The Adoption of Additive Manufacturing Technology in Sweden

This paper analyzes the adoption of Additive Manufacturing (AM) technologies in Sweden. The dataset consists of a recent and representative sample of Swedish AM users (companies, universities, and research institutes). The authors investigate two questions. Firstly, what are the current applications of AM in Sweden (e.g. Rapid Prototyping (RP), production)? Secondly, what are the factors that can explain the variation in AM adoption among the users?

Procedia CIRP 40 ( 2016 ) 7 – 12
Available online at www.sciencedirect.com
2212-8271 © 2016 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 International Scientific Committee of the 13th Global Conference on Sustainable Manufacturing
doi: 10.1016/j.procir.2016.01.036
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13th Global Conference on Sustainable Manufacturing - Decoupling Growth from Resource Use
The Adoption of Additive Manufacturing Technology in Sweden
Babak Kianian a,b*, Sam Tavassoli c,d, Tobias C. Larsson b and Olaf Diegel a
a Design Sciences Department, Faculty of Engineering (LTH), Lund University, Lund, Sweden
b Mechanical Engineering Department, Blekinge Institute of Technology (BTH), Karlskrona, Sweden
c Centre for Innovation, Research and Competence in the Learning Economy (CIRCLE), Lund University, Lund, Sweden
d Industrial Economics Department, Blekinge Institute of Technology (BTH), Karlskrona, Sweden
* Corresponding author. Tel.: [phone]; E-mail address: [e-mail]
Abstract
This paper analyzes the adoption of Additive Manufacturing (AM) technologies in Sweden. The dataset consists of a recent and representative
sample of Swedish AM users (companies, universities, and research institutes). The authors investigate two questions. Firstly, what are the
current applications of AM in Sweden (e.g. Rapid Prototyping (RP), production)? Secondly, what are the factors that can explain the variation
in AM adoption among the users? Using a regression analysis technique, the main findings are as follows. (i) There is a variation among users’
choice of AM application, and the majority of users are expanding their AM applications beyond RP. (ii) There are two factors that positively
affect the decision of firms to expand classical RP and also incorporate production and management. These two factors are using multiple AM
technologies (as opposed to single Fused Deposition Modeling technology) and being small companies. The authors discuss the implication of
these results.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the International Scientific Committee of the 13th Global Conference on Sustainable Manufacturing.
Keywords: Additive Manufacturing, 3D Printing, Application, Production Technology, Sweden
1. Introduction
Additive Manufacturing (AM) enables the fabrication of
components in a process, where slices of a virtual model are
created and produced in a layer-upon-layer additive building
process. AM thus differs radically from traditional
manufacturing which is either subtractive, where material is
removed from a block of material, or formative, in which
material is formed by a mold (which, itself, is manufactured
through a subtractive process) [1]. The technology has been
adopted, since the mid-90s by most industries involved in
product development, as it is often the best method for quickly
manufacturing prototypes. Until recently these technologies
were unable to produce components of the same strength and
quality as conventionally manufactured components.
However, some of the latest technologies have advanced to the
point where it is now possible, for certain types of
components, to produce fully functional production
components, in a fraction of the time and material needed by
conventional methods, particularly if one includes
tooling/setup times. Rapid prototyping (RP) has thus evolved
into rapid manufacturing (RM) [2,3]. AM production
capabilities have the potential to reduce the environmental
impact of manufacturing, for example by, production of
lighter, more complex and integrated parts, which require less
raw material usage in their fabrication [4]. Less raw material
usage uses less of earth’s scarce resources, which is a key
sustainable challenge relating to economic growth [4,5]. One
of the sustainable paths to continue on economic growth, via
the lens of circular economy, is decoupling economic growth
from the use of scarce resources through disruptive
technologies (e.g. AM) [5].
