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Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2017) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th C IRP Design Conference 2018.
28th CIRP Design Conference, May 2018, Nantes, France
A new methodology to analyze the functional and physical architecture of
existing products for an assembly oriented product family identification
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu
Abstract
In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of
agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production
systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to
analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and
nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production
system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster
these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable
assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and
a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the
similarity between product families by providing design support to both, production system planners and product designers. An illustrative
example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of
thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.
Keywords: Assembly; Design method; Family identification
1. Introduction
Due to the fast development in the domain of
communication and an ongoing trend of digitization and
digitalization, manufacturing enterprises are facing important
challenges in today’s market environments: a continuing
tendency towards reduction of product development times and
shortened product lifecycles. In addition, there is an increasing
demand of customization, being at the same time in a global
competition with competitors all over the world. This trend,
which is inducing the development from macro to micro
markets, results in diminished lot sizes due to augmenting
product varieties (high-volume to low-volume production) [1].
To cope with this augmenting variety as well as to be able to
identify possible optimization potentials in the existing
production system, it is important to have a precise knowledge
of the product range and characteristics manufactured and/or
assembled in this system. In this context, the main challenge in
modelling and analysis is now not only to cope with single
products, a limited product range or existing product families,
but also to be able to analyze and to compare products to define
new product families. It can be observed that classical existing
product families are regrouped in function of clients or features.
However, assembly oriented product families are hardly to find.
On the product family level, products differ mainly in two
main characteristics: (i) the number of components and (ii) the
type of components (e.g. mechanical, electrical, electronical).
Classical methodologies considering mainly single products
or solitary, already existing product families analyze the
product structure on a physical level (components level) which
causes difficulties regarding an efficient definition and
comparison of different product families. Addressing this
Procedia CIRP 91 (2020) 659–664
2212-8271 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the CIRP BioManufacturing Conference 2019
10.1016/j.procir.2020.02.224
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the CIRP BioManufacturing Conference 2019.
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2020) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2020 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the CIRP Design Conference 2020.
30th CIRP Design 2020 (CIRP Design 2020)
Evaluation Model for Mobility Design of
Learning Factories
Natalie Petruscha,*, Wolf Schliephackb, Holger Kohla,b
aFraunhofer Institute for Production Systems and Design Technology, Pascalstraße 8-9, 10587 Berlin, Germany
b Technische Universität Berlin, Pascalstraße 8-9, 10587 Berlin, Germany
* Corresponding author. Tel.: +49-30-39006-310. E-mail address: natalie.petrusch@ipk.fraunhofer.de
Abstract
Shortening product life cycles and increasing, global competition lead to the necessity for companies to become flexible and able to change. The
successful conduction of change projects depends to a large extent on the understanding and cooperation of the operating people. The acquisition
of knowledge and thus of competence at all levels is of fundamental importance for this. Learning Factories are one of the most sustainable
concepts in this respect. Concerning the industrial application, however, Learning Factories require high investment costs and are relatively
inflexible in respect to their dedicated facilities and their learning content. This reveals a connection between the success of a Learning Factory
in an industrial context and its mobility. In order to be able to evaluate approaches towards the mobility design of Learning Factories more
precisely, the decisive drivers for mobility are identified. The assessment of these drivers with regard to the extent of influence on mobility, but
also the critical consideration of possible limits, lead to an evaluation model for the mobility design of Learning Factories. Subsequently, this
model is tested by applying it to a successfully operating Learning Factory.
© 2020 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the CIRP Design Conference.
Keywords: Learning Factories; Lean Production Systems; Lean Management; mobility evaluation
1. Initial situation and motivation
Companies nowadays are facing the challenge to increase
their ability to react fast and flexible to new demands and
developments of the market as well as to be able to change
successfully, in order to succeed in the competition for market
share [1]. Furthermore, the implementation of digitalisation
elements lead to a relevant change also in terms of job
descriptions already today [2,3]. Therefore, change
programmes gain more significance, but must be supported by
the people, who still design and work for the development,
maintenance, and continuous improvement of processes,
although the level of automation is rising [4]. If they are not
motivated to change or not sharing one vision or target, they are
likely to block a change programme. Such a blockade is the
main reason, why change projects fail [5,6].
In addition to the general need for qualification, however,
the requirements for these programmes have also risen. For
instance, the ongoing digitalisation requires the development of
completely new skills in a company, which so far cannot easily
be covered by standard educational formats. Due to their
general designs, which are not very well adapted to the
company and the real workplaces, conventional location-
independent programmes such as lectures and workshops are
likely to lag behind other methods in terms of their efficiency
and long term success.
Consequently, the lack of skilled workers is already
showing an economic impact especially outside metropolitan
areas, for small and medium sized enterprises (SMEs) [7]. The
need for formats that offer both efficiency and long-time
success as well as flexible orientation and spatial independence
is therefore particularly relevant in this area. High immersive
qualification concepts such as Learning Islands or Learning
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2020) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2020 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the CIRP Design Conference 2020.
