Thesis incl. appendices

Abstract
Background: The industrial introduction of additive manufacturing technologies (AMT) in direct processing
activities of finished goods have by many been called a hype as its longstanding area of purpose have been limited
to prototyping activities innew product development. However, muchindicate that the performance capabilities of
AMT has reached a certain point in its technological maturity, in which may allow for strategic capitalization of
manufacturing organizations as a viable adoption-alternative.
Purpose: This thesis provides a preliminary adoptiondecisiontool for managers exploringthe potential of industrial
additive manufacturing (AM). Through explanation of contemporary performance capabilities of various AMT
processes, it aims to pinpoint the contemporary areas of application and future direction of strategic purpose by
reconciling these findings with theoretical concepts.
Methodology: As the novelty and potential disruptive force of the process technology not necessarily provideda fit
with pure positivist and/or interpretivist research paradigms, a pragmatic, mixed methods approach was chosen.
Hence, abductive reasoning, allowing the researcher to “fill in the blanks” with observations from the empirical
reality and match these with concepts from theory, was chosen.
Results: Based on a comparative performance capability analysis utilizingfive generic performance objectives, and
an analysis of AMT systems manufacturing, and business strategic boundaries, this thesis suggests contemporary
viability of AM in high flexibility oriented manufacturing environments where requirements for low-medium
volumes of production, and highly complex and customized parts are prevalent conditions.
Moreover, the thesis propose that contemporary viability of AM is closely linkedwithtechnology maturity, and the
results point to a proposition that when AM is proven ready for mass-adoption, established theorems within
manufacturing and supply chain operations must be taken under advisement for reconsiderations.
Conclusions: In line with predicted advances in technology maturity, the thesis further propose a development
trend of system typology from intermittent systems to concurrent systems. This shift suggests anexpansionof the
purpose boundaries for strategic areas of application. It is further suggestedthat these advances holdthe potential
to support a new, more sustainable manufacturing operations and supply chain paradigm without compromising
the ability to achieve cost efficiency.
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Acknowledgements
This section is dedicated to those who have helped and inspired me to finish this milestone achievement of my
academic journey. Without you, I could not have made it.
First, I would like to offer my eternal gratitude to Juliana Hsuan, my supervisor, mentor, andfriendthroughout this
journey. Without your brilliant expertise and motivating encouragement the journey would not have been as
rewarding as it has. Thank you, Juliana.
Second, I would like to thank my family for their support and belief in my ability to thrive on this journey by myself.
Special thanks to my Mom, Dad, Tore, Grandmother Martha, and Grandfather Kåre for all your love and support.
Third, I would like to thank my friends and classmates for your guidance, encouragement and insight in situations
where a third eye was needed to move forward. You all know who you are, thank you. Special thanks to Jørgen,
who helped be with the formalities before handing in.
Last, but not least I would like to offer a special thanks to my girlfriend Lotte. Your patience, encouragement,
overwhelming kindness and belief in me has guided me through this process. Thank you, Lotte.
Abbreviations
Terms
Abbreviation Explanation
3DP Three-dimensional printing
AM Additive Manufacturing
AMT Additive Manufacturing Technologies
BB Research Building Block(s)
CAD Computer-aided Design
CAGR Compound Annual Growth
CAM Computer-aided Manufacturing
CNC Computerized-Numerically Controlled Machine Automation
ERP Enterprise Resource Planning
IPR Intellectual Property Rights
MO Manufacturing Organization(s)
NPD New Product Development
PO Performance Objective(s)
PT Process Technology(ies)
R&D Research & Development
RBV Resource-based View
RFID Radio Frequency Identification
S&OP Sales & Operations Planning
SC Supply Chain(s)
SCM Supply Chain Management
SL Stereolithography
TM Traditional Manufacturing
TMT Traditional Manufacturing Technologies
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Table of Contents
ABSTRACT..........................................................................................................................................................................1
ACKNOWLEDGEMENTS......................................................................................................................................................2
ABBREVIATIONS ................................................................................................................................................................2
TABLE OF CONTENTS .........................................................................................................................................................3
TABLE OF FIGURES.............................................................................................................................................................6
1.0 INTRODUCTION ...........................................................................................................................................................8
1.1 BACKGROUND......................................................................................................................................................................8
1.1.2 Introduction to 3D Printing & Additive Manufacturing............................................................................................8
1.2 PROBLEM DISCUSSION...........................................................................................................................................................9
1.2.1 Developments in Macro-trends................................................................................................................................9
1.2.2 Challenges in the Current Manufacturing Operations & Supply Chain Paradigm………………………………………........10
1.3 THESIS PURPOSE ................................................................................................................................................................11
1.4 RESEARCH GAP & THESIS STRUCTURE.....................................................................................................................................12
1.5 DELIMITATIONS..................................................................................................................................................................12
2.0 RESEARCH FRAMEWORK............................................................................................................................................13
2.1 CONCEPTUAL RESEARCH FRAMEWORK....................................................................................................................................13
2.1.1 Block 1: Market Outlook & Technology Readiness.................................................................................................13
2.1.2 Building Block 2: Technology Features & Performance Capabilities ......................................................................14
2.1.3 Building Block 3: AM Strategic Purpose & Paradigmatic Appropriateness............................................................14
2.1.4 Building Block 4 Discussion: Operations & Supply Chain Performance..................................................................14
2.2 CHOICE OF THEORY.............................................................................................................................................................14
3.0 LITERATURE REVIEW..................................................................................................................................................15
3.1 GENERAL BUSINESS STRATEGY VS. OPERATIONS STRATEGY .........................................................................................................15
3.2 MANUFACTURING STRATEGY ................................................................................................................................................16
3.2.1 Scope and Definitions.............................................................................................................................................16
3.2.2 The Strategic Choice Paradigm ..............................................................................................................................16
3.3 MANUFACTURING STRATEGY TYPOLOGIES...............................................................................................................................18
3.3.1 Typologies of Generic Business Strategy & Manufacturing Strategy.....................................................................18
3.3.2 The Relationship between Generic Business Strategy &Manufacturing Strategy..................................................19
3.3.3 Primary Dimensions & Underlying Variables of Manufacturing Strategy..............................................................20
3.4 ACCOMMODATING TYPOLOGIES FOR PROCESS-TECHNOLOGY STRATEGY IN MANUFACTURING...........................................................22
3.4.1 Technical Complexity & Technical Flexibility..........................................................................................................22
3.5 MANUFACTURING & OPERATIONS PERFORMANCE....................................................................................................................23
3.5.1 Generic Performance Objectives............................................................................................................................23
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4.0 METHODOLOGY.........................................................................................................................................................26
4.1 THE NATURE OF BUSINESS RESEARCH.....................................................................................................................................26
4.2 PHILOSOPHICAL PARADIGMS & CONSTITUENTS OF SCIENTIFIC RESEARCH ......................................................................................26
4.2.1 Philosophical Paradigms in Research.....................................................................................................................26
4.2.2 Knowledge claims...................................................................................................................................................27
4.2.3 Research Strategies................................................................................................................................................27
4.3 Thesis Knowledge Claims ..........................................................................................................................................28
4.4 THE DYNAMIC RESEARCH PROCESS ........................................................................................................................................30
4.4.1 Conceptual Framework ..........................................................................................................................................31
4.4.2 Conceptual Literature – Theoretical Frame of Reference vs. the Empirical World – Evidence from Reality ..........31
4.4.3 Matching................................................................................................................................................................31
4.5 RESEARCH APPROACHES & RELATED RESEARCH STRATEGIES .......................................................................................................32
4.5.1. Building-block 1 – Market Outlook & Technology Readiness................................................................................32
4.5.2 Building-block 2 – Technology Performance Capabilities.......................................................................................32
4.5.3 Building-block 3 – Strategic Focus & Paradigmatic Appropriateness ....................................................................32
4.5.4 Building-block 4 – Strategic and Performance Implications for Operations & Supply Chains................................32
4.6 DATA SOURCES & DATA COLLECTION STRATEGY.......................................................................................................................33
4.6.1 Data Sources – Primary vs. Secondary Data ..........................................................................................................33
4.6.2 Data Collection Strategy ........................................................................................................................................33
4.7 OPERATIONALIZATION TABLES & METHODS OF ANALYSIS...........................................................................................................35
4.7.1 Building Block 2 –Process Technology Performance & Operational Capabilities...................................................35
4.8 Building Block 3 –Process Technological Manufacturing and Business Strategic Mapping......................................37
4.8.1 Building Block 4 – Operations & Supply Chain Strategy and Performance Discussion...........................................38
4.9 RESEARCH LIMITATIONS.......................................................................................................................................................38
4.10 PRESENTATION OF DATA & FINDINGS...................................................................................................................................38
5.0 PRESENTATION OF DATA & FINDINGS........................................................................................................................39
5.1 BUILDING-BLOCK 1: TECHNOLOGY & MARKET OVERVIEW..........................................................................................................39
5.2 3DP/AM PROCESS TECHNOLOGY OVERVIEW ..........................................................................................................................39
5.2.1 The Nature of Additive Manufacturing ..................................................................................................................39
5.2.2 3DP/AM Technology Definitions ............................................................................................................................39
5.2.3 Different Purposes of ALM Process Technologies ..................................................................................................39
5.3 SYSTEM CLASSIFICATION FOR DIFFERENT 3DP/AM PROCESSES...................................................................................................40
5.3.1 Process Technologies for Industrial Application.....................................................................................................41
5.3.2 Materials for Industrial Application .......................................................................................................................41
5.4 CURRENT STATE OF 3DP/AM INDUSTRY AND MARKET OVERVIEW ..............................................................................................41
5.4.1 Market Trends & Developments (Check for double statements in numbers).........................................................41
5.4.2 Public Initiatives facilitating Industry Growth........................................................................................................42
5.4.3 3D Printing Industry Value Chain ...........................................................................................................................42
5.4.4 Additive Manufacturing – Technology Life Cycle Assessment................................................................................43
5.5 FUTURE INDUSTRY OUTLOOK ................................................................................................................................................44
5.5.1 Technology Maturity Cycle.....................................................................................................................................44
5.5.2 Academic Research Contributions and Cooperative Initiatives Driving Development ...........................................44
5.5.3 Industry Development in Numbers.........................................................................................................................44
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5.5.4 Challenges & Skepticism ........................................................................................................................................45
5.6 BUILDING-BLOCK 2: PROCESS TECHNOLOGY PERFORMANCE & CAPABILITIES .................................................................................46
5.6.1 Comparative Frame of Reference...........................................................................................................................46
5.7 QUALITY PERFORMANCE......................................................................................................................................................46
5.8 PRODUCT QUALITIES ...........................................................................................................................................................47
5.8.1 Geometric Properties .............................................................................................................................................47
5.8.2 Mechanical Properties............................................................................................................................................48
5.8.3 Physical Properties .................................................................................................................................................49
5.9 PROCESS SPEED..................................................................................................................................................................50
5.9.1 Processing Speed –Build Rates...............................................................................................................................50
5.9.2 Set-up & Post-processing time ...............................................................................................................................50
5.10 COST EFFICIENCY..............................................................................................................................................................51
5.10.1 Material Costs ......................................................................................................................................................52
5.10.3 Machine Energy Consumption .............................................................................................................................55
5.10.4 Labor Costs...........................................................................................................................................................56
5.11 FLEXIBILITY......................................................................................................................................................................56
5.11.1 Product Flexibility.................................................................................................................................................56
5.11.2 Mix Flexibility .......................................................................................................................................................57
5.11.3 Volume Flexibility .................................................................................................................................................58
5.12 Dependability ..........................................................................................................................................................59
5.13 BUILDING-BLOCK 3: STRATEGIC PURPOSE & PARADIGMATIC APPROPRIATENESS...........................................................................60
5.13.1 Aerospace.............................................................................................................................................................60
5.13.2 Automotive...........................................................................................................................................................61
5.13.3 Medical.................................................................................................................................................................61
5.13.4 Consumer Goods ..................................................................................................................................................61
6.0 ANALYSIS & DISCUSSION OF FINDINGS ......................................................................................................................62
6.1 BUILDING-BLOCK 2: PROCESS TECHNOLOGY PERFORMANCE & CAPABILITY ANALYSIS ......................................................................62
6.1.1 Quality....................................................................................................................................................................62
6.1.2 Speed/Time ............................................................................................................................................................64
6.1.3 Cost Efficiency Performance...................................................................................................................................64
6.1.4 Flexibility Performance...........................................................................................................................................67
6.1.5 Dependability Performance....................................................................................................................................69
6.1.6 Summary of Results for Building-block 2: Analysis.................................................................................................70
6.1.7 Concluding Implications from BB-2 Analysis ..........................................................................................................71
6.2 BUILDING BLOCK 3 – STRATEGIC PURPOSE & PARADIGMATIC APPROPRIATENESS............................................................................71
6.2.1 Analysis of Manufacturing System Classification of AM........................................................................................72
6.2.2 Analysis of Appropriate Manufacturing & Business Strategic “fit”........................................................................72
6.2.3 Concluding Implications from building-block 3 – Analysis .....................................................................................74
6.3 BUILDING-BLOCK 4: DISCUSSION ON IMPLICATIONS FOR AM-ADAPTED SUPPLY CHAINS..........................................75
6.3.1 The Re-distribution of Manufacturing....................................................................................................................75
6.3.2 Performance Implications for Re-distributing the Geographical Landscape of Manufacturing.............................76
6.3.3 Impact on Cost Performance..................................................................................................................................77
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6.4 Supply Chain Design & Configuration Strategies ......................................................................................................78
6.5 Concluding implications from BB4-Discussion...........................................................................................................79
7.0 CONCLUSIONS............................................................................................................................................................80
7.1 ADDITIONAL CONSIDERATIONS & FUTURE RESEARCH PROPOSALS ................................................................................................83
7.2 CLOSING REMARKS .............................................................................................................................................................84
8.0 LIST OF REFERENCES ..................................................................................................................................................85
9.0 TABLE OF APPENDICES...............................................................................................................................................98
Table of Figures
Figure 1 – Conceptual research framework of the thesis. Source: Own construction........................................... 13
Figure 2 – Strategic ambition and operational reality. Source: own creation, Inspired by Paton et al, 2011, p.380.
............................................................................................................................................................................... 15
Figure 3 – Generic manufacturing Strategies. Own creation, adapted from Kim & Lee, 1993 p. 9. ..................... 19
Figure 4 – A synthesized strategic framework: a conceptual representation. Source: Own creation, adapted from
Kotha & Orne, 1989, p. 225. .................................................................................................................................. 21
Figure 5 – Typology of Production systems & practical examples- Own creation, adapted from Kim & Lee, 1993,
p. 6-7...................................................................................................................................................................... 22
Figure 6 – Differences between deductive and inductive reasoning in research. Own creation, inspired by
Bryman & Bell, 2015, p. 23.................................................................................................................................... 28
Figure 7 – Dynamics of the research process. Source: Own creation, adapted and configured from Dubois &
Gadde, 2002, p. 555............................................................................................................................................... 30
Figure 8 – Value of the AM/3DP market worldwide from 2011-2014. Own creation, adapted from Wohlers
Associates (2013, 2015)......................................................................................................................................... 41
Figure 9 – 3D Printing Industry value chain. Source: Own creation, inspired by Business insider 2012, Marketline
2013, and Frost & .................................................................................................................................................. 42
Figure 10 – Technology adoption Life Cycle. Own creation, adapted from Rogers 1995, and Moore 2007, – as
illustrated in Mellor, 2015, p. 20. .......................................................................................................................... 43
Figure 11 – Hype cycle for emerging technologies. Source: Gartner Inc., 2015.................................................... 44
Figure 12 – Quality Control Measurement Procedures for AM. Source: Own creation, adapted from Mani et al,
2015, p. 3............................................................................................................................................................... 46
Figure 13 – Cost model comparison. Source: Own creation, adapted from Hopkinson & Dickens, 2003, p. 38. .. 51
Figure 14a – The energy breakdown comparison for IM and SLS fabricated paintball handle. Source Telenko &
Seepersad, 2012, p. 477......................................................................................................................................... 55
Figure 14b – The energy breakdown comparison for IM and SLS fabricated paintball handle including mold
production. Source Telenko & Seepersad, 2012, p. 477. ....................................................................................... 55
Figure 15 – The relationships between costs vs. complexity for AM vs. TM. Source: Own creation, adapted from
Conner et al., 2014, p. 71....................................................................................................................................... 57
Figure 16a – Shop floor process flow for AM systems. Source: Own creation, adapted from Lee, 2013.............. 57
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Figure 16b – Shop floor process flow for AM systems. Source: Own creation, adapted from Lee, 2013.............. 57
Figure 17a - Output volume comparison AM vs. PIM. Source: Atzeni et al., 2010, p. 315.................................... 58
Figure 17b - Output volume comparison AM vs.HPDC. Source: Atzeni & Salmi 2012, p.1154.............................. 58
Figure 18 - AM industrial application based on AM service provider industry revenue generation in 2014. Source:
own creation, adapted from Wohlers, 2014. ........................................................................................................ 60
Figure 19 – Radar diagram of contemporary in performance capabilities between AM vs. TM. Source: Own
creation.................................................................................................................................................................. 70
Figure 20a – Analysis of AM typological system classification. Source: Based on the proposed Typology by Kim &
Lee, 1993................................................................................................................................................................ 72
Figure 20b – Analysis of AM typological system classification compared with other systems. Source: Based on
the proposed typology by Kim & Lee, 1993........................................................................................................... 72
Figure 21 – Different strategic scenarios for AM in between different industries. Source: Own creation,
conceptual typologies adopted from Kim & Lee, 1993 and Porter 1980............................................................... 73
Figure 22 – Contemporary & future manufacturing & business strategic scenario for AM. Source: Own creation,
conceptual typologies adopted from Kim & Lee, 1993 and Porter 1980............................................................... 74
Figure 23a – Digital supply chain scenario. Source: Own creation, adapted from Lee, 2013. .............................. 76
Figure 23b – Conventional Supply Chain Scenario. Source: Own creation, adapted from Lee, 2013.................... 76
Figure 24a – Lead times for AM scenario. Source: Mashhddi et al., 2015, p 8. .................................................... 76
Figure 24b – Lead times for TM scenario. Source: Mashhddi et al., 2015, p 8...................................................... 76
Figure 25 - Net benefit comparison of traditional vs. digital manufacturing supply chain. Own creation, adapted
from Lee, 2013....................................................................................................................................................... 77
Figure 26 – New supply chain pipeline selection paradigm. Source: Own creation, a revised version of the
original by Christopher et al., 2006, p. 9. .............................................................................................................. 78
Figure 27 - Strategic roadmap for AM purpose boundaries. Source: own creation, inspired and adapted from
Kotha & Orne (1989).............................................................................................................................................. 79
Figure 28 – Updated conceptual framework including key determinants for each building block. Source: Own
creation.................................................................................................................................................................. 80
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1.0 Introduction
1.1 Background
In the wake of the emerging global challenges addressed by keynote speaker Tom Goldsby at the 5th
Copenhagen
SCM summit in 2013, there were several key takeaways that inspired this thesis to explore sustainable operational
solutions that shapes the future of manufacturing operations and supply chain management (SCM).
Among the many challenges that increasing pressures from macro trends, represented by major socio-economic
forces and developments such as continued population growth and migration, rising economies’ growing buying
power, resource scarcity, and environmental climate change pose in shaping the future global business
environment, underline the fundamental need for manufacturing process innovation – defined as the steps and
activities in the improvement of the concurrent organizational processes and practices (Patton et al., 2011).
In the quest of meeting these challenges, the ever-increasing importance of technological innovation such as
integrated IT-solutions in daily operations cannot be underestimated as the emergence of computer-aided-
manufacturing (CAM), Enterprise Resource Planning(ERP) systems, Radio Frequency Identification(RFID) chips, just
to mention a few, are testaments to the revolutionary impact on the manner in which organizations manage their
operations to cope with the emerging macro-challenges. In that sense, the enabling impact of technology has
become a strategically important weapon that ensures continuous flow of value creation for its customers, and
further aid organizations to meet their goals (Ibid).
One can therefore argue that successful employment of operational innovation in manufacturing organizations
(MO) should be considered a key success factor in meeting these challenges in a sustainable and competitive
manner (SCM Summit, 2013). In addition, companies that adapt and grow through innovation, will therefore be
winners in a future paradigm characterized by enduring structural shifts that will set the business agenda for the
foreseeable future (Bain, 2011 p. 3).
1.1.2 Introduction to 3D Printing & Additive Manufacturing
3D-Printing (3DP) or Additive Manufacturing (AM) holds many of the key features of a manufacturing process-
technology-innovation that may possibly even trigger a paradigm innovation that has the potential to facilitate a
new supply chain strategies, and even: (…) change how companies frame what they do (Patton et al., 2011).
Ever since being introduced as the influx to the third industrial revolution back in 2012 (The Economist, 2012), the
publicity surrounding3D printing(3DP), Additive Manufacturing(AM), RapidPrototyping(RP), RapidManufacturing
(RM) or Stereolithography (SL) – as it initially was namedby the founder of the first RP technology, Charles Hull who
filed his patent in 1984 and later went on to found what is now considered the market leading systems provider,
3D Systems in 1986 – has sky-rocketed, leadingto anexplosioninthe fieldof researchonthe technology’s potential
in terms of industrial application and on the future implications it may have on - and within multiple areas of
production and manufacturing industries (Price, 2012).
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Regardless of what can be described as quantum leaps in 3DP/AM technological advancements the last decade,
there have been conflicting opinions among scholars and industry experts with regards to the maturity rate of AM,
and more importantly its viability and readiness to be applied in direct manufacturing of finished goods (Berman,
2012).
1.2 Problem Discussion
The following problem discussion is structured into sequential sub-sections of contemporary issues andchallenges
that have motivated this thesis. Each sub-section constitutes a natural phenomenological order inwhichthe causal
relationship between them is discussed. First, contemporary macro-trends will be addressed and discussed (the
macro-external phenomenological problem), followed by the implications these macro-trends pose for
manufacturing, operations and supply chains of organizations (the internal phenomenological problem), before the
discussion will end with a brief introduction to the proposed implications of the thesis topic of industrial additive
manufacturing (the potential solution).
1.2.1 Developments in Macro-trends
As the global economy has accelerated during the last couple of decades, creatinga global economic system where
globalization – understood as and characterized by Holton (2005) as the : (1) rising exchange of people, goods,
information, values and habits, (2) an increased degree of interdependence between national and regional
economies, and (3) a rising awareness that the world is made up of a closed system with a certain amount of
resources in which needs divided in a sustainable way to avoid risks of wide-scale consequences (as notedinHesse
& Rodrigue, 2006 p.1) – has been the key drivers for the development of a globally integrated marketplace. There
are particularly three macro-trend developments that pose challenges for the manner in which organizations
employ and utilize their pool of resources.
1.2.1.1 Globalization and global population growth
There is a general consensus that global population growth will pose a major challenge for the global community in
the near future. According to the most recent development report on forecasted population growthby the United
Nations (appendix 1), the world population reached 7.3 billion in 2015, in which imply a growth of 1 billion people
only during the last 12 years. As most populationgrowthwill come from developingandemergingeconomies (Bain,
2011), new market opportunities will rise as increased buying power among the worlds most populated emerging
economies.
1.2.1.2 Urbanization & growing middle-class in emerging economies
Urbanization and expanding prosperity in emerging economies will see leading industries shift their focus from
advanced markets to developing ones (Bain, 2011; McKinsey, 2013). As most consumption occurs in cities
(Rodrigue, 2012), traditional geopolitical and economic powerhouses such as London and New York will see
themselves surpassed in terms of consumption, as buying power in emerging and developing economies will
represent a larger share of global consumption (appendix 1). Simultaneous population and urbanizationgrowthin
developing markets such Brazil and the MINT countries; Mexico, Indonesia, Nigeria and Turkey in which according
to Goldman Sachs’ head of economic research Jim O’neill will have the potential economic and demographic
landscape to shape and further dominate the future of consumption (Business insider, 2013).
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1.2.1.3 Resource Scarcity & Environmental Change
Resource scarcity and environmental change has become a major challenge for the global community, with
potential developments such as scarcity of essential natural resources vital to the very existence of life becominga
real threat. Consequently, external legislative pressures from international political institutions such as new long
term oriented environmental sustainability initiatives exemplified by the Obama administrations “Clean Power
Plan” that aims to reduce carbon emissions from U.S. power plants to 32% below the registered 2005 levels by the
year 2030 (epa.gov, 2015), may very well mark the beginning of an era where political institutions actively
participates in an effort of increased focus on global resource scarcity and environmental sustainability, as fossil
fuels still remains the primary energy source inthe highest final energy-consumingtransport sector (Kojima & Ryan,
2010), the necessity for innovative solutions can not be underestimated.
1.2.2 Challenges in the Current Manufacturing Operations & Supply Chain Paradigm
The above-mentioned Macro-trend developments all pose major challenges for international operations
management and global supply chains defined as: “a minimum of 3 supply chain actors dispersed in different
locations of the world, involved in sequential value creation activities” (Mentzer, et.al, 2001; Stabell & Fjeldstadt,
1998, as cited in slides by Aseem Kinra in; managing global supply chains course).
Due to the rapid integration of global markets enabled by the internet eras partial erasing of cultural borders for
goods and services, evidential patterns and trends suggest an increased disintegration of production and
manufacturing, meaninga phenomenoninwhichwhere organizations findit beneficial to outsource non-core value
adding activities resulting incross-border physical separationof different parts of the productionprocess (Feenstra,
1998, Arndt & Kierzkowski, 2001).For instance, predicted forecasts on productionand manufacturing outsourcing
suggests that by the year 2020, 80 % of all goods in the world will be manufactured in a country different from
where they are consumed, compared with 20 % in the year the report was presented (Balou, 2007).
Thus, by adding up these macro-trends, a lot of indicators suggesting that manufacturers are now faced with a
paradox when trying to balance traditional location-specific advantages such as the access to cheaper factors of
production (Dunning, 2001; Guisinger, 2001) found for instance in Asia, with the ability to meet demand and
flexibility requirements for new product introductions in a time-to-market sense, that satisfy the ever-increasing
time-sensitive customers as for instance found in the fashion and apparel industry (Christopher et.al, 2006). Doing
all of this, while simultaneously satisfying what is considered every supply chains objective; profit maximization
(Manuj & Mentzer, 2008), leave MO´s in a situation where trade-offs in focus is becoming an increasingly difficult
task.
1.2.2.1 The Manufacturing & Operational Challenge
Paton et al (2011) propose the following external factors as drivers of innovation that enhance internal efficiency:
1. Increased competition: in cost and quality due to increasedglobal competition, posingchallenges for ever-
increasing standards of performance requirements
2. Increasing complexity: through the dynamics of customer pull – meaning increased demand for
differentiated, customized and functional products and technology push – meaning the ability of R&D to
continuously create more sophisticated products that conforms to the customer pull.
10

