The impact of additive manufacturing on supply chains

Abstract

Purpose: The purpose of this paper is to offer empirical insights on emerging additive manufacturing (AM) processes, barriers to their adoption and a timeline of expected impacts on the supply chain in the manufacturing industry.

Design/methodology/approach: A multi-stage survey study was conducted with a panel of 16 experts from industry and academia.

Findings: Only five out of today’s seven AM processes are of future importance, as are two emerging key processes. In total, 15 barriers to their adoption are identified, all of which are expected to be gone within ten years. Eight propositions are derived postulating as to whether and when supply chain impacts can be expected in terms of changes to supply chain structure, customer centricity, logistics and supply chain capability.

Research limitations/implications: “Soft” barriers are new to the literature, which has traditionally focused on “technical” barriers. Often-discussed barriers such as production speed and costs do not reflect the true concerns of the research panel. Furthermore, some of the supply chain implications discussed in both the academic literature and the media are found to be unlikely to materialize.

Practical implications: The study summarizes AM processes, technologies, barriers and supply chain implications solicited from experts in the field.

Originality/value: This is one of the first studies to make empirical contributions to a vastly conceptual discussion. It is also the first study to give insights on a timeline for barriers and supply chain implications.

Full text

International Journal of Physical Distribution & Logistics
Management
The impact of additive manufacturing on supply chains
Christian F. Durach, Stefan Kurpjuweit, Stephan M. Wagner,
Article information:
To cite this document:
Christian F. Durach, Stefan Kurpjuweit, Stephan M. Wagner, (2017) "The impact of additive
manufacturing on supply chains", International Journal of Physical Distribution & Logistics
Management, Vol. 47 Issue: 10, pp.954-971, https://doi.org/10.1108/IJPDLM-11-2016-0332
Permanent link to this document:
https://doi.org/10.1108/IJPDLM-11-2016-0332
Downloaded on: 23 November 2017, At: 07:31 (PT)
References: this document contains references to 48 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 273 times since 2017*
Users who downloaded this article also downloaded:
(2017),"Special issue on 3D printing: opportunities and applications for supply chain management",
International Journal of Physical Distribution & Logistics Management, Vol. 47 Iss 10
pp. 950-953 <a href="https://doi.org/10.1108/IJPDLM-08-2017-0248">https://doi.org/10.1108/
IJPDLM-08-2017-0248</a>
(2017),"Analysis of barriers to implement additive manufacturing technology in the Indian automotive
sector", International Journal of Physical Distribution &amp; Logistics Management, Vol. 47 Iss
10 pp. 972-991 <a href="https://doi.org/10.1108/IJPDLM-07-2017-0222">https://doi.org/10.1108/
IJPDLM-07-2017-0222</a>
Access to this document was granted through an Emerald subscription provided by emerald-
srm:448848 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald
for Authors service information about how to choose which publication to write for and submission
guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as
well as providing an extensive range of online products and additional customer resources and
services.
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the
Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for
digital archive preservation.
*Related content and download information correct at time of download.
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

The impact of additive
manufacturing on supply chains
Christian F. Durach
Department of Logistics, Institute of Technology and Management,
Technische Universität Berlin, Berlin, Germany, and
Stefan Kurpjuweit and Stephan M. Wagner
Department of Management, Technology, and Economics,
Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
Abstract
Purpose –The purpose of this paper is to offer empirical insights on emerging additive manufacturing (AM)
processes, barriers to their adoption and a timeline of expected impacts on the supply chain in the
manufacturing industry.
Design/methodology/approach –A multi-stage survey study was conducted with a panel of 16 experts
from industry and academia.
Findings –Only five out of today’s seven AM processes are of future importance, as are two emerging key
processes. In total, 15 barriers to their adoption are identified, all of which are expected to be gone within
ten years. Eight propositions are derived postulating as to whether and when supply chain impacts
can be expected in terms of changes to supply chain structure, customer centricity, logistics and supply
chain capability.
Research limitations/implications –“Soft”barriers are new to the literature, which has traditionally
focused on “technical”barriers. Often-discussed barriers such as production speed and costs do not reflect the
true concerns of the research panel. Furthermore, some of the supply chain implications discussed in both
the academic literature and the media are found to be unlikely to materialize.
Practical implications –The study summarizes AM processes, technologies, barriers and supply chain
implications solicited from experts in the field.
Originality/value –This is one of the first studies to make empirical contributions to a vastly conceptual
discussion. It is also the first study to give insights on a timeline for barriers and supply chain implications.
Keywords Barriers, Additive manufacturing, 3D printing, AM processes/technologies,
Supply chain implications, Time horizon
Paper type Research paper
Introduction
Imagine a world in which, whenever you want a product, all you need to do is to purchase a
digital CAD model of the product online. This digital model can then be fed into your printer
at home, and the bespoke product will be printed for you. In this world, you would neither
have to leave the house to go to the nearest hardware store, nor would anyone else have to
jump in a truck to bring the product to you. The printing technology in this fictional
example is called additive manufacturing (AM; commonly known as three-dimensional or
3D printing). AM is, probably due to increased media attention, amongst today’s best known
digital manufacturing technologies.
Though the idea of AM is not new, the technology caught media attention in 2014
when –for the first time in history –an electric car was printed by a company called Local
Motors. The printing took 44 hours (Laliberte, 2014). While today AM is still mostly used for
prototyping in some industries (e.g. apparel), it has found wider application in others
(e.g. medicine), and expectations for this technology remain high (D’Aveni, 2015).
We understand AM as “the process of joining materials to make objects from 3D model data,
International Journal of Physical
Distribution & Logistics
Management
Vol. 47 No. 10, 2017
pp. 954-971
© Emerald Publishing Limited
0960-0035
DOI 10.1108/IJPDLM-11-2016-0332
Received 28 November 2016
Revised 9 May 2017
12 June 2017
Accepted 3 August 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0960-0035.htm
The authors would like to acknowledge the financial support by the Kühne Foundation and
particularly thank Emre Akyol for his support in data collection.
954
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

