Circular Economy-oriented Simulation: A Literature Review

Grounded on Empirical Cases

Claudio Sassanelli

, Paolo Rosa

and Sergio Terzi

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

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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

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

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

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

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

official position of the supporting organization, but it

reports just the point of view of the authors.

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Circular Economy-oriented Simulation: A Literature Review Grounded on Empirical Cases