It is believed that AM, as a manufacturing technology, is
having an increasing role in many industries and that AM
capabilities now cover various ranges of applications. But,
according to the recent survey by Wohler’s Associates,
company’s benefit from using AM to fabricate functional parts
more than from other applications. The second most popular
application is prototypes for fit and assembly. This indicates
that the main advantage of using AM is in fostering product
© 2016 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 International Scientifi c Committee of the 13th Global Conference on Sustainable Manufacturing

8 Babak Kianian et al. / Procedia CIRP 40 ( 2016 ) 7 – 12
development processes by improving product quality,
reducing cost (less assembly and tooling etc.), and reducing
time to market etc. The use of AM to fabricate production
parts has also shown a continuous increase since 2003. In
2003 it was valued at 3.9% of the total global product-service
revenues from AM, while this value increased to 42.6% in
2014. Besides this, the global market growth in the part
production segment was 66% in 2014 to an estimated $1.748
billion [3].
International approaches to AM are significantly different
to that of Sweden. Governments in many countries (e.g. USA,
New Zealand, Australia, UK, Germany, Singapore and China)
are heavily investing in AM as a production technology. One
of the biggest challenges currently facing Swedish industry is
a lack of the ability to adopt AM technologies and to deploy
them to their full potential beyond just prototyping [4].
Currently Sweden lies behind its neighbours Germany, the
United Kingdom, France, and Belgium in its AM adoption
rates and is thus lagging behind on its capacity to innovate
and keep up with similar industries in other countries [3,4].
International competitors in many sectors are continuously
investing in AM, and the authors of this paper thus believe
that Sweden needs to strategically reduce the current gap on
the lack of knowledge and implementation of AM. The
authors also believe that the ‘Swedish Agenda for Research
and Innovation Within AM and 3D Printing’, which was
recently coordinated by Umeå University in Sweden is a great
start to that path. According to the agenda, Swedish industry
has adopted and utilized AM comparatively little beyond
prototyping, and the infrastructure level in Swedish
universities are well below the rest of the Europe and
worldwide [4].
The above-mentioned agenda is quite general; particularly
concerning why Sweden is lagging behind in adoption of AM
and also what factors can explain and predict the plausible
move beyond using AM solely in rapid prototyping. This can
be a problem from the perspective of users and policy makers
in Sweden, because the agenda provides little insight on how
to reduce the gap between Sweden and other advanced
countries. This paper, therefore, aims to provide a more
detailed perspective regarding the current status of AM
adoption and utilization in Sweden through investigating two
questions. Firstly, what are the current applications of AM in
Sweden (e.g. rapid prototyping, production)? And secondly,
what are the factors that can explain the variation in AM
applications among the users?
The rest of the paper is as follows: Section 2 provides a
literature review of AM state of art in particular related to AM
applications, AM technology types and raw materials. Section
3 first introduces the research design (e.g. data collection
method), and then specifies the method (empirical strategy),
which is regression analysis. Section 4 provides the results in
two sequences: description of analyses and empirical result.
Section 5 first provides a discussion on the results, and then
concludes and points out suggestions for future research.
2. Literature Review
This section provides a review of literature in order to
identify the additive manufacturing (AM) technology types,
raw materials types, and AM application areas worldwide and
in Sweden.
Innovation in production technology is viewed as a
powerful competitive weapon, which, if an industry adopts
and implements it strategically, can bring about many other
competitive advantages (e.g. superior quality, shorter delivery
cycles, lower inventories, shorter new product development
cycles) [6]. Utilizing innovation in production technologies,
such as AM, can cause improvements in market share. There
are a few cases of research and research and development
(R&D), which propose that the management of the adoption
and implementation of innovative production technologies is a
distinctive area of study in research [7]. For example, in
recent years the emergence of enterprise resource planning
systems (ERP) and radio-frequency identification technology
(RFID) has created extensive investigation and research
articles on their implementation in academia and industries.
The results of this are the creation of many process models
and frameworks to assist decision makers and managers to
implement those new innovations successfully. However, the
research and practice of AM as a production technology has
stayed relatively behind in its exploitation level [8].