usually layer upon layer, as opposed to subtractive manufacturing methodologies, such as
traditional machining”(ASTM International, 2012).
Whereas the media perception of AM is often that of a disruptive technology
that will radically change supply chain structures as we know them (Lipson, 2011;
The Economist, 2012), researchers are rather conservative and paint a picture that could
be termed a “partial adaptation and implementation of the technology, paralleling already
existing manufacturing processes.”In the academic world, though 3D printing has been
labeled as an important and timely supply chain management (SCM) issue (Ellinger and
Chapman, 2016), most researchers have concluded that it is rather unlikely that AM will
replace traditional manufacturing processes in the short and medium terms. Instead,
it is a technology that will complement today’s production processes (Holweg, 2015;
Rogers et al., 2016).
However, as of today, most of our academic research in this area is conceptual, limited to
predicting the impact of this new technology on market structures (e.g. Weller et al., 2015),
manufacturing processes (e.g. Sasson and Johnson, 2016) or supply chains (e.g. Holmström
and Partanen, 2014). For example, Weller et al. (2015) discussed how different market
structures will be affected when AM makes inroads. They distinguished monopolistic
markets from competitive markets, concluding that AM will increase profits in the former;
in the latter, conventional manufacturing processes will prevail. Sasson and Johnson (2016)
designed a scenario in which AM complements traditional mass manufacturing processes.
They suggested that even mass production could benefit from AM when smaller quantities
must be isolated from scalable mass production. Holmström and Partanen (2014) discussed
new business structures in combining AM with supply chains. Building on evolution theory,
they concluded that we are likely to see supply chains that combine conventional structures
with digital manufacturing.
Welcome exceptions to these conceptual works are studies by Chen et al. (2015) and
Mellor et al. (2014). Chen et al. (2015) discussed the impact of AM on sustainability by taking
a societal perspective. They conducted a single case study to demonstrate differences of
energy usage in AM and mass production. Mellor et al. (2014) presented a framework to
show how AM technologies can be implemented. Discussing the different AM technologies,
the authors identified factors that are important for implementation, depending on the
unique characteristics of each technology. They subsequently conducted a single case study
to enhance their findings with industry practice.
While all these studies have made valuable contributions to our field, they leave us with
few empirical insights to counter our perceptions of the impact of AM on supply chains
with the view of the media. We therefore think that our field will greatly benefit from adding
practical insights to this overwhelmingly theoretical discussion. It is the purpose of this
study to empirically inquire which AM processes will emerge, which barriers to the
adoption of such processes exist, when these barriers will be overcome, and which supply
chain impacts can be expected and at which time. We will therefore answer the following
three interlinked questions through a multi-stage survey study:
RQ1. Which AM processes will emerge from the current array of available AM processes?
RQ2. What are the barriers to the widespread adoption of these processes and when can
these barriers be overcome?
RQ3. What supply chain impacts can be expected and at which point in time?
Our study is also a response to previous studies in this field calling for a substantiation of
their conceptual findings (e.g. Holmström et al., 2016; Rogers et al., 2016). In this context,
Waller and Fawcett (2014) encourage “theory-laden academics”to provide “a clearer
documentation of what 3D printing really means to supply chain practice”(p. 99).
955
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

Research framework
Figure 1 illustrates the research framework for the timeline of the impact of AM on supply
chains. The framework assumes that due to the emergence of a variety of AM processes and
given the barriers to AM adoption in various application areas, we will see different supply
chain impacts at different points in time.
Emerging AM processes
Our research framework (Figure 1) depicts the emerging AM processes as our independent
variable. We assume that depending on the emerging processes we will see different supply
chain implications. Although several competing AM processes (with multiple technological
solutions) are now available, most work is based on the building up layers of
material. Usually, layers are built through disposing these materials –plastic, ceramic or
metal powders –through a nozzle or by melting a powder by laser or electron beam
(Wagner and Walton, 2016). AM allows the building of highly complex product structures
that could not have been built using traditional subtractive manufacturing processes.
AM designs are completely free from the design constraints imposed by traditional
processes. Instead of producing several parts, a single integrated assembly is feasible
(Atzeni and Salmi, 2012). Today, several competing AM processes allow the construction of
such complex products and their advantages and disadvantages may determine their future
adoption in industry.
Seven major AM processes are currently under discussion (Huang et al., 2012;
Zhai et al., 2014). To provide a technological backdrop for our discussion in the fourth
section, we will now briefly present each of these processes and discuss its advantages and
disadvantages. Powder bed fusion is a process in which a laser or electron beam melts
regions of the powder bed to build the product. This process has the merit of making
support structures unnecessary. Its main drawback is that the surface structure-finish is
comparatively rough. EOS, for example, applies this process for direct metal laser sintering.
Like powder bed fusion, directed energy disposition melts material by laser or electron
beam. However, in this process, the material is deposited during the AM process. The main
purpose of this process is to repair parts. Its advantages and disadvantages are similar to
those of the powder bed fusion process. The Optomec LENS system is one example. During
sheet lamination, sheets of material are bonded onto each other. A laser sequentially cuts 2D
cross sections out of these sheets. Although this process is inexpensive, the current models
are still rather inaccurate and struggling to keep control limits. This process is used in Mcor
Technologies’printers. Binder jetting joints substrate using a binder deposited through a
nozzle. The production process is fast and has low material costs, though the surface finish
remains rough and part size is limited. The ZPrinter line uses the binder jetting process.
Material jetting works similarly to a two-dimensional inkjet printer. Material is jetted onto a
surface or platform layer by layer. Material jetting is the only AM process that can combine
Barriers to…
Prototyping,
Parts production,
Production tooling, and
Spare parts production
Emerging AM
processes
time horizon
Supply chain
implications
Supply chain
structure
Logistics
Supply chain
capabilities
Customer
centricity
•
•
•
•
Figure 1.
Research framework
of AM process impact
on supply chains
956
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

different materials within the same 3D printed model. However, the only materials that can
be used are paste-like materials such as polymers or waxes. This process is used in the
PolyJet technology. During material extrusion, material is extruded from a nozzle and
deposited onto a substrate. Advantages are the wide selection of material and the low
maintenance cost of the equipment. However, the seam line between layer and the required
support structures pose some problems to technicians. The fused deposition modeling
technology is an example of this process. During vat photopolymerization, a “photopolymer in
a vat is selectively cured by light-activated polymerization”(ASTM International, 2012, p. 1).
Due to its speed and good surface finish, this technology is particularly suitable for the
manufacturing industry. However, materials are costlier and less versatile than other
AM processes. Stereolithography is one technological example. The interested reader is
referred to Berman (2012) and Hofmann (2014) for a more detailed technical overview of the
3D printing process and technologies and their potential areas of application.
Barriers
The research framework in Figure 1 indicates that the extent to which and when these
AM processes impact supply chains also depends on various barriers to their adoption.
The Oxford Dictionary describes (2016) a “barrier”as “a circumstance or obstacle that keeps
people or things apart or prevents communication or progress”. In SCM research, “barriers”
are often used to describe circumstances or obstacles preventing firms from, among other
things, being successful in developing suppliers (Busse et al., 2016), implementing SCM
techniques (Richey et al., 2009), achieving network-wide use and integration of technologies
(Murphy and Daley, 1999). In the field of AM, we understand barriers as circumstances or
obstacles to the implementation and widespread adoption of AM technologies.
Literature has speculated that the adoption of AM in industry may be hindered by
numerous barriers, such as high printer acquisition and manufacturing costs, the technical
limitations of 3D printers (i.e. variety of materials, accuracy and quality), or the development of
custom CAD systems (Berman, 2012; Gibson et al., 2015; Khajavi et al., 2014; Rogers et al., 2016).
For example, Khajavi et al. (2014) found that there are barriers to movingfrom a centralized to a
distributed spare part production. Conducting a scenario model approach, they found capital
and labor intensity as well as production cycle time as two of today’s highest barriers.
Unfortunately, the authors offer no insights into when they think that these barriers will be
overcome. It is thus the goal of our empirical study to identify today’s most dominant barriers,
and to assess when these barriers can be expected to cease hampering widespread adoption of
AM. We will present these barriers with respect to their inhibiting effect on four potential
application areas: prototyping, parts production, production tooling and spare part production.
Supply chain implications
The emergence of AM processes and their adoption in manufacturing industry, as discussed
at the start of this section, will inevitably have supply chain implications (see Figure 1).
Moreover, the remaining barriers to the adoption of AM and the timeline of when these
barriers will be overcome drive when and how supply chains will be affected.
We understand the supply chain as the interlinked network of organizations, people and
processes involved in providing products and services to the customer (Durach et al., 2015).
Impacts on supply chains are expected from, but not limited to the structure of supply
chains (i.e. the location of the manufacturing facilities), the centricity of customers in the
manufacturing process (i.e. new business models), supply chain logistics (i.e. transportation
and warehousing) and supply chain capabilities (e.g. resilience and agility).
As discussed at the beginning of this paper, research has remained either conceptual or
theoretical when it comes to the discussion of how and when supply chains will be impacted
by AM. Barz et al. (2016), for example, have computed two-stage supply chain models,
957
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

