
the preferences of the users. By following this project, the first step consists of defining
the EoL alternatives according to the characteristics of the product. Each alternative is
defined as a set of pairs (𝑒𝑙𝑒𝑚𝑒𝑛𝑡, 𝐸𝑜𝐿 𝑜𝑝𝑡𝑖𝑜𝑛) where an element corresponds to a
component of the product while an EoL option refers to the recovery strategies such as
reuse, recycling, remanufacturing and so on. Therefore, by indicating with 𝑛 the number
of components of a product, an EoL alternative is defined as follows: [(element 1, EoL
option 1), (element 2, EoL option 2), …, (element i, EoL option i), …, (element m, EoL
option m)] where 𝑚 represents the number of components obtained from the disassembly
of the product and subject to a recovery strategy, while EoL option i ∈
{remanufacturing, reclamation, recycling, incineration with energy recovery,
incineration without energy recovery, disposal to landfill}. Clearly, in case the product
is completely disassembled 𝑚 = 𝑛, which means that all the components are subject to a
recovery operation; moreover, there could be cases where different components can have
the same recovery option. By knowing that there are 6 recovery strategies and that a
product is characterized by 𝑛 components, the total number of potential EoL alternatives
is 6𝑛. It is a very big number which results not convenient to use from a practical and
operational perspective; therefore, the user is responsible for removing, from the group
of potential EoL alternatives, those solutions that are not feasible according to the
information available about technological, market and legislative constraints.
After defining the set of potential EoL alternatives, the relevant criteria should be
established. They could be either quantitative, if they are described through a numerical
scale, or qualitative, if they are expressed through a linguistic scale. In general, for each
one, the following characteristics should be indicated:
• Direction of preferences: maximization or minimization;
• Scale of measurement: qualitative or quantitative depending on the data at own
disposition;
• Unit of measurement: for example, depending on the type of product, the
dimensions can be expressed in metres (in case of big sizes) or centimetres (in
case of small sizes).
95
After that, a set of indicators to get measures of the criteria should be listed. These
indicators are developed according to three dimensions: environmental, social and
economic. The list of indicators follows a hierarchical structure where each dimension is
composed by some categories which are associated to some aspects which in turn involve
some indicators. For example, as regards the environmental dimension, by considering
natural resources as category, a possible aspect could be the energy consumption and the
related indicator could be the total non-renewable energy consumption. At this point, each
EoL alternative is evaluated for each indicator. By assuming that the number of EoL
alternatives is ℎ and the number of indicators is 𝑘, for all 𝑖 = 1, 2, . . . , ℎ and all 𝑗 =
1, 2, . . . , 𝑘, 𝐼𝑗(𝐴𝑙𝑡𝑖) stands for the score assigned to alternative 𝑖 related to indicator 𝑗.
After the assessment of the EoL alternatives, another skimming is carried out by
eliminating, for each indicator, the alternatives which report the worst score or by defining
a threshold and discarding those alternatives which are out of this threshold. The result of
the EoL alternatives assessment is a table structured as shown in the following picture
(Fig. 20).
Fig. 20: figure showing the table related to the EoL alternatives evaluation. Each row corresponds to an EoL alternative. Each column identifies an indicator63.
Next step consists of choosing the most suitable MCDA technique to find the best EoL
alternative. It’s a very important phase because there are a lot of kinds of MCDA
problems and related techniques to solve them which can lead to different results.
Furthermore, the risk to adopt an inappropriate method is quite high, as evidenced by the
literature where in some researches, the authors selected the most familiar approach to
63 SOURCE: see note 62.
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themselves by adapting wrongly the decision-making situation to the technique. The
choice of the MCDA technique should be made by considering these four levels:
1) First level: the type of problem. Basically, there are three kinds of problem. The
choice which consists of the selection of some alternatives, the sorting which is
characterized by the assignment of each alternative to a category that belongs to
a set of predefined categories, and, the ranking whose final result, as the word
itself says, is the ranking of the alternatives;
2) Second level: the type and the nature of the data, that is, for example, if they are
qualitative or quantitative or both, if they are cardinal or ordinal or both, and so
on. This dimension comprises also some other aspects like the number of
alternatives, the number of criteria, the tools used for the assessment of data, and
so on;
3) Third level: the type of decision maker, whether is a single person or a group of
people, his availability to interact with the decision-making, his experience with
the MCDA method, his objectives, interests and preferences;
4) Fourth level: the technical aspects of the MCDA approach, specifically, the
aggregation mode, the way it models decision maker’s priorities (trade-offs,
pairwise comparison, etc.) and the compensation mode (total, partial, non-
compensatory).
Once the MCDA technique is chosen, it’s necessary to identify the parameters which
define the method selected. The method most used is the criterion weight but there are
other methods that use other parameters such as ELECTRE III which associates three
parameters to each criterion: the indifference threshold, the preference threshold and the
veto threshold.
Finally, the MCDA method is completely defined and can be applied to the problem. The
result is a ranking of the EoL alternatives. Further analysis can be developed, for example
it is possible to calculate the distance of the best EoL alternative to the ideal one by
considering the weight of each criterion. The ideal EoL alternative is a fictitious solution
which reports the optimal score for each indicator.
Moreover, once obtained the best EoL alternative, before taking it as the final solution
it’s possible to add other constraints to test the feasibility of the solution under different
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perspectives. In case the verification is not satisfactory, the user basically has two paths
to follow: either he considers the successive EoL alternative in the ranking and tests it in
the same way as he has already done with the previous EoL alternative or he restarts the
MCDA method by taking into account new groups of EoL alternatives and indicators.
7.4.3 Empirical methods
According to these methods, the decision regarding the best EoL product recovery
strategy to adopt is based on the knowledge and experience gained from previous cases
which have been successfully analysed. The approach is going to be reported is a good
example to understand better how these kinds of methods work and how they can support
the decision-making in the selection of the EoL product recovery strategy. The case is
going to be described is an intelligent evaluation approach which integrates case-based
reasoning models (CBR), economic analysis model and domain expertise, with the
purpose to choose the most suitable recycling strategy for a product at its end-of-life
(reuse, repair, remanufacturing, disassembly with material recovery, shredding with
material recovery and disposition)64. CBR is considered a sort of evolution of the
traditional rule-based reasoning methods where, simply, past cases are stored in a case
database and the information associated to them are used as support to solve new
problems. CBR goes a step further by leveraging on the artificial intelligence. According
to Kolodner65, to implement CBR approach six steps should be followed:
1) collect past cases;
2) define an area within which a solution and its indices are likely correct;
3) give a justification of the solution and in case adapt the problem;
4) edit or update the case base;
5) assess the inference results;
6) store the case.
The approach proposed in this section is characterized by two stages with two different
CBR models: the first one is used to determine the best EoL recycling strategy; the second
one helps to establish recycling costs and benefits in both material recycling and
64 Shih, L., Chang, Y., Lin, Y. (2006). Intelligent evaluation approach for electronic product recycling via
case-based reasoning. Advanced Engineering Informatics (vol. 20, pp.137-145).
65 Kolodner, J. (1993). Case-based reasoning. Los Altos, CA: Morgan Kaufmann.
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disassembly stages. Finally, the domain expertise allows to determine the parameters of
the model in order to make the computation easier. In the second stage a two-step method
is defined. First at all, information related to the disassembly sequence and the
disassembly operation methods are retrieved. In this case, data concerning the
disassembly time and the percentage of weight of material to be disassembled are
extracted. Through disassembly time, disassembly labour cost is calculated; while, weight
percentage of the parts subject to disassembly operations permits to estimate the revenue
of the recovered material in the disassembly phase. Information about disassembly time
and percentage of weight of disassembled parts are obtained through a set of indices
which are based on literature reports and domain expertise.
The core process of CBR is to identify very similar past cases and extract useful
information from that cases in order to find a solution to the new problem.
The first stage of the approach works in this way. The product characteristics are defined
according to a set of pre-established indices. Some of such indices are "qualitative", such
as the "reason for redesign", and are quantified through an appropriate quantification
system. After that, the indices are normalized (the normalization is needed in order to
make calculations between indices with different unit of measures) and inserted in the
calculation of the similarity scores. The similarity scores are defined as the difference
between new and retrieved case indices and they allow to find the most similar case to
the product to be recycled. Finally, once the most similar case is found, the recycling
strategy is adopted. Here following the formulas of normalization and similarity scores
are reported.
𝑇𝑜𝑡𝑎𝑙 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 =∑ 𝑥𝑖 × 𝑠𝑖𝑚(𝑔𝑖
𝑁, 𝑔𝑖𝑂)𝑛
𝑖=1
∑ 𝑥𝑖𝑛𝑖=1
where
𝑠𝑖𝑚(𝑔𝑖𝑁, 𝑔𝑖
𝑂) = 1 −|𝑔𝑖
𝑁 − 𝑔𝑖𝑂|
[𝑚𝑎𝑥(𝑔𝑖) − 𝑚𝑖𝑛(𝑔𝑖)]
and
𝑥𝑖 represents the weight of the 𝑖-th index. Its value is included between 0 and 1 and the
sum of all the weights gives 1 as result;
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𝑛 is the total number of indices;
𝑠𝑖𝑚 is the function of similarity of the index between new case and old case, it can take
values from 0 and 1;
𝑔𝑖𝑁 stands for the index 𝑖 of the new case;
𝑔𝑖𝑂 stands for the index 𝑖 of the old case.