3. Increased focus on sustainability:suchas environmental issues, labor exploitation, tighter political policies
related to factory pollution and carbon emission fee programs impacting the transport economy.
Putting the external factors into the equation, underlines the importance of the MO´s ability to design processes
that: “produce the greatest output for the least input” (Paton et al., 2011 p. 4).
1.2.2.2 The Industrial Emergence of Additive Manufacturing
The driving characteristics of 3DP/AM that have spurred enthusiasm among industry experts as the most exciting
disruptive process technology (PT) in manufacturing, is derived from its ability to provide almost unconstrained
design freedom for the user. The computerized technology software applied in 3DP/AM known as 3-dimensional
computer-aided design (3D-CAD) is only limited by physical attributes of materials available for the printer system,
allowing almost unconstrained geometrical flexibility (Gibson et al., 2010; Holmström et al., 2010).
Regardless of the fascinating future prospects of 3DP/AM as a method of Direct Digital Manufacturing(DDM) may
hold, the existing consensus among researchers and industry experts alike, is that AM is not yet ready for mass
adoption due to the technology’s inability to support high-volume production of end-use products (AM Platform,
2014). That being claimed, industry reports indicate that companies are already exploring the technology to a
greater extent than what is being expressed (Deloitte, 2015). For instance, it is arguedthat by 2019, 10% of discrete
manufacturers will apply 3DP/AM in their part-manufacturing operations (Gartner, 2015).
1.3 Thesis Purpose
Based on the above discussion, the purpose of this thesis to provide a preliminary decision-making tool for
managers that are exploring the potential impact, implications anddisruptive force embeddedinthe emergence of
industrial AM. The manner, in which the author goes about this task, is throughidentificationandaddressingof key
adoption determinants that stimulate the decision process. The thesis further has both academic and practical
implications through the four following focus points:
(1) Technology & market overview: Identification of challenges and opportunities of AM through an
exploratory market analysis.
(2) Technology performance & capabilities: Identification of the contemporary performance capabilities of
AMT on a conversion process basis.
(3) Strategic focus & paradigmatic fit: Identification of current purpose areas and strategic fit withregards to
the following levels of strategic influence: (1) manufacturing operations, (2) general business, and supply
chain strategic orientations.
(4) Implications for operations & supply chains: Identificationof managerial implications and impact that AM
may have on operations and supply chain performance.
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1.4 Research Gap & Thesis Structure
The research gap of this thesis is defined by the author’s acknowledgement of the importance of systemsthinking
in managerial decision-making and operations management, characterized by a holistic approach to process-
oriented optimization of internal cross-functional business processes. Thus, the research gap is guided by
communication of the potential impact of AM on system properties, as it is assumed that provision of better
understanding of process improvement is integral to operational efficiency as: “all business processes and
operations are systems” (Paton et al., 2011, p. 171).
This implies that the research gap and structure of the thesis follows a reversed sequential order to that found in
the problem discussion, simultaneously recognizing the importance of the resource-based view (RBV) originally
proposed by Penrose (1959). According to the RBV, everything that happens inside the organization is just as
significant as what is happening on the outside (Paton et al., 2011). Thus, the logical vantage point of the thesis is
clarification and assessment of AM characteristics, features and capabilities, referred to in this context as the
manufacturing & operational solution – involving optimal utilization of organizational resources.
The internal phenomenological consequence is two-folded. First, this thesis explore the degree to which internal
capabilities, organizational processes, and firm attributes may be influenced by AM adoption. Second, it aims to
identify which manufacturing environments that are more supportive of the adoption-decision.
Finally, the external phenomenological consequence look at how the general disruptive characteristics of AM may
influence the manner in which organizations manage their supply chain activities.
1.5 Delimitations
Drawing on the notion by Voss (1986, 1988), this thesis distinguishbetweenadoptiondecisions andimplementation
of industrial systems. While adoption decisions is more strategically oriented and concerned with mapping of the
impact of that an adoption decision may have on current operational processes, the implementation process is
more concerned with: “early usage activities immediately followingthe decisiontoadopt aninnovationandending
when the use of innovation becomes routine practice” (Meyers et al., 1999, p.297).
This thesis is dedicated to explore the former.
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2.0 Research Framework
This thesis takes a multi-dimensional holistic approach to answering of the following driving research question:
“What are the most important considerations and key determinants for industrial AM adoption?”
2.1 Conceptual Research Framework
In order to answer the driving research question, a thorough multi-disciplinary research proposal is necessary to
explore potential capabilities and impact of AM. This section presents the conceptual framework in which the
research departures from.The framework consists of four sequential “building blocks” (BB), in whichserves as the
guiding structure in answering the main research question. These four blocks further provide a categorical
visualization of the main constituent areas of the conducted research.
Figure 1 – Conceptual research framework of the thesis. Source: Own construction.
The proposed multi-dimensional framework illustrated above involve both internal and external decision-
influencing factors, requiring utilization of trans-disciplinary data sources, empiric evidence from existingresearch
and peer-reviewed literature from an extensive body of academic journals. The following building block are
considered most important to explore when considering adoption of AMT´s.
2.1.1 Block 1: Market Outlook & Technology Readiness
In order to make strategic adoption decisions with regards to choice of PT, it is considered important to gain
preliminary understanding of the technology´s maturity and readiness through a careful exploratory assessment
and monitoring of technology – and market developments. This sectiontherefore takes anexploratory approachin
order to identify patter of opportunities and challenges noted by academics, practitioners andindustry specialists.
Main Research Question: What are
the the most important considerations
and key determinants for industrial AM
adoption?
Building-block 1:
Technology
&Market Overview
Building-block 2:
Process-Technology
Performance &
Process Capabilities
Building-block 3:
Strategic Purpose
& Paradigmatic
Appropriateness
Building-block
4:
Operations &
Supply Chain
Implications
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In addition, the investigation of historical and topical growth indicators will be looked at to assess the predicted
future outlook for the industry.
2.1.2 Building Block 2: Technology Features & Performance Capabilities
In line with the proposition by Kim & Lee (1993), it is assumed that identification of distinguishingcharacteristics of
manufacturing technologies is of high importance in the adoption decision process. Hence, identifying these
characteristics, features and performance capabilities for AM, is considered a key determinant for adoption. It is
further proposed that that an analysis of a new PT’s potential value or worth can be identifiedthrough: “exploring,
understanding and describing the strategic consequences of adopting alternatives” (Slack & Lewis 2011, p. 201).
Therefore, an investigation of the ability of AM to conform to the same processing standards set by traditional
manufacturing technologies (TMT) on a conversion process level is considered an important constituent for AM
adoption.
2.1.3 Building Block 3: AM Strategic Purpose & Paradigmatic Appropriateness
As operations can be described as: “the engine that drives the business” (Paton et al., 2011, p. 53), in which where
operations strategy constitutes the long-term guidelines, the PT’s fit withoverall business - andoperations strategy
is considered an important determinant in the adoption-decision process. Hence, the knowledge andinsight found
in BB-1 and BB-2 with regards to AM´s distinguishing features, characteristics, technology readiness and
conversion-process performance capabilities, will coupledwithobservations oncontemporary areas of application
to determine the strategic conditions under which AM adoption is most viable.
2.1.4 Building Block 4 Discussion: Operations & Supply Chain Performance
It is assumed that the potential positive impact on operations and supply chain performance constitutes the
ultimate key adoption determinant of a new manufacturing system. Hence, this section investigates the extent to
which AM may impact the current paradigm of supply chain strategy and performance.
2.2 Choice of Theory
According to Strauss & Corbin (1990) the researcher’s choice of theoretical frameworks applied in the respective
research project, are dependent on the nature and purpose of the research. For instance, in confirmatory studies,
literature may involve conceptual frameworks, in which aid in identification of “important variables, suggest
relationships among them, and direct interpretation of findings” (Dubois & Gadde, 2002, p. 559). Conversely,
theory-generating studies aim to discover new dimensions of a phenomenon. Hence, existing theory must be
viewed under scrutiny of the novel findings. Strauss & Corbin(1990) claim that the manner, inwhichthe researcher
approaches the latter, is less constrained.
As theoretical frameworks is supposedto drive the answeringof the proposedresearchquestion(s), the preliminary
literary framework for this thesis draws upon well-established concepts derived from operations, manufacturing
and process-technology strategy. The most important contributors within each area are illustrated in table 1.
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Theoretical Concepts Authors
General business strategies Porter (1980, 1985)
Manufacturing operations
strategies
Woodward (1958, 1965), Skinner (1969), Hayes & Weelwright (1984), Kotha & Orne (1989),
Platts & Gregory (1992), Miller & Roth (1994)
Operations Strategy Paton et al (2011), Slack & Lewis (2011)
Operations performance
dimensions
Paton et al (2011), Slack & Lewis (2011)
Table 1 – Overview of driving contributions & authors from the literature review. Source: Own creation.
3.0 Literature Review
This section of the thesis includes the introduction of different theoretical concepts driving the thesis. In addition,
the literature presented forms the basis for each of the identified BB in the conceptual framework foundinsection
2.1. Each BB – with the exception of BB-1, in which is restricted to explorationanddescription– has its ownspecific
theoretical literature that holds relevance in answering of the main-research question.
3.1 General Business Strategy vs. Operations Strategy
Figure 2 – Strategic ambition and operational reality. Source: own creation, Inspired by Paton et al, 2011, p.380.
Paton et al (2011) defines operations management (OM) as: “the activity of managing the resources of the
organization that delivers goods and services”. Further, Hayes (2005) argues that efficient OM is followed by an
effective operations strategy andmust support corporate strategy inorder to contribute to competitive advantage.
There are three generally acknowledged levels in which strategies are formulated within an organization. These
include: (1) the corporate level – in which where strategic guiding principles of value creation, unique to the
individual organization is defined (2) business level – in which where strategic goals and objectives related to the
competition basis of a specific business unit within its specific industry is defined, and (3) function level – in which
relates to how individual functions such as operations, marketing, finance, etc. creates a plan of action (Barnes,
2008; Paton et al, 2011).
(1) Corporate:
Top management
Mission
Values
Policies
Competitive basis
Vision
(2) Corporate:
Success Criteria
Goals
Strategies
(3) Business
unit:
Strategic
conditions
(4) Business unit:
Mission
Policies specific to
divisions
Competition basis
Vision (5) Function:
Objectives
Success criteria
Strategies
Projects
(6) Function:
Operational action plan
Financial plan
15

3.2 Manufacturing Strategy
Operations strategy is concernedwithsuccessful management of resource capabilities that meets the requirements
of the market, and the manner in which organizations deploys and utilizes its resources, is further dependent onits
ability to: “successfully pursue specific performance objectives” characterized as: “(…) criterions against which to
evaluate the performance of operations”(Barnes, 2008). However, as operations take many shapes, the nature in
which the resource capabilities that are being managed, deployed and utilized varies a lot depending on which
perspective that is employed (Swink & Hegarty, 1998). This section therefore seeks to identify the relevant
concepts of manufacturing strategy.
3.2.1 Scope and Definitions
Voss (2005) argues that literature on manufacturing operations and related strategic decisions that provide
guidelines for exceptional performance can be separated into “three distinct, but, related paradigms of
manufacturing strategy” (Voss, 2005, p.1216), including: (1) competing through manufacturing, (2) strategic
choices in manufacturing, and (3) best practices. An important distinction is that all these paradigms all have
different approaches to manufacturing strategy content, rather than the process itself.
For instance, Hill (1993) argues that competition through manufacturing is done through focus on what he defines
as order winning criteria in the specific market, including: price, delivery, quality, product designandvariety. Platts
& Gregory (1992) also provide an external approach, stating that market requirements such as delivery lead time,
reliability, product features, quality, design flexibility, output volume and price are the vantage point in which
organizations should develop their internal capabilities.
Conversely, elements of the strategic choice paradigm was first discussed in the pioneering work by Woodward
(1965) and Skinner (1969), but has later been further developed into what is considered the dominant model in
manufacturing strategy by Hayes & Wheelwright (1984). The strategic choice paradigm buildthe process structure
based on internal resource capabilities such as plant and equipment, production planning and control, labor and
staffing, product design and engineering, and organizationandmanagement (Skinner 1969; Voss; 2005). The most
recent of the three paradigms, is the best practices paradigm derived from the Japanese car-making industry. Its
emphasis is focused on world-class manufacturing throughbenchmarkingof best industry practices andcontinuous
improvement through process re-engineering (Voss, 2005).
3.2.2 The Strategic Choice Paradigm
According to Voss (2005), the strategic choice paradigm carries the highest potential for the pursuingMO. Givenits
contingency-based approaches, as for instance embeddedinHill (1993) – inwhichemphasize that process choice is
based on the interdependent requirements between market strategy, and the order winning criteria that nurture
internal and external consistency.
However the differences and commonalities between the paradigms exist. The key similarity is that all emphasize
that exceptional performance is achieved differently for each individual organization, as they all have different
prerequisites in terms of external market conditions, and internal resource availability.
16

3.2.2.1 Competitive Priorities
Hayes & Wheelwright (1984) propose two constituents of manufacturing strategy, competitive priorities and
decision categories. Competitive priorities consider manufacturing strategy from the market-based view (Porter,
1980). This view interprets manufacturing operations as an adjustable system that can be tailored to meet
customer values and expectations, and relates to the idea that all companies at least compete on the basis of one
out of the four following priorities: quality, lead-time, cost or flexibility, each priority satisfyingdifferent dimension
of market requirements. These competitive priorities are all derived from manufacturing operations, and are still
valid today, although more contemporary literature has included more dimensions. Table 2 illustrate the originally
proposed competitive priorities
Competitive priorities Description
Quality Manufacture of products with high quality and performance
standards
Delivery Reliable (on time) and fast (short delivery lead time) delivery
of products
Cost Production and distribution of the product at low cost
Flexibility Ability to handle volume and product mix changes
Table 2 - General competitive priorities. Source: Hayes & Wheelwright, 1984; Rudberg & Olhager, 2003
3.2.2.2 Decision Categories
The second constituent, involve eight decision categories that was further defined and divided into structural
and infrastructural categories by Rudberg & Olhager (2003), with four categories in each sub-category found in
table 3..
Decision categories Policy areas
Structural:
• Process choice
• Facilities
• Capacity
• Vertical integration
Process choice, technology, integration
Size, location, focus
Amount, timing, increments
Direction, extent, balance
Infrastructural:
• Manufacturing planning
and control
• Performance
measurement
• Organization
• Quality
System design, decision support
Measurements, methods of measure
Human resources, design
Definition, role, tools
Table 3 - Decision categories and related policy areas. Source: Hayes & Wheelwright, 1984; Rudberg & Olhager, 2003.
Decision categories consider manufacturing strategy from the approach of the resource-based view (RBV). In that
sense, the RBV is an integral part of manufacturing strategy. Hayes & Pisano (1994) further claim that decisions
made with regards to structural and infrastructural decision categories, impact the manner in whichorganizations
utilize their resources and dictate what types of practices the organizationchoose to employ. Tanet al (2007) refers
to these operating characteristics as manufacturing capabilities.
17

3.2.2.3 Manufacturing Capabilities
According to Größler & Grübner (2006), the two aforementioned constituents of manufacturingstrategy makes up
what is defined as the manufacturing capabilities. Hallgren (2007) elaborates on this notion and states that
manufacturing capabilities relates to “the set of practices inuse” withinthe manufacturingoperation.Inadditionto
decisional alignment between the set of practices in use such those related to choice PT, Voss (1986) argue that
market specific, order winning criteria, is key to maximize organizational performance.
Although traditional manufacturing literature often assess the achieved level of operational performance and
utilization of organizational capabilities through the lens of the four Performance objectives (PO) derived from
competitive priorities discussed above, the current body of literature is inconsistent in terms of how many, and
further which manufacturing capabilities that should be given most attention. For instance, Ferdows & de Meyer
(1990) proposed in their “sand-cone model” that all performance is rooted in the ability to provide exceptional
quality. Furthermore, Größler & Grübner (2006) has suggested that these four are interrelated, and that an
organizations ability to pursue exceptional delivery performance is dependent on its ability to achieve equally
outstanding performance in quality, and so on. Table 4 illustrate those capabilities in which Größler & Grübner
(2006) suggest to be most important
Manufacturing capability Description
Quality:
Manufacturing conformance
Product quality and reliability
Delivery:
Delivery speed
Delivery reliability
Manufacturing lead time
Flexibility:
Volume flexibility
Mix flexibility
Cost:
Labor productivity
Inventory turnover
Capacity utilization
The ability to produce in accordance with specification and
without error
The ability to deliver products quickly, and in accordance with
customer requirements
The ability to change volume, manufacturing processing time,
product mix and to innovate through new product introduction
The ability to produce at low cost
Table 4 - Manufacturing capabilities and related dimensions. Source Größler & Grübner, 2006; Barnes, 2008.
3.3 Manufacturing Strategy Typologies
Muchcontemporary work onbusiness andmanufacturingstrategy departures from the dominant business strategy
paradigm proposed by Porter (1980; 1985) in which where he suggest three generic approaches to competitive
strategy that place organizations competitive priorities into a strategic context.
3.3.1 Typologies of Generic Business Strategy & Manufacturing Strategy
Miller & Roth (1994) provideda generic taxonomy for manufacturingstrategies. Throughdevelopment of capability
profiles based on competitive capabilities identified inthe work of skinner (1969) andHayes & Wheelwright (1984),
they pooled manufacturers into three strategic categories, including: (1) caretakers – are predominantly focusedon
low prices as the dominant capability, followed by time-based competitive capabilities, (2) marketeers – seek to
obtain broad distribution and offer broad product lines and to be responsive to changing volume requirements.
18