Besides the Umeå Agenda, which is addressed in the
introduction, there are relatively few scholars investigating
current AM state of art or practice in Sweden. For example,
Kianian et al 2015, share the same opinion as the agenda,
concerning the level of adoption and deployment of AM in
Sweden, and they also provide a few examples as exceptions
to the lack of AM utilization in Sweden, which are excellent
examples of personalized high-tech manufacturing [9].
The AM applications worldwide are well summarized by
figure 1 below and, as noted earlier in the introduction, AM is
having an increasing role in many industries, and AM
capabilities now cover a wide ranges of applications. The
authors categorize AM applications for this paper’s analysis
into five categories in line with the Wohler’s report
classification in figure 1.
Figure 1. AM applications worldwide. Source: Wohler’s Associates, Inc.
The five categories are Rapid Prototyping (RP) including
general prototyping applications, and patterns for prototyping

9Babak Kianian et al. / Procedia CIRP 40 ( 2016 ) 7 – 12
tooling, pattern for metal castings, fit and assembly;
Production: including functional parts and tooling
components; Management: including visual aids and
presentation models; Research & Development and Education
(R&D and Edu) Including fundamental science and research
work in AM; and Rapid Prototyping & Production and
Management (RP & PROD and MNG) including all the AM
applications used by a single user (see table 1 in section 3.2).
There are many approaches to classifying AM processes,
like categorizing them according to a baseline technology
such as whether the process utilizes UV lasers, extrusion
technology, printer technology, etc. [10,11]. Another way is to
classify AM processes together based on the type of raw
materials (e.g. polymers, metals, ceramics) the system uses
[12]. As Gibson et al 2010 argued it is more appropriate to use
more than one classification method, as there are some issues
with relying merely on one AM process classification [13].
One of the issues, according to Gibson et al 2010, is that some
of the AM processes with similar fabricated parts are placed
in separate categories, and that some processes gather in the
same categories in an odd way. There are some attempts to
address these classification problems through such as the
classification method proposed by Pham 1998, which
categorizes AM processes based on both a technology
baselines and raw material types [13,14]. This paper’s authors
carefully consider these AM processes classification
approaches, and then propose six categories for AM
technology types, and four categories for AM material types.
These classifications are shown in table 1 (Variable names
and descriptions) in section 3.2 (empirical strategy).
The authors in this paper classify the Swedish AM users’
size class in line with the European Union’s definition [15].
Thus, the three categories of small firms (employment range
1-15), Medium firms (employment 16-100), and large firms
(employment range of 101 and more) are identified as
explanatory variables (see table 1 in section 3.2).
3. Method
3.1 Research Design
This paper is based on a quantitative research
methodology. The data comes from a survey of 55 Swedish
users of AM (e.g. companies, universities and research
institutes), which was carried out by 3dp.se between 2013-
2014 [16]. The authors of this paper also add data from 15
additional users, which have not been considered in the
survey. Therefore, a total sample of 70 users is obtained. The
authors conduct regression analysis as an analytical tool in
this paper. Regression analysis is a statistical process for
estimating the relationships among variables [17].
3.2. Empirical Strategy
The main aim of this paper, as noted earlier, is twofold:
First, the paper describes and maps the various applications of
AM that are actually chosen and utilized by the users
(companies and research institutions), and second, the paper
analyzes the determinants of such choices. The first part of the
aim is descriptive in nature and the authors will explain this
part in Section 4.1. The second part of the aim is analytical in
nature and regression analysis is used as the analytical tool.
Regression analysis is commonly utilized in social science
and medicine to analyze the effect of some potential
explanatory factors on the phenomenon of interest [17]. The
phenomenon of interest in this study is the choice of
application of AM by users. The authors employ a
multinomial logit model in order to investigate the
determinants of various AM applications choices by users.