concluding that total supply chain costs, ton-kilometers per customer, transport costs and
the number of production sites will all be improved by AM adoption. Christopher and
Ryals (2014) have predicted lower inventories and increased agility in supply chains due to
AM by calling for the emergence of a “demand chain.”Similarly, Bogers et al. (2016)
have discussed the emergence of consumer-centric business models with the advent of AM.
None of these studies have, however, consulted with practitioners and academic experts in
finding an answer to the inherent question of how supply chains will be affected. As a last
step in our theoretical framework, we will analyze and discuss what supply chain impacts
can be expected and when.
Research methodology
This research explores which AM processes emerge, what the barriers to their widespread
adoption are, how long they will exist and how supply chains will be affected by
their adoption at a certain point in time. To answer these interlinked, forecasting-oriented
research questions, it was desirable to collect empirical data in multiple rounds, preferably
from a small, heterogeneous panel of experts. The composition of the panel was designed
to be heterogeneous to foster creativity and to integrate viewpoints from a variety of
industries and from some knowledgeable academics (Okoli and Pawlowski, 2004).
Giventhisnatureandtoincrease confidence in this study’s findings, we followed
the methodological recommendations in Delbecq and Van de Ven (1971) and conducted a
multi-stage survey study (see Wieland et al., 2016 for its application in SCM).
The multi-stage survey study is akin to the Delphi technique (Rowe and Wright, 2001),
providing full anonymity to a panel of heterogeneous experts while allowing for a
moderated exchange of ideas. It is, however, less formalized in terms of the provision of
feedback to the participants and the number of survey rounds needed. The multi-stage
survey study balances the panel’s input through a moderated and anonymous process
that ensures integrity, which is in stark contrast to traditional onsite focus group
discussions where it is usually difficult to encourage more reticent members to bring their
ideas forward (Lloyd, 2011).
Panel composition
As the results of the multi-stage survey studywillbecontingentonthecompositionofthe
panel, we took great care into the selection of participants. In an initial step, we contacted
the major AM equipment producers and renowned scholars within the field of AM.
Attending AM conferences and fairs in leading AM countries like the USA, Germany and
Japan, we identified potential experts from industry and academia whom we believed
had expertise at the intersection of AM and SCM. In total, we contacted 62 individuals by
phone or in person. By talking to them, we assessed whether their qualifications
were sufficient to consider them as experts for our panel. Moreover, the personal contact
was necessary to familiarize the potential participants with the scope and process
of our study.
After this initial contact, we sent an invitation letter including a detailed overview of the
study with a short explanation of the method to 20 experts. The letter also informed the
participants that their anonymity and confidentiality would be guaranteed throughout
the entire research process.
From the 20 invited experts, 16 agreed to participate in our study. The panel consisted of
representatives from industries such as electrical equipment, industrial machinery,
management consulting for AM and from academia (Table I). Academia is represented by
university professors and research lab directors. To ensure that the panel is composed of
experts, we asked the participants to conduct a self-assessment, rating their experience in AM
on a 5-point Likert scale from 1 (“extremely knowledgeable”)to5(“not at all knowledgeable”).
958
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

The participants rated their AM experience on average with 2.43, indicating an adequate level
of knowledge to respond to our questions. The average number of years of experience in AM
was about seven years.
Research process
This study adds empirical insights to a topic which has been investigated strictly at a
conceptual level. To investigate the impact of AM on supply chains, we tapped into the
specific domain knowledge of our panel. We thus developed a two-round survey instrument
with open- and closed-ended questions, allowing our informants to provide both relevant
qualitative information –thereby determining the general direction of our study –and
quantitative ratings and rankings. For each of the survey rounds, the panelists received an
e-mail with a link to our online-survey on unipark.com, where they were given instructions
and feedback and where they were asked to respond to our questions. The survey
instrument was divided into three sections, wherein questions addressed: emerging AM
processes, the barriers to implementing AM and the implications of AM for supply chains.
The unstructured first round of the survey gathered as many qualitative insights as
possible and required creativity from the experts. They were provided with free text
fields asking to describe: which AM processes they see today as most important and which
ones will emerge as leading processes in the future; what barriers they see in the widespread
adoption of AM processes; and to name all anticipated potential supply chain impacts.
The experts had been given examples of technology to better understand the AM processes
in addition to definitions of “barriers”and “supply chains.”We then consolidated the
results to develop our second round survey.
The second round of the survey consisted of structured closed-ended questions
composed of all coded qualitative results from the first round. The panelists either had to
rank the answers or to rate them on a 5-point Likert scale. We asked for rankings in
assessing the AM processes, and for ratings of the barriers and supply chain impacts
depending on the AM process ranking. The average of each rated answer was then
calculated. We then asked the panelists questions about the time horizon: when they expect
the barriers to be overcome and when the supply chain impacts can be expected.
The participants could choose from the options: 0-5 years, 5-10 years, 10-15 years, W15 years
or never. Responses in all rounds of the research process were voluntary, and respondents
Pana-list Country Position Industry (ISIC)/academia
Number of
employees
AM experience
in years
E1 USA R&D Engineer Machinery and equipment 50 2
E2 Switzerland Head of R&D Food products 400 1
E3 Japan R&D Engineer Electrical equipment 200 10
E4 Switzerland R&D Engineer Machinery and equipment n/a n/a
E5 USA Customer Support Machinery and equipment 57 1
E6 Germany R&D Engineer Machinery and equipment 12 2
E7 Germany Researcher Scientific research –3
E8 Germany Researcher Scientific research –20
E9 Japan Researcher Scientific research –3
E10 Japan Senior Manager Management consultancy (AM) 300 1
E11 Japan CEO Machinery and equipment 45 13
E12 Japan Researcher Scientific research –30
E13 Japan Senior Manager Transportation equipment 5,000 1
E14 France CEO Electrical equipment 180 1
E15 Japan R&D Engineer Engineering services 800 25
E16 Germany Researcher Scientific research –3
Table I.
Panel characteristics
959
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