Priorities withinthe marketeer cluster were conformance quality, dependable deliveries, andproduct performance.
The Innovators – place highest emphasis on differentiation through uniqueness in designandrate of new products
introduced. Price is not considered an important variable in this category.
3.3.2 The Relationship between Generic Business Strategy &Manufacturing Strategy
Although the generic taxonomy by Miller & Roth (1994) provides a simple classification of manufacturingstrategic
orientations, based on well-known characteristic focus of performance (e.g. cost as a competitive priority), it does
not consider the importance of alignment between structural and infrastructural decision categories with generic
business strategy. For instance, Swamidass & Newell (1987) first proposed two dimensions of generic
manufacturing strategy, namely cost efficiency – and differentiation.
These two dimensions has later been the focal point for multiple authors in the development of conceptual
taxonomies that describe typologies of generic manufacturingstrategic approaches. As notedinthe sectionabove,
PO’s such as cost, quality, flexibility and delivery have dominated much of the literature involving competitive
dimensions (Kim & Lee, 1993). However, the 2x2 taxonomy proposed by Kim and Lee (1993), illustrated in fig.3
below, provide an adapted typology that place generic business strategies into the context of manufacturing. The
taxonomy is further based on the three generic approaches to business strategy proposed by Porter (1980, 1985),
including: (1) cost leadership, (2) differentiation, and (3) focus.
Figure 3 – Generic manufacturing Strategies. Own creation, adapted from Kim & Lee, 1993 p. 9.
(1) Cost leadership strategy: is first and foremost aimed towards optimizing the output volumes of non-discrete
products. Pursuant MO´s may thereby gain competitive edge in industries where scale of production is
symbolic for increased relative market share (Kotha & Orne, 1989). Two types of cost leadership is further
recognized by Porter (1980), namely; industry-wide and segment-oriented.
(2) Differentiation strategy: is associated with the goal of differentiation through for instance appealing to
customer expectations of high variations in the product mix, superior product quality, and/or short leadtimes
(Kim & lee, 1993). According to Kotha & Orne (1989), the strategic target is usually more segmenteddue to the
more customized and discrete nature of products offered. Porter (1980) emphasizes that industry-wide and
segment-oriented approach is also viable for differentiation strategies.
(3) Cost & differentiation strategy: was previously not conceivedto holdany potential strategic advantage (Kim &
Lee, 1993). However, as integration between process technologies andIT-solutions suchas CAM, CAD andFMS
have paved way for mixing cost anddifferentiationstrategy at corporate level, but also betweenbusiness units.
Pure
Differentiation
High Cost &
Differentiation
No Intended
Strategy
Pure Cost
Leadership
Low
HighLow Cost Efficiency
Differentiation
19

In more recent times, anadditional fourthstrategic orientation has openedupnew business opportunities for MO’s
(Paton et al., 2011). First proposed and popularized by Pine (1993), mass-customization is a relatively new
manufacturing strategic approach, in which first and foremost has been driven by rapid advances in machine
technology enabling increased personalization of products. In contrast to focusing on production of high volume,
standardized products as found in mass-production, where manufacturers are leveraging on the concept of
economies of scale, mass customization is more focused on achieving economies of scope, the economic concept
that place emphasis on “high variety of outputs from a single process” (Paton et al., 2011 p. 149).
3.3.3 Primary Dimensions & Underlying Variables of Manufacturing Strategy
From an internal organizational perspective, it is further assumedthat primary dimensions andunderlyingvariables
of the manufacturing structure typology proposed by Kotha & Orne (1989), dictates the appropriate external
strategic focus of the organization.
The authors propose a manufacturing structure framework composedof three structural constituents illustratedin
table 5. When combined with Porters typology of generic business strategy (Porter 1980; 1985), and/or the
adapted model by Kim & Lee (1993), form a conceptual synthesis. In addition, the synthesis distinguish itself in the
way that it in addition to the traditionally proposed dimensions: (1) process structure complexity, and (2) product
line complexity, employs a third perspective (3) organizational scope, in which takes factors related to the
geographic scope of operational activities into consideration.
Primary
dimension
Underlying Variable Dimension of underlying variable
Process
Structure
Complexity
Level of mechanization (1) Manual, (2) Machine, (3) Fixed program, (4) Programmable
Level of systemization (1) Data collection, Event reporting, (3) Tracking, (4) Monitoring, (5) Guide, (6) Control
Level of interconnection (1) Discontinuities, (2) Technological interdependences, (3) Operational flexibility
Product Line
Complexity
End product complexity (1) High product line complexity (complexity/variety/volume/maturity)
(2) Medium product line complexity (complexity/variety/volume/maturity)
(3) Low product line complexity (complexity/variety/volume/maturity)
Variety of final product
Individual product volumes
End-product maturity
Organization
al Scope
Geographic manufacturing
scope
(1) High organizational scope (global)
(2) Medium – high organizational scope (multinational)
(3) Medium organizational scope (national)
(4) Medium – low (regional)
(5) Low organizational scope (undefined)
Geographic market focus
Vertical integration
Customer – market scope
Scale
Table 5 - Manufacturing structure typology: primary dimensions and underlying variable. Source: Own creation, adapted from Kotha
& Orne, 1989.
3.3.3.1 Process structure complexities
Process structure complexity, is concerned with the capabilities of the process flow, and include elements suchas:
(1) the level of mechanization, meaning the degree to which a process is able to repeatedly perform tasks with a
satisfying degree of automation; (2) level of systemization, meaning the level to which platform interdependent
integration between direct technology applied in the conversionprocess andindirect process technologies process
technologies such as ERP systems, that facilitates high connectivity exists (Paton et al., 2011; Slack & Lewis, 2011);
20

and (3) the level of interconnection, meaning the degree to which the process flow on the shop floors are
characterized by fluency or discontinuity.
3.3.3.2 Product line complexities
Product line complexity is concerned with bridging internal resource capabilities and external markets
requirements. Elements in this dimension include: (1) end product complexity, (2) variety of final products (3)
individual product volumes, and (4) end-product maturity.
3.3.3.3 Organizational Scope
As a dimension, the organizational scope of the organization includes additional elements not traditionally foundin
other strategic typologies (Kotha & Orne, 1989). For instance, it includes structural characteristics missing in the
first two, such as considerations with regards to location-specific advantages related to easier access to factor s of
production (e.g. raw materials and labor), and factors involving proximity to the marketplace, supplier and
distribution networks.
3.3.3.4 Conceptual Synthesis
The conceptual synthesis is built on two primary assumptions. First, it is assumed that MO’s in which pursue
cost leadership, place considerable emphasis on reducing the costs associated with each step in the process
flow. Hence, the related manufacturing structure characteristics are suggested to be “low product line
complexity and high process structure complexity” (Kotha & Orne, 1989, p. 225). Conversely, the second
assumption suggests that that MO’s pursuing differentiation strategy “tend to have more complex product lines
and more discontinuities in the process structure” (Kotha & Orne, 1989, p. 225). Between these two
assumptions, the author identifies eight different manufacturing structures.
(1) Segment, neither cost nor differentiation
strategy
(2) Segment, differentiation strategy
(3) Segment, cost leadership strategy
(4) Segment, mixed strategy
(5) Industry-wide, mixed strategy
(6) Industry-wide, differentiation strategy
(7) Industry-wide cost leadership strategy
(8) Industry-wide, cost and differentiation
strategy
Figure 4 – A synthesized strategic framework: a conceptual
representation. Source: Own creation, adapted from Kotha & Orne,
1989, p. 225.
It is further noted by the authors, that: “within the context of the manufacturing structure framework, BU’s can
generate structural uniqueness – through generic strategies – by moving to the corners labeled 2, 3, 6 and 7”
(Kotha & Orne, 1989, p. 225).
7
5 6
8
3 4
21
Low
High
HighLow
Low
High
Process Structure
Complexity
Product Line
Complexity
Organizational
Scope
21

3.4 Accommodating Typologies for Process-Technology Strategy in Manufacturing
There is a general consensus among scholars that different types of process technologies thrive under different
strategic conditions (Kim & Lee, 1993). Hence, in the case of PT adoption decisions, MO´s shouldfirst andforemost
place emphasis on its operational “fit” with strategic perspective of the organization, and related guidelines found
in overall operations strategy (Slack & Lewis, 2011).
3.4.1 Technical Complexity & Technical Flexibility
One of the more recognized typologies for manufacturing system classification is that of Kim & Lee (1993). The
typology demonstrates a direct link between MO’s applied production system and manufacturing strategy. The
typology further propose two independent dimensions of process technologies and manufacturing systems, in
which recognize structural decision categories with regards to a PT’s fit with overall strategic orientation.
3.4.1.1Technical Complexity
The first dimension, technical complexity is defined as the: “complexity of the process technology”, and is further
composed of three characteristic elements, including: (1) the level of mechanization, (2) the level of predictability
and (3) the level of systematization, all somewhat corresponding with the elements included in process structure
complexity proposed by Kotha & Orne (1989).
3.4.1.2 Technical Flexibility
The second dimension, technical flexibility includes several variables including machine-, process-, product-, and
volume-flexibility. These elements are more related to product line complexity (Ibid), but also incorporate what is
considered “the most prevalent word in the manufacturing lexicon today”, flexibility (Kim & Lee, 1993, p. 6), noted
to be a key facilitating concept in mass-customization strategy and the achievement of economies of scope (Paton
et al., 2011).
3.4.1.3 Systems & Accommodating Order Types
Figure 5 – Typology of Production systems & practical examples- Own creation, adapted from Kim & Lee, 1993, p. 6-7.
Technical
Flexibility
Technical Complexity
High
High
Low
Low
Intermittent
system
Degenerate
system
Continuous
system
Concurrent
system
Technical
Flexibilit
High
High
Low
Low
Job Shop
Batch FMC
FMS
Anachronistic
factory Assembly
Line
Flexible
Assembly
Line
Transfer
LineContinuous
Flow Process
22

The figures above illustrate what Kim & Lee (1993) proposes to be four classifications of manufacturing and
production systems, and the corresponding operational process flow set-ups. The same authors elaborate that
certain system types more appropriately accommodates specific manufacturingstrategies. Patonet al (2011) notes
that certain customer order types are more appropriate for different manufacturing processing systems. Table 6
contains the key characteristics of, and relationships between type of system and accommodating orders.
Type of
manufacturing system
Characteristics Type of customer orders Characteristics
Continuous systems High volume capabilities
High levels of automation
High task specificity
High set-up costs
Make to stock (MTS) Standardized products
Made for inventory
Low customization
Forecast driven
Intermittent systems Low volume capabilities
Low task specificity
Low set-up costs
High process flexibility
High customization
Make to Order (MTO) Highly customized products
Complex products
Low levels of inventory
Concurrent systems Predefined medium lot volumes
Highly IT integrated
High levels of automation
medium task specificity
Assemble to order (ATO) “Hybrid” order type
Modular Inventory
Medium/high customization
Degenerate systems Outdated system without any
specific characteristics
N/a N/a
Table 6 – Key characteristics and relationship between system and order types. Source: Own creation, adapted from Kim & Lee, 1993
and Paton et al., 2011.
3.5 Manufacturing & Operations Performance
There is a strong link between an organizations strategic focus – understood in this context as: “the guiding
principle that differentiates one company from another” – and performance management, defined as the:
“systematic measuring, monitoringanddecision-makinggearedtowards fulfillingorganizational objectives through
operations management” (Paton et al., 2011, p. 377). However, it is noted by the same authors that because:
“measuring performance provide little value in itself”, it is the manner in which the organization selects and reacts
to performance data that aid in the creation of a future plan of action that contribute to the final value-creation.
3.5.1 Generic Performance Objectives
Contemporary performance management frameworks such as that of Slack & Lewis (2011) illustrated in table 7,
takes a generic approach of viewing operational performance based on the following five PO’s: (1) quality, (2)
speed, (3) cost, (4) dependability and, (5) flexibility.
23

Operations resources
Internal benefits include…
Performance
objective
Market requirements
External benefits include…
Error-free processes
Less disruption and complexity
More internal reliability
Lower processing costs
Quality Higher specification products
Error-free products
Reliable products
Faster throughput times
Less queuing and/or inventory
Lower overheads
Speed Short delivery queuing times
Fast response requests
Higher confidence in the operation
Fewer contingencies needed
More internal stability
Dependability On-time delivery of products
Knowledge of delivery times
Better response to unpredicted events
Better response to variety of activities
Flexibility Frequent new products
Wide range of products
Volume adjustment
Delivery adjustment
Productive processes
Higher margins
Cost Low prices
Table 7 - Internal and external benefits of excelling at each performance objective. Source: Slack & Lewis, 2011, p. 53.
What differentiates this framework from other is that it – in addition to enable establishment of relationships
between internal performance of processes and external response from the market, thereby recognizing the
relative importance of both the MBV and the RBV – also consider the fact that: “not all measures of performance
will have equal importance for an individual operation.” (Slack & Lewis, 2011, p. 66).
3.5.1.1 Quality Performance
Quality is relatively difficult to grasp as it involves many facets, is relatively subjective in nature, and is dependent
on the perceived interpretation and the contextual setting it relates to (Paton et al., 2011). Hence, several
philosophical approaches has been developed in literature, some building the central notion of quality around
customer expectations (Deming, 1986, Juran, 1988), while others have stated that product quality is a result of
exceptional management practices and high internal coordination (Crosby, 1979, Feigenbaum, 1986).
However, the most recognized conceptual classification of quality, is that of Garvin (1987), in which proposed 8
different perspectives of quality including: (1) performance, (2) features, (3) reliability, (4) conformance, (5)
durability, (6) serviceability, (7) aesthetics and, (8) perceived quality. For operations suchas manufacturing, it is the
conformance perspective of quality, understood as; “the degree to which a product’s design and operating
characteristics meet established standards” (As noted in Paton et al., 2011, p. 429), that is considered the most
important.
3.5.1.2 Speed Performance
In simple terms, speed refers to the operations ability to optimize the time-spanbetweenthe initial customer order
and the final delivery of the product or service (Slack & Lewis, 2011). In that sense speed has both internal as well
as external performance implications. From an external customer perspective the process already starts withwhat
is known as the enquiry decision time, in which relates to point when the decision to acquire a new product or
service is made. This step is followed up by the enquiry lead-time – in which is where the customer gathers
information about the ability of a provider to meet the specifications of the product they intendto purchase. As the
24

order is placed, it is what is generally understood as lead-time that unifies both perspectives that is important
(Paton et al., 2011).
From an internal operations resource perspective, speed relate to every fragmented step after the order is placed.
These steps include the service waiting time, where designing of the product in accordance with customer
expectations occurs, or the core processing time – meaning the time it takes to process the input materials to
output products, to the actual time from the product is shipped until delivery, referred to as the installation time
(Slack & Lewis, 2011).
3.5.1.3 Dependability Performance
Dependability is the other constituent of total delivery performance together with speed, in which indicate that its
contribution to performance are mainly focusedaroundsatisfyingexternal customer expectations by providingon-
time delivery of products (Slack & Lewis, 2011). The relative importance of dependability performance can be
argued to vary depending on the manufacturing strategy or competitive position taken by the individual
organization (Hallgren, 2007). For instance, anorganizationthat pursue pure cost leadershipstrategy – where order
types are typically made on a MTS basis – would most likely not relate to dependability as an important PO, as
delivery is instant and without customer involvement, while those pursuing an MTO strategy would place
considerable emphasis on high dependability performance.
3.5.1.4 Flexibility Performance
In an operational context, the definition of flexibility may relate to either an operations’ ability to: “adopt different
states – take up different positions or do different things”, or describe: “an operation that moves quickly, smoothly
and cheaply from doing one thing to doing another” (Slack & Lewis, 2011 p. 50)
The authors further distinguish betweenfour categories of flexibility, including: (1) product flexibility – the extent to
which an operation has the ability to introduce new products or modify existingones, (2) mix flexibility – the extent
to which an operation has the ability to change the variety offered within a given time period, (3) volume flexibility
the extent to which an operation has the ability to change the output of an operation quickly and, (4) delivery
flexibility – the extent to which an operation has the ability to change planned and assumed delivery dates,
respectively. According to Hallgren (2007), flexibility distinguishes itself, as it measures potential rather than
achieved performance.
3.5.1.5 Cost Performance
Cost performance – meaning the ability to produce at low costs, is widely acknowledged in academic literature as
the most significant PO, especially for those who compete onprice (Slack & Lewis, 2011). Inanoperational context,
Slack & Lewis propose a broad definition of cost to be: “any financial input to the operation that enables it to
produce its products andservices”. They further distinguishbetweenthree different dimensions or classifications of
costs. These include: (1) Operating expenditure – financial inputs needed to fund the operation (e.g. labor,
materials, rent and energy consumption), (2) Capital expenditure – relates to the financial inputs neededto acquire
the necessary equipment used in the transformationprocess (e.g. facilities, systems andprocessingmachinery), (3)
working capital – relates to the financial inputs needed to support the timeframe difference between cash inflow-
outflow from outgoing operating expenditures and received product payment.
25

4.0 Methodology
This chapter presents the framework of methodological approach aiming to aid the step-by-step maneuvering in
the answering of the main research question. In order to this, this chapter will define the guidingstructure indoing
so by elaborating on decisions made by the author with regards to philosophical paradigm, research design,
research approach, methodology for collection of data, and instruments applied in the collection of the data.
4.1 The Nature of Business Research
First, the research approach in this thesis is acknowledged by the importance of achieving evidence-based
management, understood as: “the systematic use of the best available evidence toimprove management practice”
(Reay et al., 2009, as cited in Bryman & Bell, 2015, p. 8).
The authors further emphasize a combination of four different information sources in which constitutes and
contributes to evidence-based management
• Practitioner expertise and judgement
• Evidence from the local context
• Critical evaluation of the best available research evidence
• Perspectives of those who may be affected by the decisions (Briner, Denyer, and Rousseau, 2009)
Gibbons et al., (2004) suggest that the production of scientific knowledge has two different approaches, or
“modes”. The first mode builds on the assumption that: “all knowledge production is driven primarily by an
academic agenda”(As cited in Bryman & Bell, 2015, p. 9), meaning that new knowledge should primarily take
departure from established concepts found in an existing knowledge base. The secondmode, focus more ontrans-
disciplinarity, meaning that in order to provide a holistic understanding of the research topic under scrutiny, the
problem at hand has to be viewed through a multi-disciplinary lens, as the productionof knowledge is assumednot
to be “confined to academic institutions” (Bryman & Bell, 2015, p. 9). According to Tranfield & Starkey (1998),
business research tends to be better suited for “mode 2”, as it involves several knowledge creators, including:
academics, policy-makers and practitioners. This thesis aim to reconcile bothmodes, as the evolutionof the fieldof
research holds different maturity stages from a technology perspective and business/operations management
perspective, calling for multiple approaches to answering of the research question at hand.
4.2 Philosophical Paradigms & Constituents of Scientific Research
In business research, the researcher often encounters several questions related to the: “basic belief system or
worldview that guides the investigator” in the creation of knowledge or interpretation of social phenomena
(Guba & Lincoln, 1994, p. 105). The following section address differences between philosophical paradigms,
and their business research relevance.
4.2.1 Philosophical Paradigms in Research
Guba & Lincoln (1994) suggest are three influencing elements that define a researcher´s belief system. The so-
called philosophical basis of the researcher is further determined by his/her approach to the following three
questions:
26

• Ontology: – meaning, “form and nature of reality and, therefore, what is there that can be known about it?”
• Epistemology: – meaning, “the relationship between the knower or would-be knower and what can be
known?”
• Methodology: – meaning, “How the inquirer (would-be knower) go about finding out whatever he or she
believes can be known?” (Guba & Lincoln, 1994, p.108).
Crotty (1998) proposed that the design of a research proposal should departure from a set of predefined and
interrelated elements, dictating the course of the research at hand. These elements include: epistemic position to
knowledge (objectivism or subjectivism, etc.), philosophical stance (positivism, interpretivist, etc.), and
methodological practices or instruments (interviews, conceptual modeling, content analysis, etc.) applied in the
process (as noted in Creswell, 2003, p. 4-5).
These guiding elements can according to Creswell (2003) further be translated into three sequential questions is
also referred to as elements of inquiry and include three interrelated inquiries (knowledge claims, strategies, and
methodology) that dictate the course of the researcher project (Ibid). The relationship between the elements and
their relative importance with regards to structuring of the research design framework are discussed in the
following sections.
4.2.2 Knowledge claims
The concept of knowledge claims is rooted in the predetermined set of the researcher´s assumptions with regards
to ontology, epistemology and methodology, embedded in the approach to learning aspect as well as the creation
of knowledge through the research project (Ibid). Traditional knowledge claims or so-calledparadigms inresearch,
include, post-positivism, critical theory, constructivism, and advocacy/participatory (Guba & Lincoln, 1994;
Creswell, 2003). In more recent times, an additional paradigm called pragmatism has been given increasing
attention in business, as well as social science research, domains that traditionally has been dominated by
positivistic and interpretivist approaches (Orlikowski & Baroudi, 1991). Table 8 includes some of the most relevant
knowledge claims, relevant paradigms and methodological approaches in business research.
Knowledge claim Ontology Epistemology Methodology
Post-positivism Conjectural Desired objectivity, however, the relationship between the
researcher and research problem indicate subjectivity
Quantitative
Constructivism Relativistic Subjectivity, as knowledge is created through the relationship
between the researcher and what is being researched.
Qualitative
Pragmatism Situational Consequence oriented, problem centered and pluralistic Mixed methods
Table 8 - The relationship between different philosophical paradigms and respective knowledge claims. Own creation, inspired by
Creswell, 2003.
4.2.3 Research Strategies
The approach taken by the researcher with regards to research strategy defined as: “the general plan of how the
researcher will go about answering the research questions” (Saunders et al., 2009, p. 600), is to a great extent
dependent on the knowledge claims and related philosophical perspectives of the researcher (Creswell, 2003).
Fundamentally, there are two different strategic research approaches, quantitative andqualitative research(Ibid).
27