Formally speaking, the probability that user i chooses AM
application j is given by:
Where is the vector of explanatory variables, are a set
of unknown parameters per each explanatory variable,
capturing the effect of each explanatory variables on the
probabilities of choosing each AM applications, and j is the
AM applications that users decide to choose. Starting from the
vector , it is composed of several explanatory variables,
which, as discussed in the literature review section, are
expected to have significant (positive or negative) effect on
the choice of AM application. Specifically, these explanatory
variables are as follows: the AM technology types that the
users deploy, raw material types, amount of AM machine
investments, size (number of employees) of the user, and the
location of the user (for the exact definitions, see Table 1). As
noted, are unknown parameters but they can be estimated
through a procedure called maximum likelihood estimation,
which is the output of regression analysis. And finally, ” j”
can be coded in two ways. First, in a dichotomous way, i.e. it
gets value j=0 when user i decides to use AM only and
exclusively for the rapid prototyping (RP) purposes, and it
gets value j=1 when user i decides to use AM not exclusively
for RP. Second, in an extend version, while j=0 is still the
same as dichotomous way, j=1 can be expanded as follows:
(the new) j=1 is when firms decide to still have the AM for
RP purposes but also utilize AM for production and
management purposes, j=2 is when firms decide to use AM
only in Production, j=3 is when firms decide to use AM only
for management purposes (e.g. visual aids, presentation
models), and finally j=4 is when firms decide to use AM only
in research & development and education. This implies that in
the regression analysis, there will be two alternative
dependent variables. Both of them are measuring the
phenomenon of interest, i.e. application of AM among users,
but just in two different ways. Moreover, in either of
dichotomous or extended version, the j=0 is considered to be
the “base” category (choice). This means the result of the
estimation for each j should be interpreted with reference to
this base category. It should be noted that, multinomial logit

10 Babak Kianian et al. / Procedia CIRP 40 ( 2016 ) 7 – 12
model is valid if the assumption of independence for
irrelevant alternatives (IIA) is not violated.
Table 1-Variable names and descriptions
Variable names Description
Application of AM
Rapid Prototyping (RP) Gets value 1 if user only uses AM in RP, 0
otherwise
RP & PROD & MNG Gets value 1 if user only uses AM in RP plus
production as well as management, 0 otherwise
Production (PROD) Gets value 1 if user only uses AM in production,
0 otherwise
Management (MNG) Gets value 1 if user only uses AM in
Management, 0 otherwise
R&D and EDU Gets value 1 if user only uses AM in education &
Research, 0 otherwise
Technology Type*
FDM (base) Gets value 1 if user has FDM technology in their
AM, 0 otherwise
Polyjet (ColorJet
printing)
Gets value 1 if user has Polyjet technology in
their AM, 0 otherwise
SLA Gets value 1 if user has SLA technology in their
AM, 0 otherwise
SLS Gets value 1 if user has SLS technology in their
AM, 0 otherwise
Multiple AM
Technologies
Gets value 1 if user has Multiple Technologies in
their AM, 0 otherwise
Other AM Technologies
Material Type
Polymers (base) Gets value 1 if user has polymer as the material
in their AM, 0 otherwise
Metals Gets value 1 if user has Metals as the material in
their AM, 0 otherwise
Multiple Materials Gets value 1 if user has multiple materials in their
AM, 0 otherwise
Others
Machine
investments**
The amount of investment in 3-D machines
ranges from 1 to 5
Size class
Small firms (base) Employment range of 1-15
Medium firms Employment range of 16-100
Large firms Employment range of 101 and more
Location Gets value one if the firms is located in ne of
Sweden’s Metropolitan areas (Stockholm,
Gothenburg, Malmö)
* FDM (Fused Deposition Modeling), SLA (Stereolithography), SLS (Selective Laser Sintering),
Multiple Technologies (When a firm decides to engage in various combinations of the following
technologies simultaneously: Laminated Object Manufacturing - LOM, Electron Beam Melting - EBM,
Plaster Based 3D Printing - PP, Multi-jet Printing - MJP, Digital Light Projection/Processing - DLP,
Scan, Spin and Selectively Photocure - 3SP, Direct Metal Laser Sintering - DMLS), Others (When a
firm decides to engage in only one of the above technologies)
** Scale 1 to 5 corresponds to following ranges in thousand kroners: 1 (5 to 15), 2 (16 - 100), 3(100 to
250), 4(250 to 1000), 5 (over 1 million SEK)
4. Analysis
4.1 Description
In order to have a better understanding of how users in
Sweden actually use AM, this section provides some
descriptive statistics. Table 2 reports the number of
observations, mean, standard deviation, minimum, and
maximum values for each variables.
