Circular Economy-oriented Simulation: A Literature Review
Grounded on Empirical Cases
Claudio Sassanelli
a
, Paolo Rosa
b
and Sergio Terzi
c
Department of Management, Economics and Industrial Engineering, Politecnico di Milano,
Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Keywords: Circular Economy, Simulation, Disassembly, Industry 4.0, Systematic Literature Review.
Abstract: Nowadays, manufacturers are increasingly impelled in adopting Circular Economy (CE) strategies and
consistently adequating their business models to more environment-oriented practices. However, several
barriers can be encountered during the transition from linear to circular behaviours. Here, simulation methods
can play a strategic role in supporting companies during the assessment of potential solutions to make their
products’ lifecycle circular. To this aim, simulation (as part of Industry 4.0 (I4.0) technologies), have been
detected through a literature review as one of the main technologies supporting CE. Basing on results, the
End-of-Life (EoL) stage seems to play a strategic role within CE practices, with disassembly processes as the
enabler for most of these circular strategies (e.g. reuse, reman, recycle, etc.). Moreover, a big focus is on how
to foster CE through the improvement of disassembly processes of Waste from Electrical and Electronic
Equipment (WEEEs) and Printed Circuit Boards (PCBs). Hence, a deeper analysis of how simulation
approaches can contribute to enhance these processes is presented, by defining those technologies needed to
improve specific product lifecycle stages.
1 INTRODUCTION
Circular Economy (CE) has been increasingly
considered during the last decade by manufacturers.
In order to turn both portfolios and plants under a
circular perspective (The Ellen MacArthur
Foundation, 2015), several strategies have been
identified in literature (e.g. reuse, remanufacturing,
recycling). In parallel, specific business models
(Cavallo et al., 2019) have also been suggested (Rosa
et al., 2019b) in order to apply CE practices and
gather real benefits (Rosa et al., 2019a). Here,
simulation methods can play a strategic role in
supporting companies during the assessment of
potential solutions to make their products’ lifecycle
circular. Considering the increasing importance of
managing EoL stages and the increasing amount of
wastes to be disposed of (e.g. in terms of WEEEs and
PCBs), the paper investigates how simulation (as part
of Industry 4.0 (I4.0) technologies) has been used to
support a real transition towards CE. A literature
a
https://orcid.org/0000-0003-3603-9735
b
https://orcid.org/0000-0003-3957-707X
c
https://orcid.org/0000-0003-0438-6185
review has been conducted to identify the main role
of simulation in terms of CE and Industry 4.0 (I4.0)
research contexts. The paper is structured as follows.
Section 2 provides the research context. Section 3
explains the research methodology used to conduct
the literature review. Section 4 presents results from
the literature analysis. Section 5 discusses about
results and makes some concluding remarks.
2 RESEARCH CONTEXT
2.1 Circular Economy
The CE paradigm is a new economic model taking
over - especially in the last few years - at global level
(Reuter et al., 2013). CE aims at shrinking finite
resources consumption, by focusing on intelligent
design of materials, products and systems, in order to
move from traditional economic models (based on a
Sassanelli, C., Rosa, P. and Terzi, S.
Circular Economy-oriented Simulation: A Literature Review Grounded on Empirical Cases.
DOI: 10.5220/0009989300530059
In Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2020), pages 53-59
ISBN: 978-989-758-476-3
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
53
linear process take, make, dispose) and
conceived without any built-in inclination to recycle
(Su et al., 2013; The Ellen MacArthur Foundation,
2015) towards new (closed loop) patterns balancing
economic, environmental and societal impacts.
2.2 Simulation
Simulation approaches aim at managing change,
reproducing or projecting the behaviour of a modelled
system in order to bring clarity to the reasons for
change (Barnett, 2003). Equations, state machines,
flowcharts, cellular automata represent different
types of rules that, once coupled with an experimental
design (Harrison et al., 2007), can allow to define the
future behaviour of a modelled system starting from
its present state (Borshchev & Filippov, 2004). The
most common simulation approaches are:
• System Dynamics (SD) – used for strategic (high-
level) decision-making and qualitative analysis (e.g.
knowledge management)
• Discrete Event (DE) – used for tactical and
operational (medium/low-level) decision-making. It
is based on the concept of entities, resources and
block charts to describe entity flows and resource
sharing
• Agent-Based (AB) – is used for modelling the
behaviour of an entire system, by neglecting the
behaviour of actors within the system.