4.2.3.1 Qualitative vs. Quantitative Research
As noted by Silverman (1997), the two methodologies in question were developed under two very different
approaches to ontology and epistemology, and therefore represent two distinct worldviews or paradigms
(Ibid). Creswell (2003, p.13) notes that quantitative strategies of inquiry such as experimental strategies and
surveys, constitute those in which: “invoked the post-positivistic perspectives”, and further influence the
researcher’s ability to view the world through an objective lens (Guba &Lincoln, 1994). Conversely, the same
authors suggest that qualitative approaches to research strategy such as ethnographical, phenomenological
research, and case studies, tend to fit better under constructivist paradigm assumptions of the researchers
knowledge claims are more subjective in nature.
4.2.3.2 Inductive vs. Deductive Reasoning
In cognitive science, the choices made by the researcher with regards to employment of a qualitative or
quantitative strategy approach, has additional implications for the nature of the relationship between theory and
research findings (e.g., data, observations, etc.) (Bryman & Bell (2011). Whether the researcher employs an
inductive or a deductive approach is traditionally recognized views of reasoninginresearch. Adeductive reasoning
paradigm aims for generalizationof particular observations throughdemonstrationof causal relationships between
theory and the research findings (Gulati, 2009). The relationship between theory and research findings through
deductive reasoning is characterized by testingof hypotheses that is guidedby establishedtheoretical assumptions
(Bryman & Bell, 2015). In addition, deductive reasoning is associated with positivistic/post-positivistic knowledge
claims, as observations are tested to the extent it is true or not (Creswell, 2003). Conversely, inductive reasoningis
associated with a constructivist/interpretivist worldview, and further: “involves the search for pattern from
observation and the development of explanations – theories – for those patterns through series of hypotheses”
(Bernard, 2011, p.7). Figure 6 provide a simple illustration of the different vantage points behind the two
approaches.
Figure 6 – Differences between deductive and inductive reasoning in research. Own creation, inspired by Bryman & Bell, 2015, p. 23.
4.3 Thesis Knowledge Claims
This thesis employs a pragmatic research approach. Pragmatism is especially applicable in IT and technology
research, as it is not only concerned with what is found in positivistic and interpretivist views to be the important
questions with regards to ontology of “what is”, but possess a orientation to knowledge about the world as to
“what might become” through: “explorationintosocial andtechnical potentials andopportunities” (Goldkuhl, 2012,
p.87). Three types of pragmatism can further be identified; functional pragmatism – meaning, knowledge for
actions, referential pragmatism – meaning, knowledge about actions, andmethodological pragmatism – meaning,
knowledge through actions (Ibid). As noted, this thesis adopts what (Goldkuhl, 2012) refer to as general functional
pragmatism, a sub-category of functional pragmatism that aim towards realization of widespread abstract
Theory Research Findings
TheoryResearch Findings
Deductive reasoning
Inductive reasoning
28

knowledge creation that not only provide value for a specific scientific community, but benefits cross-disciplinary
practitioners.
4.3.1 Thesis’ Pragmatic Approach to Ontology
The relationships between technology and social world has been a widely discussed as a complicated subject
among scholars (Lawson, 2007). With regards to the pragmatic ontological position, the thesis recognizes the
proposition that: “the essence of society lies in an ongoing process of action– not inapositedstructure of relations”
(Blumer, 1969, p. 71). This imply that explanation of abstract relationships between the artefact (AM) and social
reality are not driving progress, but is rather the basis from which where plans of action that encourage and
supports continuous improvement.
4.3.2 Thesis’ Pragmatic Approach to Epistemology
As discussed by Goldkuhl (2012), knowledge of “what is”, is found more important to positivists andinterpretivists
as it is limited to description, explanation and understanding. These elements are also of vital importance in this
thesis, as they are embedded in the author´s communication of the new and abstract knowledge, created for the
reader. However, they are only pillars in which potential solutions to a “world to-be” are built on. A “world to-be”,
in which the manager/practitioner, may or may not find favorable based on their interpretation of the
communicated knowledge.
Hence, the pragmatic epistemic foundation of this thesis can be characterized by its purpose of contributing a
possible/desirable solution to a practical problem (Ibid) through demonstration of evidence from reality that
supports the described, explained and understood.This canbe exemplifiedby the different types of knowledge that
each building-block attempts to create, illustrated in table 9.
Building-block Description of pragmatic epistemology
BB-1 Deviate from pure pragmatic knowledge creation, as its purpose is more in line with the
constructivist/interpretivist viewofunderstandingandexplainingcurrentphenomenological
relationship between the technological artefact (AM), and its relationship to the socially
constructed reality.
BB-2 Takes a combined approach recognized by Goldkuhl (2012) as the creation of evaluative
knowledge – meaning, diagnostic judgements through grounded reasoning, and attributive
knowledge – meaning, characterization of properties related to an object. The evaluative
knowledge creation in this thesis being the technology capability and performance
assessment of AM compared with TM methodologies, while the attributive knowledge
creation is part of the diagnostic assessment, as neither are mutually exclusive.
BB-3 Aim for the development of explanatory knowledge, meaning establishment of cause-to-
effect relationships of the implicationsofthe formertypesof communicated knowledgewith
regards to the object / technological artefact (AM). This logic derives from the assumption
that technology features, characteristics and performance assessment provide an
explanatory basis from which the strategic purpose boundaries of AM can be determined.
Table 9 – Overview of epistemic approaches, and knowledge contributions for each respective “building-block”. Source: Own
creation, Inspired by Goldkuhl, 2012.
29

4.3.3.3 Thesis´ Pragmatic Approach to Methodology
As noted, the purpose of this thesis is to provide a preliminary decision-making tool for managers when exploring
the potential impact andimplications of AM onmanufacturingandoperational performance. This further requires a
multi-dimensional methodological approach. Hence, this thesis does not employ specific methods in providing
solutions to the different constituents of adoption determinants identified in the conceptual framework. Instead
the author acknowledges and adopts the concept of the abductive research approach, in which where the
researcher is allowed to move “back and forth between framework, data sources, and analysis” (Dubois & Gadde,
2002, p. 556).
4.4 The Dynamic Research Process
Describing the dynamics between the inductive and deductive approach to research is a difficult task. As the
process is characterizedby back andforthmovement betweenthe conceptual framework, the conceptual literature
introduced in the literature review, and data/observations labeled “empirical world evidence”. Thus, a visual
simplification of the abductive research structure is provided in figure 7.
Figure 7 – Dynamics of the research process. Source: Own creation, adapted and configured from Dubois & Gadde, 2002, p. 555.
Conceptual
Literature
The empirical
world
Conceptual
Framework
Deductive
Inductive
Theoretical
frame of
reference
Theory:
1. Conceptual strategic
dimensions & generic
performance objectives
2.Manufacturing operations
and supply chain strategy
3.Manufacturing operations
and supply chain
performance objectives
Theory vs. Reality
Analysis:
1.AM technology´s fit with
process-level
performance dimensional
requirements
2.AM fit with
manufacturing operations
and business strategy
3.AM impact on
manufacturing,
operations and supply
chain Performance
Reality:
1.Technology characteristics
features, and performance
dimensional capabilities
from the empirical world
2.Current industrial
application and examples
of strategic purpose
boundaries
3.Manufacturing, operations
and supply chain
performance implications
Evidence
from reality
30

4.4.1 Conceptual Framework
The main constituent of the dynamic research process is the conceptual framework, in which is built onfour pillars
(building-blocks) of key adoption determinants. With the exception of BB-1, each block is dependent on a
theoretical frame of reference in whichinterpret the evidence from reality. Hence, the abductive researchapproach
is vital to the process as the author constantly moves back and forth between the theoretical frame of reference
and the evidence from reality. The conceptual framework cantherefore be understoodto set the boundaries at the
very beginning for what to be researched as well as provide a bridge between the two domains.
4.4.2 Conceptual Literature – Theoretical Frame of Reference vs. the Empirical World – Evidence
from Reality
The theoretical frame of reference consists of scientific concepts and theoretical frameworks that guides the data,
observations and general findings of observed reality to fit into a relatable theoretical context. It is assumedby the
author that a the quantitative data and qualitative findings with regards to AM’s performance capabilities on a
conversion process level (BB-2) determines the relevant literature with regards to the appropriate dimensions of
strategic fit (BB-3), in which again determines the relevant literature of operational performance and strategic
influence found in (BB-4).
The logic that process-technology characteristics is followed by related strategic purpose areas is thereby an
integral part of the proposed back and forth moving dynamics between literature and observations by Dubois &
Gadde (2002).
4.4.3 Matching
Reconciling theory and reality through what is known as matching, takes place when the researcher is: “taking
advantage not only of the systemic character of the empirical world, but also of the systemic character of
theoretical models” (Dubois & Gadde, 2002). Doing so is considered difficult through pure inductive or deductive
reasoning (Kakkuri-Knuuttila et al., 2008).
Hence, an abductive approach allows the researcher to modify the theoretical body as new insights are gained
throughout the study. The research is therefore structured so that already established theoretical frameworks in
terms of strategic focus, and related performance metrics constitutes the vantage point from which where data,
observations and findings are analyzed. However, as the research process unfolds, the strategic dimensions and
performance related elements is subject to revision in accordance with those identified as more appropriately
describing the empirical character of AM.
4.4.4 Direction & Redirection
In simple terms, the process of direction and re-direction of a study include utilizing multiple sources in which:
“allow the investigator to address a broader range of historical, attitudinal, and behavioral issues” (Yin, 1994, p.
92). It is further noted by Dubois & Gadde (2002) that directing and re-directing the study is important inachieving
matching between theory and reality.
The sequential order of the four building-blocks identified in the conceptual framework is in that respect the basis
from which triangulation between different domains of knowledge is made possible. While BB-1 is primarily
31

focused towards commercial sources, BB-2 place these non-scientific findings into a scientific perspective from
which the empirical findings on current application in BB-3, and operations & supply chain performance and
strategy in BB-4 can be scientifically justified. Hence, multiple sources are utilized and constantly revisitedinorder
achieve matching between the domains of theory and evidence from reality.
4.5 Research Approaches & Related Research Strategies
Synonymous with a pragmatic/abductive combination is the employment of mixed methods methodological
approach involving the simultaneous gatheringof bothquantitative andqualitative data inorder to best answer the
research problem at hand (Creswell, 2003). The followingsectionelaborates onthe building-block specific research
approaches and related research strategies.
4.5.1. Building-block 1 – Market Outlook & Technology Readiness
The research approach for BB-1 is purely exploratory anddescriptive, but involve gatheringof bothquantitative and
qualitative data. The purpose of the researcher is to obtain an overview of the 3DP/AM technology from an
external market perspective. The method used in gathering of qualitative andquantitative data, is througharchival
literature analysis that aim to track the relatively contemporary historical developments withregards to technology
life cycle and maturity assessment, public documents on government initiatives, forecasted development after
expiration of present patents, as well as potential challenges and opportunities. In addition, related development
for complementary markets such as markets for software, materials and service providers is also explored.
4.5.2 Building-block 2 – Technology Performance Capabilities
The research approach and related data collection process for BB-2 is narrower, and include both qualitative and
quantitative data. The quantitative data gatheredinBB-2 is mostly relatedto PT performance, and cantherefore be
argued to serve technology-qualifying purposes through a comparative assessment of AMT and TMT configured
processes on an industrial scale. The qualitative data is mostly extracted from scientific research and academic
journals and aim to place the quantitative data into an empirical context.
4.5.3 Building-block 3 – Strategic Focus & Paradigmatic Appropriateness
The approach taken in BB-3 distinguishes itself from BB-2 as it is more explanatory, and further aim for matching
between empirical observations of current AMT application, and theoretical concepts within strategy. Hence, it
takes both a deductive approach – as it utilize manufacturing theoretical concepts such as decision categories of
employed process technology, and typologies of relatedmanufacturingstrategies –, andaninductive approach–as
findings related to current industrial application benchmark the manufacturing strategic paradigm and related
industries in which adoption proves more appropriate.
4.5.4 Building-block 4 – Strategic and Performance Implications for Operations & Supply Chains
Although the research approach taken in BB-4 is discussion-based, it still follows a research strategy. The collected
secondary data on the proposedimpact AM may have onmanufacturingoperations andsupply chains is adaptedto
fit into a theoretical frame of reference involving established theorems within strategy and performance. Hence,
the inductive perspective involve a focal point of the researchthroughpredefinedtheoretical scope of what theory
says about strategy and performance, in which is challenged through the deductive approach by potentially
32

identified disruptions between theory and observations from the empirical world that may, or may not, re-direct
the theoretical frame of reference.
4.6 Data Sources & Data Collection Strategy
4.6.1 Data Sources – Primary vs. Secondary Data
The data collected for in this thesis are solely based on secondary data sources. This means that they are collected
by an unknown third-party, and not specifically collected for the purpose of the research at hand (Frankfort-
Nachmias & Nachmias, 1996). As a consequence, they may be less relevant, and not satisfy the requiredspecificity
of the research. However, it can also be argued that secondary data are: "mere substitutes for better, but more
expensive, primary data" (Cowton, 1998, p. 430). Hakim (1982) furtherargues that secondary data gatheredwithin
an approximate time-span of its intended purpose, may overcome problems of recall (as citedinHarris, 2001). This
thesis further includes both quantitative and qualitative data sources. The data sources usedas empirical evidence
from reality for this thesis is based on a multi-disciplinary approach.
4.6.2 Data Collection Strategy
The data collection strategy for this thesis unconventionally departures from the generic model for literature
studies by Sørensen (2004). Although its application is primarily limited to literature studies, the thesis author’s
choice of applying secondary data as basis for the evidence based data gathering from the empirical world– that is
later analyzed and reconciled with theoretical concepts – is used as rationale for keeping the strict line inselection
of proposed concepts and papers, that in turn secures the required validity of the research. The generic model of
how to conduct the literature search, is according to Sørensen (2004), intended to: “(a) frame the purpose under
scrutiny; (b) identify relevant concepts, methods/techniques andfacts, and; (c) positionthe study” (Sørensen, 2004;
Ghauri et al., 1995).
The author further proposes the following five steps:
(1) Definition of the domain or sources for the research reviewed – meaning defining the population.
(2) Definition of the selection criteria – meaning the process of identifying the key words, subjects and authors
used in selecting the literature.
(3) Definition of the relevance criteria – meaning the research literatures level of academic relevance or “fit”
that supports in answering of the research question.
(4) Definition of the validity criteria – meaning the degree of the methods appliedis inaccordance withthe type
of study performed
(5) Definition of the completeness of the contribution – in which can be measured based upon for instance
number of citations used in other academic research and related works.
As the research contains four different aspects of AM (building-blocks), all will have a different population of
sources and criteria related to selection, relevance and validity. Each building-block therefore has its own
explanatory description, accommodating each step of the generic model by Sørensen (2004).
33

4.6.2.1 The Domain of Sources
According to Sørensen (2004), the prerequisite for the literature search is to define the purpose of the thesis by
establishing the domain or the population of sources for the reviewed literature. As the driving purpose of this
thesis is answering of the following research question: “What are themost important considerations and key
determinants for industrial AM adoption?”, the four respective “building-blocks” eachrepresents animportant area
with its own domain of sources. Consequently, the domain of sources is pooled into subject categories rather than
relative to each BB. Hence, classifications of data and findings are mostly gathered from peer-reviewed articles
spanningareas suchas: productionandmanufacturing, material science, manufacturingengineering, organization–
and business management, operations, logistics and supply chain management. Besides from online-based
academic journals, additional sources include: books, magazines, web-based articles, Industry reports, consultant
reports, industry specialist web-pages, and online based statistical databases.
4.7.2.2 Selection & Relevance Criteria
It is assumed by the thesis author that the requirements for an appropriately defined approach in the selection of
data sources that meets the desired relevance is of highimportance whensecondary data sources are used.Hence,
a trusted source approach was taken where a preliminary body of focused data sources based on peer-review
citation criteria was selected as basis of the thesis. However, although the data search has primarily been focused
around a predetermined set of journal sources, it is impossible to ignore the important role that snowballing search
strategy – meaning backward investigation of related literature discussed in the preliminary articles of relevance
(Sørensen, 2004) – has had in the process of constant revision of the preliminary sources. As new insight was
gained throughout the study, additional complementary data sources enabled the author to always keep the
current format of the thesis, and what is understood as “truths” under scrutiny.
4.7.2.3 Literature Search Terms & Key Words
Relevant search terms using citation databases like Scopus, business source complete and Elsevier, statistical
databases such as Statista, and general journal searches through CBS lib-search was used in the search for
keywords. Table 10 below illustrates three different segments of search terms. The logical manner in which the
search terms were approached is that a primary search term was either exclusively search for, or paired together
with a secondary or tertiary search term.
Primary Search terms Secondary search terms Tertiary search terms
3D-printing
AMT
Additive manufacturing
Computer-aided design
Computer aided manufacturing
Computer Technology
Rapid prototyping
Rapid manufacturing
Selective laser sintering
Selective laser melting
Manufacturing technology
Industrial application
Material properties
Operations
Operations management
System Properties
Supply chain
Product(s)
Adoption
Complexity
Customization
Disruption
KPI’s
Performance (Quality, Speed, Cost,
Flexibility, Dependability)
Strategic impact
Strategic planning
Variety
Volume
Table 10 - Overview of literature search terms& key words in the data collection process. Source: Own, creation.
34

4.7.2.4 Validity & Completeness Criteria
In order to maintaining a high degree of research validity, most of the relevant sources of data are extracted from
acknowledged journals, symposium – and forum papers. Given the extensive efforts made by the author in the
preliminary phases of the thesis to gain in-depth understanding of the research topic, the above-mentioned
snowballing search strategy has familiarized the author with the most cited and fundamentally important
contributions within the different aspects of the technology. In that sense, the thesis author has been able to
maintain the primary source of the respectively cited contribution, a criterion has been followed to support a high
level of completeness.
4.7 Operationalization Tables & Methods of Analysis
4.7.1 Building Block 2 –Process Technology Performance & Operational Capabilities
The comparative performance capability assessment between two different processing paradigms (AM vs. TM)
departs from the competitor performance standards approachproposedby Slack et al (2004), as it is consideredthe
most appropriate.
The manner in which this section of analysis is approach is so that TM methodologies serve as the benchmark for
what is considered satisfying standards of process performance, and therefore aidinconfirmingand/or disproving
what findings in “BB-1 – technology & market overview” suggests to be with regards to AM technology viability. It
thereby serves as the qualifyingcriteria for AM, andconstitutes animportant element for the analytical approachin
BB-2-4 as the approach works well when mapping out strategic improvement (Slack et al., 2010).
4.7.1.1 Metrics
The metrics identified to support the radar diagram was selectedonthe basis of matchingthe theoretically defined
primary performance dimensions proposed by Slack & Lewis (2011), and various underlying variables from the
literature review, with data andobservations for those performance related dimensions foundthroughthe revision
of academic research literature on process technology performance, including: quality, speed, costs
(operationalized into cost efficiency), flexibility, and dependability.
4.7.1.2 Measurement Instrument
The 5-point measurement instrument for determining contemporary technology performance capabilities is
illustrated in table 11.The reference frame for categorization of scores ranges from 1-5 where the arithmetic mean
(average) is calculated to determine the overall value for each primary performance dimension (Base
metrics).However, as some performance dimensions are considered to carry more relevance than others, a
weighted average approach is taken.
35

Base metrics Dimensional Variables Weight
(%)
Calculation method Calculation
variables
Category range
(label)
Quality • Geometric properties 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨
=
𝟏𝟏
𝒏𝒏
(𝒂𝒂𝟏𝟏 + 𝒂𝒂𝟐𝟐+ … 𝒂𝒂𝒏𝒏)
1-5 points Very low, low,
satisfying, good
very good
• Mechanical properties
• Physical properties
Speed/Time • Cycle time/ build rates 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨
=
𝟏𝟏
𝒏𝒏
(𝒂𝒂𝟏𝟏 + 𝒂𝒂𝟐𝟐+ … 𝒂𝒂𝒏𝒏)
satisfying, good
very good
• Set-up & processing time
• Assembly time
Cost • Materials costs 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨
=
𝟏𝟏
𝒏𝒏
(𝒂𝒂𝟏𝟏 + 𝒂𝒂𝟐𝟐+ … 𝒂𝒂𝒏𝒏)
satisfying, good
very good
• Machine costs
oInvestment costs
oMachine processing
costs (pre, in, post-
processing)
oEnergy consumption
• Labor costs
oOperator costs
Flexibility • Product flexibility 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨
=
𝟏𝟏
𝒏𝒏
(𝒂𝒂𝟏𝟏 + 𝒂𝒂𝟐𝟐+ … 𝒂𝒂𝒏𝒏)
satisfying, good
very good
oCustomization
oComplexity
• Mix flexibility
oProcess flow flexibility
oVariety of products
• Volume flexibility
o Volume capacity
o Output flexibility
Dependability • Machine automation 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨
=
𝟏𝟏
𝒏𝒏
(𝒂𝒂𝟏𝟏 + 𝒂𝒂𝟐𝟐+⋯ 𝒂𝒂𝒏𝒏)
satisfying, good
very good
• Machine reliability
• Machine consistency
Table 11 - Operationalization table for analysis of process performance capabilities of AM vs. TM. Source: Own creation.
4.7.1.3 Scores & Ranges
The measurement instrument in the above section includes a point score system of 1-5 points with categorization
label ranges from: 1 – very low to 5 – very high. Table 12 provides a general definition of the points and
characteristics given to each label.
1 Point - Very
Low
2 Points - Low 3 Points -
Satisfying
4 Points - High 5 Points - Very high
The lowest possible
score suggesting a
disqualifying score
not acceptable for
adoption.
Not an optimal score,
however not
necessarily a
disqualifying score as
it is just inside the
“trade-off”
boundaries (Scores of
2,3,4 points).
A score that
equals a satisfying
degree of
performance for
most general
products.
A score that satisfies the
label “good” may imply a
“trade-off” between one
area of performance and
with another. (e.g. low
flexibility for high cost
efficiency).
A score of very good suggests
that some aspects of the
technology´s performance may
be leveraged as a competitive
weapon in the operational
activities of the MO and should
therefore be subject to further
implementation decisions.
Table 12 - General definitions and point score descriptions for AM vs. TM process performance capabilities. Source: Own creation.
36

The classification system used for determining the scores was considered a difficult task, as both quantitative and
qualitative data was found to support the different claims about the overall performance capabilities of AM. Hence,
the final scores for each primary performance dimension have to the extent deemednecessary by the author been
subject to intuitive reasoning and logical interpretation.
4.7.1.4 Score Significance
The score criteria and boundaries for AM technology adoption viability and qualification, is set so that “1-point” in
any of the performance dimensions automatically result in disqualification. Hence the red color tone in the table.
Further, if a score of “5-points” in any of the five areas of performance are achieved, the indication might be that
AM may potentially hold a significant competitive advantage for MO’s focusing onthat particular Performance for
their processes. In addition, the thesis takes into considerationthat anaverage overall score of 3.0 – points must be
achieved in order for AM to qualify as a viable substitute option to TM methodologies.
4.8 Building Block 3 –Process Technological Manufacturing and Business Strategic Mapping
Based on the categorically developed operationalization table (table 13) comprised of different theoretical
concepts from the literature review and pairing between them, the approach to the analysis BB-3 is two-folded. By
combining the evidence from the analysis with regards to PT performance capabilities in BB-2 analysis and
observations of current application in the researchfindings in BB-3, the analytical content andresearchfindings are
interpreted through the lens of the operationalization table to determine: (1) What type of system classification
typology AM fall in under, and (2) What type of manufacturing, and business strategic-conditions AM provide the
best fit with.
Manufacturing
strategic
orientations
Performance
focus
System type Process flow
structure
Supplementing concepts &
strategic characteristics
Technical
Complexity vs.
Technical Flexibility
Cost Leadership Cost, Speed Continuous
production
systems
Transfer
lines
Assembly
lines
Mass-Production (MTS)
Non-discrete products
High volume
High set-up costs
High Low
Differentiation
Innovators Strategy
Flexibility,
Quality
Intermittent
production
systems
Job shops
Batch
production
Customization
Discrete product
Low Volumes
Low High
Cost &
Differentiation
Strategy
Marketeers
Cost,
Flexibility,
Delivery
Speed
Depndability
Concurrent
production
systems
Assembly
lines
FMS
Mass-Customization
(MTO/ATO)
Modularization
Non-discrete/Discrete
Medium Volumes
High High
No intended
strategy
Cost Degenerate
production
systems
Anachronisti
c process
flow
structure
Not specified Low Low
Table 13 - Operationalization table of differences between strategic orientations, and “fit” with various manufacturing system types.
Source: Own creation, inspired by Porter, 1980, Kotha & Orne, 1989, Kim & Lee, 1993, Paton et al., 2011.
37