30th CIRP Design 2020 (CIRP Design 2020)
Evaluation Model for Mobility Design of
Learning Factories
Natalie Petruscha,*, Wolf Schliephackb, Holger Kohla,b
aFraunhofer Institute for Production Systems and Design Technology, Pascalstraße 8-9, 10587 Berlin, Germany
b Technische Universität Berlin, Pascalstraße 8-9, 10587 Berlin, Germany
* Corresponding author. Tel.: +49-30-39006-310. E-mail address: natalie.petrusch@ipk.fraunhofer.de
Abstract
Shortening product life cycles and increasing, global competition lead to the necessity for companies to become flexible and able to change. The
successful conduction of change projects depends to a large extent on the understanding and cooperation of the operating people. The acquisition
of knowledge and thus of competence at all levels is of fundamental importance for this. Learning Factories are one of the most sustainable
concepts in this respect. Concerning the industrial application, however, Learning Factories require high investment costs and are relatively
inflexible in respect to their dedicated facilities and their learning content. This reveals a connection between the success of a Learning Factory
in an industrial context and its mobility. In order to be able to evaluate approaches towards the mobility design of Learning Factories more
precisely, the decisive drivers for mobility are identified. The assessment of these drivers with regard to the extent of influence on mobility, but
also the critical consideration of possible limits, lead to an evaluation model for the mobility design of Learning Factories. Subsequently, this
model is tested by applying it to a successfully operating Learning Factory.
© 2020 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the CIRP Design Conference.
Keywords: Learning Factories; Lean Production Systems; Lean Management; mobility evaluation
1. Initial situation and motivation
Companies nowadays are facing the challenge to increase
their ability to react fast and flexible to new demands and
developments of the market as well as to be able to change
successfully, in order to succeed in the competition for market
share [1]. Furthermore, the implementation of digitalisation
elements lead to a relevant change also in terms of job
descriptions already today [2,3]. Therefore, change
programmes gain more significance, but must be supported by
the people, who still design and work for the development,
maintenance, and continuous improvement of processes,
although the level of automation is rising [4]. If they are not
motivated to change or not sharing one vision or target, they are
likely to block a change programme. Such a blockade is the
main reason, why change projects fail [5,6].
In addition to the general need for qualification, however,
the requirements for these programmes have also risen. For
instance, the ongoing digitalisation requires the development of
completely new skills in a company, which so far cannot easily
be covered by standard educational formats. Due to their
general designs, which are not very well adapted to the
company and the real workplaces, conventional location-
independent programmes such as lectures and workshops are
likely to lag behind other methods in terms of their efficiency
and long term success.
Consequently, the lack of skilled workers is already
showing an economic impact especially outside metropolitan
areas, for small and medium sized enterprises (SMEs) [7]. The
need for formats that offer both efficiency and long-time
success as well as flexible orientation and spatial independence
is therefore particularly relevant in this area. High immersive
qualification concepts such as Learning Islands or Learning
660 Natalie Petrusch et al. / Procedia CIRP 91 (2020) 659–664
2 Author name / Procedia CIRP 00 (2020) 000–000
Factories often require specialized facilities in order to deliver
the desired quality in education and thus the aimed learning
outcome. As a result, these programmes are often expensive,
require a high amount of planning and thus are less flexible
once they are designed compared to conventional qualification
programmes [1,7]. Nonetheless, the concept of Learning
Factories is a recognized method to impart qualification of
people successfully for the long term. However, existing
realisations often require a comparatively high investments and
show a lack of flexibility regarding their location and the
learning content [8,9].
In this paper, as a reaction to the lack of a framework to
design a mobile Learning Factory, the authors take a look at
different concepts of mobility in general and introduce a
possible evaluation model of a mobile Learning Factory based
on relevant drivers of mobility.
2. Learning Factories
2.1. Definition
Kolb defines learning as “the process whereby knowledge is
created through the transformation of experience” [10]. This
occurs in two different modes of learning, direct formal
learning and mostly incidental informal learning. Both modes
are intensively promoted in Learning Factories, whereby a
close relation to the actual work content characterizes an
effective learning environment [8,11]. According to the
accepted definition, a “Learning Factory in a narrow sense is a
learning environment specified by
Processes that are authentic, include multiple stations, and
comprise technical as well as organizational aspects,
A setting that is changeable and resembles a real value
chain,
A physical product being manufactured, and
A didactical concept that comprises formal, informal and
non-formal learning, enabled by own actions of the
trainees in an on-site learning approach” [12].
In a broader sense, an additional distinction must be made
between different types of Learning Factories:
“Set up in a hybrid or fully virtual environment,
Focus on a service product,
Based on remote learning.” [12].