As already mentioned above, the second stage of the approach is characterized by an
economic evaluation model which allows to estimate recycling costs and revenues.
Specifically, this phase comprises two steps:
1) through a CBR model the disassembly time, needed for the computation of the
disassembly cost, and the disassembled weight percentage of materials, necessary
to calculate the revenue coming from the material saved, are calculated;
2) the information estimated at point 1) regarding disassembly cost and revenue from
material reclaimed are integrated with data about recycling operation cost,
disposal cost and transportation cost in order to carry out the cost-benefit
assessment.
The cost-benefit analysis is conducted following the structure reported in the figure below
(Fig. 21).
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Fig. 21: structure used to conduct the cost-benefit analysis in the second step of the second stage of the intelligent evaluation approach66.
As regards facility and equipment costs shared by one EoL product, they are allocated by
considering the weight of the product in ratio with the recycling plant’s capacity defined
in weight. Finally, it’s possible to summarize the cost-benefit analysis with the following
expression:
𝑇𝑜𝑡𝑎𝑙 𝑏𝑒𝑛𝑒𝑓𝑖𝑡 = 𝑅𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 − 𝐿𝑑𝑖𝑠𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑒 − 𝐿ℎ𝑎𝑛𝑑𝑙𝑒 − 𝐿𝑑𝑖𝑠𝑝𝑜𝑠𝑎𝑙 − 𝐿𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡
where
𝑅𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 represents the revenue coming from the material recovered;
𝐿𝑑𝑖𝑠𝑎𝑠𝑠𝑒𝑚𝑏𝑙𝑒 is the loss due to the disassemble process;
66 SOURCE: see note 64.
101
𝐿ℎ𝑎𝑛𝑑𝑙𝑒 is the loss due to the recycling process;
𝐿𝑑𝑖𝑠𝑝𝑜𝑠𝑎𝑙 is the loss due to the disposal process;
𝐿𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 represents the cost for the transportation.
To summarize the intelligent evaluation approach just explained, it's possible to say that
it's made up of two stages based on two CBR models. In the first stage, CBR is used to
find the most suitable strategy for the EoL product to be recovered. In the second stage,
CBR is used to estimate the disassembly time and the portions of materials disassembled
which are two of the inputs of the economic evaluation model used to conduct the cost-
benefit analysis. To better understand this process, it's possible to have a look at Fig. 22
where the flowchart of the intelligent evaluation approach is reported.
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Fig. 22: figure showing the flowchart of the proposed CBR-based approach67.
67 Reworking of a figure extracted from the following scientific paper: see note 64.
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8. CONCRETE EXAMPLE OF DSS FOR THE
CIRCULAR ECONOMY
The DSS is going to be described as example for the research is a computer-based
program which supports the user in the decision-making process of choosing the best
recovery strategy for a product at the end of its life cycle. It consists of a useful tool which
allows to evaluate the recovery strategies (repairing, refurbishing, reuse and disposal) for
a damaged/used product by taking into account economic and environmental impacts.
Basically the DSS works in this way: the user, who could be a manufacturer or wholesaler,
chooses a used/damaged product which needs to be recovered from a list provided by the
DSS; after that he decides which processes to perform on that product and which recovery
strategies to evaluate by providing also the criteria weights related to the impacts (human,
economic, energetic and environmental); finally, the DSS, with all the information at its
disposition, makes the calculations and gives the final result to the user which is a vector
containing the total benefit that each recovery option selected by the user can generate.
The total benefit is calculated as the revenue minus the total costs.
Specifically, the economic and environmental impacts are:
• Human effort
• Energy consumption
• Total costs: cost of processes + reverse logistics costs
• CO2 emissions
• Ozone depletion
• Part matter
• Photoozone
• Eutrophication
• Land use
• Water depletion
• Distance
• Time
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Actually, only human effort, energy consumption, total costs and CO2 emissions are used
as optimization criteria. Therefore, it’s important to define how they are measured. The
human effort is expressed in PM effort which stands for Person Month effort. Basically,
the Person Month effort is calculated as how many hours of how many people are
necessary for the task. The energy consumption is calculated using kWh as unit of
measure. Total costs are expressed in Euro (€) and, finally, CO2 emissions are computed
as the total amount of CO2 equivalent.
From the circular economy perspective, this tool is used to support and optimize the
process of closing the loop by extending the life of products. Fig. 23 helps to better
understand this process. At the end of the life, the product, according to its state, can be
re-integrated into different stages of the cycle through different correspondent recovery
strategies. Specifically, through maintaining/repairing/upgrading activity the product
comes back to the last user, through reusing and redistributing options it is sent to the
distribution phase where is transferred to another user, in case of refurbishing or
remanufacturing it returns to the manufacturing stage where is regenerated as if it was
new and, finally, through the recycling process, some parts of the product are recovered
and become raw materials either for the same process which made it or for other processes
which use the same materials as raw materials. In case no recovery strategy can be applied
to the product, it is disposed through the traditional process.
Fig. 23: figure showing the circular economy's concept of closing the loop: the reintroduction of the product, at its end-of-life, in the different stages of the cycle according to its current state.
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Before describing the architecture of the DSS and explaining in detail all the modules
which compose it, it’s important to understand the process followed by the tool, from the
information filled by the user to the results given by the DSS. In this regard, let’s assume
the user’s point of view.
1) The user selects a product from the list provided by the DSS;
2) The characteristics and a picture of the product selected are shown by the DSS.
Sometimes, if it’s filled, also a survey related to the current state of the product is
reported;
3) The user selects the strategies he wants to evaluate for that product. The options
are the following: repairing, refurbishing, reuse and dispose. Moreover, for
repairing and refurbishing strategies, he can choose the specific processes to
perform on the product; in this way sub-strategies are created. For example, by
looking to Fig. 24, let’s consider a table which needs to be repaired because a leg
is broken. In the repairing strategy (as well as in the refurbishing strategy), it’s
possible to build more than one scenario by selecting different sequences of
actions to be carried out on the product. For instance, one scenario (“SCENARIO
1” in the picture) could be composed by the process for repairing the leg and the
painting process, another scenario (“SCENARIO 2” in the picture) could include
other actions in addition to the processes of leg repairing and painting. As regards
reuse and dispose processes, instead, since no actions are envisaged, only
transportation costs (to transport the product to recover to the new user for the
reuse process, and to transport the item to the disposal facility for the dispose
process) and environmental impacts are considered. In this phase, the user chooses
also the weights (values between 0 and 1) to assign to the different optimization
criteria according to his needs and preferences. The optimization criteria are the
human effort (PM effort), the consumption of energy (kWh), the total costs (€)
and the environment (CO2 emissions measured through CO2 equivalent).
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Fig. 24: example of repairing strategy to be evaluated by the DSS. Each scenario has a different sequence of actions to be performed on the product and represents a sub-strategy.
4) The DSS makes the calculations and shows the report of the results to the user
which is a vector composed by four spaces, each one associated to a recovery
strategy (the first space refers to the repairing case, the second cell to the
refurbishing option, the third cell to the reuse strategy and the fourth space to the
disposal case). The values in the vector represent the total benefit, calculated as
total revenue minus total costs, generated by the recovery strategy.
5) The user selects the most suitable recovery strategy for the product selected
according to the results provided by the DSS and his specific circumstances.
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8.1 DSS ARCHITECTURE
It consists of a model-driven DSS and, as such, is composed by a user interface which
allows the user to easily communicate with the system, a knowledge base which contains
the database storing the information which needs to be processed by the DSS, an inference
engine which combines the data given by the user with the information coming from the
database to create the inputs to send to the calculation module and shows the report of the
results to the user, and a model base composed by the algorithms used to process the
information and obtain the results which are finally sent to the inference engine.
In Fig. 25 the architecture of the DSS along with the relationships between the modules
is reported.
Before going to describe in detail each module, let’s start to define the flows of
information which travel between the different sections of the DSS. It is possible to say
that flow 1 is equal to flow 2, as well as flow 16 is equal to flow 17 because in both cases
there is the user interface which serves only as a link between the user and the system.
The user interface does not process the information in inputs which, therefore, are equal
to those in output. Basically, the user interface allows the user to insert the data, which
are needed to the system, in the easiest possible way. Anyway, flow 1/2 contains user’s
data, that is, the characteristics of the product selected to be recovered (ID, name, size,
mass, latitude, longitude and so on), the recovery strategies to be compared and evaluated
along with the sub-strategies for the repairing and refurbishing options and the weights
assigned to the criteria related to human effort, energy consumption, total costs and
environmental impacts. This information is sent to the inference engine. Flow 3 comes
from the database placed in the knowledge base and goes to the inference engine. Flow 3
refers to the data related to the locations of the facilities and to the processes which can
be performed on the products to be recovered present in the list provided by the DSS.