Table 2- Descriptive Statistics
Variables Observations Mean Std. Dev. Min Max
APP* 70 .6571429 .4780914 0 1
FDM 70 .3714286 .4866755 0 1
Polyjet 70 .0857143 .281963 0 1
SLA 70 .0571429 .2337913 0 1
SLS 70 .0428571 .2039973 0 1
Multiple AM Technologies 70 .3571429 .4826171 0 1
Other AM Technolo gies 70 .0857143 .281963 0 1
Polymer 70 .7714286 .4229444 0 1
Metal 70 .0571429 .2337913 0 1
Multiple Materials 70 .1571429 .3665631 0 1
Other Materials 70 3.2 1.499758 1 5
Small Firms 70 .5857143 .496155 0 1
Medium Firms 70 .2142857 .4132886 0 1
Large Firms 70 .2 .4028881 0 1
Location 70 .4142857 .496155 0 1
* APP is the dichotomous version of the dependent variable.
Looking at APP (our dichotomous dependent variable), it
is interesting to note that about 65% of users are using AM
not exclusively for rapid prototyping (RP), but either using
AM for RP together with other applications (such as in
production) or exclusively in other applications. Further
looking at the data reveals that the majority of these 65%
users have not given up applying AM in their RP, rather they
are using AM in RP together with production and
management (about 41%), while only 6% are using AM
exclusively in their production, 4% exclusively for
management purposes (e.g. visual aids, presentation models)
and about 14% for education and research purposes. When it
comes to technology types, as expected, Fused Deposition
Modeling (FDM) is the most popular type with 37% of users
exclusively adopting this technology. What is perhaps
surprising is that using “multiple technologies” is almost
equally as popular as FDM, i.e. about 36% of users. When it
comes to material types, the dominance is for polymer with
about 77% of users using polymer raw materials. An
interesting observation is about the size of the users, which
adopt and utilize AM technologies. About 58% of the users
are small organizations (1 to 15 employees) and the medium
and large size organizations are about 20% each.
4.2. Empirical Results
The author conducts regression analysis in order to have a
proper understanding regarding the effect of explanatory
variables on the phenomenon of interest. As discussed in
Section 3.2, a multinomial regression model is used to
estimate the effect of each potential explanatory variable on
each choice of AM application that users choose. Table 3
reports the estimation results.
Table 3- Determinants of Applications of Additive Manufacturing for users
Explanatory
Variables
Other
departments
than
only DP (1)
Other departments than only DP (2)
DP plus PROD
& MNG (2.1)
Only
PROD
(2.2)
Only
MNG
(2.3)
Only
EDU
(2.4)
Polyjet 0.123
(0.618)
0.065
(1.127)
34.920
(22,453)
-20.766
(17,616)
-18.209
(12,115)
SLA 0.717
(0.741)
0.952
(1.333)
37.952
(10,828)
-18.285
(64,521)
47.720
(50,091)
SLS 0.088
(0.912)
-19.445
(17,658)
141.822
(44,234)
-21.904
(32,996)
1.344
(2.285)
Multiple AM
Technologies
1.581**
(0.617)
2.416*
(1.448)
74.710
(14,232)
-0.396
(3.253)
2.842
(2.066)
Other AM
Technologies
1.437
(1.187)
15.695
(2,966)
124.999
(19,268)
1.073
(4,113)
32.518
(5,171)
Metals -0.716 -16.163 -63.017 -0.602 17.860

11Babak Kianian et al. / Procedia CIRP 40 ( 2016 ) 7 – 12
(1.143) (2,966) (3.630) (4,113) (3,762)
Multiple
Materials
-0.208
(0.644)
-1.168
(1.513)
-1.434
(7,443)
-18.451
(10,404)
0.335
(1.564)
Machine
Investments
0.004
(0.167)
0.424
(0.385)
-35.175
(5,982)
2.185
(1.479)
-1.448*
(0.795)
Small Firms 0.580
(0.573)
2.817**
(1.419)
64.220
(19,227)
21.291
(5,184)
-70.491
(27,416)
Medium Firms 0.499
(0.569)
1.812
(1.400)
63.531
(23,870)
22.047
(5,184)
-1.195
(1.618)
Location 0.062
(0.366)
0.403
(0.709)
2.053
(10,440)
3.489
(2.877)
-0.830
(1.238)
Observations 70 70
Notes: The Table reports the estimated parameters in Equation 1 ( with standard errors in the
parenthesis. The signs * and ** means the estimated parameters are statistically significant at 90% and
95% level respectively. The reference category for both model (1) and (2) are “using AM only in DP”.