In particular, SD considers continuous timeframes,
while DE and AB consider discrete timeframes
(Borshchev & Filippov, 2004).
3 RESEARCH METHOD
In order to identify relevant studies describing both
the relation between simulation and CE and its
practical adoption in lab-scaled and industrial
contexts, a literature analysis has been conducted in
both Scopus® and Science Direct®. Looking at titles,
abstracts and keywords, the terms “Circular
Economy” and “Simulation” have been coupled with
other terms like “application”, “industrial” or
“laboratory”, by gathering 153 documents. In
addition, no constraints on the publication year have
been considered and only papers written in English
have been evaluated. The review process has been
carried out in three steps (collection, evaluation and
analysis). Firstly, the assessment of abstracts reduced
the initial set to 21 documents. Secondly, a full
reading of these manuscripts allowed to consider a
final set of 19 documents assessing the role of both
digital technologies and simulation tools in
supporting CE practices. Table 1 reports the three
strings used to carry out the searches on Scopus® and
Science Direct® databases, leading to 153 results,
becoming 149 after redundancies removal among
results. Figure 1 shows the research strategy used in
the systematic literature review (Sassanelli, Rosa, et
al., 2019; Sassanelli, Rossi, et al., 2019; Smart et al.,
2017). 3 documents were taken in consideration
through cross-referencing processes and 2 through
hand search. Finally, 7 more documents were
suggested by experts to be added to the list. Applying
the three criteria, the set of documents found was
reduced to 19 articles to be fully analysed. The
selection and examination of documents was
conducted by two authors, carrying it out
autonomously to avoid bias of analysis along the
review. Finally, their results were compared and
made consistent to each other, leading to the research
presented in this article. The selection was based on
the relevance of documents, by considering only
those contributions proposing simulation approaches
and tools fostering the adoption of CE practices. As
shown in the next section, all the contributions have
been analysed and grouped basing on the purpose of
exploiting simulation to support the adoption of CE
.
4 RESULTS
The set of 19 documents selected have been analysed
through the SLIP (Sort-Label-Integrate-Prioritize)
method (Maeda, 2006). At the end, six main groups
have been detected: a) design alternatives selection,
b) decision-support tools, c) online platforms and
monitoring for Industrial Symbiosis (IS), d) recycling
performance and Key Performance Indicators (KPIs),
e) material and technological properties and f)
Table 1: Searches by keywords and documents selection.
Scopus Science Direct
“Circular Economy” AND “Industry 4.0” 30 4
“Circular Economy” AND “Simulation” 111 14
“Circular Economy” AND “Simulation” AND
(“application” OR “industrial” OR “laboratory”)
33 14
Total per database 174 32
Total per database discarding redundancies among searches 135 18
IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics
54
benefits and business impacts. All contributions were
focused on exploring the role of simulation (as part of
Industry 4.0 technologies) in supporting the adoption
of CE. Most of them (5) were aimed at strengthening
and highlighting benefits and business impacts
deriving from the adoption of CE. Others (4)
considered simulation as a mean to provide recycling
performances and KPIs metrics and evaluations.
Others, considered simulation as a mean to support
decision-making processes (3), selection of different
design alternatives (2), evaluation of material and
technological properties (2) and adoption of online
platforms and monitoring tools at all Industrial
Symbiosis levels (2). Table 2 reports some details
about the final set of 19 papers.
All the detected roles played by simulation under
a circular perspective (see Figure 2 below), have been
analysed in the following sub-sections.
Figure 1: Research strategy (adapted by Smart et al. (2017)).
Table 2: The six roles of simulation and digital technologies to foster circularity adoption.
Simulation roles in supporting CE
Design alternatives
selection
Decision-support
tools
Online platforms
and monitoring
for Industrial
Symbiosis (IS)
Recycling
performance and
Key Performance
Indicators (KPIs)
Material and
technological
properties
Benefits and
business impact
Total 2 3 2 4 2 5
Figure 2: Scheme of Simulation roles fostering CE adoption.