Flow 4 represents the input for the main function of the DSS present in the model base
where calculations are made. Specifically, such input is the combination of data coming
from the database and information provided by the user through the user interface. Flows
from 5 to 14 consist of exchange of information between the main function of the DSS,
where the main algorithms needed to solve the decision-making process are present, and
some other functions called by the main function in order to carry out some basic
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operations. For example, flow 5 represents the input for the distance calculator function
and flow 6 the output of this function. The same mechanism occurs for the reverse
logistics API function (flow 7 is the input, flow 8 is the output), the cost matrix function
(flow 9 is the input, flow 10 is the output), the update minmax values function (flow 11
is the input, flow 12 is the output) and the fitness function (flow 13 is the input, flow 14
is the output). In the following section, each function will be described in detail. After
that the main function has made all the calculations, the output containing the results of
the decision-making process is sent to the inference engine (flow 15). Finally, the
inference engine shows the report of the results to the user through the user interface (flow
16 which is equal to flow 17).
Notice that some arrows are black, and some are reds: red arrows represent flows of
information which occur once per product, while black arrows stand for flows of
information which occur more than once per product. For instance, the distance
calculator function is used for the assessment of each recovery strategy chosen by the
user for the product selected and, since it is possible to talk about decision-making
problem if at least two recovery strategies per product are selected to be evaluated
(otherwise obviously, with only one strategy selected there would be no decisions to take),
this function is always used at least twice per product.
Finally, it is possible to observe that all the components in each module are blue except
for the functions cost matrix, update minmax values and fitness which are used only for
the evaluation of repairing and reuse strategies.
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MODEL BASE
INFERENCE
ENGINE
KNOWLEADGE BASE
DSS MAIN FUNCTION
DISTANCE
CALCULATOR
USER
INTERFACE
USER
REVERSE
LOGISTICS
API
COST
MATRIX
UPDATE
MINMAX
VALUES
FITNESS
FUNCTION
TEST DSS DATABASE DATA GENERATOR
1
2
v
3
v
15
4
v
5
v
6
7
v
8
v
9
v
10
v
11
v
12
v
13
14
v
16
17
Fig. 25: figure showing the architecture of the DSS.
Fig. 25
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8.2 DSS COMPONENTS AND MODULES
First at all, all the modules have been developed through the MATLAB68 software except
for the user interface which has been built separately from the DSS.
8.2.1 Knowledge base
The knowledge base is composed by the database. Before starting to describe the structure
and the contents of the database, it’s necessary to specify that the data it stores are not
real but are plausible and are generated randomly by the module data generator.
Basically, the database is characterized by two tables of type structure array (struct):
location and process. A structure array in MATLAB is a data type which groups related
data using data containers called fields. Each field can accept data of any kind or size.
locations is a structure array with 5 fields (the dimension of locations depends on the
number of existent facilities which varies because data are generated randomly and each
time there is the possibility to have a different amount of total locations) which means
that is composed by 5 data containers which are id of type character, lat of type numeric,
long of type numeric, owner of type cell array (data type similar to structure array where
data containers are called cell; the main difference with structure arrays is that cell arrays
are indexed by integers while structure arrays by key-value pairs and they store data in a
hierarchy) and action of type structure. Clearly, id represents the identification code of
the facility, lat and long are respectively the latitude and the longitude which identifies
the location of the facility, owner contains the name of the owner of the facility and
actions is a structure array (the dimension of actions depends on the number of actions
such facility can perform and varies because data are generated randomly and each time
there is the possibility that a location can carry out different amount of actions) with 2
fields which are action of type cell array containing the ID of the action that can be
performed, and modifier which contains the vector of multipliers to be used to calculate
the human effort, the energy consumption, the CO2 emissions and the process costs for
that action (each facility for each process it can perform has different modifiers; there’s
the possibility that two facilities can carry out the same process but with different values
of human effort, energy consumption, CO2 emissions and process costs according to the
68 MATLAB and Statistics Toolbox Release 2019b, The MathWorks, Inc., Natick, Massachusetts, United
States.
9
v
111
different value of the modifiers). To better understand how the table location is organized,
it is suggested to have look to Fig. 26.
Fig. 26: table "location" containing the list of the facilities which can process the products to be recovered with the related main information.
The table process is a 1x6 structure array with 7 fields which are name of type character
containing the name of the process, id of type character storing the ID of the process,
description of type character reporting a very short description of the process, pmEffort,
kWh, co2 and cost of type numeric which contain respectively the values of human effort,
energy consumption, CO2 emissions and cost of the correspondent process. Fig. 27
reports the table process.
Fig. 27: table "process" containing the list of processes and the main information associated to them which can be carried out on the products to be recovered.
8.2.2 Inference engine
The inference engine is composed only by the MATLAB document testDSS which carries
out the following actions:
• It takes the data filled by the user through the user interface and the information
related to the processes, stored in the variable processDB, and to the facilities,
stored in the variable locationDB from the database in order to create the input for
the main function of the DSS;
• It calls the main function of the DSS by giving as input the data and the
information described in the previous point;
112
• It shows the results obtained from the main function of the DSS to the user through
the user interface.
Specifically, the data inserted by the user through the user interface are:
• The selected product to be recovered along with its main characteristics which are
stored in a 1x1 structure array called product. This structure array has 7 fields
which are id of type character containing the ID of the product, type which is a
cell array reporting the type of the product (“Chair”, “Table” “Sofa”, ”Cupboard”,
“Shelving” unit”, “Chest of drawers”), name of type character showing the name
of the product, description which is a character containing a short description of
the product, size which is a 1x1 structure array where the fields are the dimensions
of the product, that is, the height, the length and the width, and finally, mass and
price of type numeric which report respectively the mass and the price of the
product. Fig. 28 shows an example of the structure array product.
Fig. 28: table of the information of the product selected by the user for the evaluation of the recovery strategies.
• His personal information regarding the location, specifically, longitude and
latitude. This information is stored in the 1x1 structure array called productCords
which is reported in the figure below (Fig. 29).
Fig. 29: figure showing the structure array "productCords" which stores the location of the user defined by the latitude and the longitude.
• The recovery strategies he wants to evaluate for the product selected. As explained
previously, the recovery options the user can choose are repairing, refurbishing,
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reuse and disposing. The information related to the recovery strategies selected
by the user is collected in a row vector of 4 spaces, as the number of available
recovery options, where the first space corresponds to repairing, the second to
refurbishing, the third to reuse and the fourth to disposing. If the space contains
the value 1, it means that the recovery strategy associated to it is selected,
otherwise not and, in that case, the space has the value 0. Therefore, for instance,
the row vector [1 0 0 1] means that the user wants to evaluate only the repairing
and the disposing strategies. This information is stored in the numeric field
circularStrategies which belongs to a 1x1 structure array called dssConfig.
• The weights associated to the impacts to evaluate for each recovery strategy. As
in the previous point, there is a row vector with four spaces where the first element
refers to the human effort, the second one to the energy consumption, the third
one to the total costs and the fourth one to the environmental impacts. The weights
can vary from 0 to 1. For example, a row vector [1 1 1 1] means that all the impacts
are evaluated with the same weight, that is, the user gives the same importance to
all the impacts. This vector is stored in the second numeric field of the structure
array dssConfig which is called optimOptions. The figure below (Fig. 30) shows
an example of how the structure array dssConfig is built.
Fig. 30: figure reporting the 1x1 structure array "dssConfig" along with the numeric fields "circularStrategies" and "optimOptions" associated to it.
• The information related to each recovery strategy. Specifically, for the repairing
and the refurbishing cases, the sequences of actions and the revenue associated to
each sub-strategy are reported. In the reuse option, only the revenue and the user
geographical coordinates (latitude and longitude) are reported and, finally, for the
dispose strategy, in addition to such data, kWh consumed, CO2 emissions and total
process costs are collected. Notice that in the dispose case the revenue is zero
because this option implies only costs. The 1x4 structure array dssActions stores
this information. dssActions is composed by 4 rows, each one corresponding to a
recovery strategy, and 8 fields which are name of type character which reports the
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name of the recovery option, actionGroup that is a structure array whose
dimension depends on the number of existent sub-strategies (for instance, if the
repairing case is characterized by 4 sub-strategies, actionGroup, for the row which
refers to the repairing option, will be a 1x4 structure array), revenue of type
numeric storing the revenue associated to each recovery alternative,
newUserCords which is a 1x1 structure array with two fields referring to the
latitude and the longitude defining the user location, kWh (numeric type), co2
(numeric type), cost (numeric type) and disposalCords (1x1 structure array with
two fields related to the latitude and the longitude of the disposal facility) which
are filled only for the disposal case and respectively correspond to the energy
consumption, the CO2 emissions, the process costs and the location of the disposal
facility. dssActions is represented in Fig. 31.