2.2. Advantages and Limitations of Learning Factories
The concept of Learning Factories is recognised for many
advantages in terms of qualification programs [8]. It enables
both self-regulated and externally guided learning, leading to
an active integration of the learner. Active learning in this
context refers to the solving of real challenges as well as the
alternating phases of generating and application of knowledge,
to create competencies. Learning Factories additionally provide
possibilities for self-organized learning in groups and through
their active participative character enhance the motivation of
the learners [5]. Furthermore, in addition to these versatile
possibilities of didactic structure, however, Learning Factories
above all bring another immense advantage in contrast to other
concepts. Especially the representation of a realistic working
environment, which corresponds to that of the participants,
enables a reduction of the requirements for transfer of the
acquired knowledge and thus paves the way for a direct
implementation of the gained competence at one's own
workplace [13]. These inherent advantages of Learning
Factories directly link to the key factors of successful learning,
namely constructivist learning environments, situated learning,
active learning or problem-based learning [5]. Additionally,
through their characteristic ability to generate an advanced
learning loop, Learning Factories can support the creation,
conduction and conservation of change projects.
However, besides these advantages, Learning Factories also
carry some limitations. Many design variants require
specialized facilities and resources, a high amount of planning,
and tailored learning content as well as professional coaches
and assistants. These factors lead to high investments and
running costs also including travel expenses regarding
participants and additionally limit the flexibility regarding the
produced product, the learning content and the target audience
[8,14]. The mobility evaluation of Learning Factories, in form
of a systematic approach to overcome these limitations, is
practically non-existent, at least regarding physical and hybrid
Learning Factories. Still, there are attempts in the course of
realisations that directly consider these aspects. [5,8,9]
2.3. Mobility of Learning Factories
In general, mobility describes “the ability [of an object] to
move freely or be easily moved” [15], meaning it is not
constrained to a fixed location and thus is mobile. In the
physical context of production sites or a factory, mobility
describes the ease for relocating the resources and facilities of
the factory, including people, machines, products, and even
building structures [16]. In a wider sense, mobility may be
expressed as the possibility to adapt, modify or change the
behaviour of the plant or factory in reaction to alternating
circumstances.
Furthermore, mobility is one of the five main transformation
enablers for change of objects on the factory level besides
universality, scalability, modularity and compatibility [17,18].
However, since these enablers influence each other and do not
correspond to a categorisation according to the MECE rule
(Mutually Exclusive and Collectively Exhaustive), the
influence of the other enablers must also be considered in the
case of mobility. For instance, a high degree of modularity and
compatibility leads to advantages in mobility and vice versa
[16]. By transferring the said advantages of mobility to the
concept, Learning Factories would become able to
Provide a high quality and site-independent practical
education environment for learners in a wider range of
geographical locations,
Support learners to overcome local deficiencies of
educational infrastructures,
Author name / Procedia CIRP 00 (2020) 000–000 3
Provide learners the opportunity to interact with current
production technologies, either directly or indirectly,
Make technologies affordable and globally available,
Maximize the utilization of training facilities by sharing
with other educational institutes and companies,
Minimize investments and costs for planning, operation
and maintenance, and
Create socio-economic value within on-site training. [14]
3. Development of evaluation model
Currently though, the established morphology of Learning
Factories does not provide specific modules for the design of
the mobility of Learning Factories [19]. In addition, no
guideline supporting the planning and design of the mobility
exists at the moment. In order to develop a model for a clearer
evaluation of the mobility of Learning Factories, the relevant
influencing factors have to be identified. Though mobility is
not explicitly addressed, the morphology of Learning Factories
contains some factors, which, according to the definition of
mobility, are relevant and can therefore be adopted directly.
Further factors can also be derived from the concepts of
existing Learning Factories, laboratories and production
systems, resulting in a comprehensive model for determining
and evaluating the mobility of Learning Factories. Following
the definition of Learning Factories, a distinction is made
between physical, virtual and remote access Learning Factories
for the analysis of realisations.
3.1. Physical Learning Factories
Conventional physical Learning Factories offer the learning
advantages active, collaborative hands-on learning including
direct interaction among participants as well as with the
supervisor and thus immediate feedback in a realistic
environment [14,20]. Besides that, their restrictions regarding
usage times and their location as well as the high investment
requirements often hinder their widespread implementation
[14].
One example to overcome these disadvantages is the
“factory-in-a-box” project, which main objectives are the
development and implementation of a flexible and
reconfigurable production system. Mainly focusing on using
standardized modules and an increased level of automation,
they assure flexibility and reconfigurability and thus enhance
the mobility of this specific realisation [21,22]. The “mini
factory” is another implementation example of such a modular
system. By using a flexible “plug-in” system for setting up
manual workstations, these can not only be set up and
dismantled in a short time, but are also compatible with each
other and with universal profiles, so that additional equipment
can be avoided [23]. In terms of changeability and
reconfigurability, the concept of reconfigurable modules with
standardized elements and interfaces, which enable a "plug and
produce" capability [18,24], are of particular importance for
mobility. In addition, the general size and needs of
supplemental media of the installations as well as standards and
requirements for logistics must also be taken into account
[16,22,24–27]. The model must therefore include in particular
the versatile aspects of the workstations, such as the number of
machines, interfaces, but also product specifics, etc. From the
morphology of Learning Factories, additional basic factors for
mobility can be derived, e.g. the required space, required
installation media, the degree of automation, etc.
