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New Promises AI Brings into Circular Economy Accelerated
Product Design: Review on Supporting Literature
Malahat Ghoreishi1, and Happonen Ari2
1LUT University, School of Business and Management, 53850 Lappeenranta, Finland
2LUT University, School of Engineering Science, 53850 Lappeenranta, Finland
Abstract. Promoting and applying circular strategies in the product planning stage by industrial designers
have significant environmental impacts. Product design have an enormous influence on sustainable ecology.
Huge amount of data analysis in designing circular products as well as reducing human biases in testing and
prototyping are the main reasons of urging digital technologies in industries. Digitalization assets in
ecodesign in collaboration with human and as a complement for human skills. This study found the circular
design tools and strategies which can help organizations in their product designs and the way artificial
intelligence enhances product circularity. Real-time data transformation and analysis ability can help in
massive data analysis which is less time consuming and less energy consumption is needed. In addition,
rapid prototyping and fast testing will reduce the waste in design process. Furthermore, AI transfers precise
data and information on materials and products’ availability, condition and accessibility which makes easy
monitoring and enables remote maintenance as well as reuse, remanufacturing and repair opportunities.
1 Introduction
The Circular Economy (CE) approach has been
significantly discussed on industrial development
globally and has gained attentions of industries, scholars
and policy makers worldwide over the last decades.
According to Ellen Macarthur Foundation [1], CE is a
“system restorative and regenerative by design, which
aims to maintain products, components and materials and
their highest utility and value”. Despite the outstanding
benefits that CE brings to businesses, transition from a
linear economy, “make, use, dispose”, towards a circular
model requires huge efforts of governments and policy
makers in cooperation with industries and end users.
However, Macarthur Foundation [2] argues that beside
the numerous advantages of circular business models,
which are considered as sustainable business models,
renewing and restoring materials can be highly costly for
industries. Hence, certain changes on business models are
required for companies which can generate additional
challenges. These things companies need to be able to
tackle, include new operational needs in asset
management, innovative approaches for supply chain
novelty, logistics approaches reinvention for currently
unfamiliar waste products as well as designing
manufacturing services and enhancing quality control [3].
On the other hand, designing theses aspects increases the
complexity of CE workflow, thus product design
development and related business model improvements
are essential in accelerating the move towards circularity
[4].
Since few changes can be made on the products once
the resources, characteristics and specifications are
allocated for designing a certain product, integrating
circular strategies at the early stage of product design
process has a vital role in value creation as well as supply
chain [5,6]. Accordingly, companies need more
innovative solutions on product design strategies of
narrowing or closing the resource loops. Bressanelli et al.
[7], discuss that the fourth industrial revolution (4IR)
such as Big Data, Artificial Intelligence (AI) and Internet
of Things (IoT) can pave the way to overcome the
challenges towards circularity for industries. According
to Ellen MacArthur Foundation [8], AI plays an
important role and is a subset of the technologies that
asset in enhancing product circulation as well as smart
management and predictive maintenance. AI is discussed
in this study as an accelerator in circular product design
by data collection and analysis, fast testing and
prototyping.
2 Background & research focus
2.1 Circular product design
In the world of CE, product circularity aims to maximize
the value of components and materials of products during
the longest time period. Product life extension which can
be attained by repair, remanufacturing and refurbishment
is the central economic and social model [9].
Kraaijenhagen et al. [4], argues that in transition towards
CE, the challenge is how to reduce natural resource
utilization and working on promoting positive societal
and environmental impacts by rethinking of the ways
helping in maximizing the value in offered products.
Therefore, product design strategies are essential in value
creation along with “customer value proposition, supply
chain, value networks of the companies and capturing the
value of new offerings”[10].