Fig.31: figure showing an example the structure array "dssActions".
8.2.3 Model base
The model base is the module containing the main function of the DSS called DSS_fcn
along with all the other shorter functions needed to DSS_fcn to complete the calculations.
The shorter functions are:
• distanceCalculatorGoogle_fcn which, by receiving as input the latitude and the
longitude of both the starting point (lat1, long1) and the end point (lat2, long2),
and an API key of Google Maps, gives back the total distance to walk in km
(totalDistance is the name of the output in the code of the
distanceCalculatorGoogle_fcn function, while in the main function of the DSS it
becomes tempDistance) and the total time needed in hours (totalTime is the name
of the output in the code of the distanceCalculatorGoogle_fcn function, while in
the main function of the DSS it becomes tempTime). Basically, the API key
allows to connect the system with Google maps in order to get the information
about the total distance and the total time in the most accurate possible way
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according to the input data which express locations through latitude and
longitude;
• reverseLogisticsAPI which, given the structure array with 2 fields (the dimension
depends on the number of existent routes which is random because it depends on
the locations which are generated randomly. Basically, given a sequence of
actions for a sub-strategy, different paths defined as sum of routes can be
identified according to where, that is, in which location, the actions of the
sequence can be carried out. Since locations and the actions they can perform are
generated randomly, also the paths, which depend on them, can vary) called
Distancias containing the list of all the possible routes for a given sequence of
actions (the two fields are: name of type character which refers to the name of the
route and distance of type numeric which contains the distance of that route), the
type of vehicle (TipoVehiculo), the weight (PesoCarga) and the volume
(VolumenCarga) of the cargo to transport, returns a structure array rlResults
composed by 4 fields (the dimension depends on the number of existent routes)
which are name of type character representing the name of the route, distance of
type numeric reporting the distance in km for that route, cost of type numeric
containing the transportation costs for that route and the 1x1 structure array
environmental_impact which is composed by 7 fields of type numeric referring
to the 7 voices related to the reverse logistics impacts for that route (CO2
emissions, ozone depletion, part matter, photo ozone, eutrophication, land use
and water depletion). In Fig. 32, the structure array Distancias (a), which is the
main input for this function and the structure rlResults (b), which is the output of
the function are reported;
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(a) (b)
Fig. 32: (a) example of the structure array “Distancias” which is one of the input of the function; (b) example of the structure array “rlResults” which is the output of the function.
• costMatrix_fcn which creates a numeric column matrix (realValuesMatrix)
containing 14 voices (rows) associated to the reference route which are in order:
the human effort measured in PM effort, the energy consumption measured in
kWh, the total costs, measured in €, which are the sum of the transportation costs
(row 6) and the process costs (row 5), the environmental impact represented by
CO2 emissions expressed as CO2 equivalent, the process cost, the transportation
cost, the ozone depletion, the part matter, the photo ozone, the eutrophication, the
land use, the water depletion, the total distance and the total time needed to walk
such distance. The reference route is temporary stored in the 1x1 structure array
tempCostValues composed by 7 fields which are start of type character
containing the name of the starting point of the route, end of type character
containing the name of the end point of the route, distance of type numeric
reporting the distance in km of that route, time storing the time in hours needed
to walk that route, name of type character which contains the name of that route,
level of type numeric which reports the level which that route belongs to
(according to where the actions of a sequence related to a sub-strategy can be
performed, different paths can be followed since there’s the possibility that the
same action can be carried out at different facilities; therefore, the existence of
alternatives implies the creation of a tree where each level corresponds to an
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action to be performed, for instance, level 1 represents the situation where the
first action of the sequence should be carried out) and the 1x1 structure array
impacts characterized by 12 fields representing the reverse logistics impacts and
costs for that route. Here following, in Fig. 33, an example of tempCostValues
structure (a) and realValuesMatrix matrix (b) are shown;
(a) (b)
Fig. 33: (a) example of “tempCostValues” structure array which is input for the function; (b) example of “realValuesMatrix” column matrix which is the output of the function.
• updateMinMaxValues_fcn which creates two matrices: minMatrix containing the
minimum value for each impact voice among all the paths evaluated at that
moment and maxMatrix having the highest value for each impact voice among
all the paths evaluated at that moment. These two matrices are used for the
normalization operation, therefore, this function is used only for the repairing and
refurbishing cases where normalization is needed (specifically, normalization is
needed to allow to sum impacts having different units of measures, so that, later,
it’s possible to compare the different sub-strategies). The input, in addition to the
revenue used to transform the total costs into inverse benefit, and minMatrix and
maxMatrix, which should be updated at each cycle (each path to evaluate), is the
tempRealValuesCosts matrix (which becomes tempCostValues in the code of the
function) which has 14 rows, as the number of impact voices considered for each
path, and a number of columns equal to the number of routes composing the
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reference path (under evaluation). In Fig. 34, an example of
tempRealValuesCosts matrix (a) and an example of minMatrix and maxMatrix
matrices (b) are illustrated;
(a) (b)
Fig. 34: (a) example of "tempRealValuesCosts" matrix which is input for the function; (b) example of "minMatrix" (on the left) and "maxMatrix" (on the right) which are input for the function.
• fitness_fcn which is used to find the path for a sub-strategy which minimizes the
total costs normalized. Also this function, as updateMinMaxValues_fcn function,
is used only for the repairing and the refurbishing strategies. Basically, for each
sub-strategy, the user can select a sequence of actions. According to where (in
which facility) each action can be performed, different paths can be followed.
Notice that a path is defined as a sequence of routes. Each path implies multiple
impacts which have different unit of measures. The normalization allows to
define a common denominator among the different impacts which, in this way,
can be summed by giving the total impact for each path. Through the fitness_fcn
function, normalization is made and, finally, the path recording the minimum
total impact is identified and stored in the variable of type numeric minCost.
Among the others input (the weights assigned to the different kinds of impact,
the revenue used to transform total costs into inverse benefits, the matrices of
minimum and maximum values for each impact used for the normalization
operation), the structure array detailedTripMatrix (which becomes
tempCostValues in the code of the function) represents the main input for this
function. Its dimension depends on the number of possible existent paths and the
fields are stops of type cell array which contains the sequence of facilities
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composing the path, evaluationResults of type numeric which stores the matrix
of the impacts of each route composing the path (14 rows, one for each impact,
and a number of columns equal to the number of routes composing the path), lvlld
of type numeric which is a column matrix containing the sequence of levels
associated to each action composing the path. This last definition can be better
understood through the following example: let’s suppose that the sequence of
actions is AC001-AC007-AC004-AC006. In the following figure (Fig. 35), the
list of facilities where each action can be performed is reported. The red path has
the following sequence of levels [1;1;1;1] where all is 1 because for each action
is chosen the first facility available from the list; while, the green path has the
following sequence of levels [2;1;1;2] where for the first and the last actions are
chosen the second facilities available from the correspondent lists.
Fig. 35: example of a sequence of actions with the correspondent lists of facilities where each action can be performed. The red and the green paths are two examples of possible paths which can be followed to carry out the
correspondent sequence of actions.
In the next figure, Fig. 36, an example of the structure detailedTripMatrix is
reported.
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Fig. 36: (a) example of “detailedTripMatrix” structure which is input for the function; (b) first part of the code of the "fitness_fcn" function; (c) second part of the code of the "fitness_fcn" function.
In the next section, the main function of the DSS is described and the shorter functions
just explained can be better understood because it will be clear the context where they
will be used. First at all, it’s important to notice that the most complex calculations are
made for the repairing and refurbishing cases where it’s necessary to select the best sub-
strategy which will be, later, compared with the results of the other strategies.
Specifically, in these cases, the problem to solve is a multi-objective optimization
problem (for reuse and disposal cases, there is only one alternative, therefore, no
comparing calculations are needed). Basically, for each sub-strategy represented by a
sequence of actions, different paths are evaluated and the system selects the best one
according to four optimization criteria which are the human effort, the energy
consumption, the total costs and the CO2 emissions. The weighted global criterion method
is adopted to find the best path, while, the different paths are discovered through the
Depth-First Search algorithm (DFS). Since no preference information of the decision
maker is considered, the weighted global criterion method belongs to the class of no-
preference methods. This method is based on the following main function:
𝑀𝑖𝑛 𝑍 = ∑𝑓𝑗𝑡𝑟𝑎𝑛𝑠 ∙ 𝑤𝑗
𝑁
𝑗=1
(1)
where 𝑤𝑗 is the weight assigned to criteria 𝑗 and 𝑓𝑗𝑡𝑟𝑎𝑛𝑠 represents the value of criteria 𝑗
normalized through the following formula:
𝑓𝑗𝑡𝑟𝑎𝑛𝑠 =
𝑓𝑗(𝑥, 𝑦) − 𝑓𝑗𝑚𝑖𝑛
𝑓𝑗𝑚𝑎𝑥 − 𝑓𝑗
𝑚𝑖𝑛 (2)
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𝑓𝑗(𝑥, 𝑦) is the criteria 𝑗;
𝑓𝑗𝑚𝑎𝑥 is the maximum value for the criteria 𝑗 among all the possible paths of a scenario;
𝑓𝑗𝑚𝑖𝑛 is the minimum value for the criteria 𝑗 among all the possible paths of a scenario.