were asked to skip any questions that they did not feel qualified to answer. Whenever
clustering of our study results was necessary, for example, when assigning barriers to AM
application areas, or when assigning barriers to supply chain implications, the authors
conducted a Q-sort exercise (Ellingsen et al., 2010).
The structured second round survey quantified the domain knowledge of the experts
and elicited predictions and importance ratings on the summaries of answers from the first
round. Unfortunately, personal commitments kept two of our panelists from participating in
the second round so we had to exclude their insights from those results. Nonetheless, we are
confident that the panel quality has not been significantly compromised by their absence,
and that our study results may still provide valuable insights to our field. The next section
will present our findings and contrast them with statements, claims and predictions from
previous scholarly studies of AM.
Results and discussion
3D printing: emerging AM technologies
From the seven AM processes which we introduced in the presentation of our research
framework, only five were identified as relevant by the panel (Figure 2). In the first round of
the survey, the panelists were given a list of all seven AM process and short technological
descriptions. They were then asked in two separate questions to select those AM processes
which they consider relevant to manufacturing today and in the future. They had the option
to name additional AM processes in free text fields in addition to the seven that we
presented. No other processes were mentioned by the panel, and there was anonymous
agreement on the relevance of five out of the seven processes. In the second round of the
survey, the panel was provided with the five processes identified as relevant and again
asked in two separate questions to rank these processes according to their current and
future importance (1 ¼most important; 5 ¼least important).
The five AM processes that we identified as relevant are powder bed fusion, directed
energy deposition, material jetting, material extrusion and vat photopolymerization. They
are seen as today’s leading processes and are likely to play a leading role in the future. It is
interesting to see that both sheet lamination and binder jetting were not considered relevant
by the panel, neither for today nor for the future. Both processes present a fairly inexpensive
1
2
3
4
5
Current importance Emerging importance
Ranking
Powder bed fusion
Directed energy
deposition
Material jetting
Material extrusion
Vat
photopolymerization
Binder jetting
(considered
irrelevant)
Sheet lamination
(considered
irrelevant)
Figure 2.
Importance ranking
of emerging AM
technologies
960
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

AM process in comparison to the other processes. However, the disadvantages of these two
processes –their inaccuracy in keeping control limits and the rough surface finish of the
manufactured products –seem to outweigh their advantages.
Looking at the data, we make two key observations. First, we found that material
extrusion is soon expected to lose its leading role in AM. While it is the most widely used of
the five processes (first place), we can soon expect it to decline (third place). Clearly, material
extrusion is amongst today’s cheapest AM processes; however, its drawbacks are likely to
prevent its future widespread adoption. It can, for example, only use polymers and plastics,
manufacturing speed in material extrusion is slower than that of other processes, and the
final product quality is rather poor and limited to material nozzle thickness.
Second, we found that powder bed fusion and material jetting are expected to take over
the two leading roles. In contrast to material jetting, which can only use wax-like materials,
powder bed fusion can operate with metals, polymers, ceramics and composites
(e.g. cermets). This allows the production of parts for critical applications and makes it
more attractive for deployment in parts production, production tooling and spare parts
production (King et al., 2014). Material jetting, with its high accuracy but limited range of
material, is applied mostly for prototyping (i.e., visual and form/fit testing). Material jetting
and material extrusion using non-metal materials, thereby, cover a similar application area,
with the final quality of material jetting being higher. We therefore conclude that powder
bed fusion and material jetting are likely to lead the AM market in the future. The main
application areas of these two processes –parts production, production tooling and spare
parts production for powder bed fusion and prototyping for material jetting –may also
explain the order of these two processes. This is because we see a stark increase in the use
AM processes in everyday manufacturing, while the rise of AM for prototyping has been
less steep, causing a slight increase in the importance of powder bed fusion (Wohlers, 2016).
Having identified and compared important current and emerging AM processes we will
now analyze our results pertaining to the barriers to the widespread adoption of AM.
Barriers to the adoption of AM
The research framework, presented at the beginning of this paper, discussed the need for
identifying barriers that inhibit the widespread adoption of AM processes (Figure 1).
In what follows, we will not just list these barriers but also outline their importance, indicate
the AM areas of application that are affected, and speculate on when these barriers can be
expected to be overcome (Table II).
In the first round of the survey, we gave our panel the following prompt: “In order to
achieve the widespread adoption of AM by the manufacturing industry in the future, what
are the main barriers regarding AM processes that need to be addressed?”Each respondent
was provided with free text fields to enter his/her answers. The first round yielded
31 barriers. In the second round, a consolidated list of 15 barriers was presented to the panel.
In consolidating the list, synonymous barriers were grouped together based on logical
deduction. The panel was then asked to rate the impact of each barrier on the widespread
use of AM on a 5-point Likert scale (1 ¼extremely important; 5 ¼not at all important).
Interestingly, the ranking of the 15 barriers, resulting from the second survey round,
did not reflect the sum of how many participants have had mentioned these barriers in the
first round. This means that the AM barriers with which we are regularly confronted in
our everyday work life (e.g. low production speed and high costs) do not reflect the
importance of such barriers. The opposite may be true, given that barriers such as
production speed and costs often overshadow the true barriers such as the limited variety
of materials and the difficulties to develop new materials. On the one hand, the
often-mentioned barriers, “high manufacturing and equipment costs”and “consumer
awareness”had low scores. On the other hand, barriers which were mentioned by only one
961
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

Round 1 Round 2 Barriers will be overcome within …
Barriers
Number of
mentions Rating Rank 0-5 years (%) 5-10 years (%)
10-15
years (%)
W15
years (%) Never (%)
Affected
application areas
Limited variety of materials 3 1.54 1 21 43 36 ––P, PP, PT, SP
Difficulties regarding the development of new
materials 1 1.62 2 29 29 42 ––PP, PT, SP
Insufficient quality of (metal) parts 2 1.77 3 43 29 28 ––PP, PT, SP
Stability and reliability of the AM process 1 2.00 4 29 36 35 –PP
Education about AM–compatible design needed
for AM engineers 3 2.08 5 23 54 23 ––PP, PT, SP
Low production speed 4 2.15 6 7 64 29 ––PP
Limitations of 3D printing processes (e.g. size
limitations) 1 2.23 7 21 36 15 14 14 P, PP, PT, SP
Low accuracy and quality 3 2.23 7 29 50 14 7 –PP, PT, SP
High material costs 2 2.38 9 7 50 43 ––PP, SP
Regulations relating to materials (e.g.
biocompatibility) 1 2.31 9 14 36 29 21 –PP
Costs regarding the investigation of less-known
AM material properties 2 2.62 11 21 57 15 7 –PP
Consumer awareness and acceptance 2 2.77 12 54 31 7 8 –PP, SP
High manufacturing costs 2 2.85 13 21 50 15 14 –PP
High equipment costs 3 3.00 14 23 38 24 15 –PP
Limited long-term usability 1 3.23 15 14 43 22 21 –PP, PT, SP
Notes: Application areas abbreviation: prototyping (P), parts production (PP), production tooling (PT), spare parts production (SP). Numbers were rounded to add
up to 100
Table II.
Barriers to the
adoption of AM
962
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