2.2 Technologies in CE
According to the recent research, digital technologies
have an urgent role and increasing involvement to
enhance CE solutions to overcome current challenges
[27,7]. Digital technologies contribute in product
visibility through intelligent sensors and provide
information on assets, location, condition and
availability[11]. Currently one of the most important
roles of circular business models is to share and lease the
products instead of selling [12,13]. Waughray et al. [14]
mention that digitalization is one of the key solutions in
accelerating the move towards circularity. Moreover,
utilizing digital technologies such as AI, IoT or
Blockchain enhances the ways in developing and
improving transparency and traceability throughout
products lifetime [15]. In a digital world, producers have
the opportunity to monitor, control, analyse and optimize
products’ performance through smart, connected products
and to collect valuable data of usage of materials,
components and goods [16].
Correct use of those technologies can boost efficient
reverse logistics & materials and goods considerations
that then gain second life and also it accelerates CE
concept worldwide with suitable recycling process, which
uses limited resource [2]. But e.g. for efficient reverse
logistics, talented and knowledgeable logistics operators
will be needed. Operators who know how to position
themselves correctly between manufacturers and
subcontractors [34]. Combination of digitalization driven
technologies and novel business models innovation may
provide significantly new opportunities towards more
sustainability for industries in terms of value creation,
value capturing and CE [17]. For example, by using
digital design processes to build one of configurable
products [61], in sustainable manner. Sitra [18] mentions
that manufacturing industries can gain tangible benefits
by digital reinvention of industry to move towards
Circularity. However, some technologies are known to
be prone of risks that must be considered and balanced
with possible higher rewards and benefits these
technologies offer compared to more traditional options.
As an example, one way of doing this is to do feasibility
analysis for the new technology [22] and compare it
directly against know and well matured traditional
(golden standard) solutions.
2.3 Artificial intelligence and CE
AI enables automatic and remote monitoring of the
efficiency during manufacturing process as well as
product’s end lifecycle in CE. AI enhances massive data
analysis which are generated over the manufacturing
process, disposal or use [3]. According to Ellen
MacArthur Foundation [8], Since AI has the ability to
deal with complexity and can improve huge amount of
data awareness, it is considered as a complement for
human’s capabilities which helps in more efficient
learning from feedbacks. Faster and rapid prototyping,
learning process by repeated designing cycles and
feedback collection are requirements in accelerating the
transition of the complexity of redesigning key features to
a better economic model. Accordingly, AI can play a
significant part in enabling the systematic shift.
3 Literature review process and
finding
According to European commission [19], all the products
during their lifecycle have an impact on environment in
all the phases from the use of raw materials and natural
resources, manufacturing, packaging, transport, disposal
and recycling. The environmental impact of over 80% of
the products is determined at the phase of design.
According to [20], “circular product design: elevates
design to a system level, strives to maintain product
integrity; is about cycling at a different pace, explores
new relationships and experiences with products, and is
driven by different business models”. Product design
plays an essential role in CBM and affects the company’s
competitive advantage [21]. Van den Berg and Bakker
[62]define five main characteristics of circular products
from the inner loop to the outer loop as: Future proof,
Disassembly, Maintenance, Remake and recycling (Fig.
1). In circular product design, products are designed to
last and use longer (future proof), can be disassembled,
maintained, remade (components) and recycled
(materials).
Fig. 1. Circular product design (adapted from Van den Berg and
Bakker 2015)
According to these characteristics, Van den Berg and
Bakker [62] introduced a guideline as a tool in product
design process (Fig. 2) which can be used in discussion
within a design team to define the aspects that requires
consideration for CE and to realize on which area and
what degree the product needs to be improved. As it was
mentioned previously, the product designs can be seen as
key factor already in early stages of product development
processes for circularity. As such the designers of these
products play an important role in developing changes to
product for enhanced disposability and they can build
improved relationships / bridges between end users needs
and the product to be designed [23]. The characteristics
of the products in questions will have a defined and direct
influence into the process of creating the and the way it
will be managed [24]. Basically it means that the design
has a big and vital role when supply chain loops are
considered to be closed up [25]. Additionally, proper
supply chain partners and collaborative measures [60]
will be needed to be able to run all these design actions
effects efficiently trough the supply chain.