3.2. Virtual Learning Factories
In the broader sense of definition, Learning Factories are
considered not only to be physical learning environments, but
also in a virtual context [5]. Although such approaches offer no
interaction with real equipment and other learners or the
supervisor, nor do they deliver direct or at least limited
feedback, they have advantages over physical Learning
Factories [5,14]. In fact, Virtual Learning Factories are more
cost effective in maintaining, provide a large capacity to
manage numerous users at one time and are not restricted to
specific facilities or usage times [28]. The use of technologies,
such as virtual reality (VR), augmented reality (AG) and digital
twins, offer a variety of options to develop a virtual Learning
Factory and therefore enhance the aspect of mobility [29].
Nevertheless, the precise development of the real environment
is the biggest challenge to this approach and limitations
regarding communication and other soft skills need to be
considered. [14,28] Thus, the model must also include factors
such as the IT infrastructure, the degree of simulation, while
regarding the morphology the aspect of IT integration is of
particular interest.
3.3. Remote Access Learning Factories using ICT
Remote Access facilitates the access to virtual (learning)
content and thus also enhances the mobility of Learning
Factories in the broader sense of meaning [5,30,31]. The
application of information and communications technologies
(ICT) infrastructure and equipment enables participants to
access, learn and interact remotely inside a Learning Factory
environment regardless geographical restrictions, similar to
Virtual Learning Factories, but with an additional physical
component [14,32]. For instance, by using different modules,
which are networked together but do not necessarily have to be
at the same location, such a concept has been implemented with
the Cube Factory 2 [33]. Another advantage of ICT integration
is a simplified ability to modify the product, as shown by the
application of the concept for Learning Factories to the
requirements of special machinery assembly [34].
Furthermore, remote learning laboratories enable numerous
participants at different locations to choose the learning content
and tempo fitting best for their needs and into their schedule.
Open education models such as Makerspaces or massive open
online courses (MOOCs) together with the design of
interconnected Learning Factories complete the existing
portfolio of remote access learning possibilities [35].
Therefore, aspects such as the type of communication channel,
the connection network, etc. must also be taken into account in
the model.
Summarised, the general morphology together with specific
realizations already offer a broad spectrum of relevant factors,
which can enhance the mobility of a Learning Factory.
Natalie Petrusch et al. / Procedia CIRP 91 (2020) 659–664 661
2 Author name / Procedia CIRP 00 (2020) 000–000
Factories often require specialized facilities in order to deliver
the desired quality in education and thus the aimed learning
outcome. As a result, these programmes are often expensive,
require a high amount of planning and thus are less flexible
once they are designed compared to conventional qualification
programmes [1,7]. Nonetheless, the concept of Learning
Factories is a recognized method to impart qualification of
people successfully for the long term. However, existing
realisations often require a comparatively high investments and
show a lack of flexibility regarding their location and the
learning content [8,9].
In this paper, as a reaction to the lack of a framework to
design a mobile Learning Factory, the authors take a look at
different concepts of mobility in general and introduce a
possible evaluation model of a mobile Learning Factory based
on relevant drivers of mobility.
2. Learning Factories
2.1. Definition
Kolb defines learning as “the process whereby knowledge is
created through the transformation of experience” [10]. This
occurs in two different modes of learning, direct formal
learning and mostly incidental informal learning. Both modes
are intensively promoted in Learning Factories, whereby a
close relation to the actual work content characterizes an
effective learning environment [8,11]. According to the
accepted definition, a “Learning Factory in a narrow sense is a
learning environment specified by
Processes that are authentic, include multiple stations, and
comprise technical as well as organizational aspects,
A setting that is changeable and resembles a real value
chain,
A physical product being manufactured, and
A didactical concept that comprises formal, informal and
non-formal learning, enabled by own actions of the
trainees in an on-site learning approach” [12].
In a broader sense, an additional distinction must be made
between different types of Learning Factories:
“Set up in a hybrid or fully virtual environment,
Focus on a service product,
Based on remote learning.” [12].