expert in the first round were assessed as having a high impact in the second
(e.g. “stability and reliability of the AM process”).
Overall, the panel identified “limited variety of materials,”“difficulties regarding the
development of new materials”and “insufficient quality of (metal) parts”as the most
important impediments to the dissemination of AM technologies. Comparing our results
with the conceptual AM studies, we found not only similar but also new barriers, all of
which are ranked and shown in Table II. For instance, Berman (2012) discussed higher
costs for larger production runs, low material availability, less precision, limited strength
and low resistance to heat and color stability. This listing is broadly similar to the barriers
found by our study. Other scholars have emphasized the high printer acquisition costs or
the development of custom CAD systems as important barriers (Rogers et al., 2016).
The ranking of the different barriers, however, shows that the often-mentioned direct
costs of the technology pose only a minor concern to experts compared to the indirect
costs related to quality, accuracy and reliability. Furthermore, in the reviewed literature
the spotlight tends to be on technical issues and the costs of AM technologies. However,
we also found that “soft”factors such as “education of the engineers,”“regulations toward
the materials”or “customer awareness”are also significant. We suppose that these
“soft barriers”have not been sufficiently discussed in the academic literature, probably
because of the limited real-life experience with AM by practitioners and academics. Hence,
we argue that by having AM users on our panel, we can now paint a more realistic picture
of present and future barriers.
The barriers shown in Table II show their inhibiting effect on potential AM application
areas (i.e. prototyping, parts production, production tooling and spare parts production).
Current barriers to the widespread adoption of AM seem to inhibit its application in parts
production, production tooling and spare part production. There are now very few
impediments to the use of AM in prototyping. As a last step, we asked the panelists to
predict when they expect each of these barriers to be overcome. Only for “customer
awareness”did a majority expects this barrier to be overcome within the next five years.
However, a look at the timeline of the other barriers shows that most of the panelists
expect all other barriers to be overcome within five to ten years. We were surprised and
encouraged by this finding; surprised because in some industries (e.g. automotive,
electronics) product lifecycles fall within this short time frame; and encouraged because it
is a clear sign that business practice will increase efforts to assess the need for adopting
AM technologies, thereby substantiating our research efforts. Practitioners should assess
the ways in which these technologies could be integrated into the production lines of the
next generation of products.
Having identified the barriers to the widespread adoption of these processes and having
predicted when these barriers will be overcome, we can evaluate AM supply chain impacts
and their timeline.
Supply chain implications
Using the same approaches as when we solicited our set of barriers from the panel, we asked
the panel in the first round of the survey to answer the following question: “What do you
think will be the impact of AM on manufacturing supply chains?”The participants entered
their response into free text fields, yielding 20 “supply chain impacts.”In the second round,
a consolidated list of nine supply chain impacts was presented to the panel –the list was
again consolidated by clustering synonymous terms based on logical deduction. The panel
was then asked to rate the impact of each “supply chain impact”on a 5-point Likert scale
(1 ¼extremely likely; 5 ¼not at all likely). In analyzing the respondents’perceptions, we
considered average scores below 2.5 as impacts that are unlikely. The panelists were then
asked to predict when each of the supply chain impacts is likely to materialize. If a supply
963
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

chain impact was evaluated as rather likely (i.e. scores above 2.5) we chose the longest time
frame that at least half of the respondents selected as adequate for the time of the “expected
supply chain impact”and included it in our propositions. This must be seen as a tendency
rather than a definite timing. We will now discuss each of the supply chain impacts,
its likelihood and timeline as reported in Table III, following the four categories of supply
chain impact as depicted in Figure 1.
Supply chain structure.Inour“home fabrication”example at the beginning of this paper,
we described a scenario in which a consumer downloaded the CAD design of a product and
printed it at home. Several research studies have anticipated that AM consumers are likely to
become significantly more involved with and integrated into the design and manufacturing
process of their product, making such a print at home scenario very likely (e.g. Rayna and
Striukova, 2016; Rogers et al., 2016). Berman (2012) went a step further in his conceptual study
and labeled home fabrication as likely within the next five years (until 2017).
Others, however, are less optimistic (e.g. Khajavi et al., 2014). These scholars concur that
the production of the future will be more decentralized and local; however, their scenario
falls between home fabrication and traditional supply chain structures. In spare part supply
chains, for example, Khajavi et al. (2014) predicted more decentralized local manufacturing
systems if AM technologies become less capital-intensive and more autonomous, and if they
offer shorter production cycles.
Interestingly, the idea of home fabrication is in stark contrast to the changes that have
been made to supply chains over the last decade; managers pooled manufacturing
capacities to increase their economies of scale. In combination with outsourcing of
non-core activities to low-cost countries, single production facilities were built to produce
goods for entire countries or even continents. AM has the potential to reverse this
development, since AM allows production in small quantities. Consequently, production
Round 1 Round 2 Expected supply chain impact within …
Impact of AM on
supply chains
Number of
mentions Rating Rank
0-5 years
(%)
5-10 years
(%)
10-15 years
(%)
W15 years
(%) Never
Business models that
integrate customers in the
value creation (P2a) 1 1.69 1 54 38 0 8 –
Logistics service providers
entering AM market by
providing 3D printing
services (P2c) 1 1.85 2 31 46 8 15 –
Reduction of product
development/production
lead time (agile supply
chain) (P4) 4 1.92 3 46 38 16 ––
Decentralized, local
manufacturing (P1b) 5 2.46 4 0 54 23 23 –
Mass customization/
Individual products (P2b) 1 2.62 5 15 46 24 15 –
Less or no inventory/
warehouse (P3a) 4 2.69 6 17 25 25 33 –
Reduction of
transportation costs (P3b) 2 2.69 6 15 31 39 15 –
Purchasing CAD model
data and 3D printing at
home instead of purchasing
the product (P1a) 1 3.31 8 0 38 39 23 –
Note: Numbers were rounded to add up to 100
Table III.
Supply chain
implications
964
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

sites could again be located closer to the customers, which could lead to a revival of
manufacturing in Western countries. However, regardless of whether home fabrication
or local manufacturing will result from the emergence of AM, both scenarios necessitate
removing all barriers that hinder parts manufacturing, as depicted in Table II. As more
than half of the panel concur that all barriers to parts manufacturing are overcome within
five to ten years, it is logical to believe that at least one of the two supply chain structure
implications will emerge within this time frame.
This study’s data indicated that the latter assumption about the supply chain structure
impacts of AM is more likely, supporting a less radical local manufacturing scenario than a
home fabrication scenario for the future. Thus, we propose:
P1a. It is unlikely that AM will change supply chain structures in such a way that we see
large-scale “home fabrication.”
P1b. It is likely that AM will change supply chain structures in such a way that we see
local manufacturing emerging in the next five to ten years.
Customer centricity. Based on the assumption of an emergence of local manufacturing, this
development will inevitably lead to the production of parts and products closer to the
customers. Companies will thereby be enabled to better integrate their customers into
product design and manufacturing. In addition, when consumers take a more active role in
the value chain, the opportunity arises for the manufacturers to offer new services and to
create new business models (Bogers et al., 2016; Rogers et al., 2016). Bogers et al. (2016, p. 228)
spoke of a “direct co-creation with users,”which in extreme cases, may lead to products
designed by the user. Christopher and Ryals (2014) even spoke of a “demand chain”in which
AM gives unprecedented power to the customer. Kathawala and Wilgen (2005) called it
“build-to-order supply chains.”
Furthermore, Berman (2012) observed that the increased possibility of integrating
consumers in 3D printing processes, which are still limited to one-off production, could
completely replace mass manufacturing. This, in turn, can have important consequences for
global supply chains. AM could allow mass production while satisfying the customer’s
needs for customization. Furthermore, shoes, textiles or computers are already mass
customized, allowing consumers to choose individual features of the product.
In these more localized supply chains, on-demand 3D printing service providers could
perform a range of services from design to manufacturing, leveraging the versatile
manufacturing capabilities of direct manufacturing outside of firms’and consumers’
homes (Rayna and Striukova, 2016). 3D printing services encompass and combine
design- and manufacturing-related services to fulfill a customer’sneedforapartor
product (Rayna et al., 2015). A company that offers AM services does not have to limit its
production capability and capacity to its own devices. 3D printers are usually limited in
terms of chamber size, printing speed, or material. Suitable services can provide a vaster
array of device specifications (Rogers et al., 2016). The strongest impediment to
the development of such new services and production strategies around AM is likely the
awareness and acceptance of potential AM customers for this new technology. More than
half of the panel agrees that within five years, this barrier will no longer exist.
These impediments can thus be expected to be short-lived and will not impede the
emergence of new services and production strategies for long. Nevertheless, with regards
to consumer-centric supply chains, our participants have mixed expectations of the
likelihood of their occurrence. While the panel is optimistic that new actors like 3D
logistics service providers will enter the market, and that customers will be integrated into
the value chain, the experts are at odds to the use of AM for mass customization. Building
on our panel, we suggest that the main impacts of AM on customer centricity will be
965
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