3.1 Circular Design strategies
Vanegas et.al [19] define three product design in the
vision of CE: “increasing material efficiency, product life
extension, improving recycling efficiency”. Bocken et.al
[12] divided CE into three fundamental categories of
narrowing, slowing and closing the resource loop (Fig. 3)
in which, “narrowing” the loops is related to the resource
efficiency, “slowing” the loops focus on designing the
products with long lifetime whereas “closing” the loops
focuses on materials and products recycling as well as
system’s leakages removal [12,26]. According to Atlason
et.al [28], proper functionality of such strategies at the
End-of-Life design are gained when producers’ and end
users’ intentions on handling the end use of products are
aligned. For example, if the consumer tends to repair or
reuse the product after the useful lifetime, it is not
desirable for producer to design for better recyclability.
Pocock et al. [59] mention that materials circulation
through products lifetime extension generate revenue for
the businesses. Essoussi and Linton [29], discuss that
willingness of users to purchase products with reused or
recycled was related to the perceived functional risk of
achievement pave the way towards implication of
Industry 4.0 [32] products. Therefore, where the
perceived risk is high, and the margin price is low, users
like to buy new products. Moreover, users’ tolerance to
uncertainty defines the willingness to pay for the
perceived quality of refurbished product [30].
According to [10], circular design options can develop
downstream circularity, therefore they might be part of
business model innovation.
Fig. 2. Circular product design tool (adapted from Van den Berg and Bakker 2015)
3.2 Industry 4.0 and CE
Although circular strategies aim to close material flows
and to extend lifetime of the products, [31], companies
face serious challenges in transition towards a circular
model. Challenges which prevent sustainability goal.
Here, digitalization provides precise information such as
location and availability of the products to help closing
the material loops which facilitates companies in
transition towards a more circular sustainable model.
[11]. Moreover, utilizing digital technologies lead to
reduce waste, prolongs life expectancy of the products
and minimizes transaction costs which enables efficient
processes in organizations [14]. Thus, it helps to enhance
circular business models of closing/slowing and
narrowing the loops by increasing resource efficiency
[11].
Fig. 3. Slowing, closing and narrowing the loops strategies
(adapted from Bocken et al., 2016)
According to [33–35] the term Industry 4.0 (Table 1)
is “a combination of AI, Cyber-physical systems (CPS),
Internet of Things (IoT) and Industrial internet, in other
words internet services”. Industry 4.0 contributes in
optimizing sustainable solutions of reducing emission
from industrial systems via transforming and utilizing
information generated from various smart devices [36].
While 4IR is not capable to overcome all the challenges
towards circularity, such technologies offer more cost
efficient tools [37].
Integration of CE and Industry 4.0 leads towards new
levels of sustainability, which can work as a motivation
enabler for business organizations to move towards
sustainable supply chains as well as a new outlook for
production and consumption [32]. Industry 4.0, which is
also known in production industry context as smart
manufacturing, helps managers in decision-making by
providing the real time information on machines, flow of
components and production, monitoring performance and
tracking parts and products [38]. New Industry 4.0 based
technologies are enablers that will pave the way in
integrating CE principles through tracking products post-
consumption and recovering components [39]. Fig. 4
illustrates 4IR solutions for circularity. CE business
models could benefit by Industry 4.0 with applying these
technologies in the form of sensors and apps; for
example, to plan, monitor, predict and control the
lifecycle of the products [14,40]. Precise demand
forecasts will make it easy to implement the CE
principles, thus more precise plan to reuse and
preparation of used materials can be made [41].
Moreover, digital technologies can help in product design
and making decisions on production through sustainable
operations management by providing data on the
resources to reduce resource consumption, improving
productivity and extending the lifecycle of products [7].