By summarizing, basically, the weighted global criterion method allows to solve the
multi-objective optimization problem of finding the best path for a sub-strategy where the
multiple objectives are the minimization of human effort, the minimization of energy
consumption, the minimization of CO2 emissions and the minimization of total costs. For
each path, through the normalization calculation, it is assigned a value which represents
the sum of the economic and environmental impacts generated by the choice of that path.
Even if economic and environmental criteria have different units of measure, they can be
summed through the normalization process well shown in the equation (2). Equation (1),
instead, is used when the best path, in each sub-strategy of repairing or refurbishing cases,
should be chosen: for each path, environmental and economic criteria are multiplied by
the correspondent weights and then summed; after that, the path with the lowest value is
selected. Once that, for each sub-strategy (or scenario), the best paths have been selected,
the system chooses as best, the one with the lowest normalized value. Finally, the total
benefit for the recovery strategy under analysis is calculated as the difference between
the total revenue and the total costs (not normalized) of the best path (representing the
best sub-strategy) chosen before.
As regards the depth-first search approach, its logic is used to discover all the possible
paths for a sequence of actions representing a sub-strategy. The depth-first search
approach is defined as an algorithm to explore tree or graph data structures. Basically, the
process consists of starting from the root node (for graph data structures the root node is
chosen randomly) and exploring as deep as possible each branch before backtracking. To
better understand, an example is shown here following. Consider a solution A,
representing the root node, which is not feasible and from which solutions B and C derive
(Fig. 37).
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Fig. 37: problem state after the first branching.
Then, solution B is explored. It’s not feasible and gives two other solutions: D and E (Fig.
38).
Fig. 38: problem state after the second branching.
According to the first-depth search approach, before exploring solution E, and later, by
backtracking, solution C, the left branch of A, where the exploration process started, must
end to be studied, therefore, all the solutions coming from it are analysed before E and C.
In this case, the process must proceed by exploring solution D. D is not feasible and
generates the solutions F and G (Fig. 39).
Fig. 39: problem state after the third branching.
For the same reason explained before, the next solution to be explored is F and since it is
feasible, the process must be stopped and now the backtracking must start from the
solution G. G is feasible so it must stop (Fig. 40).
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Fig. 40: problem state after exploring solutions F and G. The green circle represents the root node where the process started, the blue circles stands for the solutions which have been branched because not feasible and the yellow
circles are the feasible solutions.
Then, after G, solution E is explored and since is feasible is not be branched and the
process can move to explore C (Fig. 41).
Fig. 41: problem state after exploring solutions F, G and E.
The last solution to be studied is C and, since like solutions F, G and E, is feasible,
therefore, the process definitely stops (Fig. 42).
Fig. 42: final tree representing the problem.
In the specific case of the DSS which is being described, the process just explained is
used to discover all the possible paths for a sub-strategy. Therefore, instead of solutions
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(A, B, C and so on), there are the facilities where the product can go to be processed. To
better understand, by considering the pictures used to describe the depth-first search
algorithm (from Fig. 37 to Fig. 42), the first path would be A-B-D-F, after that it’s
possible to start the backtracking, therefore, in correct order, the following paths would
be A-B-D-G, A-B-E and finally A-C.
After this premise, it’s possible to describe the main function of the DSS. First at all, the
inputs of the function are:
• The characteristics of the product selected by the user (product);
• The coordinates of the product (productCords);
• The list of the recovery strategies to be evaluated selected by the user and the
weights assigned to the impact factors (dssConfig);
• The information related to the recovery strategies to be evaluated selected by the
user (dssActions);
• The list of the facilities with the correspondent information (locationDB);
• The list of the processes which can be performed on the product selected with the
correspondent information (processDB).
The outputs of the function are:
• totalCost which is a numeric row vector containing the total benefit of each
recovery strategy evaluated; it represents the most important output because it’s
from this row vector that the user takes the decision about the most suitable
recovery strategy to adopt for the product selected; in Fig. 43 is presented an
example of row vector totalCost;
Fig. 43: example of "totalCost" row vector. The first cell refers to the repairing case, the second one to the refurbishing case, the third one to the reuse strategy and the last one to the disposal option.
• detailedCosts which is a structure containing the detailed information regarding
the total costs of each recovery strategy evaluated. Specifically, it is composed by
four structure arrays (one for each row) each one corresponding to a recovery
strategy (repairingCase, refurbishingCase, reuseCase, disposalCase). Then, each
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of these structures is composed by other two structures, one storing the
information of the best scenario (optimumRepairingScenario,
optimumRefurbishingScenario, optimumReuseScenario,
optimumDisposalScenario) and one saving the information regarding all
scenarios (scenarioResult). This information is the total normalized costs
(totalCostPU), the total costs not normalized (totalCost), the total revenue
(totalRevenue) and the results regarding the economic and environmental impacts
(evaluationResults). For the repairing and refurbishing cases there are also the
details related to the paths expressed through the variables lvlld (concerning the
levels which define a specific path) and stops (which stores the sequence of the
facilities which compose the path). Actually, both stops and lvlld are present also
in the reuse and disposal cases but are empty since the only existent path is from
the place where the product to recover is stored, to the new user, for the reuse
case, or to the disposal facility, for the disposal case. In the figure below (Fig. 44),
it’s possible to see the hierarchical structure of detailedCosts. Notice that for reuse
and disposal cases, optimumReuseScenario/optimumDisposalScenario is equal to
scenarioResult because there is no more than one scenario, therefore, the only one
existent is also the optimal one. Actually, in this case, also the refurbishing option
has optimumRefurbishingScenario equal to scenarioResult because, in this
specific example, is characterized by only one scenario. For this reason, in the
picture, except for the repairing case which has more than one scenario, for each
recovery strategy only the variable storing the optimal results is reported because
the other one, which would contain the information of the other scenarios, is the
same.
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Fig. 44: hierarchical graph representing the structure "detailedCost".
Now, it’s possible to describe the code associated to the main function of the DSS step
by step.
In the first part, there is an if to check whether the user has selected at least a recovery
strategy to evaluate, otherwise the process ends immediately because there is nothing to
assess. Moreover, the vector totalCost and the structure detailedCost along with all its
sub-sections are created and are, obviously, empty for the moment.
After that, the next section opens with an if (if1) to verify whether the repairing option
has been selected by the user for the evaluation or not (if the first element of the vector
dssConfig.circularStrategies is 1, the repairing case has been selected and the following
instructions are run, otherwise not and the process goes to the next recovery strategy
which is the refurbishing). From this moment on (specifically, from instruction 1 to
instruction 26), the code structure is the same also for the refurbishing case.
1. A for cycle (f1) is opened to allow to evaluate each scenario;
2. The temporary structure tempActionList is created to store the sequence of actions and
the correspondent revenue associated to the current scenario; this information is
extracted from dssActions;
3. The revenue of the current scenario is stored in revenue;
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4. A for cycle is opened (f2) to process each action of the sequence associated to the
current scenario;
5. In tempId is stored the position of the current action inside the database processDB;
6. Using the position stored in tempId from the database processDB the information
regarding the human effort, the energy consumption, CO2 emissions and costs is
extracted and saved respectively in the variables pmBase, energyBase, co2Base and
costBase;
7. The structure tempLocationList which will be used to store the list of facilities where
the current action can be performed is created;
8. A for cycle (f3) is opened to find the facilities which can perform the current action;
9. Through f3, for each facility, it’s checked, through an if condition (if2), if, in the
database locationDB, the current action is present in the list of actions that the current
facility can carry out; if yes, its position in the list is stored in the temporary variable
tempPos which is used to extract the modifiers from the database locationDB and use
them to update the impact costs of the facility (by multiplying the modifiers by the
values contained in pmBase, energyBase, co2Base and costBase); this information is
stored in the structure tempLocationList;
10. From the database locationDB, the ID, latitude and longitude of the current facility
are extracted and stored in the structure tempLocationList;
11. If2 is closed;
12. f2 is closed;
13. f3 is closed;
14. Now, tempLocationList is a structure whose number of rows is equal to the number
of actions composing the sequence of actions associated to the current scenario (each
row corresponds to an action); there is only one field which is list, a structure whose
number of rows corresponds to the number of facilities which can perform the current
action; the fields of list are id, referring to the ID of the facility, lat and long,
respectively the latitude and the longitude that define the location of the current
facility, pmEffort, kWh, co2, cost which are the human effort, the energy consumption,
the CO2 emissions and the costs for the current facility to perform the current action;
15. To the field list of the structure tempLocationList is added a new row at the beginning
reporting the ID and the geographical position (latitude and longitude) of the user; in
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the figure below (Fig. 45) an example of how tempLocationList appears is reported;
it’s possible to notice that the first row corresponds to the user.