4.8.1.1 Manufacturing System Classification
The analysis of manufacturing system classification utilize the typology framework of Kim & Lee (1993) foundinthe
literature review to place AM into a conceptual context for easy interpretation and understanding. The typology
applies the two concepts; technical flexibility and technical complexity on the XY-axis, both in which will be
discussed in relation to the implications found in BB-2 on PT characteristics and performance capabilities.
4.8.1.2 The Appropriate Manufacturing Operations, and Business Strategic Fit for AM
When the system type classification is established, the secondstepof the analysis includes determiningthe process
technology’s fit with different generic manufacturing and business strategic orientations. Here, we draw on the
manufacturingstrategic frameworks proposedby Kotha & Orne (1989), typological manufacturingclassifications by
Miller & Roth (1994), and the generic business strategic orientations from the seminal work of Porter (1980). Based
on the observations on the current application and case examples from the empirical world, the analysis is able to
match the above-mentioned theoretical models with evidence from reality that depicts the current strategic
boundaries for AM.
4.8.1 Building Block 4 – Operations & Supply Chain Strategy and Performance Discussion
Given the additional complexity of – and difficulties in – defining many categories of variables in measuring of
performance implications of AM on an operations and supply chain scale, BB-4 is basedona discussionof potential
differences between operational and/or supply chain paradigms (AM-adapted paradigm vs. traditional
paradigm).The discussion will however be underpinned by theoretical concepts within the domains of operations
and supply chain strategy, as well as performance. In that sense, a red thread in the thesis methodology is
obtained.
4.9 Research Limitations
The author recognizes the most significant limitation of the thesis to be its breadth and dimensional diversity.This
may be exemplified by the “openness” of the main researchquestion, inwhichwhere in-depthresearchwithinone
or more areas or so-called “building blocks” have been compromised for the sake of providing preliminary broad
knowledge about many areas. In addition, the lacking collection and use of primary data is further considered a
limitation. Primary data is considered particularly important inOM research, as the rapidevolutioninOM practices
and managerial methods enabled by technological innovation(Lewis, 1998) require situational specific data to fully
capture the practical nature of the field of research.
4.10 Presentation of Data & Findings
The presentation of the research findings is distributed in accordance with the sequential order of the different
building blocks (1-3). However, some research findings may have sequential, cross-block application, meaning
that a causal link between what is for instance presented in BB-2 and BB-3 may exist, and therefore have cross-
block relevance.
38

5.0 Presentation of Data & Findings
5.1 Building-Block 1: Technology & Market Overview
Building-block 1 is three-folded. While the first section contains a process technology overview, the secondsection
focuses on the current state of the 3DP/AM industry. The third section investigates what researchers and industry
experts have written about the future industry outlook.
5.2 3DP/AM Process Technology Overview
5.2.1 The Nature of Additive Manufacturing
3DP/AM is an “additive layer manufacturing” (ALM) technique, in which “employs an additive manufacturing
process whereby products are built on a layer-by layer basis, through a series of cross-sectional slices (Berman,
2012, p. 155). In addition, AM systems are capable of fabricating three-dimensional components and finished
products from raw materials directly from designs developed in what is called three-dimensional computer-aided-
design (3D-CAD) software, prior to fabrication (Baumers et al., 2016).
Due to 3DP/AM’s unique additive rather than subtractive nature as for instance found in standard conventional
manufacturing processes such as injection molding – one of the most commonly used method for fabrication of
plastic parts (appendix 2a), and die casting – one of the most commonly applied subtractive process for metals
(appendix 2b), 3DP/AM has been recognized as a disruptive technology, a concept introduced and defined by
Christensen (1997) as a technology that possess a combination of characteristics that has the ability to create new
markets or change the dynamics of current ones. Appendix 3 outlines the generic AM fabrication process.
5.2.2 3DP/AM Technology Definitions
3DP/AM is not a new concept of process materials as it derives from the evolution of Rapid Prototyping (RP), the
name adopted in most industrial contexts. RP has been an important tool in research and development (R&D) for
new product development (NPD), as the PT’s initial purpose and area of application has mostly been focused
around its contribution to the design phase of new products, in order to increase the speed of prototyping before
final release or commercialization (Gibson et al., 2010).
5.2.3 Different Purposes of ALM Process Technologies
It is noted that AM is the: “formalized term for what used to be called rapid prototyping (RP) and what is popularly
called 3D Printing” (Gibson et al., 2010, p.1). This formalization is embedded in the term-standardization of the 7
different approaches to additive layer manufacturing(ALM) inwhichin2010 was bundledtogether under the wider
concept of Additive technologies (AMT), a formalization that is acknowledged through both the American Society
for Testing and Materials (ASTM), and the International Organization for Standardization (ISO under the joint
standardization code: ISO/ASTM 52901 (ISO, 2015). Traditionally AM has found three purpose areas including:
• Rapid Prototyping (RP): RP remains the primary area of application for ALM with80% of all parts are being
produced for RP purposes and ranging from assembly aids for engineers and visual aids for toolmakers, to
presentation models in marketing and architecture (Econolyst, 2014).
39

• Rapid Tooling (RT): RT is a method of RP in which bridges the purposes of RP into a manufacturingcontext
as it is commonly used for fabrication of highly customized industrial low volume equipment such as
injection molds, casts, forging and other tooling equipment (Groover, 2007).
• Additive Manufacturing (AM): The final frontier on its evolutionary path of applicatory potential, RP
through Direct Digital Manufacturing (DDM) (Barnatt, 2013) may facilitate a total restructuring of how
MO’s manage their operations.
5.3 System Classification for Different 3DP/AM Processes
Regardless of the fundamental additive vs. subtractive distinctions, similarities in the DNA between them such as
utilization of the same materials, and employment of computerized design software exists (Gibson, 2010).
Gibson et al (2010) propose the classification system developed by Pham (1998) in which includes a trifecta of
classifications, both the two aforementioned in addition to the different process they relate to. Gibson (2010)
identifies 7 different PT categories of 3DP/AM (See appendix 4 for detailed descriptions). Due to recent
technological advancements, an updatedversionbuildingonPham´s principal format of classificationis providedin
table 14. A comprehensive abbreviation table for materials and related processes is found in appendix 5.
AM Processes Direct Energy
Deposition
Powder Bed
Fusion (PBF)
Material
Extrusion
Material
Jetting
Binder
Jetting
Sheet
Lamination
Vat
Photopoly-
merization
Material
s
Plastic x x x x x x
Metal x x x x
Ceramic x x
Composite x x x
Others Wax,Photopol
ymer
Sand Paper Resin, liquid
photo-
polymer
Energy Source Laser, electron
beam
Laser, electron
beam/ion
beam
Heating coil Heating coil,
UV light
N/A Laser,
ultrasonic
UV light, X-ray
or Y-rays
Relevant
Terms
LENS, DMD,
LBMD, EBF,
DLF,LFF, LC, CMB,
IFF
SLS, SLM,
DMLS, DMP,
EBM, SPS,
Laser Cusing
FDM, FFF,
FLM
Inkjet, PolyJet,
MJM, Aerosol
Jet, ThermoJet
3DP, LPS,
DSPC
LOM, UC,
UAM
SL, SLA, MPSL,
DLP, FTI
Leading
Manufacturer
Optomec, DM3D
TRUMF,
Fraunhofer
3D Systems,
EOS, Concept
Laser, SLM
Solutions
3D Systems,
Stratasys
3D Systems,
Stratasys,
Solidiscape
3D
Systems,
Voxeljet,
ExOne
Mcor,
Cubic,
Fabrisonic
3D Systems,
EnvisionTEC,
RapidShare
Part
Durability
Detail
Precision
Surface
Roughness
Build Speed Slow Slow Medium Medium Fast Fast Medium
High Duarability Low
LowPresicionHigh
High Surface Roughness Low
40

Cost High High Low Low Medium Medium Medium
Support No Yes Yes Yes No No Yes
Post Process Yes Yes Minimum Minimum Yes Yes No
Table 14 – Comparison of AM technologies. Source: Own creation, adapted from DNVGL Strategic Research & Innovation, 2014, p.15.
5.3.1 Process Technologies for Industrial Application
Powder-bed-fusion (PBF) processes, such as selective laser sintering (SLS), selective laser melting (SLM) and direct
metal laser sintering (DMLS), respectively, has been given more attention than others with regards to industrial
application. Especially SLS process technologies have been singled out as having the highest cost efficiency
potential in terms of plastic based AM technologies (Cotteleer et al., 2014). Appendix 6 provides a more
comprehensive description of the relevant PBF processes.
5.3.2 Materials for Industrial Application
Industrial AM is currently limited by the number of materials compatible for application (Berman, 2012). As noted
by (Alpern, 2010) in an interview with a Stratasys VP: “the industry doesn’t have a catalogue of 20.000 materials
available today. Right now, I’d say it sits around 50” (Alpern, 2010, p. 47). However, as of more recent, new
materials such as ceramics and thermoplastics has expandedthe range of applications for the technology (Wohlers,
2015). According to Guo & Leu (2013), the currently most commonly applied powder materials in AM further
include: (1) polymers, (2) metals, (3) ceramics and (4) composites. A comprehensive description of PBF material
types is found in appendix 7.
5.4 Current State of 3DP/AM Industry and Market Overview
AM has been singled out as having the potential to be the biggest disruptive technological leap that has impacted
global industry since the assembly line was introduced in the early twentieth century (Manners-Bell & Lyon, 2014).
Due to rapid advancements in a wide variety of 3DP/AM techniques as a direct consequence of heavy investments
by private in current systems providers, the industry can be characterized by fierce competition throughout the
entire value chain, as all the market players are still trying to developthe dominant designthat will set the standard
for everyone else to follow (Wohlers, 2015).
5.4.1 Market Trends & Developments (Check for double statements in numbers)
The most recent Wohlers Report (2015) presents an overall industry growth of 35.2% (CAGR) in 2014 for both
industrial systems (AM) and desktop commercial printers (3DP). This estimation includes all AM products and
services on a global scale in what now constitutes a $ 4.103 billion industry.
Figure 8 – Value of the AM/3DP market worldwide from 2011-2014. Own creation, adapted from Wohlers Associates (2013, 2015).
1,71 2,2 3,033 4,103
10,8
0
5
10
15
2011 2012 2013 2014 2021
MarketValuein
billionU.Sdollars
41

Compared with the industry-wide growth from 2013, 2012, 2011 illustrated in figure 8, the industry has recorded
an increase of respectively 32.7%, 29.4% and 24.1%, a trend suggesting a relative, but steady increase. However, if
one rely on Wohlers (2015) forecasted CAGR of $ 10.8 billion by 2021, it is evident that the industry is picking up
pace in terms of growth. Further, in terms of overall growth in units sold pr. year, Deloitte (2015) predicts that
there will be sold nearly 220,000 3DP/AM units worldwide in 2015 and represent $ 1.6 billion of the total CAGR.
This represent an astonishing forecasted increase of 100% in unit growth, but “only” 80% growth in dollar value
compared with 2014, as low-cost commercial printers will represent most of the growth (Deloitte, 2015).
5.4.2 Public Initiatives facilitating Industry Growth
The interest in 3DP/AM has further beenprovento stretchfar beyondprivate investors, as governmental initiatives
such as the 2012 opening of the National Additive ManufacturingInnovationInstitute (NAMII) in Youngstown, Ohio
suggest governmental interest. Composed of over 40 large companies (including IBM, Boeing and Lockheed
Martin), 9 research universities (includingPennState University, Carnegie MellonUniversity andLehighUniversity),
and 11 non-profit organizations, the initially pledged $ 40 million by the U.S government, is only a puzzle in a
planned $ 1 billion overall investment in a national network of up to 15 similar manufacturinginnovationinstitutes
around the United States (Commerce.gov, 2014).
Further, the European Union (EU) and the European Commission (EC) has also specified initiatives related to the
future of AM under their biggest EU research and innovation program Horizon 2020, a € 80 billionfundedinitiative
from 2014 that aim to place the EU on the map as leaders in sustainable innovative developments. Recognizedby a
series of measures built on closer collaboration that bridges private and public sector interests, the future
competitive importance of AM is not only in focus on an organizational profit-making basis, it also exemplifies
optimism, in which where national and regional infrastructures buildtheir competitive platform around. (EC,2014).
5.4.3 3D Printing Industry Value Chain
The 3DP/AM industry value chain is comprised of 5 very broad, but fragmented categories, including: (1) material
providers, (2) design & software developers, systems manufacturers, service providers, and developer/user
communities (Business insider, 2012; Frost & Sullivan, 2014). Fig.10 provides a simple illustration of the related
segments, what is offered, and market actors. Appendix 8 includes more comprehensive descriptions of eachvalue
chain segment.
Figure 9 – 3D Printing Industry value chain. Source: Own creation, inspired by Business insider 2012, Marketline 2013, and Frost &
42

5.4.4 Additive Manufacturing – Technology Life Cycle Assessment
Regardless of the technological advances and industry growth experienced throughthe last decades, there are still
a lot of uncertainties regarding the technology´s ability to become a viable option as a disruptive manufacturing
paradigm. To present where the current pace of technology development, Wohlers (2013) draws onRogers’ (1995)
Technology Adoption Life Cycle, in which aim to pinpoint the technology life cycle maturity representedby a model-
curve for categorical classification of the market and its reaction to a high-tech innovation. The market,
represented by a normally distributed curve (figure 10), constitute the technology’s life cycle separated into five
classifications, namely: (1) innovators, (2) early adopters, (3) early majority, (4) late majority and (5) laggards.
Figure 10 – Technology adoption Life Cycle. Own creation, adapted from Rogers 1995, and Moore 2007, – as illustrated in Mellor,
2015, p. 20.
The author further argues that each segment has its own distinct set of needs, product criteria and reactions to
new innovations. Later, Moore (2007) added something that he called the chasm in which is a gap between early
adopters and early majority. According to Moore (2007), this stage in the life cycle is where most high-tech
products fail. The explanationgivenby Wohlers Associates (WA) (2013) for why AM adoptionis currently delayingis
due to the fact that there is yet to emerge a dominant design, characterizedas the standardtechnological platform
in which all improvements takes departure from until a new disruptive innovationtakes its place(Patonet al.,2011).
Terry Wohlers, CEO of WA explains that:
“Twenty years is roughly the span of one human generation and is often the time it takes for technology to fully
mature, according to futurist Joel Orr. AM is indeed mature for prototyping, but it is still in the “innovators”
phase for the production of parts for final products” (Wholers Report, 2013, as cited in Mellor, 2015, p. 20).
43

5.5 Future Industry Outlook
5.5.1 Technology Maturity Cycle
According to the annual technology hype-cycle developed by globally recognized Gartner Inc. – a leading
information technology research and advisory company – Enterprise 3D-printing and consumer 3D-printing are
currently experiencing different levels of anticipated development. The curve in which aim to illustrate forecasted
maturity and adoption rates of emerging technologies, show that Enterprise 3D-printing currently situated in the
Slope of enlightenment. This indicates that it is already applied in some industrial contexts, but has not yet reached
the plateau of productivity, meaning widespread adoption.
For commercial application, consumer 3DP is at the end of the phase “peak of inflatedexpectations”, andexpected
to reach the next plateau; through of disillusionment in5-10 years. Appendix 9 shows the hype-cycle developments
from 2012-2015, and further give an impression of the pace of technology maturity.
Figure 11 – Hype cycle for emerging technologies. Source: Gartner Inc., 2015
5.5.2 Academic Research Contributions and Cooperative Initiatives Driving Development
Most of the research that has lead the way for advances in 3DP/AM technology the past decades has come from
well-renowned academic institutions within advanced engineering and manufacturing, such as Massachusetts
Institute of Technology (MIT), University of Nottingham (UoN), Loughborough University (LU) and University of
Texas (UTA), just to mention a few. All of these institutions have dedicated and received monetary resources to
research on technological development through collaborations with systems providers such as in the case of the
Core 3DP patent from MIT, a foundational systems patent licensed by EXOne, Voxeljet and 3D Systems (Wohlers,
2015).
5.5.3 Industry Development in Numbers
Regardless of the different views on where the industry is heading, what impact it will have, and at what pace its
getting there, the growth of the industry is impossible to ignore when looking at the hard data such as market
growth, revenue streams of the largest industrial systems providers and annual growth in system shipments.
44

In 2014, Gartner Inc. forecasted a compound annual growth rate (CAGR) of 106.6% in worldwide unit shipments of
3D printers between 2012 and 2018 anda revenue growthof 87.7% for printer system providers inthe same period
(Forbes, 2014). Further, Bloomberg (2014), through a global survey of industry leaders, estimates a market-size
increase of $ 1.3 billion in 2014 to $ 5.4 billion in 2018. However, as the manufacturing technology is yet to have
had enough time to prove the forecasts right or wrong, there are clear indications that some of the most important
global industries have faith in 3DP/AM as a viable technology that can shape a future manufacturing paradigm.
5.5.4 Challenges & Skepticism
Although 3DP/AM has been predicted a promising prospect, skepticism surrounding the technology is also to be
found as it has been branded “over-hyped” by academics and industry specialists alike.
5.5.4.1 Patents & Distribution of Market Power
Regardless of the fact that all actors and segments in the AM value chain (materials, machine hardware, software,
service providers, and developer/user communities) all contribute to increasingthe technologies value andmarket
position, the industry is suffering from oligopolistic distribution of power with regards to the various value chain
components(Berger, 2013; Frost & Sullivan, 2014; Weller et al., 2015). For instance, universal technology patents
have mainly been developed through collaborations between universities and private companies. The result has
been an industry dominated by few large system providers, in which has also held control over the supply of
materials (Wohler’s, 2015).Hence, relatively low competitioninterms of price, quality and lackingcompetitionthat
drives development pose a future challenge for the industry.
However, with last basic patent – but also foundational SLS technology patent held by University of Texas, Austin
expiring in June, 2014 – more competitors have announced their arrival in the commercial printer as well as inthe
industrial market, one should expect increased competition on product qualities and prices (Wohlers, 2015).
5.5.4.2 AM Conflicting Traditional Concepts of Business Economics
Laseter & Hutchison-Krupat (2013) argue that when looking at the technology through a multi-lensed analytical
perspective composed by 3 well tested concepts, respectively; (1) the experience curve – shows how cost-
reductions takes place over time by using variables such as the rate in which volume grows while the rate of cost
decline at the same time, (2) economies of scale – shows how output volumes increases with fewer resources or
input used, and (3) total landed cost – shows the trade-offs between differences in costs of scale economies, labor
arbitrage, and transportation costs. According to the authors, fundamental differences in the manufacturing
technology’s nature, demands a new approach when assessing the long term benefits and impact of 3DP/AM.
5.5.4.3 The Role of Complementing Rather Than Replacing
Morgan Stanley (2014) argues that the question whether or not AM will replace traditional CNC-machines is a
wrong question to focus on, given that the CNC market is worth $93 billion compared to the above mentioned
valuation of $ 4.103 billion as of 2014 (Wohlers, 2015). In addition, Morgan Stanley considers the CNC market a
“mission critical” especially in the capital goods industry. Hence the probability of AM to out-compete TM process
technologies for all operations, are considered minimal.
45

5.6 Building-Block 2: Process Technology Performance & Capabilities
It is considered of high importance that AM process technologies satisfy the same level of performance
expectations as those demonstrated by TM methodologies.This sectionis therefore dedicatedto observations and
data findings related to technology performance and strategic capabilities. The data is presented in a systematic
manner guided by the five generic PO’s (Slack & Lewis, 2011). In addition, it is assumed that distinguishingfeatures
and characteristics play a central role in some aspects of the technology’s performance capabilities. Hence, related
findings and observations will be mentioned.
5.6.1 Comparative Frame of Reference
Contemporary research identifying differences between AM and traditional manufacturing (TM) process
technologies have mainly been focused on the comparison of polymer-based processes. Hence, the comparative
frame of reference in this section will therefore – to the extent literature is available – be focused around plastic
polymer materials fabricated by selective laser sintering (SLS) and plastic injection molding (PIM). In addition, due
to the increasing technological maturity andimportance of AM for metal parts, metal basedAMT processes suchas
selective laser melting (SLM) and direct metal laser sintering (DMLS) will be comparedwithtraditional metal based
processes such as high-pressure die casting (HPDC).
5.7 Quality Performance
The ability of manufacturing technologies to satisfy the desired product conformance quality –constituting the
ultimate observable and measurable aspect that directly impacts customer satisfaction – is considered integral to
any process-choice decision. Fig 12 illustrates what Mani et al (2015) propose to be the three interdependent
factors in the assessment of overall part quality. Although all of these are important with regards to conformance
quality, it is the post-process product qualities in which directly corresponds with the manufacturing based
approach of Garvin (1987), that is primarily emphasized in this thesis (as noted in Paton et al 2011, p. 415). Thus,
the identified underlying performance dimensions driving conformance quality include: (1) Geometric properties,
(2) mechanical properties, and (3) physical properties.
Figure 12 – Quality Control Measurement Procedures for AM. Source: Own creation, adapted from Mani et al, 2015, p. 3.
However, research suggests the significance of other in-process measurement andcontrol systems suchas process
parameters and process signatures cannot be overemphasized, as this is considered to be the key for future AM
Process
Parameter
s
•Controllable (e.g., laser power, scan speed)
•Predefined (e.g., powder size, material distribution)
Process
Signatures
•Observable (e.g., melt pool shape, temperature)
•Derived (eg., melt pool depht, residual stress)
Product
qualities
•Geometric Measurements
•Mechanical Measurements
•Physical Measurements
46

reliability and widespread industrial adoption, as it is noted that: “Process control today is based on heuristics and
experimental data, yielding limited improvement in part quality” (Mani et al., 2015, p. iii).
5.7.1.1 Process Parameters
Process parameters are inputs and/or settings that is configured by the operator prior to the fabrication process
begins. Controllable parameters include: energy distribution, laser power, scan speed, layer thickness and
temperature, all in which influence the chamber heat, melting point of the powder materials and solidification
(Mani et al., 2015). Examples of predefinedparameters include: material, geometric specifications of the object and
size/dimensions of the build platform (Ibid).
5.7.1.2 Process Signatures
Process signatures can either be observable, hence measured, or derived andcantherefore only be “(…) calculated
with a numerical model, such as maximum depth of a melt pool” (Mani et al., 2015). Process signatures are
considered to be the key determinant of final product qualities.
5.8 Product Qualities
5.8.1 Geometric Properties
It is noted by Singh et al (2012) that because material properties of powder act differently than in a liquid or solid
state, the reliability for geometric properties in AMT fabricatedproducts differ from those manufacturedwithTMT
techniques. The same authors further demonstrated that powder materials tend to shrink when reaching certain
levels of heat. This view is also supported by Gibson et al (2010), in which advocates that typical part shrinkage is
found to be 2.0-4.0%. These observations point to low reliability for repeatable consistency of AM.
5.8.1.1 Dimensional Accuracy
A major disadvantage of AM, more specifically powder-based technologies is noted by numerous academic
contributions to be dimensional part inaccuracy (Gibsonet al., 2010; Singhet al., 2012). The sectionbelow provides
comparative quantitative data on dimensional accuracy across manufacturing technologies.
AM dimensional accuracy for plastic parts:
Singh et al (2012) demonstrated the dimensional inaccuracy
of SLS for 6 plastic parts simultaneously fabricated in the
same build chamber by the use of DuraForm® Nylon-12 (PA)
powder materials and Vanguard HS printer. The simulation
results showed an average errorof0.140% with the maximum
error of 0.192%.
TM dimensional accuracy for plastic parts:
According to Stan et al (2008), moldingaccuracyof +/-0.150%
for high precision PIM with dimensions below 150mm can be
achieved, while an accuracy level of +/- 0.300% can be
achieved for technical injection. As for dimensions above 150
mm, the accuracy requirements for high precision PIM is +/-
0.250% and +/- 0.400% for technical injection. Appendix 10
illustrates some of the influencing factors on dimensional
variation for PIM.
47