When we integrate products with sensors, we basically
allow performance monitoring and as example this
performance data could be used for predictive
maintenance and future products maintenance related
requirements specification purposes. Therefore,
organizations can provide high quality services to the
customers. Furthermore, these technologies enable
connectivity and sharing information related to supply
and demand; for example, by website and apps which
connects people to organizations [39]. In addition, since
such technologies could be used to collect the
information on consumers’ behaviour, they could also
help the organizations in improving design of products
and services for a better and more user friendly
equipment, that would then meet the customer’s needs
and satisfaction more completely [42]. With the ability to
collect data from operations, processes and objects,
digital technologies can help to identify possible failures
which creates waste and to prevent further failures [39].
In addition, referring to CE and sustainable
manufacturing, improvement in using data, machinery,
equipment and software can reduce the need of limited
resources as well as ecological footprint of the production
is leading to new business models [41]. However,
manufacturing industries would face challenges such as
cybersecurity concerns, developing new talent, new
business models and definition of the new strategy in
attempting to implement Industry 4.0 [43]. A pioneering
roadmap towards an Industry 4.0-based CE business is
illustrated in Fig. 5.
Table 1. A framework of Industry 4.0 in Intelligent Manufacturing System (adapted from Zhong et al., 2017)
Design Machine Monitoring Control Scheduling
Smart design
Smart prototyping
Real-time control and
monitoring
Collaborative decision-
making
Real-time information
sharing Data driven
modelling
Big data analytics
Marketing
Warehouse management
Smart controller
Data-enabled
prediction
Transports
Smart sensors
Fig. 4. Industry 4.0 solutions for circularity (adapted from World Economic Forum and Accentures Strategy, 2019)
3.3 Industrial AI
According to McCarthy [44], “Artificial intelligence is
the science and engineering of making intelligent In the
end short authors view how that might indicate a research
gap and how on another hand the non-full SLR nature of
literature review might need additional machines,
especially intelligent computer programs. It is related to
the similar task of using computers to understand human
intelligence, but AI does not have to confine itself to
methods that are biologically observable.”. According to
Kaplan, in his book AI: What Everyone Needs to Know,
AI relates to the computer programs which are capable to
behave in such a way that if demonstrated by human, it
would be considered as intelligent [45]. Hence, the
definition of AI concerns the comparison and alignment
between human and machines. Lee et al [46] distinguish
AI as “a cognitive science”, which enhances research
activities in the areas of natural language processing,
machine learning, image processing, robotics etc.
Brynjolfsson and Mcafee [47] mention that AI has huge
advances on both areas of perception and cognition AI
algorithms can be used in various functionalities such as
pattern recognition, prediction, optimization & planning,
and integrated solutions with robots. Fig. 6 presents the
areas of AI development AI techniques future states.
Fig. 5. Roadmap towards Industry4.0 and CE (adapted from Jabbour et al., 2018)
The progress of AI relates to the advancement in three
enablers of real innovations: learning algorithm,
computing power and training data. Advanced robotics
among many digital technologies progress in 4IR, has
been identified as a significant alternative in the entire
value chain. It is estimated that 1.8 million industrial
robots will operate in production system globally which
represents approximate market of $35billion worldwide
[48] . According to research done by Jacoby and Paltsev
[49], Artificial Intelligence is capable of reducing costs
for business actions, based on its capability to predict
future things, based on current knowledge. AI has the
ability in data collection from the human on the
information that they have and to generate novel
information that didn’t exist. Market competition and
demands are the challenges that industries have faced
during the past years worldwide and Industry 4.0 can
bring a radical innovative solution in businesses. Machine
learning along with AI techniques bring solutions in a
systematic way and discipline for industrial applications.
[46]. The aim of Industrial AI is to validate, develop and
deploy different machine learning algorithms with a
sustainable performance for industrial applications.