restricted to new business models that build on customer co-creation and the emergence
of 3D logistics service providers. Mass customization is as a rather unlikely scenario.
We therefore derive the following propositions:
P2a. It is likely that AM will change supply chains in such a way that in the next five
years we will see AM business models that integrate customers in decentralized,
local manufacturing.
P2b. It is unlikely that AM will change supply chains in a way that brings large-scale
mass customization.
P2c. It is likely that 3D logistics service providers will enter the AM market with 3D
printing services within the next ten years.
Logistics. In theory, AM production arguably decreases the need for transporting finished
goods, thereby resulting in a decrease of transportation costs. Building on this idea,
Barz et al. (2016) conducted a computational study in which they developed a two-stage
supply network to assess the impact of AM on transportation costs. They calculated three
stylized instances of their model, and found that after the introduction of AM, total
transportation costs decreased by around 50 percent.
Besides the reduction of transportation costs, AM impacts are commonly expected in
terms of reduced warehousing needs in supply chains (Mavri, 2015). In traditional supply
chains, manufacturers usually source a wide array of parts and components from multiple
suppliers and production sites, resulting in high volume part flows. In contrast, AM processes
require a smaller number of different input materials, simplifying supply chains and allowing
for the aggregation of flows of goods and reducing safety stocks (and transportation costs).
The lower variety of input materials thus keeps inventory levels lower. Furthermore,
a consumer centric supply chain, where goods are produced only when consumers pull them
out of the system, is said to lead to less inventory (Hopp and Spearman, 2011). Lastly,
inventory levels could also be reduced due to less or no scrap of AM production.
In contrast to the variety of arguments suggesting reduced transportation costs and a
decrease in inventory levels due to AM, one must simultaneously expect to see more
decentralized, local manufacturing structures (see P1b). These supply chain structures
would probably hinder a reduction in supply chain inventories and reduce the potential for
pooling transportation volume. Considering this counterargument, it is a logical
consequence that our panel –seeing decentralized, local manufacturing structures as a
likely future scenario –does not expect a significant decrease in inventories and
transportation costs due to AM. Only in the most extreme, yet rejected, scenario of “home
fabrication”could lower transportation and inventory costs be expected. Consequently,
based on the study data, we propose:
P3a. It is unlikely that AM will change supply chains in such a way that we see a
significant reduction in supply chain inventories.
P3b. It is unlikely that AM will change supply chains in such a way that we see a
significant reduction in transportation costs.
Supply chain capabilities. The supply chain integration and supply chain risk management
literature has speculated about the impact of AM on the development speed of products and
production lead times in supply chains (Caccamo, 2016; Khajavi et al., 2015). These studies
have argued that AM has the potential to increase supply chain agility in a way that allows
a much quicker response to changing market demands and customer feedback. Agility,
as defined in this study, focuses on lead time compression, not the elimination of waste.
The agility brought by AM thus allows for the fast reconfiguration of products and
966
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

processes in both design and volume to accommodate changing consumer demand
(Tuck et al., 2006). Tuck et al. (2006) posit that AM also enables supply chains to move
the order decoupling point (i.e. the point in a supply chain where a product is linked to the
customer) closer to the customer, significantly increasing agility.
In particular, the increase of production speed within the next five to ten years,
as expected by more than two-thirds of the panel (depicted as a current barrier in Table II),
should yield 3D printing technologies that allow for a quicker response to changing market
demands, resulting in more agile supply chains. Moreover, a more decentralized production
network (as suggested in P1b) makes supply chains more flexible because
production facilities are located closer to the customer.
These assumptions are corroborated by this study’s panel, where it has been argued that
3D printing is likely to make supply chains more agile. Consistent with the panel’s
expectation about the emergence of decentralized manufacturing (P1b), the expected
emergence of agile supply chains falls into the same period. The simultaneous emergence of
AM services would result in an additional increase in companies responding to changing
demands by flexibly adjusting their production capacities (Sasson and Johnson, 2016).
We thus propose that supply chain agility, as a fundamental supply chain capability,
is likely to increase as a result of AM introduction:
P4. It is likely that AM will change supply chains in such a way that we see increased
supply chain agility in the next ten years.
Conclusion
This study contributes to the sparse literature at the intersection of AM and SCM and is a
direct response to Holmström et al.’s (2016) and Rogers et al.’s (2016) call for further research
on AM. As Rogers et al. (2016) have pointed out, of the more than 2,400 articles discussing
3D printing, only 12 addressed its potential impact on SCM. We believe this study is
amongst the first toward addressing this gap.
There are high hopes for AM today. AM technologies are expected to reduce costs,
production time and tooling requirements all at the same time, allowing for easier
customization of products. However, the future success and the economic impacts of AM have
largely been discussed on a conceptual level, often disregarding that supply chain impacts
depend on the emerging AM processes and the barriers to their adoption. These aspects may
also drive the timeline of when these impacts can be expected, as these impacts will probably
not all occur at the same time. We therefore conducted a multi-stage survey study with a panel
of 16 experts from industry and academia to answer three interlinked research questions:
RQ1. Which AM processes will emerge from the current array of available AM processes?
RQ2. What barriers inhibit the widespread adoption of these processes and when can
these barriers be overcome?
RQ3. Which supply chain impacts can be expected and at which point in time?
The study results indicate that, of the seven AM processes discussed in the literature
(Huang et al., 2012), only five are likely to emerge as leading processes that will experience a
widespread use in the manufacturing industry: powder bed fusion, directed energy deposition,
material jetting, material extrusion and vat photopolymerization. Two out of these
five processes –powder bed fusion and material jetting –are expected to be most successful in
this intense technological competition taking over today’s leading role of material extrusion.
The analysis of barriers has generated some additional valuable insights to the AM
literature. In particular, our methodological setting pinpointed a set of “soft”barriers that
967
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