AM dimensional accuracy for metal parts:
Similar studies for metal parts, more specifically stainless
steel was conducted by Abd-Elghany & Bourell (2012). By
using Selective laser melting (SLM) – a type of PBF based
process technology for metal parts – they demonstrated a
of 2.0-4.0% dimensional expansion of the fabricated steel,
before post-processing blocks (see appendix 11 for
example).
TM dimensional accuracy for metal parts:
HPDC is considered a very effective manufacturing method
related to production of small-to medium-sized metal parts
with exceptional mechanical properties, surface finish and
dimensional tolerance of 0.005% – 0.01 % and is capable of
producing large volumes with short cycle times (Sadeghi &
Mahmoudi, 2012).
Table 15 - Dimensional accuracy comparison data for AM vs. TM. Source: Own creation, inspired by collected data.
5.8.2 Mechanical Properties
Grenda (1997) found that lack of consistency in mechanical part properties is holding back widespread industrial
adoption of AMT (as noted in Jones et al., 2015). More than a decade later, the initiative named roadmap for
additive manufacturing (RAM) confirmed the validity of these findings. Through a survey study of 65 industry
experts, Bourell et al (2009) concluded that the:
“inability to guarantee material properties for any given process is holding back the adoption of AM
technologies as industry does not have the confidence that manufactured parts will have the required
mechanical properties which are required to meet specific structural needs” (Bourell et al., 2009, p. 8).
5.8.2.1 Tensile Properties
It is noted that mechanical properties can be characterized as a set of measurable and characteristic properties
found in the material (Roylance, 2008). The most common properties inthe mechanical category include: (1) tensile
strength – meaning “the ability to resist breaking under tensile stress”, (2) tensile elongation at break – in which is
the: “percentage increase in length that occurs before it breaks”, (3) tensile modulus –in which determines: “the
ratio of stress to elastic strain in tension”, and (4) flexural modulus – “the ratio of stress to strain in flexural
deformation” (matweb, 2015).
The table below illustrates a technical data comparison of tensile property differences betweenSLSandPIM based
on the above-mentioned properties collectedfrom different SLSadaptedNylon-12 (PA) powder material providers.
The data suggests that material specifications are relatively uninfluencedby variationinprocess-technique. Infact,
as demonstrated in the table comparison below, SLS show an on average higher tensile strength and better
elongation at break ratios than that of PIM, while tensile modulus is lower and flexural modulus equal.
Tensile Properties AM Nylon-12 (PA) TM Nylon 12 (PA)
Tensile Strength (psi / MPa) 6.382 psi (44.6 MPa) 5.946 psi (41 MPa)
Elongation at Break (%) 16.33% > 10%
Tensile Modulus (psi / MPa) 241.053 psi (1.662 MPa) 200.007 psi (1.379 MPa)
Flexural Modulus(psi / MPa) 189.854 psi (1.309 MPa) 189.999 psi
Density/porosity (g/cm³) 0.925 g/cm³ -
Table 16 – Physical properties of Nylon 12 PA for PIM and AM according to different sources. Source: Own creation, data acquired
from: Stratasys, 3D systems, 3T RPD
Directly contradicting this comparison is that of Ajoku et al (2006), inwhichprovidedempricial evidence that PA-12
Nylon fabricated materials with SLS had 10% lower tensile modulus than when applied under pressure during
injection molding. Moreover, Kruthet al. (2007) demonstratedby the use of notchedizod-impact testing– anASTM
48

standard method for determining impact strength of an object or material – a lower tensile strength for SLS
processed PA-12 Nylon, than when compression molded. Observed densities for SLS fabricated PA-12 Nylon
signified 0.95-1.00 g/cm3, while IM showed densities of 1.04 g/cm3. As for metal parts, more specifically for
stainless steel, Abd-Elghany & Bourell (2012) showed that process parameters such as scan speed and layer
thickness had impact on the tensile strenght of AM fabricated steel parts, as slower speed resulted in stronger
parts.
5.8.3 Physical Properties
As noted by Jones et al (2015), AMT’s lack applied in-process pressure found in most TMT processes. The lack of
pressure therefore impacts the tensile properties of AM fabricated parts in which further impacts the physical
quality properties (ibid). Commonly referredto physical property differences betweenAMT andTMT manufactured
objects include: (1) residual stress, and (2) porosity/density of products.
5.8.3.1 Residual Stress
Residual stress can be described as: “the internal stress distribution locked into a material” (proto manufacturing,
2015). For metal parts, Knowles et al (2012) measured residual stress and structural integrity implications for SLM
of Ti-6Al-4V alloy, the most widely adopted titanium alloy in manufacturing. They found that AM fabricatedTi-6Al-
4V alloy may inflict premature fatigue crack initiation. These fatigue cracks sugges lower mechanical properties,
leading to lower product qualities for AM fabrication than when processed by TM methodologies.
5.8.3.2 Porosity/Density
Through their experiment with DuraForm® Nylon-12 (PA) powder materials and Vanguard HS printer, Singh et al
(2012) demonstrated that positioning of the objects onX, Y, Z axis inthe buildchamber hadmajor influence onpart
densities of SLS fabricated objects (Appendix 11). By simultaneous fabricationof 5 similar parts (see appendix 11 for
example), they found that variation of object orientation on the build platform influenced part densities. These
specimen variations indicate that quality assurance for AM is inconsistent with the process parameters
S No. Mechanical Properties Values of different parts
Part 1 Part 2 Part 3 Part 4 Part 5
1. Tensile Strength (MPa) 47.49 47.48 47.48 47.48 47.45
2. Elongation at break(%) 17.3 17.2 17.2 17.0 17.1
3. Density (gm/cc) 0.960 0.958 0.558 0.57 0.595
Table 17 – Mechanical properties of SLS. Source: Own creation, adapted from Singh et al.,2012.
Spierings et al (2012) also measured part densities of SLM fabricated cylinderedblanks usingstainless steel 17-4PH
/ AISI-630 materials (Appendix 11). They found that the most interesting ability of AM fabrication from a material
perspective was that it: “allows also designing the mechanical material properties of the parts, e.g. the stiffness, by
allowing a specific degree of porosity” (Spierings et al., 2012, p. 453).
49

5.9 Process Speed
The rate in which a process technology can produce output in the conversion process is considered an important
capability that sets the boundaries for its strategic purpose and application. The underlying performance
dimensions in terms of process speed identified through revision of research data includes time spent on: (1) build
rates/throughput times, (2) set-up & processing, and (3) post-processing.
5.9.1 Processing Speed –Build Rates
The scientific terminology for AMT processing speed is the buildrate, andis measuredincubic centimeters pr. hour
(cm3
/h) (Lindemann et al., 2012). However, most research use build time pr. hour (T).Due to variabilities inprocess
parameters for each build, it is noted that buildtime estimationis relative, andvaries not only for eachbuildset-up,
but also between different AMT processes (Di Angelo & Di Stefano, 2011). The table below illustrates comparative
quantitative data found on throughput times (build rates) and cycle times across manufacturing technologies.The
calculations by Atzeni et al (2010) and Atzeni & Salmi (2012) for build time (T), whereas for TM, cycle time (T) is the
standard notation.
AM build rate for plastic parts:
Atzeni et al (2010) demonstrated the viability of AM on a
medium lot production basis in their AM vs.IM cost
comparison for a lamp holder. Intheircomparative simulation
of two different AM machines (EOS P730 and EOS P390),they
found that EOS P390 was able to produce 2520partspr.jobat
a build rate of 87.50 hours (h), and 5460 parts pr. job at a
build rate of 124.60 hours (h) for the EOS P730 (See appendix
12).
AM cycle time for plastic parts:
Atzeni et al (2010) notes that cycle time pr. hour is the
indicatory equivalent for machine set-up and post-processing
time for AM. The time spent on productionofoutputvolumes
between 20.000 and 100.000 lamp holders was set to 0.0056
hours (h) (See appendix 12).
AM build rate for metal parts:
Atzeni & Salmi (2012) found that buildratesfor more complex
4 SLS fabricated aerospace landing gear parts was 54 hours
(See appendix 13).
TM cycle time for metal parts:
Cycle time for HPDC produced aerospace landing gear was
calculated to be 0.001 (h)
(See appendix 13).
Table 18 – Build rate comparison data for AM vs. TM. Source: Own creation, inspired by data collection.
5.9.2 Set-up & Post-processing time
Besides build rates, the most important time-related variables for AM are time spent on system
calibration/machine set-up and post-processing. However, they are usually calculated together (Atzeni, 2010,
Atzeni; 2012). Holmström et al (2010) propose that drawbacks such as time consuming machine calibration
procedures limits the technology, and suggest standardization based barriers still to be overcome. In addition to
predefined parameters (scan speed, layer thickness and laser power), the machine operator has to take into
consideration the calibration of variables such as materials utilized, volume of the build, geometrical complexity
specifications of the object beingfabricated (Ruffo et al., 2006, Di Angelo & Di Stefano, 2011, Lindemannt al., 2012).
The table below includes comparative quantitative data on machine set-up and post-processing times across
manufacturing technologies.
50

AM machine set-up & post-processing time for plastic Parts:
Atzeni et al (2010) found that the total time consumption for
set-up and processing activities constituted 3 (h) pr. build for
both systems (EOS P390 and EOS P730). As 2520 and 5460
parts are respectively fabricated pr. build, time spent is
relatively short pr. part (See appendix 12).
AM machine set-up & post-processing time for plastic parts:
Atzeni et al (2010) does not consider activities related to pre-
processing and system set-up for PIM. However, on average,
typical lead times for quantities between 10.000 –100.000 is
months, while it can be feasiblewithin weeks(custompartnet,
2016).
AM machine set-up & post-processing time for metal parts:
Atzeni & Salmi (2012) notes that time spent on system set-up
activities pr. build was 1.2 (h) pr. build, while post-processing
time pr. build was 3 hours (h). This included post-processing
of 4 aluminum landing gear parts, suggesting post-processing
activities to be a time consuming process. (See appendix 13)
TM machine set-up & post-processing time for metal parts:
Atzeni & Salmi (2012) does not consider activities related to
pre-processing and system set-up for HPDC. However, on
average, typical lead times for quantities between 10.000 –
100.000 is months, while it can be feasible within weeks
(custompartnet, 2016).
Post-processing time for HPDC, was calculated by Atzeni &
Salmi (2012) be 0.100 (h) pr. assembly and significantly lower
than that of SLS (See appendix 13).
Table 19 – Set-up & Post-processing comparison data for AM vs. TM. Source: Own creation, inspired by data collection.
According to Atzeni et al (2010) time spent onpre-, andpost-processingvaries amongsystems due to the variations
found among the different process technologies´ need for post processing and manual removal of for instance
support structures. However, on a general basis the authors further suggest that part simplification through part
consolidation offer reduction of 67% in assembly time compared with TM methodologies (Ibid).
5.10 Cost Efficiency
Perhaps the area that has been given most attention in AM research is the disruption it brings to traditional
economic concepts such as economies of scale. As illustrated in figure 13, cost pr. part does not decrease as scale
increase. While scale of production is important in many industries, it is noted that AM: “increases flexibility and
reduces the capital required to achieve scope” (Marchese et al, 2015).
Figure 13 – Cost model comparison. Source: Own creation, adapted from Hopkinson & Dickens, 2003, p. 38.
Development of cost models for AM has to this point been driven by two well-renowned contributions, more
specifically research by Hopkinson & Dickens (2003), and Ruffo et al. (2006). While Hopkinson & Dickens (2003)
provided the first approximate break-even comparison between SLS and PIM, demonstrating how the technology
disrupts the traditional concept of economies of scale in manufacturing (fig. 13) by focusing on material anddirect
costs such as: labor, machine costs and material costs, Ruffo et al (2006) placed more emphasis on material
4000 8000 12 000 16 0000
Number of parts
Injection molding
Additive Manufacturing
Cost pr. part
0
8
6
4
2
51

utilization and different aspects of process speed/build rates. Thus, most of the associated research includes a
combination of these. However, inline withHopkinson& Dickens (2003) cost model, the most commonly identified
elements of cost performance found in the data collection for this thesis includes: (1) material costs, (2) machine
costs, and (3) labor costs.
5.10.1 Material Costs
The additive nature of processing materials for AM fabrication is considered an economical sustainability enabler
for manufacturers that reduces the cost of material usage. This reduction in AM material costs and utilization is
grounded in two fundamental capabilities of AMT’s. First, AMT has the ability to produce full versionend-products
parts based on lattice structure designs (appendix 15), in which enable production of lighter products with less
waste than AMT processes (Maheshwaraa et al., 2007). Second, AMT can fabricate products with fewer and more
complex parts, through what is known as part consolidation (Gibson et al., 2010), resulting in an increase in
material utilization and cost cutting of manufacturing activities (Huang, 2012).
5.10.1.1 Material Costs pr. Part
Sherman (2009) established that in terms of cost; “materials suitable to 3-D printing can run 10-100 times more
than typical injection molding thermoplastics”. Table 20 illustrates comparative quantitative data onmaterial costs
across manufacturing technologies. The calculations by Atzeni et al (2010) and Atzeni & Salmi (2012) for material
cost pr. part (MP) for AM include the following components: (1) material cost pr. build (MB) and, (2) the output
volume (N). Whereas calculations for material cost pr. part for TM methodologies include: (1) material cost pr. kg
(M), and part weight (W). Making up the following equations:
Additive Manufacturing Traditional Manufacturing
MP = MB/𝑁𝑁 MP = 𝑊𝑊𝑊𝑊𝑊𝑊
AM material costs for plastic parts:
Atzeni et al (2010) provided a cost model comparison
between SLS and PIM for plastic parts. They found that
material costs for PIM pr. part ranged between € 0.38 (EOS P
730) and € 0.36 for a simple lamp holder. This constitutes
between 29.1% and 30.4% of total costs of production (See
appendix 12).
TM material costs for plastic parts:
Atzeni et al (2010) found material costs pr. part for PIM in the
same cost comparison to vary at different volumes. They
found that at volume of 5 000, 20 000, 100 000, and 500 000
parts, material costs was constant at € 0.011,and significantly
lower and varying from 0,2 % at the lowest volumes to 2,7 %
of total costs of production at the highest volumes (See
appendix 12).
AM material costs for metal parts:
Atzeni & Salmi (2012) also provided a cost model comparison
for metal parts. Their study demonstrated a cost pr. part pf €
25.81 for AM fabrication of titanium alloy landing gear device
for aerospace application, and therefore 4.9% of total costs
pr. part (€ 526.31) (See appendix 13).
TM material costs for metal parts:
Atzeni & Salmi (2012) found material costs pr. part for HPDC
to be significantly lower than that of AM. They found that at
volume of 10, 20, 50, and 100 pcs. material costs was
constant € 2.59 (See appendix 13)
Table 20 – Material cost comparison data for AM vs. TM. Source: Own creation, inspired by data collection
52

5.10.1.2 Material Utilization & Material Cost Saving
It is further noted that when the cost of raw materials for AM – in which still constitutes the largest barrier for
widespread adoption drop, even larger production volumes may be more viable, challenging traditional PIM
techniques (Morgan Stanley, 2013). Looking at it differently, research on material utilizationsuggests that additive
fabrication of products through AM can result in a 96% decrease in raw material usage compared with subtractive
methodologies (Wagner, 2010). Petrovic et al (2011) further argue that a major processing related cost benefit
related to AMT is the ability to recycle materials. They note that as much as 95-98% of all material waste can
potentially be recycled. Thus, utilization of material-wise, AM is in accordance with lean manufacturing principles.
5.10.2 Machine Costs
The most commonly used variables for determining total machine costs include a combination of following
elements: (1) Machine investment costs, (2) Machine processing costs (set-up, processing, and post processing
costs).
Currently, the investment costs for AM may range from an upper scale of around $ 1 million, to hundreds of
thousands of dollars pr. machine (Deloitte, 2015). It is further noted by Lindemann et al (2012) that machine
investment cost constitutes one out of the two major cost drivers for AM systems. For TM, the major cost driver is
suggested to be the mold itself (Atzeni et al., 2010). Mold costs pr. part (KP) is anestimatedaverage basedonthree
factors including: (1) standard components cost (SC), (2) mold cavities andslide costs (K), (3) ancillary costs (A), and
volume (V).
5.10.2.1 Machine Investment costs
AM machine investment cost pr. plastic part:
Atzeni et al (2010) identified that the major AM cost driver to
be the machine investmentcost,representingbetween 58.7%
and 65.9% pr. part produced (See appendix 12).
TM machine investment cost pr. plastic part:
Atzeni et al (2010) highlighted that the major cost driver for
PIM is the mold, accounting for 84.6%-97.7% of total
production costs (See appendix 12).
AM machine investment cost pr. metal part:
Atzeni & Salmi (2012) also identified machine investment
costs as the major cost driver for AM fabricated aluminum
alloy. They do not however, provide an estimate of total cost
pr. part. However, Lindemann et al (2012) confirmed machine
investment costs as the major cost driver forstainless steel.In
their study found that machine investment costs accounted
for 73% pr. part for metal part fabrication (See appendix 13).
TM machine investment cost pr. metal part:
Atzeni & Salmi (2012) also found the major cost driver for
HPDC to be the mold. The mold investment cost pr. part
ranged from € 2100 accounting for 99% of total costspr.part)
to € 210 accounting for 91% of total costs (See appendix 13).
The authors note that in line with the principles of economies
of scale, mold costs (the indicatory equivalentforpre-process
HPDC pre-processing costs equals € 21.000, with (1) standard
component costs (SC) at € 1.900, (2) mold cavities and slide
costs (K) at € 15.400 and ancillary costs (A) at € 3.700 (See
appendix 13).
Table 21 – Machine investment cost comparison data for AM vs. TM. Source: Own creation, inspired by data collection
53

5.10.2.2 Machine Pre-Processing Costs
As in the case of processing speed, the cost element of processing costs in the lamp holder case study by Atzeni et
al (2010) does not consider pre- and, post-processing costs individually. Thus, no hard data could be extracted.
However, the calculations by Atzeni & Salmi (2012) for pre-processing costs pr. part (CP) include the followingcost
and time: (1) machine operator cost pr. hour (O), (2) set up time pr. build (A), and(3) number of pre-processedpart
(N). Mold costs pr. part (KP) is covered in table 21. Hence, the following equation for AM pre-processing costs is:
𝐴𝐴𝐴𝐴 = 𝑂𝑂 ∗ 𝐴𝐴/𝑁𝑁
AM machine pre-processing costs pr. metal part:
Atzeni & Salmi (2012) calculate pre-processing costs pr. part
to sit at € 8.00 constituting only 1.5% of total assemblycostat
€ 526.31 (See appendix 13).
TM machine pre-processing costs pr. metal part:
See table 21 for data.
Table 22 – Pre-processing cost comparison data for AM vs. TM. Source: Own creation, inspired by data collection
5.10.2.3 Machine Processing Costs
The calculations by Atzeni & Salmi (2012) for machine processing costs pr. part (CP) for AM include the following
cost and time elements: (1) machine cost pr. build (CB), and (2) number of parts pr. build (N). Whereas processing
cost pr. part (CP) for HPDC include the following elements: (1) machine cost pr. hour (P), (2) labor cost pr. hour of
processing (PL), (3) percentage (%) of operator time (PT), and (4) cycle time (T). Makingupthe followingequations:
CP = 𝐶𝐶𝐶𝐶/𝑁𝑁 CP = (𝑃𝑃 + 𝑃𝑃𝑃𝑃 ∗ 𝑃𝑃𝑃𝑃) ∗ 𝑇𝑇
AM machine processing cost pr. plastic part:
Atzeni et al (2010) notes that processing costs for SLS
include multiple cost elements such as: (1) machine
amortization, (2) part design and testing, (3) labor, and (4)
post processing. At respective output volumes of 2520 for
the EOS P390, and 5460 for the EOS P730, machine
processing costs pr. part was calculated to be € 0.694, and €
0.867, respectively (See appendix 12).
TM machine processing cost pr. plastic part:
Atzeni et al (2010) calculated the processing cost for PIM at
output volumes of 5.000, 20.000, 100.000, and 500.000.
They found that machine cost pr. part to be € 0.020, €
0.010, € 0.008, and € 0.004, respectively. The results
therefore show that PIM processing costs are significantly
lower than that of SLS (See appendix 12).
AM machine processing cost pr. metal part:
Atzeni & Salmi (2012) notes that processing costs for 4 SLS
fabricated titanium alloy landing gear devices equals €
472.50 pr. part, accounting for 89.7% of total costs pr.
assembly (See appendix 13) .
TM machine processing cost pr. metal part:
While mold creation and pre-processing constitutes a large
portion of Atzeni & Salmi (2012), the cost of processing is
considerably lower than that of SLS. The example given by
the author indicates that processing costs is consistent at €
0.26 regardless of output volume (See appendix 13).
Table 23 – Machine processing cost comparison data for AM vs. TM. Source: Own creation, inspired by data collection
54

5.10.2.4 Machine Post- processing Cost
Atzeni & Salmi (2012) notes that calculations of post-processing costs (BP) for AM include the following cost and
time elements: (1) machine operator costs (O), (2) post-processing time pr. build (B), and (3) heat treatment cost
pr. build (HT). Whereas for HPDC the cost elements for post processing (AP) includes: (1) Heat treatment cost per
part (HT), (2) machine operator cost (MO), (3) labor cost pr. hour (AL), and (4) operator time (AT). Making up the
following equations:
BP = (O ∗ B + HT)/𝑁𝑁 AP = 𝐻𝐻𝐻𝐻 + 𝑀𝑀𝑀𝑀 + AL ∗ AT
AM machine post-processing costs pr. metal part:
Post-processing costs for SLS fabricated landing gear was
calculated by Atzeni & Salmi (2012) to constitute € 20.00 (See
appendix 13).
TM machine post-processing costs pr. metal part:
Atzeni & Salmi (2012) notes that post-processing costs for
HPDC of the landing gear to be € 17.90 (See appendix 13).
Table 24 – Machine post-processing cost comparison data for AM vs. TM. Source: Own creation, inspired by data collection
5.10.3 Machine Energy Consumption
Telenko & Seepersad (2012) presented a direct comparison between the energy consumption of SLSandPIM for a
paintball handle made out of nylon (plastic). By analyzing life-cycle inventories (LCI) a significantly representative
factor of LCA, they found that energy used in material refining, and the energy consumption of the machine itself
was significantly higher during fabrication of the part. However, when the production of the mold used in IM
production was included, so-called small builds (50 parts or less) was more cost efficient. Acomplete breakdownis
illustrated in fig.14a and fig.14b.
Figure 14a – The energy breakdown comparison for IM and
SLS fabricated paintball handle. Source Telenko & Seepersad,
2012, p. 477.
Figure 14b – The energy breakdown comparison for IM and
SLS fabricated paintball handle including mold production.
Source: Telenko & Seepersad, 2012, p.478.
55