3.4 Smart manufacturing
Smart manufacturing, also known as intelligent
manufacturing, belongs to the concept of manufacturing
in which advanced information and manufacturing
technologies optimize production and product
transactions [50]. According Zhong et al. [51],
smart/intelligent manufacturing is a modern
manufacturing model which is based on intelligent
technologies and science in which design, management
and productions are significantly upgraded . Holubek and
Kostal [52] mention that intelligent manufacturing system
consists of intelligent design, intelligent operation,
intelligent control, intelligent planning and intelligent
maintenance. Various smart sensors, advanced materials,
intelligent devices, adaptive decision-making models and
data analytics are used to facilitate the whole product
lifecycle [38]. Accordingly, product quality, production
efficiency and service level will be improved [53].
3.5 Intelligent manufacturing
AI provides features such as reasoning, acting and
learning in industrial manufacturing system and therefore
is capable to play a key role in future manufacturing
systems development efforts. With the use of AI
technology as a complement of human’s skill which deals
more effectively with complexity, human involvement in
IMS is minimized [8,51]. The application of AI in
product lifecycle mainly consists “intelligent cloud
product design technology, intelligent cloud innovation
design technology, intelligent cloud production
equipment technology, intelligent cloud operation and
management technology, intelligent cloud simulation and
experiment technology, and intelligent cloud service
guarantee technology” [38].
Fig. 6. AI development and the future state of AI techniques (World Economic Forum and A.T.Kearney, 2017)
AI can utilize production optimization in
manufacturing companies. For example, machine
learning techniques can be employed to be used in
identification tasks, where the system needs to do root
cause related identifying tasks for ranking purposes to be
able to define probable root causes of production
challenges [54]. AI can help product designers in IMS to
create multiple prototypes versions and more efficient
testing [55]. However, implementing AI is not easy and
requires experts for algorithm development, preparation
of training data as well as translating the algorithm output
into the meaningful results for humans [8]. In addition, to
train the algorithm, the availability of sufficient high-
quality data is required. Poor quality outputs result from
badly engineered data, in other words rubbish in, rubbish
out.
3.6 Artificial Intelligence and CE
When considering the impact of AI in a role of an enabler
and innovations enhancer of CE, according to Ellen
MacArthur Foundation (2019), AI’s role can be divided
to three categories:
1. By improving the sorting and disassembling
products processes, remanufacturing the components and
recycling materials: AI can help in closing the loops by
building and improving the reverse logistics
infrastructure.
2. To expand innovative circular business models: AI
can help to combine real time and historical data from
users and products to increase product circulation and
asset utilization through pricing and demand prediction,
predictive maintenance and smart inventory management.
3. Design and develop circular products, components
and materials: By rapid prototyping and testing through
machine-learning-assisted design processes AI can help
in design out waste for food in CE and generate the
potential value of USD 127 million by 2030 [8].
3.6.1 Infrastructure optimization business model
AI can offer help into circular infrastructure optimization
needs, for example by affecting material and components
recycling actions and flows. AI can be used in waste-
sorting improvement and efficiency by increasing the
value of recycled and recovered materials [8]. Moreover,
robots and automation are enablers of precise waste-
sorting and therefore they can enhance current and
generate new opportunities for material reuse cases,
based on current materials seen as waste [56].
3.6.2 Operate innovative circular business model
In order to develop successful and profitable innovative
circular business model, organizations require functions
such as pricing, marketing, sales and after sales services
as well as customer support [8] . From digitality point of
view, digitalization enabled platforms and business
models build upon platform ideologies do offer new
service provision opportunities, material sharing business
models, and in general the allow value generation for
both, the companies and the users [58]. Dynamic pricing
and matching algorithms are the two main role of digital
platforms which enhances extension of products lifecycle
to achieve and retain their innovative circular objectives
[8,56,58]. by the use of AI-based analytical model
companies can make faster decisions on the next use
cycle of returned products through huge data collection
and analysis of data from products and customers in a
more feasible way [8]. Best practices of such application
models are the online flea markets in which buyers can
find the more desirable prices and sellers can sell
unnecessary second-hand products.