are utterly new to the AM technology literature. These “soft”barriers focus on issues such
as employee trainings, regulations and customer awareness, complementing the more
technical barriers discussed to date. The results are also remarkable, as they show that
the often-discussed barriers of production speed and costs frequently overshadow true
barriers for AM adoption, such as the lack of material variety. With this study, we have
been able to shed some light on what truly interferes with the success of AM.
Through analyzing potential supply chain implications and their timeline, we found four
dimensions of supply chain impacts (i.e. supply chain structure, customer centricity, logistics
and supply chain capabilities) and derived eight propositions. The findings indicate that some
often-discussed supply chain impacts such as home fabrication, mass customization or a
significant reduction of inventory are rather unlikely scenarios. Other scenarios such as an
increase in decentralized manufacturing or the rise of 3D printing services providers are much
more likely and may even become true within the next five to ten years. We conclude that AM
deserves attention from industry and academia. However, some of the industry implications
discussed in both academic literature and the media are rather unlikely to materialize. AM is
therefore better understood as a groundbreaking technology rather than a disruptive
technology that changes supply chains as we know them. Nonetheless, though, the
assessment of company readiness for AM can only take place on a case-by-case basis,
managers are advised to monitor AM and to assess its potential for future adoption.
The interested reader is referred to Conner et al. (2014), who discussed the circumstances
under which companies should adopt AM developing a discrete range of product categories.
In terms of study limitation and future research, the methodology used here is similar to the
Delphi technique. That is to say, the panel of study participants was purposefully selected to be
heterogeneous allowing for creativity and the integration of viewpoints from various industry
sectors as well as from research. However, while this heterogeneous panel helped us to address
some of the urgent questions in this new technology field, it came at the expense of contextual
insights for particular industry sectors or even for organizational structures. Future research
should take a more nuanced approach to some of the questions addressed in this study.
In contrast to the Delphi technique, we did not assess the stability of responses gathered
in the second survey round. Any judgments regarding the stability of the responses in a
potential third round are therefore illegitimate. However, judging from our experts, we are
confident that their opinions would remain stable despite the diverging opinions of their
peers. The testing of the propositions developed in this study also needs to be subject to
future research. This testing will be possible and should be conducted at the time of the
expected supply chain impacts. Researchers then need to assess whether the barriers
depicted in the manufacturing industry still exist, and whether predicted changes to the
supply chains in comparison to the status quo have appeared. We consider it would also be
interesting to study the redistribution of power in supply chains following the introduction
of AM. The technology might affect the interaction between organizations. Some supply
chain relationships are likely to be substituted by printing machines (or the service
providers). Even companies that may not be interested in adopting 3D printing could use 3D
printing service providers, substituting a partnership with a machine vendor. Therefore, it
would be worthwhile to investigate whether and how power will be redistributed in
networks based on manufacturing/printing capabilities and resource availability.
References
ASTM International (2012), Standard Terminology for Additive Manufacturing Technologies:
Designation F2792-12a, ASTM International, West Conshohocken, PA.
Atzeni, E. and Salmi, A. (2012), “Economics of additive manufacturing for end-usable metal parts”,
International Journal of Advanced Manufacturing Technology, Vol. 62 Nos 9-12, pp. 1147-1155.
968
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

Barz, A., Buer, T. and Haasis, H.-D. (2016), “A study on the effects of additive manufacturing on the
structure of supply networks”,IFAC-PapersOnLine, Vol. 49 No. 2, pp. 72-77.
Berman, B. (2012), “3-D printing: the new industrial revolution”,Business Horizons, Vol. 55 No. 2,
pp. 155-162.
Bogers, M., Hadar, R. and Bilberg, A. (2016), “Additive manufacturing for consumer-centric business
models: implications for supply chains in consumer goods manufacturing”,Technological
Forecasting and Social Change, Vol. 102, pp. 225-239.
Busse, C., Schleper, M.C., Niu, M. and Wagner, S.M. (2016), “Supplier development for sustainability:
contextual barriers in global supply chains”,International Journal of Physical Distribution &
Logistics Management, Vol. 46 No. 5, pp. 442-468.
Caccamo, W. (2016), “3D printing”,Supply & Demand Chain Executive, Vol. 17 No. 4, pp. 34-37.
Chen, D., Heyer, S., Ibbotson, S., Salonitis, K., Steingrímsson, J.G. and Thiede, S. (2015), “Direct digital
manufacturing: definition, evolution, and sustainability implications”,Journal of Cleaner
Production, Vol. 107, pp. 615-625.
Christopher, M. and Ryals, L.J. (2014), “The supply chain becomes the demand chain”,Journal of
Business Logistics, Vol. 35 No. 1, pp. 29-35.
Conner, B.P., Manogharan, G.P., Martof, A.N., Rodomsky, L.M., Rodomsky, C.M., Jordan, D.C. and
Limperos, J.W. (2014), “Making sense of 3-D printing: creating a map of additive manufacturing
products and services”,Additive Manufacturing, Vol. 1-4, pp. 64-76.
D’Aveni, R. (2015), “The 3-D printing revolution”,Harvard Business Review, Vol. 93 No. 5, pp. 40-48.
Delbecq, A.L. and Van de Ven, A.H. (1971), “A group process model for problem identification and
program planning”,Journal of Applied Behavioral Science, Vol. 7 No. 4, pp. 466-492.
Durach, C.F., Wieland, A. and Machuca, J.A.D. (2015), “Antecedents and dimensions of supply chain
robustness: a systematic literature review”,International Journal of Physical Distribution &
Logistics Management, Vol. 45 Nos 1/2, pp. 118-137.
Ellinger, A.E. and Chapman, K. (2016), “IJPDLM’s 45th anniversary: a retrospective bibliometric
analysis and future directions”,International Journal of Physical Distribution & Logistics
Management, Vol. 46 No. 1, pp. 2-18.
Ellingsen, I.T., Størksen, I. and Stephens, P. (2010), “Q methodology in social work research”,
International Journal of Social Research Methodology, Vol. 13 No. 5, pp. 395-409.
Gibson, I., Rosen, D. and Stucker, B. (2015), Additive Manufacturing Technologies: 3D Printing, Rapid
Prototyping, and Direct Digital Manufacturing, 2nd ed., Springer, New York, NY.
Hofmann, M. (2014), “3D printing gets a boost and opportunities with polymer materials”,ACS Macro
Letters, Vol. 3 No. 4, pp. 382-386.
Holmström, J. and Partanen, J. (2014), “Digital manufacturing-driven transformations of service supply
chains for complex products”,Supply Chain Management: An International Journal, Vol. 19
No. 4, pp. 421-430.
Holmström, J., Holweg, M., Khajavi, S.H. and Partanen, J. (2016), “The direct digital manufacturing(r)
evolution: definition of a research agenda”,Operations Management Research, Vol. 9 Nos 1/2,
pp. 1-10.
Holweg, M. (2015), “The limits of 3D printing”,Harvard Business Review, Digital Article, available at:
https://hbr.org/2015/06/the-limits-of-3d-printing (accessed November 21, 2016).
Hopp, W.J. and Spearman, M.L. (2011), Factory Physics, 3rd ed., Waveland Press, Long Grove, IL.
Huang, S.H., Liu, P., Mokasdar, A. and Hou, L. (2012), “Additive manufacturing and its societal impact:
a literature review”,International Journal of Advanced Manufacturing Technology, Vol. 67
Nos 5/8, pp. 1191-1203.
Kathawala, Y. and Wilgen, A. (2005), “The evolution of build-to-order supply chain and its implications
with selected case studies”,International Journal of Services and Operations Management, Vol. 1
No. 3, pp. 268-282.
969
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