16. Through three for cycles, the structure distanceMatrix containing the list of all the
possible routes (which will define all the possible paths) associated to the current
sequence of actions and the correspondent list of facilities for each action, along with
their distance (distance), time to walk (time), name (name) and level of the route in
the tree (level), is created; notice that the distance is calculated through the function
distanceCalculatorGoogle_fcn; in the following pictures (Fig. 46 and Fig. 47), an
example of distanceMatrix with the correspondent tree are reported.
Fig. 46: example of "distanceMatrix" structure.
Fig. 45: example of "tempLocationList" structure.
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Fig. 47: example of a tree generated from "distanceMatrix" shown in figure 36. The red circles with a white number represent the levels of the tree.
17. Through the function reverseLogisticsAPI the economic and environmental reverse
logistics impacts are calculated for each route and stored in the variable rlResults;
18. distanceMatrix becomes costMatrix;
19. costMatrix is updated by adding, through a for cycle, the environmental and economic
reverse logistics impacts calculated before; specifically, they are stored in the new
field of the structure costMatrix called impacts which can be seen in Fig. 48;
Fig. 48: figure showing an example of "costMatrix" structure. Notice that htis structure is equal to "distanceMatrix" but with one more field called "impacts".
20. Through a series of while and for constructs, and the use of the functions
costMatrix_fcn and updateMinMaxValues_fcn, the first-depth search algorithm is
implemented to find all the possible paths for the current scenario; the result is the
structure detailedTripMatrix;
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21. Through the fitness_fcn function the normalized impacts and costs are calculated and,
according to them, the path which shows the best result is selected; moreover, the
information related to the best path are stored in the structure scenarioResult;
22. f1 is closed;
23. Once that this procedure has been performed for each scenario, scenarioResult is
composed by the best paths of each scenario and among them the best is selected and
represents the best scenario for the repairing strategy;
24. The necessary information related to the best scenario are stored in detailedCosts;
25. The total benefit which is stored in the structure totalBenefit is the difference between
the total revenue and the total costs (not normalized) of the optimal scenario.
26. if1 is closed.
As regards the refurbishing strategy, the instructions just explained are exactly the same,
therefore, it’s possible to describe directly the third recovery option which is the reuse.
As far as the reuse case is concerned, there are fewer instructions since no processes are
performed on the product and this implies that facilities are not taken into account and
the only costs to consider are transportation costs from the current location of the product
to the location of the new user. Therefore, the only commands are:
1. Through the distanceCalculatorGoogle_fcn function, the distance (reusalDistance)
and the travel time (tripTime) to go from the product location to the location of the
new user are calculated;
2. Through the reverseLogisticsAPI function, the economic and environmental reverse
logistics impacts are calculated and stored in rlResults;
3. Reverse logistic and process impacts and costs are stored in impactCostsMatrix, a
column matrix composed by 14 rows, each one referring to an impact factor; observe
that the process costs and impacts (row 1, which refers to the human effort expressed
in PM effort, row 2 associated to energy consumption measured in kWh and row 5
related to process cost in Euro) are zero since, as it has been already explained before,
no process is performed on the product in the reuse case. In the following figure, Fig.
49, an example of impactCostsMatrix column matrix is proposed;
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Fig. 49: example of “impactCostsMatrix” matrix.
4. The necessary information regarding the reuse case is stored in detailedCosts;
5. The total benefit is calculated in the same way it has been computed in the repairing
case and is finally stored in totalBenefit.
The procedure followed in the reuse case, is used also for the evaluation of the disposal
case. The only difference is that, in the disposal case, process costs and impacts
(associated to the disposal process) are implied; therefore, the column matrix
impactCostsMatrix is not zero at rows 2 and 5. Moreover, it’s important to notice that the
disposal strategy will have always a negative total benefit because it represents only a
cost and, as such, does not imply any revenue (revenue always equal to zero). Once that
all the recovery strategies selected by the user have been evaluated, the structure
totalBenefit is saved as totalCost which, together with detailedCosts, represents the
output for the main function of the DSS.
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9. CONCLUSIONS
This work, through numbers, data and even natural phenomena, reveals that the linear
economic model is failing, and, for this reason, the Circular Economy concept is
spreading and growing year by year. Specifically, CE is growing a lot at micro and meso
levels, as testified by the presence of many solutions at company and industrial sector
levels, while, at macro level, the only example is represented by China. Despite it is still
a young concept without a univocal definition and with a lot of nuances surrounding it, it
has clear and shared objectives which are:
• Design out waste and pollution
• Keep products and materials in use
• Regenerate natural systems
Nowadays, global economy is living the transition phase from the development of CE’s
concept, towards the practical and concrete realization of the idea. In this regard, the
technological revolution which is characterizing humanity in the last 20 or even 30 years,
is contributing to foster and make faster this transition. There are a lot of digital
technology solutions which are supporting the implementation of Circular Economy at
all levels. The variety of these solutions is quite huge: from databases and applications to
Internet of Things and blockchains, there are a lot of types of devices. Some of them are
already existent, while others are emerging. Specifically, these tools can be divided in the
following three categories according to the scope they have been created:
• Enhance relationships and information sharing
• Make products, processes and services more circular
• Affect and empower consumers and citizens
However, among the digital technology solutions, the Decision Support System is not
much exploited yet. The work reveals that the tools of this type are few in the field of the
Circular Economy. It is possible to state that nowadays DSSs have much more experience
in the medical field, in the banking sector, in the agricultural sector and in university
management. In this report, an example of DSS for each of these fields is described.
This work shows only two examples of existent DSS for Circular Economy: a DSS for
the eco-design and a DSS to support industrial symbiosis. Nevertheless, there are many
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models and methods for Circular Economy which could be implemented in a DSS. In this
study, for example, as regards the branch of Reverse Supply Chain Management, a
mathematical programming model to solve reverse distribution problems and a hybrid
multi-objective metaheuristic (HMM) algorithm to find the most suitable disassembly
process for an end-of-life product are described. Moreover, as far as the process of
selection of end-of-life product recovery strategy is concerned, the following examples
are illustrated:
• A multi-objective meta-heuristic algorithm used to determine the best joint
decision-making on automated disassembly system scheme selection and
recovery route assignment
• A multi-criteria decision-aid (MCDA) approach for the selection of the best
recovery strategy for a product
• An intelligent evaluation approach which integrates case-based reasoning models
(CBR), economic analysis model and domain expertise to choose the most
suitable recycling strategy for a product at its end-of-life
Finally, a practical example of DSS is reported. It is developed through MATLAB
software and its scope is to evaluate the recovery strategies selected by the user for a
specific product (itself chosen by the user) according to user’s preferences (expressed
through criteria weights assigned directly by the user) and economic and environmental
impacts.
134
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T. (2010). From closed-loop to sustainable supply chains: the WEEE case. International
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139
[53] Sauvé, S., Bernard, S., Sloan, P. (2016). Environmental sciences, sustainable
development and circular economy: Alternative concepts for trans-disciplinary research.
Environmental Development (Vol. 17, pp. 48-56).
[54] Preston, F. (2012). A Global Redesign? Shaping the Circular Economy. Briefing
Paper, London: Chatham House.
[55] EEA (European Environment Agency) (2014). Resource-efficient Green Economy
and EU policies, Luxembourg: Publications Office of the European Union.
[56] Mitchell, P. (2015). Employment and the circular economy - Job Creation through
resource efficiency in London. Report produced by WRAP for the London Sustainable
Development Commission, the London Waste and Recycling Board and the Greater
London Authority.
[57] Heck, P. (2006). Circular Economy related international practices and policy
trends: Current situation and practices on sustainable production and consumption and
international Circular Economy development policy summary and analysis. Institut für
angewandtes Stoffstrommanagement (IfaS).
[58] Su, B., Heshmati, A., Geng, Y., Yu, X. (2013) A review of the circular economy in
China: Moving from rhetoric to implementation. Journal of Cleaner Production (Vol. 42,
pp. 215-227).
[59] Bastein, T., Roelofs, E., Rietveld, E., Hoogendoorn, A. (2013). Opportunities for a
Circular Economy in the Netherlands. TNO, Report commissioned by the Netherlands
Ministry of Infrastructure and Environment.
[60] EEA (European Environment Agency) (2016). Circular Economy in Europe -
Developing the knowledge base. EEA Report No. 2/2016.
[61] Ghisellini, P., Cialani, C., Ulgiati, S. (2016). A review on circular economy: the
expected transition to a balanced interplay of environmental and economic systems.
Journal of Cleaner Production (Vol. 114, pp. 11-32).
[62] ADEME (French Environment and Energy Management Agency) (2014). Economie
Circulaire: Notions.
140
[63] Ellen MacArthur Foundation (2013). Towards the Circular Economy. Economic and
Business Rationale for an Accelerated Transition. https://tinyurl.com/hzfrxvb.
[64] Ellen MacArthur Foundation (2013). Towards the Circular Economy, Opportunities
for the Consumer Goods Sector. https://tinyurl.com/ztnrg24.