Model (1) is based on logit estimation (as a specific case of Multinomial estimation) and Model (2) is
based on Multinomial estimation.
Table 3 shows the determinants of applications of additive
manufacturing (AM) in various departments within the
seventy user firms in Sweden. Model (1) is based on logit
estimation (a specific case of multinomial model) and model
(2) is based on multinomial estimation. The results in model
(1) shows that if a firm decides to use multiple AM
technologies (as opposed to use only fused deposition
modeling), then the firm tends to significantly use the AM in
not only rapid prototyping, but also other departments. In
model (2), the authors further break down such “other
departments” into its components, as follows: (i) in rapid
prototyping plus production and management
(simultaneously), (ii) only in production, (iii) only in
management, and (iv) only in R&D and education. These four
breakdowns corresponds to models (2.1), (2.2), (2.3), and to
(2.4) respectively in Table 3. It turns out that the observed
significant effect of multiple AM technologies is associated
with “simultaneously” using the multiple AM technologies in
rapid prototyping, production and management departments.
Using multiple AM technologies does not affect the
probability of using AM exclusively in any of management,
production, or R&D and education (relative to using the
multiple AM technologies in rapid prototyping only).
5. Discussion and Conclusion
This section provides a discussion based on the results of
the empirical analyses, which are provided above in section 4.
The section then concludes and suggests some research
opportunities as future work.
The main findings of this paper are as follows. (i) There is
a variation among users’ choice of AM application and the
majority of users are expanding their AM applications beyond
rapid prototyping. (ii) There are two factors that positively
affect the decision of firms to expand classical rapid
prototyping and incorporate production and management as
well. These two factors are using multiple AM technologies
and being small sized companies.
5.1. Swedish Industry Trends in Additive Manufacturing
Utilization Beyond Prototyping
AM capabilities are used in various applications and play
important roles in many industries worldwide (Wohler’s
Report 2014). Sweden lies behind its neighbors (e.g.
Germany, the United Kingdom, France, and Belgium) in its
AM adoption rates and thus is lagging behind on its capacity
to innovate and keep up with similar industries in other
countries [4]. This also means that Sweden is underutilizing
its chance to capture the sustainable benefits of utilizing AM
as a disruptive technology leading to a more circular
economy. Despite all of these facts, the result of this paper
shows that the majority of the Swedish AM users (65%) are
expanding AM capabilities beyond just rapid prototyping
(RP) in order to take advantage of AM full potential and
hopefully reap the rewards of a more sustainable
manufacturing process. This outcome can be considered as a
milestone and indicates that Swedish AM users are starting to
acknowledge that, as Sweden’s international competitors in
many sectors are continuously investing in AM, if Sweden
wants to increase its global competitiveness, it needs to
investigate means to redress its lack of ability to adopt AM
technologies and to deploy them to their full potential beyond
just prototyping.
5.2. Factors Affecting Swedish Industry’s Decision to Expand
Beyond Rapid Prototyping
There are two factors that positively affect the decision of
Swedish users to expand classical rapid prototyping (RP) and
incorporate also AM production and management
applications. These two factors are using multiple AM
technologies and being small sized companies. When it comes
to the former factor (technology types), fused deposition
modeling (FDM), as expected, is the most popular type as
37% of users exclusively adopting this technology. What is
perhaps surprising is that using “multiple technologies” is
almost equally as popular as FDM, i.e. about 36% of users.