Khajavi, S.H., Partanen, J. and Holmström, J. (2014), “Additive manufacturing in the spare parts supply
chain”,Computers in Industry, Vol. 65 No. 1, pp. 50-63.
Khajavi, S.H., Partanen, J., Holmström, J. and Tuomi, J. (2015), “Risk reduction in new product launch:
a hybrid approach combining direct digital and tool-based manufacturing”,Computers in
Industry, Vol. 74, pp. 29-42.
King, W.E., Barth, H.D., Castillo, V.M., Gallegos, G.F., Gibbs, J.W., Hahn, D.E., Kamath, C. and
Rubenchik, A.M. (2014), “Observation of keyhole-mode laser melting in laser powder-bed fusion
additive manufacturing”,Journal of Materials Processing Technology, Vol. 214 No. 12, pp. 2915-2925.
Laliberte, N. (2014), “No time for car shopping? Click ‘print’to make your own”,The New York Times,
September 26, available at: www.nytimes.com/2014/09/28/automobiles/no-time-for-car-
shopping-click-print-to-make-your-own.html (accessed October 10, 2016).
Lipson, H. (2011), “3D printing: the technology that changes everything”,New Scientist, available at:
www.newscientist.com/article/mg21128236-100-3d-printing-the-technology-that-changes-
everything/ (accessed October 10, 2016).
Lloyd, S. (2011), “Applying the nominal group technique to specify the domain of a construct”,
Qualitative Market Research: An International Journal, Vol. 14 No. 1, pp. 105-121.
Mavri, M. (2015), “Redesigning a production chain based on 3D printing technology”,Knowledge &
Process Management, Vol. 22 No. 3, pp. 141-147.
Mellor, S., Hao, L. and Zhang, D. (2014), “Additive manufacturing: a framework for implementation”,
International Journal of Production Economics, Vol. 149, pp. 194-201.
Murphy, P.R. and Daley, J.M. (1999), “EDI benefits and barriers: comparing international freight
forwarders and their customers”,International Journal of Physical Distribution & Logistics
Management, Vol. 29 No. 3, pp. 207-217.
Okoli, C. and Pawlowski, S.D. (2004), “The Delphi method as a research tool: an example, design
considerations and applications”,Information & Management, Vol. 42 No. 1, pp. 15-29.
Rayna, T. and Striukova, L. (2016), “From rapid prototyping to home fabrication: how 3D printing is
changing business model innovation”,Technological Forecasting and Social Change, Vol. 102,
pp. 214-224.
Rayna, T., Striukova, L. and Darlington, J. (2015), “Co-creation and user innovation: the role of online
3D printing platforms”,Journal of Engineering and Technology Management, Vol. 37, pp. 90-102.
Richey, R.G., Chen, H., Upreti, R., Fawcett, S.E. and Adams, F.G. (2009), “The moderating role of barriers
on the relationship between drivers to supply chain integration and firm performance”,
International Journal of Physical Distribution & Logistics Management, Vol. 39 No. 10, pp. 826-840.
Rogers, H., Baricz, N. and Pawar, K.S. (2016), “3D printing services: classification, supply chain
implications and research agenda”,International Journal of Physical Distribution & Logistics
Management, Vol. 46 No. 10, pp. 886-907.
Rowe, G. and Wright, G. (2001), “Expert opinions in forecasting: the role of the Delphi technique”,
in Armstrong, J.S. (Ed.), Principles of Forecasting: A Handbook for Researchers and Practitioners,
Springer, New York, NY, pp. 125-144.
Sasson, A. and Johnson, J.C. (2016), “The 3D printing order: variability, supercenters and supply chain
reconfigurations”,International Journal of Physical Distribution & Logistics Management, Vol. 46
No. 1, pp. 82-94.
The Economist (2012), “A third industrial revolution”,The Economist, April 21, available at:
www.economist.com/node/21552901 (accessed October 10, 2016).
The Oxford Dictionary (2016), “Barrier –definition of barrier in English |Oxford Dictionaries”,
Oxford Dictionaries |English, available at: https://en.oxforddictionaries.com/definition/barrier
(accessed October 10, 2016).
Tuck, C., Hague, R. and Burns, N. (2006), “Rapid manufacturing: impact on supply chain methodologies
and practice”,International Journal of Services and Operations Management, Vol. 3 No. 1,
pp. 1-22.
970
IJPDLM
47,10
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)

Wagner, S.M. and Walton, R.O. (2016), “Additive manufacturing’s impact and future in the aviation
industry”,Production Planning & Control, Vol. 27 No. 13, pp. 1124-1130.
Waller, M.A. and Fawcett, S.E. (2014), “Click here to print a maker movement supply chain: how
invention and entrepreneurship will disrupt supply chain design”,Journal of Business Logistics,
Vol. 35 No. 2, pp. 99-102.
Weller, C., Kleer, R. and Piller, F.T. (2015), “Economic implications of 3D printing: market structure
models in light of additive manufacturing revisited”,International Journal of Production
Economics, Vol. 164, pp. 43-56.
Wieland, A., Handfield, R.B. and Durach, C.F. (2016), “Mapping the landscape of future research themes
in supply chain management”,Journal of Business Logistics, Vol. 37 No. 3, pp. 205-212.
Wohlers, T.T. (2016), “3D printing and additive manufacturing state of the industry: annual worldwide
progress report”, Wohlers Associates, Fort Collins, CO.
Zhai, Y., Lados, D.A. and LaGoy, J.L. (2014), “Additive manufacturing: making imagination the major
limitation”,JOM, Vol. 66 No. 5, pp. 808-816.
About the authors
Christian F. Durach (Dr-Ing., Technische Universität Berlin) is a Senior Researcher at the Department
of Logistics, Technische Universität Berlin, and Manager of the Kühne Foundation Center for
International Logistics Networks. Christian has published several peer-reviewed journal articles and
sits on the Editorial Advisory Board of the International Journal of Physical Distribution & Logistics
Management. He graduated from the Zurich University of Applied Sciences, the Worcester Polytechnic
Institute and the Technische Universität at Berlin. Christian is a frequent speaker at both business
and research conferences. He is a Member of the Center for Supply Networks (CaSN) at the
Arizona State University, the Beta Gamma Sigma International Honor Society, and the Global
Manufacturing Research Group (GMRG).
Stefan Kurpjuweit is a Research Associate and PhD candidate at the Chair of Logistics
Management at the Swiss Federal Institute of Technology Zurich (ETH Zurich). He graduated from the
Karlsruhe Institute of Technology (KIT) in the field of Industrial Engineering and Management.
His research interests lie in the areas of supply chain innovation, entrepreneurial suppliers and supply
chain risk management.
Stephan M. Wagner (PhD, University of St Gallen) is a Professor, holds the Kühne
Foundation-sponsored Chair of Logistics Management, and is the Director of the Executive MBA in
Supply Chain Management at the Swiss Federal Institute of Technology Zurich (ETH Zurich).
His research interests lie in the areas of supply chain management, purchasing and supply
management, logistics, and transportation management –with a particular emphasis on strategy,
networks, relationships, behavioral issues, risk, innovation, entrepreneurship and sustainability. He is
an active researcher and has published 10 books, and over 100 professional and academic articles in
journals such as Academy of Management Journal,Journal of Management,Journal of Operations
Management,Decision Sciences,Journal of Supply Chain Management,Journal of Business Logistics,
Interfaces,orCalifornia Management Review. Stephan M. Wagner is the corresponding author and can
be contacted at: stwagner@ethz.ch
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
971
Additive
manufacturing
on supply
chains
Downloaded by EIDGENOSSISCHE TECHNISCHE HOCHSCHULE ZURICH At 07:31 23 November 2017 (PT)