Circular Economy-oriented Simulation: A Literature Review Grounded on Empirical Cases
55
4.1.1 Design Alternatives Selection
Low and Ng (2018) built a methodological
framework supporting flexible design of
remanufacturing systems. Through an application
case study based on remanufacturing of laptop
computers for the Cambodian market, they also
demonstrated as Monte Carlo simulation can be
adopted to gauge the efficacy of different flexible
design strategies in managing uncertainties. To
support both design alternatives selection and End-of-
Life (EoL) options evaluation, Ameli, Mansour and
Ahmadi-Javid (2019) developed a simulation-
optimization model and applied it on a real-world
case. The proposed model is useful to producers for
evaluating EoL performances of their products and to
policymakers for foreseeing reactions of producers
against a defined set of CE strategies.
4.1.2 Decision-support Tools
Matino, Colla and Baragiola (2017) quantified the
electric energy consumption and environmental
impact of unconventional electric steelmaking
scenarios, by concurrently monitoring steel
composition. Through a Decision Support Tool
(DST) they highlighted as the scrap quality strongly
affects the monitored energy and environmental
KPIs. Moreover, simulations highlighted a slag
reduction and yield improvement, by conserving steel
quality and marginally improving electric energy
consumption. Yazan and Fraccascia (2019) adopted
an enterprise input-output model providing a cost–
benefit analysis of IS integrated with an AB model for
simulating how companies share the total economic
benefits deriving from IS. The proposed model, under
the form of a DSS, allowed to explore the space of
cooperation, by enabling users to evaluate if the IS
relationship is enacted and define the cost-sharing
policy. Pfaff et al. (2018) paired a macroeconomic
simulation model and a substance flow model to
define both sectoral copper demand and availability
of secondary copper. They modelled and simulated
several scenarios, aiming at diminishing primary
copper demand and rising the supply of secondary
copper.
4.1.3 Online Platforms and Monitoring for
Industrial Symbiosis (IS)
Fraccascia and Yazan (2018) designed an AB model
to simulate the emergence and operations of self-
organized IS networks. Three platform-oriented
scenarios (no information-sharing, information
sharing of geographical location of wastes and
information sharing of sensitive data about IS
operating costs) were simulated in two businesses
(marble residuals reused in concrete production and
alcohol slops reused in fertilizers production).
Simulations were useful to demonstrate how online
platforms could improve the economic and
environmental performance of IS networks. Gaspari
et al. (2017) proposed a remanufacturing-oriented
simulation model based on a modular framework
enabling users in managing process settings and
production control policies (e.g. token-based
policies). The model allowed the assessment of
logistics performances, by enabling the selection of
optimal production policies in specific businesses. In
addition, an application case in a real
remanufacturing environment was proposed.
4.1.4 Recycling Performance and Key
Performance Indicators (KPIs)
van Schaik and Reuter (2016) developed a Recycling
Index (RI) (embedding a new material-RI) based on
minerals and metallurgical processing simulation
models, aimed at measuring recycling performances
of a product and its embedded materials.
(Wiedenhofer et al., 2019) extended the Economy-
Wide Material Flow Analysis (EW-MFA) framework
jointly addressing material flows, in-use stocks of
manufactured capital and waste. Through a fully
consistent Material Inputs, Stocks and Outputs
(MISO) model, they enabled a dynamic and complete
appraisal of resources, stocks and wastes in a socio-
economic metabolism. Innocenzi et al. (2018)
simulated a solvent extraction process to determine
the mass and energy balance of the whole recycling
treatment of spent lamps. The process consisted in the
recovery of rare earth elements from sulfuric leaching
solutions achieved by a dissolution of fluorescent
powders of lamps. Wakiru et al. (2018) adopted a
Discrete Event Simulation (DES) model to analyse
the effect of two CE strategies (remanufacturing and
maintenance) on power plants availability and
maintenance time. A thermal power plant located in a
remote region was considered as a demonstration
case.
4.1.5 Material and Technological Properties
Karayannis (2016) studied the development of
building bricks through a pilot-plant simulation of
industrial processes for red bricks manufacturing.