When it comes to the latter factor, an interesting observation
is related to the size of companies which adopt and utilize AM
technologies. About 58% of the users are small organizations
(1 to 15 employees) while the medium and large size
organizations are at about 20% each.
5.2.1. Multiple AM Technologies
AM includes a range of technologies that offer advantages
over traditional manufacturing. Until recently these
technologies were unable to produce components of the same
strength and quality as conventionally manufactured
components. However, some of the latest technologies have
advanced to the point where it is now possible, for certain
types of components, to produce fully functional production
components, in a fraction of the time needed by conventional
methods, particularly if one includes tooling/setup times [1,3].
In addition to these technological advancements, there are
some AM patents, which have been expired recently, and
others that will be expiring in next few years. These facts
foster the access to many AM technologies types, as both the
technologies’ cost reduces and upgrading process cycles
shorten.

12 Babak Kianian et al. / Procedia CIRP 40 ( 2016 ) 7 – 12
5.2.2. Small Sized Companies
Small enterprises and medium-sized firms are classified by
their size, balance sheet or turnover. In Sweden, all
enterprises that have less than 250 employees are identified as
medium size enterprises and those who have less than 50
employees are identified as small enterprises [15]. Small and
medium-sized enterprises (SMEs) represent the prominent
portion of economic activities in Sweden, as about 99% of all
type of enterprises are SMEs in Sweden. Based on the result
of this paper (as described in section 4 and 5.2 above), SMEs
show to be the main driver of additive manufacturing (AM)
adoption and utilization in Swedish industry. This is the result
of the evidence in this study that the combination of small size
users (58%) and the medium size users (20%) are the
dominants AM users in Sweden (78%), as they show AM
implementation beyond rapid prototyping. The authors
believe that one of the main reasons behind this SMEs
behavior is that AM removes existing performance trade-offs
in two elemental ways. Firstly, AM decreases the initial
capital needed to fulfill economies of scale. Secondly, AMs
flexibility reduces the capital needed to fulfill economies of
scope [18]. A reduction in the initial capital required is
extremely beneficial to SMEs, as it enables them to try their
ideas with lower risk. This increased use of AM will lead to
an increased competitiveness for SMEs in a global market,
where innovation cycles are becoming faster and the demand
for a decreased time-to-market is high. By the use of AM, the
SMEs are able to shorten their innovation processes and are
able to create added value for their products and services.
Since the use of AM will enable the companies to manage the
complete chain of the product, from innovation to
manufacturing, all in-house, it will empower the possibility to
maintain the manufacturing jobs in Sweden, for customized
parts or parts in short series, this will help the SMEs to be
able to compete in an international market [9,16]. This is a
particularly important factor as according to Swedish Agency
for Economic and Regional Growth (Nutek) there is an
increase in Swedish SMEs internationalization. One of every
four Swedish SMEs is internationalized [19].
During the analysis in this study care is taken in order to
point out the reasons for which the explanatory variables are
distinguished; and to also point out variables (e.g. multiple
technologies, small firms), which may be common features to
AM applications and implementations, all in order to provide
a source for a potentially more generic solution.
It is essential to acknowledge the nature of this case study
and the number of observations presented in this work. The
scenario under investigation is limited to 70 AM users (e.g.
companies, universities, research institutes) in Sweden
coming from various backgrounds. However, there are
currently no statistics available on the total number of
companies that use AM in Sweden. The authors expect that
the effect and significance of this work outcome will be
determined by the scenario under study. Therefore future
works may look to extend the number of observations
regarding the users of AM in Sweden. Another research
opportunity will be comparing the various variables
determining the AM applications and its implementations in
different countries (e.g. Scandinavian countries).
Acknowledgement
The authors would like to express their sincere thanks to
Mattias Kristiansson of 3dp.se for his cooperation with
providing the initial descriptive result of the survey, which
was conducted by 3dp.se, and also his continuous support of
this work.
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