2.2. Advantages and Limitations of Learning Factories
The concept of Learning Factories is recognised for many
advantages in terms of qualification programs [8]. It enables
both self-regulated and externally guided learning, leading to
an active integration of the learner. Active learning in this
context refers to the solving of real challenges as well as the
alternating phases of generating and application of knowledge,
to create competencies. Learning Factories additionally provide
possibilities for self-organized learning in groups and through
their active participative character enhance the motivation of
the learners [5]. Furthermore, in addition to these versatile
possibilities of didactic structure, however, Learning Factories
above all bring another immense advantage in contrast to other
concepts. Especially the representation of a realistic working
environment, which corresponds to that of the participants,
enables a reduction of the requirements for transfer of the
acquired knowledge and thus paves the way for a direct
implementation of the gained competence at one's own
workplace [13]. These inherent advantages of Learning
Factories directly link to the key factors of successful learning,
namely constructivist learning environments, situated learning,
active learning or problem-based learning [5]. Additionally,
through their characteristic ability to generate an advanced
learning loop, Learning Factories can support the creation,
conduction and conservation of change projects.
However, besides these advantages, Learning Factories also
carry some limitations. Many design variants require
specialized facilities and resources, a high amount of planning,
and tailored learning content as well as professional coaches
and assistants. These factors lead to high investments and
running costs also including travel expenses regarding
participants and additionally limit the flexibility regarding the
produced product, the learning content and the target audience
[8,14]. The mobility evaluation of Learning Factories, in form
of a systematic approach to overcome these limitations, is
practically non-existent, at least regarding physical and hybrid
Learning Factories. Still, there are attempts in the course of
realisations that directly consider these aspects. [5,8,9]
2.3. Mobility of Learning Factories
In general, mobility describes “the ability [of an object] to
move freely or be easily moved” [15], meaning it is not
constrained to a fixed location and thus is mobile. In the
physical context of production sites or a factory, mobility
describes the ease for relocating the resources and facilities of
the factory, including people, machines, products, and even
building structures [16]. In a wider sense, mobility may be
expressed as the possibility to adapt, modify or change the
behaviour of the plant or factory in reaction to alternating
circumstances.
Furthermore, mobility is one of the five main transformation
enablers for change of objects on the factory level besides
universality, scalability, modularity and compatibility [17,18].
However, since these enablers influence each other and do not
correspond to a categorisation according to the MECE rule
(Mutually Exclusive and Collectively Exhaustive), the
influence of the other enablers must also be considered in the
case of mobility. For instance, a high degree of modularity and
compatibility leads to advantages in mobility and vice versa
[16]. By transferring the said advantages of mobility to the
concept, Learning Factories would become able to
Provide a high quality and site-independent practical
education environment for learners in a wider range of
geographical locations,
Support learners to overcome local deficiencies of
educational infrastructures,
Author name / Procedia CIRP 00 (2020) 000–000 3
Provide learners the opportunity to interact with current
production technologies, either directly or indirectly,
Make technologies affordable and globally available,
Maximize the utilization of training facilities by sharing
with other educational institutes and companies,
Minimize investments and costs for planning, operation
and maintenance, and
Create socio-economic value within on-site training. [14]
3. Development of evaluation model
Currently though, the established morphology of Learning
Factories does not provide specific modules for the design of
the mobility of Learning Factories [19]. In addition, no
guideline supporting the planning and design of the mobility
exists at the moment. In order to develop a model for a clearer
evaluation of the mobility of Learning Factories, the relevant
influencing factors have to be identified. Though mobility is
not explicitly addressed, the morphology of Learning Factories
contains some factors, which, according to the definition of
mobility, are relevant and can therefore be adopted directly.
Further factors can also be derived from the concepts of
existing Learning Factories, laboratories and production
systems, resulting in a comprehensive model for determining
and evaluating the mobility of Learning Factories. Following
the definition of Learning Factories, a distinction is made
between physical, virtual and remote access Learning Factories
for the analysis of realisations.
3.1. Physical Learning Factories
Conventional physical Learning Factories offer the learning
advantages active, collaborative hands-on learning including
direct interaction among participants as well as with the
supervisor and thus immediate feedback in a realistic
environment [14,20]. Besides that, their restrictions regarding
usage times and their location as well as the high investment
requirements often hinder their widespread implementation
[14].
One example to overcome these disadvantages is the
“factory-in-a-box” project, which main objectives are the
development and implementation of a flexible and
reconfigurable production system. Mainly focusing on using
standardized modules and an increased level of automation,
they assure flexibility and reconfigurability and thus enhance
the mobility of this specific realisation [21,22]. The “mini
factory” is another implementation example of such a modular
system. By using a flexible “plug-in” system for setting up
manual workstations, these can not only be set up and
dismantled in a short time, but are also compatible with each
other and with universal profiles, so that additional equipment
can be avoided [23]. In terms of changeability and
reconfigurability, the concept of reconfigurable modules with
standardized elements and interfaces, which enable a "plug and
produce" capability [18,24], are of particular importance for
mobility. In addition, the general size and needs of
supplemental media of the installations as well as standards and
requirements for logistics must also be taken into account
[16,22,24–27]. The model must therefore include in particular
the versatile aspects of the workstations, such as the number of
machines, interfaces, but also product specifics, etc. From the
morphology of Learning Factories, additional basic factors for
mobility can be derived, e.g. the required space, required
installation media, the degree of automation, etc.