They found that extruded and fired bricks produced
with up to 15 wt% recycled steel industry by-products
is feasible without compromising their technological
properties. Odenbreit and Kozma (2019) performed
IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics
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15 large scale push-out tests, two large scale
composite beam tests and several finite element
simulations for demountable flooring and beam
systems. These applications were useful to determine
the suitability of dis-/re-assembly processes and some
inner material characteristics.
4.1.6 Benefits and Business Impacts
Bosch et al. (2017) used a dynamic business model
simulation for estimating CE business impacts. Dong
et al. (2017) conducted a SD-based simulation, whose
results stated that manufacturing transition towards
CE can foster coal power and cement companies to
decrease waste emission and improve economic
profits. Reuter (2016) focused on the metallurgical
industry. Given that: i) all metals have strong intrinsic
recycling potentials and ii) a digital integration of
metallurgical reactor technologies and systems can
support dynamic feedback control loops, they used
modelling, simulation, and optimization tools to
perform real-time measurement of ore and scrap
properties in intelligent plant structures, by enabling
CE-oriented big data analysis and process control of
industrial metallurgical systems. Results were used to
elaborate the resource efficiency of CE systems. Hao
et al. (2012) proposed a similar SD model and applied
it on coal-dependent systems with a full lifecycle
perspective. Thirteen development projects divided in
two types of scenarios were run on the model.
Simulation results were analysed through the efficacy
coefficient method in order to determine the best
project and demonstrate benefits coming from CE
adoption. Teekasap (2018) used SD models to
enlighten benefits deriving from CE in countries
without resource shortage issues. Simulation
demonstrated as, despite investment costs, countries
can obtain economic benefits through a lower raw
material cost in a long run.
5 DISCUSSION AND
CONCLUSIONS
This research consisted in a systematic literature
assessment aiming at understanding the real
contribution of simulation methods and tools to foster
the adoption of CE. The purpose of exploiting
simulation to support CE practices has been split in
six groups: a) design alternatives selection, b)
decision-support tools development, c) online
platforms and monitoring tools development for
Industrial Symbiosis (IS), d) recycling performance
and KPIs quantification, e) material and technological
properties evaluation, f) benefits and business
impacts assessment. Benefits and business impact
assessment and recycling performance and KPIs
quantification are the two most explored simulation
roles to foster CE practices. Moreover, results show
that disassembly processes (among other EoL
practices) are becoming the enabler for great part of
circular strategies detected, raising the need for
automated solutions. So far, only scattered attempts
have been done in this direction, by exploiting
simulation as reference approach (Ameli et al., 2019;
Kobayashi & Kumazawa, 2005; Wang & Wang,
2019). In addition, new actions must be focused on
the improvement of disassembly processes in the
WEEE sector (Ongondo et al., 2011). About this last
issue, a recent work (Rocca et al., 2020) presented a
lab-scaled case were different I4.0-based
technologies have been exploited to support CE
practices. Here, a virtual testing of a WEEE
disassembly plant configuration was implemented
through a set of dedicated simulation tools. They
highlighted as service-oriented (event-driven)
processing and information models can support the
integration of smart and digital solutions in current
CE practices at the factory level. Limitations/main
issues of the literature assessment presented in this
work can be considered the limited amounts of
scientific databases considered, the limited number of
works classified and the absence of a classification in
terms of automation processes exploiting traditional
simulation approaches that could support also CE
practices. This would push CE to an even stronger
integration with I4.0 technologies (Rosa et al., 2020).
Future researches could assess how the previously
detected simulation purposes should be extended and
analysed in terms of:
• Adopted means (e.g. methods, tools, algorithms,
rules, virtual environments, system architectures)
supporting circularity issues,
Improved lifecycle phases,
• Involved technologies,
Selected simulation types (e.g. physics-based
modelling or virtual/augmented reality),
• Improved human-machine interactions,
• Optimized variables,
Addressed issues along systems’ lifecycle.
ACKNOWLEDGEMENTS
This work has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 760792. In any
case, the present work cannot be considered as an
Circular Economy-oriented Simulation: A Literature Review Grounded on Empirical Cases
57
official position of the supporting organization, but it
reports just the point of view of the authors.
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