[65] Ellen MacArthur Foundation (2015). Towards a Circular Economy: Business
Rationale for an Accelerated Transition. https://tinyurl.com/zt8fhxw.
[66] European Commission (2015). Closing the loop - An EU action plan for the Circular
Economy. Communication from the Commission to the European Parliament, the
Council, the European Economic and Social Committee and the Committee of the
Regions.
[67] Johnson, M. R., McCarthy, I. P. (2014). Product recovery decisions within the
context of Extended Producer Responsibility. Journal of Engineering and Technology
Management (vol. 34, pp. 9-28).
[68] Thierry, M., Salomon, M., Van Nunen, J., Van Wassenhove, L. (1995). Strategic
Issues in Product Recovery Management, California Management Review (vol. 37, pp.
118-120).
10.2 WEBPAGES
[1] https://www.caixabankresearch.com/en/emergence-middle-class-emerging-country-
phenomenon
[2] https://www.ellenmacarthurfoundation.org/circular-economy/concept/schools-of-
thought
[3] https://www.ellenmacarthurfoundation.org/circular-economy/concept/infographic
[4] http://www.dmi.unict.it/mpavone/nc-cs/materiale/ABC_GCP.pdf
[5] https://www.investopedia.com/
[6] https://www.managementstudyhq.com/components-of-decision-support-
systems.html
[7] https://www.decision-making-confidence.com/decision-support-systems.html
141
[8] https://www.sustainabilityexchange.ac.uk/the_european_waste_catalogue_ewc
[9] https://en.wikipedia.org/wiki/Decision_support_system
[10] https://en.wikipedia.org/wiki/Depth-first_search
[11] http://www.businessdictionary.com/definition/reconditioning.html
[12] https://www.epa.gov/recycle/recycling-basics
142
11. ANNEX
Source Definition
Sauvé et al.
(2016)69
Circular economy refers to the “production and consumption of goods through
closed loop material flows that internalize environmental externalities linked
to virgin resource extraction and the generation of waste (including
pollution)’’.
Preston
(2012)70
“Circular economy is an approach that would transform the function of
resources in the economy. Waste from factories would become a valuable
input to another process – and products could be repaired, reused or upgraded
instead of thrown away”.
EEA (2014)71
Circular economy “refers mainly to physical and material resource aspects of
the economy – it focuses on recycling, limiting and re using the physical inputs
to the economy, and using waste as a resource leading to reduced primary
resource consumption’’.
Mitchell
(2015)72
A circular economy is an alternative to a traditional linear economy (make,
use, dispose) in which we keep resources in use for as long as possible,
extracting the maximum value from them whilst in use, then recovering and
reusing products and materials.
Heck
(2006)73
The utilisation of sustainable energy is crucial in a circular economy. The
transition to a circular economy would require addressing the challenge of
establishing a sustainable energy supply as well as decisive action in several
other areas such as agriculture, water, soil and biodiversity.
69 Sauvé, S., Bernard, S., Sloan, P. (2016). Environmental sciences, sustainable development and circular
economy: Alternative concepts for trans-disciplinary research. Environmental Development (Vol. 17, pp.
48-56).
70 Preston, F. (2012). A Global Redesign? Shaping the Circular Economy. Briefing Paper, London:
Chatham House.
71 EEA (European Environment Agency) (2014). Resource-efficient Green Economy and EU policies,
Luxembourg: Publications Office of the European Union.
72 Mitchell, P. (2015). Employment and the circular economy - Job Creation through resource efficiency in
London. Report produced by WRAP for the London Sustainable Development Commission, the London
Waste and Recycling Board and the Greater London Authority.
73 Heck, P. (2006). Circular Economy related international practices and policy trends: Current situation
and practices on sustainable production and consumption and international Circular Economy
development policy summary and analysis. Institut für angewandtes Stoffstrommanagement (IfaS).
143
Su et al.
(2013)74
The focus of the circular economy gradually extends beyond issues related to
material management and covers other aspects, such as energy efficiency and
conservation, land management, soil protection and water.
Bastein et al.
(2013)75
The circular economy transition “is an essential condition for a resilient
industrial system that facilitates new kinds of economic activity, strengthens
competitiveness and generates employment’’.
EEA (2016)76
“A circular economy provides opportunities to create well-being, growth and
jobs, while reducing environmental pressures. The concept can, in principle,
be applied to all kinds of natural resources, including biotic and abiotic
materials, water and land”.
Ghisellini et
al.
(2016)77
The radical reshaping of all processes across the life cycle of products
conducted by innovative actors has the potential to not only achieve material
or energy recovery but also to improve the entire living and economic model.
ADEME
(2014)78
The objective of the circular economy is to reduce the environmental impact
of resource consumption and improve social well-being.
Ellen
MacArthur
Foundation
(2013a79;
Circular economy is “an industrial system that is restorative or regenerative by
intention and design. It replaces the ‘end-of-life’ concept with restoration,
shifts towards the use of renewable energy, eliminates the use of toxic
chemicals, which impair reuse, and aims for the elimination of waste through
the superior design of materials, products, systems, and, within this, business
models’’. The overall objective is to “enable effective flows of materials,
energy, labour and information so that natural and social capital can be
rebuilt’’.
74 Su, B., Heshmati, A., Geng, Y., Yu, X. (2013) A review of the circular economy in China: Moving from
rhetoric to implementation. Journal of Cleaner Production (Vol. 42, pp. 215-227).
75 Bastein, T., Roelofs, E., Rietveld, E., Hoogendoorn, A. (2013). Opportunities for a Circular Economy in
the Netherlands. TNO, Report commissioned by the Netherlands Ministry of Infrastructure and
Environment.
76 EEA (European Environment Agency) (2016). Circular Economy in Europe - Developing the knowledge
base. EEA Report No. 2/2016.
77 Ghisellini, P., Cialani, C., Ulgiati, S. (2016). A review on circular economy: the expected transition to a
balanced interplay of environmental and economic systems. Journal of Cleaner Production (Vol. 114, pp.
11-32).
78 ADEME (French Environment and Energy Management Agency) (2014). Economie Circulaire: Notions.
79 Ellen MacArthur Foundation (2013). Towards the Circular Economy. Economic and Business Rationale
for an Accelerated Transition. https://tinyurl.com/hzfrxvb.
144
2013b80;
2015a81)
European
Commission
(2015a)82
The circular economy is an economy “where the value of products, materials
and resources is maintained in the economy for as long as possible, and the
generation of waste minimised’’. The transition to a more circular economy
would make “an essential contribution to the EU's efforts to develop a
sustainable, low carbon, resource-efficient and competitive economy’’.
Tab. 2: table reporting some today's definitions about Circular Economy.
80 Ellen MacArthur Foundation (2013). Towards the Circular Economy, Opportunities for the Consumer
Goods Sector. https://tinyurl.com/ztnrg24.
81 Ellen MacArthur Foundation (2015). Towards a Circular Economy: Business Rationale for an
Accelerated Transition. https://tinyurl.com/zt8fhxw.
82 European Commission (2015). Closing the loop - An EU action plan for the Circular Economy.
Communication from the Commission to the European Parliament, the Council, the European Economic
and Social Committee and the Committee of the Regions.
145
Name Existing/
Emerging
Type of
device
Function General aim
Trash to Trend
Existing
Online
platform
It provides information about
sustainable production of
garments and textile
upcycling.
Gather and
exchange
information
European
Resource
Efficiency
Knowledge
Centre
Existing
Platform
It provides information about
how improving resource
efficiency. It cooperates,
above all, with small-and-
medium size businesses.
Gather and
exchange
information
IUCLID
Existing
Database
It reports information about
chemicals and checks if they
are adequate according to
REACH regulation83.
Gather and
exchange
information
BOMcheck and
International
Material Data
System (IMDS)
Existing
Database
They record substances used
by the industry (automotive
industry).
Gather and
exchange
information
EC4P
Existing
Cloud-based
platform
It assists companies at
complying recycling
requirements for electrical
and electronic equipment
waste, batteries and
packaging across the EU and
world.
Gather and
exchange
information
83 REACH stands for Registration, Evaluation, Authorisation and Restriction of Chemicals. It consists of a
European Union regulation promulgated the 18 December 2006. It provides useful information regarding
the production and use of chemical substances, specifically, their potential impacts on both human health
and the environment.
146
ECHA’s
database
Emerging
Database
It provides information about
hazardous substances in
products and materials.
Gather and
exchange
information
Evolution3
Existing
Sensor
system
It provides real-time data
about tires temperature and
pressure that are turned into
actionable knowledge to
prevent tires damage.
Improve
information
FerryBoxes
Existing
Big data
It collects and analyses data
about marine litter and water
quality to support
policymaking regarding
measures to clean seas.
Improve
information
SimaPro, GaBi
and openLCA
Existing
Software
They help the
implementation of LCA (Life
Cycle Assessment).