3.2. Virtual Learning Factories
In the broader sense of definition, Learning Factories are
considered not only to be physical learning environments, but
also in a virtual context [5]. Although such approaches offer no
interaction with real equipment and other learners or the
supervisor, nor do they deliver direct or at least limited
feedback, they have advantages over physical Learning
Factories [5,14]. In fact, Virtual Learning Factories are more
cost effective in maintaining, provide a large capacity to
manage numerous users at one time and are not restricted to
specific facilities or usage times [28]. The use of technologies,
such as virtual reality (VR), augmented reality (AG) and digital
twins, offer a variety of options to develop a virtual Learning
Factory and therefore enhance the aspect of mobility [29].
Nevertheless, the precise development of the real environment
is the biggest challenge to this approach and limitations
regarding communication and other soft skills need to be
considered. [14,28] Thus, the model must also include factors
such as the IT infrastructure, the degree of simulation, while
regarding the morphology the aspect of IT integration is of
particular interest.
3.3. Remote Access Learning Factories using ICT
Remote Access facilitates the access to virtual (learning)
content and thus also enhances the mobility of Learning
Factories in the broader sense of meaning [5,30,31]. The
application of information and communications technologies
(ICT) infrastructure and equipment enables participants to
access, learn and interact remotely inside a Learning Factory
environment regardless geographical restrictions, similar to
Virtual Learning Factories, but with an additional physical
component [14,32]. For instance, by using different modules,
which are networked together but do not necessarily have to be
at the same location, such a concept has been implemented with
the Cube Factory 2 [33]. Another advantage of ICT integration
is a simplified ability to modify the product, as shown by the
application of the concept for Learning Factories to the
requirements of special machinery assembly [34].
Furthermore, remote learning laboratories enable numerous
participants at different locations to choose the learning content
and tempo fitting best for their needs and into their schedule.
Open education models such as Makerspaces or massive open
online courses (MOOCs) together with the design of
interconnected Learning Factories complete the existing
portfolio of remote access learning possibilities [35].
Therefore, aspects such as the type of communication channel,
the connection network, etc. must also be taken into account in
the model.
Summarised, the general morphology together with specific
realizations already offer a broad spectrum of relevant factors,
which can enhance the mobility of a Learning Factory.
662 Natalie Petrusch et al. / Procedia CIRP 91 (2020) 659–664
4 Author name / Procedia CIRP 00 (2020) 000–000
Furthermore, concepts of manufacturing planning show
additional aspects that need to be considered with regard to the
definition of mobility. Four of these factors stand out, which are
of particular importance when evaluating the mobility of a
Learning Factory.
Standardization of modules, interfaces, networking
technologies, and platforms [16,23] to create a “plug-and-
produce” design in a physical way as well as in terms of IT
[12].
Design of modules to be mobile, external as well as
internal, highly compatible, reconfigurable, scalable (e.g.
to small model size) and universally usable with modular
products [16,23].
Design of modules based on manual assembly with an
increased degree of automation and dynamically adaptive,
enabling a wider range of products [21,23].
Transportation in standardized carriers, e.g. containers,
using high payload vehicles (truck/train) [21], and
transportation-friendly and space-saving design of modules
[5].
4. Evaluation Model and exemplary application
In order to achieve a structured guideline for the evaluation
process, the identified factors and required characteristics were
further grouped into three main analysis levels (infrastructure
mobility, workstation mobility and product mobility) [36].
These analysis levels are presented in a top-down manner,
starting with the overall infrastructure assessment down to the
mobility evaluation on workstation and product level.
However, the levels do not necessarily have to be processed
consecutively, but can rather be regarded as independent of
each other and thus merely reflect the scope of observation.
Furthermore, the resulting model was applied to an existing
Learning Factory to check its consistency and correctness.
The Learning Factory under consideration is located in the
premises of a leading pharmaceutical company. For the
company's own production system, which takes up many
elements from the Lean Production System, both managers and
employees are prepared for their role and trained in the
appropriate methods and tools [20,37]. Fig. 1 shows the general
structure of this particular Learning Factory.
Fig. 1. Overview of the design of the Learning Factory to which the model was
applied.
Analysis level 1, the evaluation of the infrastructure
mobility, is divided into several categories, serving to describe
the Learning Factory under consideration.
Fig. 2. Evaluation of the infrastructure mobility of the considered Learning
Factory in the context of analysis level 1 of the model.
The evaluation of the infrastructure of the Learning Factory
under consideration shown in Fig. 2 shows standardised
connection types, but despite the reduction still a high space
requirement and a low level of automation as well as no
simulation model. Furthermore, the entire concept is designed
for onsite learning.