5.10.4 Labor Costs
In terms of labor costs, there were no findings that suggest any differences between plastic and metal AMT
processes. However, the most contemporary researchsuggests that labor costs constitute a relatively small portion
of production costs for AM (Thomas & Gilbert, 2014). Accordingto Hopkinson& Dickens (2003) AM labor costs was
on average 2% of total costs of fabrication. Additional research by Ruffo et al (2006) also established the average
AM labor cost to sit around 2-3% of total costs of production. The following cost examples of AM labor costs by
Atzeni et al (2010) and Atzeni & Salmi (2012) include cost calculations of machine operator costs (PL) across
manufacturing technologies.
AM machine operator costs for plastic parts:
Atzeni et al (2010) notes that machine operator costs
per hour (EUR/h) for SLS fabrication of plastic parts is
calculated to be € 14.00 (See appendix 12).
TM machine operator costs for plastic parts:
Atzeni et al (2010) notes that machine operator costs
for PIM are the same as for AM, and therefore also
calculated to be € 14.00 pr. hour (See appendix 12).
AM machine operator costs for metal parts:
Atzeni & Salmi (2012) notes that machine operator costs
include just one cost element, and are calculated pr.
hour (EUR/h). The study suggest that total operator
costs are equal to € 20.00 pr. hour (See appendix 13).
TM machine operator costs for metal parts:
Atzeni & Salmi (2012)notes that operator costs for HPDC
include the following two cost elements: (1) labor cost
per hour processing(EUR/h), and (2) labor cost per hour
post processing (EUR/h). For in-processingactivities, the
hourly rate is calculated to be € 35.00, while for post-
processing activities the hourly rate is € 25.00 (See
appendix 13).
Table 25 – Operator cost comparison data for AM vs. TM. Source: Own creation, inspired by data collection
5.11 Flexibility
As noted in the literature review, operational flexibility refers to the ability of an operation to shift from one
systematic way of task-execution to another (Slack & Lewis, 2011). The smoother, cheaper and/or quicker the
transition is executed is further used as benchmark for determining the operations flexibility capabilities. From an
operational perspective flexibility is oftenevaluated onthe operations product-, mix-, andvolume – flexibility (Slack
& Lewis, 2011).
5.11.1 Product Flexibility
Conner et al (2014) suggests that the evaluation of flexibility for AMT is underpinned by the PT’s capabilities in
terms of the extent to which it can offer a higher degree of product complexity anddegree of product customization
than processes such as PIM and HPDC.
5.11.1.1 Product Complexity
Hopkinson & Dickens (2003) demonstrated that the cost of product complexity for AM is constant, meaning that
costs incurred are unaffected by the nature and shape of the product. Cohen et al (2014) elaborates that part
consolidation also positively affects complexity costs, as less materials are utilized. Fig 15 illustrates the different
curves experienced for AM vs. TM methodologies interms of complexity costs. Elaborates onthis andstate that, as:
“design re-iterations are cheap because they basically require changing the instructions in the CAD software”
(Bianchi, 2014 & Åhlström, p. 6).
56

Figure 15 – The relationships between costs vs. complexity for AM vs. TM. Source: Own creation, adapted from Conner et al., 2014, p.
71.
5.11.1.2 Product Customization
The high degree of system flexibility enabledby 3D-CAD software designs directly translates into the ability to “truly
customize products” (Bianchi, 2014 & Åhlström, p. 6), and thereby accommodate the high customization
requirements posed by the modern customer (Frazier, 2014). The rationale provided by Conner et al (2014) is
related to what is illustrated in figure 15 with regards to “free complexity”. Conner et al (2014) argue that since the
relationship of no added cost between complexity and cost exist, full individual customization of products should
also be true and conceivable.
5.11.2 Mix Flexibility
Another reference point for determining PT flexibility is that of time and costs spent on process set-up
demonstrated in the example of Atzeni & Salmi (2012), Holmström, et al (2010) further notes that as AM offer
removal of pre-processing activities such as tooling procedures, increased process flexibility through AM is
experienced removal of these pre-processing activities further implies a decrease in costs related to changing the
process and changing the product mix is considerably faster than that of PIM and HPDC (See appendix 12-13 for
example).
Process flow flexibility
Figure 16a – Shop floor process flow for AM systems. Source:
Own creation, adapted from Lee, 2013.
Figure 16b – Shop floor process flow for TM systems. Source:
Own creation, adapted from Lee, 2013.
As illustrated in fig16a and fig16b, the shop floor process and material flow of for AM vs. TM systems differ in one
fundamental way; part and component inventory is obsolete for AM and replaced by raw materials (Lee, 2013). As
the models suggest, the impact on process flexibility when the necessity of inventory is removed is that changes in
Cost pr. part Additive Manufacturing
Complexity
Traditional Manufacturing
3D
printer
Raw Materials
Parts
Parts
Sub-Assembly
Assembly
Finished
product
Inventory
Parts Components
Sub-Assembly
Assembly
Components
Finished
product
57

what is being produced are unaffected by potential stock-out situations for different parts and components. This
new process flow paradigm is what Holmström al (2010) refers to as spare part redundancy.
5.11.2.1 Variety of products
It is noted AM offer a significantly increase in the variety of products produced as: “the lack of toolingrequirements
implies that altering products does not require retooling, but only changing the instructions in the software”
(Bianchi, 2014 & Åhlström, p. 6). Moreover, this also implies a considerable reduction in the timespan from the
design phase to fabrication, as design challenges are more easily overcome. Other researcher’s support this claim
as it is stated that: “separation of product design from manufacturing capabilities is a major advantage of Additive
Manufacturing” (Mashhadi et al., 2015, p. 1).
5.11.3 Volume Flexibility
In the case of volume flexibility, the general notion of issues mentioned in contemporary research is not relatedto
the ability of AMT processes to change volumes, but rather the PT’s volume capacity capabilities (Berman, 2012).
5.11.3.1 Volume Capacity
Hopkinson & Dickens (2003) was among the first to establish that the upper scale limit of outputs in which AM
could compete in, where set to 14,000 pieces before costs wouldexceedthat of traditional IM. This low production
volume was rationalized by the high priced nature of AM raw materials, inwhichona general basis is consideredto
be 10-100 times higher than for plastic materials used in traditional injection molding (RAoE, 2013; McKinsey,
2013).
Figure 17a - Output volume comparison AM vs. PIM. Source:
Atzeni et al., 2010, p. 315.
Figure 17b – Output volume comparison AM vs. HPDC. Source:
Atzeni & Salmi 2012, p. 1154.
However, the benchmark research examples of Atzeni et al (2010) andAtzeni & Salmi (2012) are pavingthe way for
further discussion on AM volume production capabilities as the authors demonstrated the viability of AM on a
medium lot production basis. The evaluation costs model and break-even analysis in figure 17a illustrates viable
volumes of respectively 73.000 parts for the EOS P730 and 87.000 parts for the EOS P390. In terms of similar
research on metal materials, Atzeni et al (2012) presented comparative research between SLS and HPDC. The
break-even analysis illustrated in17billustrate that volumes of 42 parts canbe producedmore economically before
traditional HPDC becomes more cost effective.
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5.12 Dependability
From a PT perspective of performance capabilities, dependability can be understood as the extent to which a
process that use a specific system are: (1) repeatable – meaning, they can repeatedly perform tasks with a
satisfying degree of automation, (2) consistent – meaning they can deliver the same output or result every time,
and (3) reliable – meaning, they are predictable in their operating patterns and offer low levels of breakdown risk
(Paton et al., 2011).
The following areas of dependability performance have been found as commonly discussed: (1) machine
automation, (2) machine reliability, and (3) machine consistency. As in the case of flexibility performance, the
followingsections include researchoncontemporary and differences independability performance offered through
AM, rather than directly comparing it to TM processes.
5.12.1 Machine Automation
In line with the issues identified under the quality section with regards to the significant impact of process
parameters and signatures on product qualities andprocessingspeedfor AM fabricatedproducts, Holmström et al,
(2010) notes that time consumingmachine calibration – requiringhighcompetency andmanual interventionby the
operator – is currently an automation related drawback still to be overcome. The author further suggests that AM
systems have yet to leverage on the same high levels of mechanization and automation as that of TMT processes.
5.12.2 Machine Reliability
AM currently suffer from high build time variabilities (Holmström et al, 2010). It is further noted by Kechagias et al
(2004) that a standardized build time estimator is yet to emerge, as different AM processes require independent
estimation models. Di Angello & Di Stefano (2011) presented a specifically designedartificial neural network (ANN)
approach to estimate build time independent of applied AM method. They found that the error of deviation
between real and estimated build times varied from 6-20%, results that according to the authors are relatively
good. However, the lacking of technical standardization is considered a reliability related limitation of AM.
5.12.3 Machine Consistency
According Holmström et al, 2010 AMT processes currently suffers from high rework rates. It is further noted that
given the novelty of the PT variabilities in properties and material processing capabilities for different AMT’s exist.
The lacking standardization with regards to traditional design rules and quality assurance (QA) of dimensional
accuracy, surface quality, residual stress, etc. on an earlier stage of product development currently limits the
consistency of AM (Lindemann, 2015).
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5.13 Building-Block 3: Strategic Purpose & Paradigmatic Appropriateness
This section is dedicated to phenomenological observations and case based data findings related to current
application and best practice use of AM. Given the novelty of the process technology in a manufacturing context,
this section serves as a substitute for case-based evidence. As the depicted figure below illustrates, there are
particularly five industries where AM has found general application. Below three of these is givenfurther attention,
while consumer goods has two.
Figure 18 - AM industrial application based on AM service provider industry revenue generation in 2014. Source: own creation,
adapted from Wohlers, 2014.
5.13.1 Aerospace
Contributing around 12% ($ 492 million) of AM´s $ 4.103 billion global revenue in 2014, the aerospace industry is
embracingwidespreadadoptionof AM technology (Wohlers, 2014). As noted, AM removes geometrical restrictions
found in TM. Consequently, it has caught the attention of industries with high requirements to geometric freedom
such as that found in Aerospace.
AM enable production of lighter components through what is referred to lattice structured design (appendix 15).
Consequently, the buy: fly ratio –meaning the relative difference between the raw material used and the
component itself – can be dramatically lowered and result in fuel savings, and increasedpassenger capacity (Allen,
2006). It is noted that airlines may save as much as $ 2.5 million annually if they substitute conventionally
manufactured metal brackets that is used to connect the cabin structures with AM components in which is
recorded to be 50-80% lighter without compromising quality (Mellor, 2015).
A practical business case example is that of Boeing, in which already applies over twenty thousand components in
their military and commercial airplanes (Freedman, 2012). Allen (2006), further suggests that the overall cost
savings of AM over conventional manufacturing techniques for selected components, may be as much as 30%.
Another practical business case example is that of the “LEAP” jet engine producedby General Electrics andSnecma.
The two companies have found a way to incorporate additively manufactured fuel nozzles using SLS technology in
manufacturing of jet engines (see appendix 16). While traditional techniques weld 20 parts to produce the fuel
nozzle, AM offer the same procedure in a single operation. In addition to a 25% weight reduction, the nozzle is five
Industrial Business
Machines
20%
Consumer Products
19%
Motor Vehicles
18%
Medical
16%
Aerospace
12%
Academic Institutions
6%
Government/Military
5%
Other
4%
AM Industrial Application in 2014
60