Improve
information
FoodCloud and
Too Good To
Go
Existing
Apps
They aim at tackling food
waste by connecting
consumers and charities with
restaurants and retailers.
Facilitate
partnership
Urban Mine
Platform
Existing
Database
It provides information about
valuable materials coming
from high tech products
through “urban mining”; it
focuses on the recovery and
value retention of secondary
raw materials.
Facilitate
partnership
147
BE CIRCLE
Emerging
Web-based
platform
It supports industrial
symbiosis; the platform
allows to visualize water,
materials, energy stocks and
flows of nearby industrial
facilities to facilitate possible
synergies.
Facilitate
partnership
CCMS
Existing
Online
platform
It allows to close the textile
cycle by encouraging
collaborations across the
value chain and maintaining
a database and tracking
materials. CCMS relies on
QR codes to trace data
throughout a product’s life
cycle.
Enable
information to
travel across
value chain
FiliGrade (name
of the company
which
developed this
system)
Existing
Watermarks
It consists of watermarks
applied onto plastic products;
these labels can be scanned
to get valuable information
about products.
Enable
information to
travel across
value chain
TagItSmart
Existing
Smart tags
This system of smart tags
allows stakeholders to track
items and to obtain further
information by scanning the
correspondent QR codes.
Enable
information to
travel across
value chain
Auchan (name
of the company
which
developed this
system)
Emerging
RFID
technology
This technology allows
Auchan to keep track of the
crates for the reverse
logistics.
Enable
information to
travel across
value chain
148
Circularise
(name of the
company which
developed this
system)
Emerging
Blockchain
Through blockchain
Circularise improves
transparency and
communication across value
chains; it makes also data
sharing and data protection
more efficient.
Enable
information to
travel across
value chain
Tab. 3: table which reports the main digital solutions for Circular Economy whose main aim is to enhance relationships and information sharing.
149
Name Existing/
Emerging
Type of
device
Function General aim
The
Accelerated
Metallurgy
project (funded
under H2020)
Emerging
Artificial
Intelligence
It aims at creating
new materials by
searching for
environmentally
friendly metal alloys
Improve
design
Google (name
of the company
applying the
device)
Existing
Machine
Learning
It uses Machine
Learning for energy
efficiency; Doing
this, Google has
already reduced the
energy consumption
by 40%.
Improve
production
and processes
GreenLab Skive
(an industrial
park developed
as a public-
private
partnership in
Danish Skive
Municipality)
Existing
Integrated
intelligent
infrastructure
This device allows
to exchange energy
between businesses
with the aim of
optimizing its usage.
Improve
production
and processes
Winsun (the
name of the
company which
is using the
device)
Emerging
3D printing
Thanks to 3D
printing, costs have
been cut by 50%
and construction
material usage has
been cut by 305 up
to 60%.
Improve
production
and processes
Bosch and
Siemens (name
of the
companies
Emerging
Smart digital
factories built
on AI and
This solution
enables companies
to reduce energy
consumption and
waste generation
during production
Improve
production
and processes
150
applying the
device)
machine
learning
eBay and
Gumtree
Existing
Online
trading
platform
They are
marketplaces for
used products
Improve
reuse, repair,
disassembly
and
durability of
products
Gen Byg Data
Existing
Online
trading
platform
It provides
information about
the available
materials and allows
asset-tracking in a
building before its
demolition with the
help of a geographic
information system.
Improve
reuse, repair,
disassembly
and
durability of
products
Excess Material
Exchange
Existing
Online
trading
platform
It permits
companies to
exchange excess
materials with each
other
Improve
reuse, repair,
disassembly
and
durability of
products
iFixit
Existing
Open-source
online
platform
The main purpose is
the repairing of
electronics,
machinery and car
components. It
incorporates repair
instructions, Q&A
forums and user
generated updates
on existing and
prospective
equipment.
Improve
reuse, repair,
disassembly
and
durability of
products
151
DAQRI
Existing
Augmented
reality glasses
They can provide
workers with
necessary
information to repair
the product.
Improve
reuse, repair,
disassembly
and
durability of
products
Thyssenkrupp
(name of the
company
exploiting the
tool)
Existing/emerging
Connected
machines
Tool used for
conducting
predictive
maintenance. A
machine can inform
the staff about a
particular problem
through IoT for
instance.
Improve
reuse, repair,
disassembly
and
durability of
products
Whim
Existing
App
It provides citizens
with the access to
multiple
transportation
modes.
Enable
service-based
business
models
Tale Me
Existing
Online
platform
Rental service for
maternity and
children’s clothes
Enable
service-based
business
models
HP Instant Ink
Existing
Connected
machines
It is an ink cartridge
replacement service
which allows
printers to send to
HP information
about ink level. In
this way, when the
level is getting low,
the company
automatically ships
Enable
service-based
business
models
152
replacement
cartridges.
Rezycl
Existing
App and
online
platform
A custom-designed
software which
allows companies to
handle their waste.
It gives easier access
to pertinent data and
waste statistics, and
the ordering of
waste collection.
Improve
waste
management
SUEZ (name of
the company
applying the
tool)
Existing
Infrared and
digital twin
technologies
SUEZ exploits these
technologies for
advanced waste
characterisation in
order to improve
waste sorting and
recycling.
Improve
waste
management
ZenRobotics
(name of the
company
applying the
tool)
Emerging
Robots
Robots allow faster
and more precise
waste sorting.
Improve
waste
management
Tab. 4: table which reports the main digital solutions for Circular Economy whose main aim is to make products, processes and services more circular.
153
Name Existing/
Emerging
Type of
device
Function General aim
Amazon’s
Second Chance
Existing
Webpage
It provides user-friendly
instructions for recycling
packaging, repairing
equipment and buying
(certified) refurbished
products
Providing
information
Bext360 (name
of the company
using the tool)
Existing
Blockchain
Through the blockchain
technology, it has the
possibility to monitor
critical supply chains,
such as timber, cotton and
minerals, in a complete
and measurable way
Providing
information
AskREACH
(name of the
project to
develop this
tool)
Emerging
Database
connected to
an app
The database collects
information about
defective substances
provided by the suppliers.
The connection to the app
serves to facilitate the
access to information.
Providing
information
myEcoCost
(name of the
project which
developed this
tool)
Existing
Data analysis
through
machine
learning and
AI
This project aims at
estimating a consumer’s
ecological footprint by
analysing data across the
whole value chain of a
product
Stimulating
behaviour
change
My Little Plastic
Footprint
Existing
App
It incentives and
encourages consumers to
reduce their plastic waste
production by providing
them data and information
about plastic waste
production
Stimulating
behaviour
change
154
SIRPLUS
(name of the
project based on
this tool)
Existing
IoT
Through IoT, this project
is focus on selling surplus,
expired and deformed
groceries for up to 70%
less than its usual price,
by combatting, in this
way, food waste
Stimulating
behaviour
change
Oscar Existing Chatbot It helps waste sorting Incentivising
recycling
Plastic Bank
(name of the
project based on
this tool)
Existing
Digital
currency
This digital currency is
given in exchange of
plastic waste left at
appropriate recycling
export collection areas
Incentivising
recycling
LitterGram
Existing
App
This app basically aims at
reducing litter in UK.
Citizens can use the app
for sharing pictures and
locations of litter whit
their local authorities
who, later, take care of it
Co-creating
knowledge
Tab. 5: table which reports digital solutions for Circular Economy whose main aim is to affect and empower consumers and citizens.
155
Recovery
strategy
Definition
Remanufacturing
"the rebuilding of a product to specifications of the original
manufactured product using a combination of reused, repaired and
new parts".84
Repairing The aim of repairing is to bring the used or damaged products back
to their “working order”.85
Reconditioning
“Servicing, readjusting, and recalibrating equipment or
instruments to bring them to near-new or original operational level.
Reconditioned goods are of later model and usually in better
condition than refurbished goods.”86
Refurbishing
“Servicing and/or renovation of older or damaged equipment to
bring it to a workable or better-looking condition. Refurbished
goods are of older model and usually in worse condition than
reconditioned (see reconditioning) goods.”87
Repurposing Modify or not a used product in order to use it for other purposes.
Cannibalization
Instead of reusing the entire product, the purpose of
cannibalization is to recover only a small proportion of
components of the used product which will be used for repairing,
refurbishing or remanufacturing of other products or parts.88
Recycling
“The process of collecting and processing materials that would
otherwise be thrown away as trash and turning them into new
products.”89
Tab. 6: table showing the definitions of the most important product recovery strategies.
84 Johnson, M. R., McCarthy, I. P. (2014). Product recovery decisions within the context of Extended
Producer Responsibility. Journal of Engineering and Technology Management (vol. 34, pp. 9-28).
85 Thierry, M., Salomon, M., Van Nunen, J., Van Wassenhove, L. (1995). Strategic Issues in Product
Recovery Management, California Management Review (vol. 37, pp. 118-120).
86 http://www.businessdictionary.com/definition/reconditioning.html.
87 See note 86.
88 See note 85.
89 https://www.epa.gov/recycle/recycling-basics.