3.6.3 AI design circular product business model
According to Ellen MacArthur Foundation [8], AI
technologies help to reveal high potential circular
opportunities in designing circular products, components
and materials. Design innovation allows cycles of reuse,
repair, refurbishment and recycling of technical
components as well as looping of biological nutrients. AI
techniques can help scientists to evaluate a huge amount
of data on the materials’ properties and structure in
designing a new material b rapid analyzing. In addition,
AI could help the tedious work of doing material toxicity
analysis for safety purposes or e.g. chemicals analysis
and detection to be 1) more efficient, 2) faster and 3)
economical and efficient in multiple ways trough proper
algorithms developing efforts. AI can help in closing and
slowing the materials loops by reducing the faults in
designing and prototyping products and materials,
making less waste in manufacturing processes. Looking
things from designers point of view, according to World
Economic Forum and Accenture Startegy [37], AI-based
application supports them by connecting data on
alternatives to harmful or hard-to-recycle materials within
a product. The results of the product modularity and
durability evaluation lead to and overall circularity index
for the designed product. For more understanding of
Below are two case examples of food and electronic
companies taken from [8].
4 Results & managerial implications
This publication makes an argument for the importance
of digital technologies, especially Artificial Intelligence
in context of circular product designs. The review
includes circular products design tool, which a design
team could follow for gains in efforts to produce
materials, whole products and their components with
circular characteristics in mind, that will be beneficial for
the companies and businesses as well. Additionally,
smart systems will boost circularity by utilizing the finite
design related resources better, with processes such as
rapid prototyping and enhanced testing. Therefore,
implementing innovative AI enhancement in business
models, that shall support circularity are essentials for the
growth and competitiveness of the industries.
Accordingly, to recognize the opportunities Artificial
Intelligence could bring with it for an organization, one
should understand what AI can or cannot do. Specially in
their own industry context. For example, it is a good
question to think about, that how the business can
integrate AI and CE in all the different areas of the
organizations and then take into account the product /
service design elements as we have been discussing here,
it is the make or break part of products life cycle
circularity.
5 Limitation and further research
potentials
According to the area specific literature, there is a big gap
in studies related into the role of circular product design
and relating strategies and business models. Basically, the
scientific publications seem to be countable in tens in this
research realm and we highly suggest a systematic
literature research and/or mapping study to be done in
that cross section of topics to give definite answer how
big of a research gap or in what specific areas of business
models and product design strategies for circularity there
actually is. First of all, there could be more work to be
done on infusing AI into product design courses,
similarly like authors school did to infuse design thinking
to software engineering teaching activities [62] or to
enhance digital design process thinking teaching making
more configurable products [63] to take the re-usability
into account. On other hand, more research and studies
on the guidelines of designing circular products will be
required, to give guidance for practical designers, as most
of the studies and research are actually focused on other
phases of CE such as recycling and related business
models or shared platforms. Furthermore, we like to state
that digitalization in CE is a novel topic which has
recently gained lot of attention from both the businesses
and from researchers and it is developing rapidly. More
research is also needed with deep details on what is /
should be the role of AI in different phases of whole
ecosystems of Circular Economies and where it fits best
in different industries going towards circularity enhanced
practices. For example, more detailed studies are required
to figure out what challenges organizations might face
when implementing digital technologies within their
circular business models and what are the advantages
they can gain in return. This could be helped with
different community wisdom harnessing models [64]
(e.g. to map reusability concept from wide audience to
teach the AI to support that sort of design features). And
Different style of new AI solution design hackathons
could be used within research units and product
developing companies cross-sections [65, 66] to boost
new innovation generation in these fields. Additionally,
case examples with ROI calculations and SWOT analysis
for e.g. educational purposes would be beneficial for
academic education context. And in bigger picture, it
would make a lot of sense to look the end user side too,
e.g. if AI is used for product design, it should be
connected into the solutions in use to educate the people
who generate the waste to recycle the valuable resources
more efficiently [67]. Finally, longitudinal studies result
to reveal in which phases of CE it is more beneficial and
important to utilize digitalization in each industry would
help e.g. politicians to fine tune regulations to support
faster CE efforts ongoing in different industry areas.
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