Analysis level 2, workstation mobility, is divided in a similar
manner to level 1, leading to following result:
Fig. 3. Evaluation of the workstation mobility of the considered Learning
Factory in the context of analysis level 2 of the model.
Regarding the individual workstations, Fig. 3 shows a lower
overall space requirement and regular transport of the
machines, however the weight of these must be taken into
account. Another advantage in terms of mobility is the modular
design of the workstations and the high degree of
standardization.
On the analysis level 3 the product mobility is represented
as the last step of the evaluation model (see Figure 4).
Fig. 4. Evaluation of the product mobility of the considered Learning Factory
in the context of analysis level 3 of the model.
Prod uct mobility
size of pro duct/pa ckage special carrier needed fits into standardized carrier
form of prod uct bulk cargo ge ner al ca rgo
number of c omponents > 100 ... 6-20 ... 1
not a vailable sim plified ve rsion av ail abl emarke t avai labil ity
didactically
sim plified f un cti on al without func tionav ail ab emarketability of product
ex hibitio n / displ ay gi ve aw ay sale / d isposalre use / r ecycl efurther produc t use
le gal acce pta nce ill ega l le gal
In fra stru ctu re m obi lit y
environmenta l scale scaled down life size
a re a
electrical
ve ntila tion
IT infrastructure
communicat ion channel remo te c on nec tion onsit e learning
complex p a rti al l y rarely or non- existentIT inte gra tion
< 100 m² ... ... ... > 1000 m²
none partl y automated ful ly autom ateddegree of a utomation
none pa rt i al digit al twin
complexity of
simulation mode l
Sel f- co ntai ned sp eci a lis ed
Sel f- co ntai ned sp eci a lis ed
Sel f- co ntai ned sp eci a lis ed
standardised
standardised
standardised
Wor kstati on mobi lit y
size of machi nes fitte d in c ontain er ov e rs i ze d
number of machi nes < 5 6 -10 ... ... > 50
wei gh t of m ac hine s > 200 kg 51-200 kg ... ... < 32 kg
type of t ransport special transport standard tra nsport (forklifts, trucks etc.)
modular desi gn non modular modu lar
mounted platf orm specialized standardised
mobility of platform imm obile mobil e
vacuum air cushion lo c kabl e whee lsfi xed s tan dtype of mobile platform
hardware interfaces
between modules specialized standard ized
connection network specialized standardiz ed
softw are interface
between module specialized standardiz ed
Natalie Petrusch et al. / Procedia CIRP 91 (2020) 659–664 663
4 Author name / Procedia CIRP 00 (2020) 000–000
Furthermore, concepts of manufacturing planning show
additional aspects that need to be considered with regard to the
definition of mobility. Four of these factors stand out, which are
of particular importance when evaluating the mobility of a
Learning Factory.
Standardization of modules, interfaces, networking
technologies, and platforms [16,23] to create a “plug-and-
produce” design in a physical way as well as in terms of IT
[12].
Design of modules to be mobile, external as well as
internal, highly compatible, reconfigurable, scalable (e.g.
to small model size) and universally usable with modular
products [16,23].
Design of modules based on manual assembly with an
increased degree of automation and dynamically adaptive,
enabling a wider range of products [21,23].
Transportation in standardized carriers, e.g. containers,
using high payload vehicles (truck/train) [21], and
transportation-friendly and space-saving design of modules
[5].
4. Evaluation Model and exemplary application
In order to achieve a structured guideline for the evaluation
process, the identified factors and required characteristics were
further grouped into three main analysis levels (infrastructure
mobility, workstation mobility and product mobility) [36].
These analysis levels are presented in a top-down manner,
starting with the overall infrastructure assessment down to the
mobility evaluation on workstation and product level.
However, the levels do not necessarily have to be processed
consecutively, but can rather be regarded as independent of
each other and thus merely reflect the scope of observation.
Furthermore, the resulting model was applied to an existing
Learning Factory to check its consistency and correctness.
The Learning Factory under consideration is located in the
premises of a leading pharmaceutical company. For the
company's own production system, which takes up many
elements from the Lean Production System, both managers and
employees are prepared for their role and trained in the
appropriate methods and tools [20,37]. Fig. 1 shows the general
structure of this particular Learning Factory.
Fig. 1. Overview of the design of the Learning Factory to which the model was
applied.
Analysis level 1, the evaluation of the infrastructure
mobility, is divided into several categories, serving to describe
the Learning Factory under consideration.
Fig. 2. Evaluation of the infrastructure mobility of the considered Learning
Factory in the context of analysis level 1 of the model.
The evaluation of the infrastructure of the Learning Factory
under consideration shown in Fig. 2 shows standardised
connection types, but despite the reduction still a high space
requirement and a low level of automation as well as no
simulation model. Furthermore, the entire concept is designed
for onsite learning.
Analysis level 2, workstation mobility, is divided in a similar
manner to level 1, leading to following result:
Fig. 3. Evaluation of the workstation mobility of the considered Learning
Factory in the context of analysis level 2 of the model.
Regarding the individual workstations, Fig. 3 shows a lower
overall space requirement and regular transport of the
machines, however the weight of these must be taken into
account. Another advantage in terms of mobility is the modular
design of the workstations and the high degree of
standardization.
On the analysis level 3 the product mobility is represented
as the last step of the evaluation model (see Figure 4).
Fig. 4. Evaluation of the product mobility of the considered Learning Factory
in the context of analysis level 3 of the model.
Prod uct mobility
size of pro duct/pa ckage special carrier needed fits into standardized carrier
form of prod uct bulk cargo ge ner al ca rgo
number of c omponents > 100 ... 6-20 ... 1
not a vailable sim plified ve rsion av ail abl emarke t avai labili ty
didactically
sim plified f un cti on al without func tionav ail ab emarketability of product
ex hibitio n / displ ay gi ve aw ay sale / d isposalre use / r ecycl efurther produc t use
le gal acce pta nce ill ega l le gal
In fra stru ctu re m obi lit y
environmenta l scale scaled down life size
a re a
electrical
ve ntila tion
IT infrastructure
communicat ion channel remo te c on nec tion onsit e learning
complex p a rti al l y rarely or non- existentIT inte gra tion
< 100 m² ... ... ... > 1000 m²
none partl y automated ful ly autom ateddegree of a utomation
none pa rt i al digit al twin
complexity of
simulation mode l
Sel f- co ntai ned sp eci a lis ed
Sel f- co ntai ned sp eci a lis ed
Sel f- co ntai ned sp eci a lis ed
standardised
standardised
standardised
Wor kstati on mobi lit y
size of machi nes fitte d in c ontain er ov e rs i ze d
number of machi nes < 5 6 -10 ... ... > 50
wei gh t of m ac hine s > 200 kg 51-200 kg ... ... < 32 kg
type of t ransport special transport standard tra nsport (forklifts, trucks etc.)
modular desi gn non modular modu lar
mounted platf orm specialized standardised
mobility of platform imm obile mobil e
vacuum air cushion lo c kabl e whee lsfi xed s tan dtype of mobile platform
hardware interfaces
between modules specialized standard ized
connection network specialized standardiz ed
softw are interface
between module specialized standardiz ed
Author name / Procedia CIRP 00 (2020) 000–000 5
The Result of the evaluation illustrated in figure 4 represents
the consideration of the product that is manufactured in the
Learning Factory under consideration. It shows an
advantageous design in terms of packaging, availability and
downstream use. The results regarding the number of required
components, however, are less positive in terms of mobility.
The evaluation of the mobility of the Learning Factory under
consideration is therefore quite heterogeneous in every step. In
summary, this Learning Factory has a high degree of
standardised elements and a compact product. The machines
and workstations used also require less storage space. The
model clearly shows, however, that the entire structure must be
viewed critically with regard to mobility due to the required
size and the respective weight of the machines as well as their
ability to be moved. In addition, the lack of availability of
digital elements and the overall degree of automation hinder an
even better mobility of the Learning Factory in terms of a
remote access. Hence, the critically assessed components of the
realisation give a direct indication of the points of action if an
improvement in mobility is to be targeted. However, some
questions remain open, especially with regard to the
prioritisation of the elements to be improved. In addition, the
handling of the model has proven to be insufficiently intuitive
in some places. The versatile possibilities of realizing a
Learning Factory require a comprehensive generalization of the
selection options, so that a clear assignment was not possible
in some places.
5. Conclusion and Outlook
The current design of the evaluation model already shows a
fundamental structuring of the relevant factors for the mobility
of Learning Factories with the first application, whereby the
given options make an exact assignment quite difficult in some
places. Further application of the model needs to be conducted
in order to specify options as well as advance the degree of
specification and thus gain an easy to apply design. In addition,
a comprehensive explanation of the factors and options could
be helpful in the future. Currently, it is already possible to
identify the relevant factors that would improve the mobility of
an existing Learning Factory in a systematic way.
Nevertheless, the identification process of the factors has so far
focussed on existing realisations and the general morphology.
As a next step also the process of the initial design of a
Learning Factory as well as more aspects from related fields
need to be considered in order to expand also the number of
factors and therefore improve the model regarding a holistic
approach. As the different factors have individual shares of the
impact in mobility this also needs to be taken into account by
including a weighting of the factors. Finally, the evaluation
system needs to be further specified for instance by
implementing a point scale.
The introduction of the modified morphology for the
mobility of Learning Factories delivers an improved guideline
towards the successful identification of the mobility status of a
Learning Factory. Still, though tested at the given example, the
provided mobility model needs to be further tested and possible
improvements as stated above may be developed and
implemented.
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