times more durable and reduces fuel burn by 15% (GE Capital, 2013). As the “LEAP” engine is the GE Aviation
branch’s best-selling engine in history, and recorded pre-order numbers of 6 700 from 20 countries (GE Reports,
2014), the technology has been employed by GE Aviation in their new plant in Auburn, where 300 machine
operators are set to fill an entire shop floor of machines (Ibid).
5.13.2 Automotive
Contributing around 18% ($ 738 million) of AM´s $ 4.103 billion global revenue in2014, AM technology adoptionin
the automotive industry could be considered as quite extensive.
It is noted that original equipment manufacturers (OEM), has to this point mainly focused the application of AM
around RP and RT purposes (Griffi et al, 2015). For instance, Ford was able to reduce tooling costs in their product
development stage of a new engine by millions of dollars, as cost-intensive tooling and investment casting
procedures was eliminated (Griffi et al., 2015). Another example is that of British AM-service provider 3T RPD. 3T
RPD fabricated a racing-gearbox using AM, and their CEO Ian Holloway, confirmed that the results achieved was
faster gear-changes and a reduced weight of 30% (The Economist, 2012).
5.13.3 Medical
Contributing around 16% ($ 656 million) of AM´s $ 4.103 billion global revenue in 2014, the medical technology
industry is considered an important industry where AM will prosper and become an integral part of products
offered to patients (Wohlers, 2014). Characterized by the highest degree of required customization related to
patient specific products, the term mass-complexity manufacturing has been used to describe the true disruptive
potential of AM in the medical industry (Conner et al., 2014). Especially in sub-sectors such as that for medical
implants, prosthetic and orthotic parts, but also for hearing aids and dental crowns (Conner et al, 2014). For
instance, in the hearing aid industry, the adoption of AM has been proven to shorten lead time for patient-specific
aids to one day, achieving a first time fit percentage of 95% (Ruffo et al., 2007).
Another practical business case example of AM application in the medical technology industry canbe foundinthat
of Kabloee Design, in which found that traditional techniques in treatment of medically conditioned enlarged
prostate required 10 iterations to customize it to the patient. Kabloee concluded that traditional injectionmolding
techniques would be too costly, and therefore turned to RP in order to arrive at the ultimate design in a more
timely fashion. This change resulted in cutting of costs equivalent to $250.000 and simultaneously shortening the
product development time by 12 weeks (Deloitte, 2014).
5.13.4 Consumer Goods
Contributing around 19% ($ 780 million) of AM´s $ 4.103 billion global revenue in 2014, the consumer goods
industry is clearly finding application for AM (Wohlers, 2014). However, it is difficult to justify the widespread
adoption of AM in this all-encompassing category. Three examples of current applicationprovidedinthe following
sections.
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5.13.4.1 Footwear
One consumer goods sub-sector where AM has caught on, is the footwear industry. Much like the medical device
industry, it is characterized by high requirements to customization, requiring many iterations and process steps
(TNO, 2014).
A practical business example where AM has found application is that of New Balance (NB), a sports footwear
manufacturer that has developed a concept running shoe model using AM technology (appendix 17a). By
substituting traditional injection molding techniques withSLSinthe constructionof the sole, NB was able to reduce
the number of process iterations, improve the fit and stiffness of the shoe, and achieve a 5% weight reduction. In
addition, the business manager of New Balance Studio Innovation, Katherine Petrecca suggests that SLShas made it
possible for NB to: “manufacture on demand, fluidly adjust our process to accommodate different sizes andwidths,
and update designs without the continuing capital investment required by injection molding” (, 2014).
5.13.4.2 Fashion & Apparel
The fashion industry provides another industry example that has experimentedwithAM. In2013, designer Bradley
Rothenberg made a costume for the Victoria´s secret show using AM (Appendix 17b) and Brooklyn-based design
label, Continuum has been pioneering software-based fashion by offering everything from shoes to dresses solely
made out of 3D-printed materials (appendix 17c). However, current application has been limited to high profile
couture (Lewandrowski, 2014), and literature searches made indicates low industrial application. Danit Peleg, a
designer of AM apparel, provides a rationale for why it is not yet feasible in the garment industry. She states in an
interview with Sculpteo (2015) that: “the speed of the printers and better materials” are needed for it to become
viable as it took her over 2000 hours to print her collection.
6.0 Analysis & Discussion of Findings
6.1 Building-block 2: Process Technology Performance & Capability Analysis
From a traditional process technology point of view, the direct comparison between AMT and TMT such as plastic
AM fabrication vs. plastic injection molding (PIM), andmetal AM fabrication and high-pressure die-casting(HPDC),
of metals, seemed relatively easy to accomplish. However, certain issues made it more difficult than anticipated.
Thus, some assumptions had to be made, particularly with regards to quality, speed, and cost performance. The
scores distributed has further been subject to weighted averages, as some dimensions of performance has been
considered as more relevant and important to the overall analysis for the respective primary performance
dimensions. A more comprehensive dataset of the average calculations can be found in appendix 18.
6.1.1 Quality
Due to variations in process parameters, signatures andproduct qualities for eachbuild– meaning, no set-upfits all
products – significantly impact the quality properties in different ways, a comparative quality performance proved
difficult. Further, as product qualities differed for plastic and metal parts, calculations has been made for both
material categories. A breakout of the allocated scores is provided in table 26.
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Quality Performance Additive Manufacturing Traditional Manufacturing
Plastics (SLS) Metals (SLS/SLM) Plastics (PIM) Metals (HPDC)
Geometric properties (33.4%) 2 2 4 4
Mechanical properties (33.3%) 3 3 4 5
Physical properties (33.3%) 3 4 3 4
Weighted average score 2.64 3.0 3.67 4.0
Process technology averages 2.82 4.33
Table 26 – Outbreak of point scores for AM vs. TM product quality performance. Source: Own creation.
6.1.1.1 Discussion of Results
Geometric properties: Contemporary issues with regards to geometric properties of AM fabricatedparts is related
to the PT’s inability to assure the required dimensional accuracy in line with those specifications that are
programmed into the CAD-software design tools prior to fabrication.
For plastic parts, material shrinkage is an evident issue, while for metal parts, material expansioncause distress. As
suggested by the data in table 15, AMT’s suffer current shortcomings in terms of geometric quality properties
compared with TMT. However, it is not deemed low enough to disqualify it, and is therefore awarded 2-points,
while both TMT’s receive scores of 4-points each due to its significantly more predicable property capabilities.
Mechanical properties: The findings suggests that overall tensile property differences between AMT and PIM
fabricated plastic objects are relatively unaffected by process technology if the machine settings are properly
calibrated (table 17).
However, some research indicates higher tensile strengths and higher part densities for PIM than those found in
PIM. Due to this inconsistency among contributions, plastic AM fabricatedproducts are therefore awardeda lower
score than PIM, 3-points and 4-points, respectively. For metal parts, it was demonstrated that process parameters
significantly influence tensile strengths, as slower fabrication speed resulted in stronger parts. However, HPDC is
noted to provide exceptional mechanical properties and surface finish and is awarded 5-points, while AM
fabrication of metals receives a score of 4-points due to the ability of the user to adjust the mechanical properties
the way he/she see fits.
Physical properties: SLS fabricated ti-6AI-4V showed higher levels of premature fatigue cracks residual stress.
However, metal materials showed flexible capabilities, meaning that properties could be manipulated to a further
extent than through TM methodologies.
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6.1.2 Speed/Time
As the data and findings on speed and time spending inthe conversion process suggests, AMT’s canproduce a part
in one single operation while PIM, HPDC and other techniques may require a multitude of set-ups, and extensive
process planning. A breakout of the allocated scores is provided in table 27.
Speed/Time Performance Additive Manufacturing Traditional Manufacturing
Plastics (SLS) Metals (SLS/ SLM) Plastics (PIM) Metals (HPDC)
Cycle times / build rates (50%) 2 2 5 5
Pre- & post-processing (25%) 4 4 3 3
Assembly times (25%) 5 5 3 3
Weighted average score 3.25 3.25 4 4
Table 27 – Outbreak of point scores for AM vs. TM process speed/time performance. Source: Own creation.
Cycle times / build rates: As suggested in the findings and data (table 20), the rate andscale inwhichoutput canbe
produced through AM under thoroughly process planned circumstances are very low compared with TMT cycle
times. Moreover, the examples given with regards to differences between processed materials also suggest that
plastics for two different systems (2520 parts in 87.50 hours and 5460 parts in 124.60 hours) can produce higher
output rates in a relatively shorter period of time than metal materials (4 parts in54 hours). This implies that at this
point in time, medium build environments are feasible for plastic AM while only very-low/low environments are
feasible for metal materials.
Pre- & post-processing time: Time spending on pre-processing activities suchas machine set-upandcalibrationfor
AM techniques are calculated to be 3 hours pr. build for plastic material fabrication and 1.2 hours pr. build for
metal materials (table 21). In the case for TMT’s, set-upactivities are not consideredinthe data provided. However,
it is suggested that machine tooling and general process planning activities related to TMT processes constitute a
major portion of overall lead times, in whichis suggestedto be betweenweeks andevenmonths. Time comparison
for post processing activities suggests that AM through removal of support structures etc. are more time-
consuming than those found in TM paradigms.
Assembly times: While lead times for manufacturing of a highly complex object may take weeks evenby the use of
5-axis high-speed CNC machining, AMT processes may only use a few hours. This is exemplified in the findings, in
which suggests that lattice structural design capabilities couple with part consolidation may offer as much as a
reduction of 67% in time spent on assembly. Removal of iterative process steps therefore offer a competitive edge
for AMT’s over TMT’s.
6.1.3 Cost Efficiency Performance
As suggested in the findings, AMT’s are not capable of leveraging on economies of scale, meaning that production
costs (e.g. cost of mold, machine, operator cost) does not dropas volume increases. However, the data suggest that
economies of scope can be leveraged to a greater extent thanTM methods. Hence, a cost efficiency comparisonon
a process level between technologies departing from a scale andscope perspective provedchallenging. Abreakout
of the allocated scores is provided in table 28.
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Cost Efficiency Performance Additive Manufacturing Traditional Manufacturing
Plastics (SLS) Metals (SLS/ SLM) Plastics (PIM) Metals (HPDC)
Material costs pr. part 1 1 5 5
Material utilization 5 5 2 2
Total score materials costs (35%) 3 3.0 3.50 3.50
Investment costs 4 3 3 3
Pre-processing costs 4 4
Processing costs 1 1 5 5
Post-processing costs x 3 x 3
Energy consumption costs 2 x 4 x
Total score machine costs (45%) 2.75 2.75 4.00 4.00
Total score labor costs (20%) 3 3 3 3
Weighted average score 2.89 2.89 3.63 3.63
Table 28 - Outbreak of point scores for AM vs. TM Cost-efficiency. Source: Own creation.
Material costs: As noted in section (5.10.1), AM material costs constitute a large portion of total costs pr. part,
especially for metal parts, and is currently considered a barrier to adoption.
• Material costs pr. part: As the data findings suggests (table 20), material costs pr. part for plastics are
considered significantly higher constituting as much as 29.1% and 30.4% in both AM machine cases. While
for PIM, it ranged between 0.2 % and 2.7%. As in the case of the more advanced metal part case study
example (table 20), the material costs was € 25.81 constituting a share 4.9% of total costs of € 526.31,
while significantly lower at for HPDC methodologies at € 2.59. This implies that AM fabricationof high-cost
parts can to a greater extent justify material costs production, as no relationships betweenpart complexity
and material costs exist.
• Materials Utilization: Moreover, the identified additive vs. subtractive processing differences between
AMT’s and TMT’s suggest a higher level of material utilization, exemplified by a potential reduction in
material waste of 95-98% based on two respective sources (see section 5.10.1.2). However, these are
potentials and must be taken under further advisement.
Machine costs: The overall machine cost comparison is comprised of four cost elements (investment, pre, in, and
post-processing costs).
• Investment costs: While machine investment costs was pointed out as the major cost driver for AM
accounting for 56.7%-65.9% pr. part for plastic material fabrication , PIM mold costs was considered even
higher and noted to account for as much as 84.6%-97.7% of total costs of production. In the case of
investment cost pr. metal part for AM systems, research from another study than the benchmark study
used throughout the data comparison suggested machine investment costs of 73% pr. part. Given the
reasonable similarity to that in the plastics material case, it is deemed valid.
• Pre-processing costs: Pre-processing cost data for AM processed plastic parts could not be retrieved.
However, as these are relatively low at € 8.00 and accounting for only 1.5% of total assembly costs of €
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526.31 for metal materials, it is assumed that these rates have cross-material application. In the case for
pre-processing costs for PIM processes, no data was neither retrievable. However, in line with what it is
assumed in terms of investment costs pr. part for mold creation for each process set-up, pre-processing
costs for PIM is considered to be high.
As for HPDC processes the data suggests that pre-processingactivities accountedfor € 21.000 of total costs
of production. This included acquisition costs of components (€ 1.900), mold cavities and slide costs (€
15.700), and ancillary costs (€ 3.700). These pre-processing and set-up costs therefore far exceed those
offered by AMT’s.
• Processing costs: Following the observational principle along the data samples suggesting that AM
machine and processing costs for plastics and metal materials are somewhat equal, it is assumed that the
example given for metal parts where processing costs accounted for as much as € 472.50, a percentage
share of 89.7% of the € 526.31 total cost of assembly is the same for plastic material fabrication. Thus, the
performance scores for processing costs are both considered low plastic and metal materials.
While the allocated costs pr. part for AM fabricated plastic material parts ranged from € 0.694, to € 0.867
for the different systems at output volumes of 2520 and 5460, respectively (EOOS P390 and EOS P730).
Processing costs for PIM constituted a significantly lower portion of total costs, registering allocated costs
pr. part of € 0.020, € 0.010, € 0.008, and € 0.004 at output volumes of 5.000, 20.000, 100.000, and500.000.
The large differences in processing costs across PT provide conclusive results that support the business
case for scale production through TMT and scope production for AMT.
• Post-processing costs: In the case of post-processing costs, no data was retrievable for neither plastic AM
fabrication nor PIM production. In the fear of making wrongful assumptions with regards to similarities
between AMT and PIM in terms of post-processing costs, plastic materials are not included inthe analysis.
However, the general notion as suggested through the data comparisonof AM for metals andHPDC is that
post-processing costs pr. part are relatively similar and accounting for € 20.00 for AM and € 17.90 for
HPDC.
• Energy consumption costs: The data findings onenergy consumptiondifferences betweenAM andPIM (fig.
15a and b), suggests that PIM – when taking into account elements such as energy usage in material
refining and energy consumption of the machines – provide a more sustainable solution to AMT’s under
any circumstances, especially for large builds (100 parts or more), but also for small builds (50 parts or less)
when mold creation was not considered. Energy consumption measured in megajoule (MJ) for small build
AM fabrication was Found to constitute 602 MJ, while PIM, with and without mold creation, used 185 MJ
and 646 MJ, respectively. Although slightly less energy is consumed for low volume fabrication, it is not
significant enough to justify the trade-offs offered by TMT’s. On the other hand PIM is a mature
technology, and has pushed the limits for sustainable manufacturing over it existence. Thus, energy
efficient AM still has potential to leap beyond TM.
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Labor costs: Based on the data example given and findings, the observations made onlabor cost differences across
PT is that regardless of the different activities embedded in labor costs for AMT’s and TMT’s, costs are seemingly
the same. In the case for plastic materials, operator costs for AM were calculated to be € 14.00 pr. hour, in which
also was the case for PIM, recording labor costs of € 14.00 pr. hour. For metal fabrication, labor costs, in which
suggests relatively low costs in both cases.
6.1.4 Flexibility Performance
The analysis of flexibility performance differences between AMT and TMT processes deviate from the approach
followed for quality, speed/time, and cost, as the concept of flexibility is more related to measurement of non-
metrical “potential” than those of a metrical nature in the above sections. A breakout of the allocated scores is
provided in table 29.
Flexibility Performance Additive Manufacturing Traditional
Manufacturing
Product complexity 5 3
Product customization 5 3
Total score product flexibility (40%) 5 3
Process structure flexibility 4 3
Variety of products 4 3
Total score mix flexibility (30%) 4 3
Volume capacity 2 5
Output flexibility 5 2
Volume flexibility (30%) 3.5 3.5
Weighted average score 4.25 3.15
Table 29 – Outbreak of point scores for AM vs. TM flexibility performance. Source: Own creation.
Product flexibility:
• Product complexity capabilities: As noted in the research on customization capabilities differences
between AMT’s and TMT’s, and complimented by the analysis of underlying performance dimensions of
cost and time related processing-capabilities, the combination of CAD-software design and additive
fabricationenable almost unconstrainedgeometric freedom inthe manufacturingprocess. Consequently,
prototyping activities and expensive iterations and re-iterations inthe process-chainof activities become
obsolete, as product specifications are predefined using CAD-software.
Thus, as demonstrated in figure 15, AM complexity costs are constant and in that regards comes for
“free”. However, there is no suggestion that PIM, HPDC and other TMT’s cannot achieve the same
product complexity, althoughmanufacturingof products withhighcomplexity may require a multitude of
additional set-ups. Complexity capabilities for AM are therefore awarded the highest possible score of 5-
points, while the score of 3-points is allocated to TM methodologies, as it is deemed satisfying, but not
optimal.
• Product customization capabilities: Basedonthe same principles of digitalizationof the designphase, and
additive, particle-by-particle fabrication of products, the research findings on product customization
suggests that true customization of products are feasible. As design iterations are cheap due to minimal
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changes in the CAD software is the only thing required, customization, as in the case for complexity, is
“free”. TMT customization capabilities on the other hand is in many cases based on modularization of
standard components in which the customer put together to fit their preferences.
On that basis TMT’s are only awarded a score of 3-points based on today’s standards of what is
achievable, while AM can facilitate a paradigm shift in terms of what is perceived as customization from
both an internal operational and external market perspective. Thus it is awarded a score of 5-points.
Mix flexibility:
• Process flow flexibility: As suggested in figure 16a and16b, the shopfloor process andmaterial flow inAM
operations differ. As no inventory of parts and components are necessary and replaced by raw materials,
the ability to change the operation is not decided by inventory held, and potential stock out situations.
This further implies a change to the material procurement function. Moreover, cross-functional dialogue
and S&OP between supply and demand functions becomes easier, as the assortment of raw materials
used are limited compared to the vast variety of standard parts and components available.
That being said, these scenarios are hypothetical, and remain to be fully tested on a scale in which makes
it significant enough to be used as scientific truths. However, assuming that researchers are correct, this
structural shift in operational process-flows point to a potentially dramatic increase in process flow
flexibility, a score of 5-points is therefore awarded, while traditional flows are labeled satisfying and
awarded 3-pints.
• Variety of products: In terms of product variety, the researchsuggests that product portfolios throughAM
fabrication can be drastically expanded as the set-up time and costs are very low compared with TMT’s
(See section ? costs), and the number of output produced are predefined in the CAD software prior to
each build of objects are fabricated. More importantly, the already mentioned enabling feature of
consolidating parts, limit the number of post-process iterations needed, and reduce number of activities
such as assembly. It is therefore assumed that the lacking extensiveness in planning and execution of
complex predefined process-flow set-ups suchas those foundinTM process flows, canraise the standards
for variety of products offered. Hence, AM is awarded 5-points for product variety flexibility, comparedto
the satisfying 3-points score of TM techniques.
Volume flexibility:
• Volume capacity: As suggested by the research findings, the relationship between output volume and
capacity in which AMT’s can operate in a more sustainable way thanTMT processes, is closely linkedto the
performance perspective of cost. Figure 17a and 17b illustrates a break-even analysis on output volumes
for AM and TM techniques. According to the model, the volume capacity for AM as the more viable option
in the plastic material parts case (fig 17a), is set to range between 73.000- 87.000 parts before PIM
becomes more economically sustainable. In the case for more complex metal material parts, the volume
capacity in which AM provide the more economically sustainable solution to HPDC is calculated to be 42
parts, a radical difference from that in the case of plastics.
Hence, the analysis suggests that the appropriate manufacturingenvironment for AM is dependent onthe
relationship between volume, complexity, and customization.
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• Output Flexibility: The analysis has to this point suggested high output flexibility for AM. Hence, without
repeating too much, the score awarded to AMT’s is 4-points, while TMT’s receives a score of 2 points. This
is rationalized by the volume/variety trade-off continuum, where high volume capabilities for TM
methodologies are related to low levels variety and output flexibility.
6.1.5 Dependability Performance
Out of all the performance dimensions, dependability performance is perhaps – together with flexibility – that in
which to the furthest extent been subject to logical interpretation and rational explanation. Moreover, the
achieved dependability performance of a PT can also be argued to be the one in which is the most relative, as
advances in machine and/or system technology is subject to technologic progression and may shift rapidly. The
contemporary differences in machine dependability are further based on the variables below, and the related
allocated scores is provided in table 30.
Dependability Performance Additive Manufacturing Traditional
Manufacturing
Machine automation (20%) 2 5
Machine reliability (40% 2 4
Machine consistency (40%) 2 4
Weighted average score 2.0 4.2
Table 30 - Outbreak of point scores for AM vs. TM dependability performance. Source: Own creation.
Machine automation: Directly related to mechanization capabilities, the research findings suggests that machine
automation is currently limited by time-consuming machine calibration and high necessity of process supervision
due to variabilities in process parameters and in-process measurement equipment important in the quality
assurance for each build.
Moreover, high competency and manual interventionby the operator betweeneachbuildis currently holdingback
full automation. As for TM systems, high levels of automation are often retained through predefinedsequences of
operations with high levels of task specificity, in which is designed for efficient mass-production.
Machine reliability: In addition to employment of highly sophisticated CAM systems and other mechanization
enabling IT integrated solutions, state if the art TM based facilities offer higher degrees of consistency between
work cells, as the experience curve for TMT’s suggests better process-planning and work-flow mapping that
ultimately results in low re-work rates. However, in the case for AMT the findings suggest the existence of
evidential limitations such as high re-work rates due to variabilities in both surface finishes, and dimensional
accuracies for each build.
Machine consistency:
As of now TMT’s are considered more predictable in their operating patterns. Often identified by a predefined
set of operations through assembly lines, easy detection of errors for each process step is enabled by high-tech
equipment and IT-integrated solutions that provide very accurate estimates in terms of relevant process
parameters. As noted, this is not the case for AMT due to differences among system processing capabilities and
lacking development of standardized build-time estimators.
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6.1.6 Summary of Results for Building-block 2: Analysis
Figure 19 is based on the dataset in appendix 18 and provides a visualization of what the analysis has suggested
with regards to contemporary performance capability differences between PBF-based AMT process and TM
methodologies represented by PIM and HPDC. The following section explains the relationships discovered and
implications these hold for the forward analysis in BB-3.
Figure 19 – Radar diagram of contemporary in performance capabilities between AM vs. TM. Source: Own creation
Quality performance: It can be argued that a major drawback for quality performance is the currently experienced
inconsistency across all material property dimensions. Perhaps the most important observation made is the
influence in which machine dependability performance has on product output quality. This point to the fact that
future developments in AM machine technology will potentially enhanced quality performance.
Further, as the point-score outbreak in table 26 suggests, SLS and SLM fabricatedmetal parts is provento a greater
extent to meet those satisfying requirement standards in the score system than plastic material fabrication.
However, if only considering quality, TMT processes such as HPDC shows by far the most promise to fall into the
competitive potential category of quality performance with a point score of 4.33, in which should be difficult to
compete against.
Speed/time performance: TMT’s such as PIM and HPDC is – at this point in time – much faster than AMT’s on a
single-run process basis, making it an arguably more viable option for mass-production of standardized goods.
However, removal of pre-processing activities and tooling procedures such as for instance mold and die creation
suggests a shorter timeframe from design to fabrication in which is further reflected in the enhanced flexibility
performance.
In addition, the overall assessment of speed/time performance should be considered relative, as lead times for
HPDC and PIM may take weeks. Thus, we can conclude that for simple, non-discrete products with low conversion
process complexity, AM falls short of TM. However, for geometrically complex parts requiringmany post-processing
iterations, the relativity of process speed speaks in the favor of AMT’s.
Cost efficiency performance: There are particularly two important takeaways from the cost efficiency performance
comparison. First, demonstrated by a significantly higher material cost pr. part, material costs for AM are still a
barrier for widespread adoption. However, as noted in BB-1, the oligopolistic market landscape for products and
0
1
2
3
4
5
Quality
Speed/Time
Cost efficiencyFlexibility
Dependability
Traditional Manufacturing
70

services are changing as patents expire, meaning that material cost competition will increase and material prices
drop resulting in more cost efficient volume production. Second, in terms of the three cost elements of processing
costs (pre, in, post-processing), AM vs.TM methodologies offer complimentary trade-offs.
While pre-processing and assembly costs are favorable in the case of AM, the high significance of in-process costs
suggests that high volumes are favorable for TM techniques. One can therefore argue that the high investment
costs for AM machines needs to be justified by high variety infabricatedproducts, while PIM andHPDC needs to be
justified by low variety and high degrees of pre-determinedprocess-structures, as new molds anddies are required
for each part or component that is being produced.
Flexibility performance: It is evident that the area inwhichAM canoffer a true competitive edge for manufacturers
is in terms of flexibility oriented operations. Exemplified by potential paradigmatic shifts inmaterial process flows,
increased variety in product offerings, “free” complexity, and true customization capabilities, AM has proven its
ability to produce small batches of products with high conversion process complexity.
As it is noted that much as 25% of total expenditure in manufacturingcompanies is inducedby product andprocess
complexity (Steinhilper et al., 2012), AMT may provide a solutionthat helpaidinreducingcosts inthese areas. With
a performance score of 4.25 points, compared with a significantly lower 3.15 points for TMT, certain industries
should find this interesting enough to give further attention.
Dependability performance: Exemplified by lacking standardization of in-process control systems, technology
consistency, automation and reliability, all point to the fact that contemporary limitations withregards to AMT’s is
maturity related and reflected through the low AM dependability score (2.00 points). Compared with TMT’s, it is
considerably worse in this area of performance.
6.1.7 Concluding Implications from BB-2 Analysis
In conclusion, given the high degree of operational flexibility offered, justified by an average score of high – very
high (4.25 points), a quality score that is almost satisfying (2.82 points), low machine dependability (2.0 points) and
cost efficiency (2.89 points) performance scores does not necessarily disqualify the technology. In fact, the most
interesting discovery is the influence that low machine dependability has on overall quality performance.
As the technology matures, the technology should experience a performance increase in both areas. To sum it up,
on could argue that the relationships identified with regards to speed/flexibility and volume/variety trade-off
concepts, AM should find complementary purposes to those that are served by TM methodologies.
6.2 Building Block 3 – Strategic Purpose & Paradigmatic Appropriateness
Departing from the operationalization table (table 13), this section of analysis aim to place the technology under
the scope of the addressed typologies of strategic orientations found in the literature review with regards to
manufacturing system employment. The analysis is further two-folded as it aims to determine: (1) What type of
system classification typology AMT’s fall in under, and (2) What type of manufacturing, and business strategic-
conditions AM provide the best fit with.
71

6.2.1 Analysis of Manufacturing System Classification of AM
Figure 20a – Analysis of AM typological system classification.
Source: Based on the proposed Typology by Kim & Lee, 1993
Figure 20b – Analysis of AM typological system classification
compared with other systems. Source: Based on the proposed
typology by Kim & Lee, 1993.
The implications from the analysis in “building-block 2” propose that on a process level, traditional processes such
as PIM and HPDC provide the best solution in high volume made for inventory manufacturing environments with
emphasis on speed, cost, and process dependability in line with those desirable characteristic features of a
continuous manufacturing systems found to be related to cost leadership manufacturing strategies (see table 13).
As illustrated in figure20a and 20b, AM provides a fit with those characteristics related to intermittent systems,
defined by thoroughly planned process flow layouts of sequential activities between work cells due to low task
specificity (Paton et al.,2011).
Exemplified by a low machine dependability performance score – operationalized into a system typological
strategic context as low technical complexity (KIM & Lee, 1993) –, and high flexibility performance scores –
translated into high technical flexibility (Ibid) – the interpretation of system classificationtype for AMT’s suggests a
positioning in the matrix within the typology of Intermittent systems. These systems retain high flexibility, and can
produce “one-offs” through highly competent job shops and batch production process flows, tailored for MTO
delivery strategies. However, these systems do not hold the capacity to support cost-oriented manufacturing and
business strategic positions.
6.2.2 Analysis of Appropriate Manufacturing & Business Strategic “fit”
On a manufacturing and business strategic level, the analysis in BB-2 – combined with the preliminary explorative
findings on market developments in BB-1–, propose that AM hold several characteristics in which suggest a
technology development that may potentially shift towards what is recognized in the operationalization table for
the strategic analysis (section 4.8) as concurrent systems. These systems are associatedwithsupportinghighlevels
of differentiated products through mass-customization, while simultaneously pursuing cost leadership. These
Technical
Flexibility
High
High
Low
Low
Intermittent
system
Degenerate
system
Continuous
system
Concurrent
system
AM
Technical
Flexibility
High
High
Low
Low
Job Shop
Batch FMC
FMS
Anachronistic
factory Assembly
Line
Flexible
Assembly
Line
Transfer
LineContinuous
Flow Process
AM
72

assumptions are confirmed by the findings from current application in the case-substitute for empirical evidence
proposed in BB-3 findings.
6.2.2.1 Current Application
As suggested in figure 21, two different paradigms of strategic purpose exist for contemporary application of AM.
Moreover, the following industrial trends suggest an expansion of AM application boundaries in the selected
industries.
Figure 21 – Different strategic scenarios for AM in between different industries. Source: Own creation, conceptual typologies adopted
from Kim & Lee, 1993 and Porter 1980
Aerospace (1): Characterized by high capital investments in manufacturing of complex, material cost exhaustive
parts, the application of AM in the aerospace industry probably pinpoints the technology’s maturity stage
application potential better than any other industry. Exemplified by a 50-80% reduction in component weight, the
combination of additive fabrication andapplicationof CAD-software, enable manufacturingof highly differentiated
parts using lattice structured designs, while simultaneously achieving high cost efficiency through less fuel
consumption and lower buy: fly ratio.
Automotive (2): The ford example in section 4.13.2 suggests that AM is currently limited to RP activities in the
automotive industry. However, as it is suggested that the aerospace industry has found application beyond RP
purposes, technology maturity, exemplified by the combination of technology related improvements such lower
material costs, increased output rates, increased machine dependability, andimprovedmanagerial understanding
and development of AM process flow structures, indicate that the automotive industry – characterized by high
capital investment in modularized parts to meet high customer requirements – should find extended purposes for
AM in the near future.
Medical (3): Characteristically termed a mass-complexity drivenindustry (Conner et al., 2014), the medical industry
place AM into a contemporary context of consumer orientedareas of applicationthat holdbothdifferentiationand
cost advantages. Exemplified by the hearing aid industry, in which where the findings suggest a first time fit
percentage of 95% for patient specific hearing aids, shows the ability of AM to facilitate effective demand-based
manufacturing on a consumer level, meaning the ability to produce only to the extent of demand. Moreover, the
technology is also proven within even more complexity driven medical sub-sectors, exemplified by a 12-week
High
Low
Cost Efficiency
Different-
iation
Low High
2 & 4 1 & 3
73

shortening of lead-times for solutions that treat medically conditioned enlarged prostate, validates the viability of
AM on a consumer product level.
Consumer goods (4): Although it can be concluded that lacking business model structures and technology
immaturity is currently limiting application of AM in manufacturing of consumer goods to RP, managers should be
aware not to dismiss its potential. Exemplified by the New Balance example, it is evident that those industries
characterized by high volatility in terms of seasonal demand, high customization demands and relatively
low/medium goods prices, will benefit most as AM technology adoption first-movers.
6.2.3 Concluding Implications from building-block 3 – Analysis
On the basis of the above discoveries made with regards to developing trends of industrial application, it is
reasonable to argue that two interrelated determinants exist with regards to the technology’s current strategic
purpose.
First, industry characteristics such as level of customization, material utilization and manufacturingcomplexity are
currently determining the technology’s fit with strategic focus. Second, the trend in development suggests that as
the technology matures, we will see a shift from intermittent system/pure differentiationstrategic purpose area or
focus, to a concurrent system/cost and differentiation paradigm.
This implies a strategic manufacturing strategic paradigm that is unaffected by the high alternative capital
investment needed to support AM as an adoption decision alternative. Figure 22 illustrate this notionof a naturally
experienced strategic boundary shift triggered by these developments where “1” is the current situation for most
industries, while “2” demonstrate the trending shift.
Figure 22 – Contemporary & future manufacturing & business strategic scenario for AM. Source: Own creation, conceptual typologies
adopted from Kim & Lee, 1993 and Porter 1980
Moreover, the industries from the findings is arguably those in which will set the benchmark for AM adoption and
application within their respective sector of product offerings. As of now, it can be argued that the adoption
decision is driven by a combination of internal operational flexibility benefits that meet the high customer
requirements for customization, and that cost opportunities related to the internal flexibility offered, supersedes
the extra costs of offering personally customized products. The medical industry is a good indicator of this
phenomenon where flexible processes both have internal cost-saving repercussions through fewer iterations
Technical
Flexibility
High
HighLow
Low
High
Low
Cost Efficiency
Different-
iation
Low High
AM
”1”
AM
”1”
AM
”2”
AM
”2”
74

needed in manufacturing of the product (Kabloee example), while simultaneously adding extra value for the
customer through decreasing lead-times.
Furthermore, it is reasonable to argue that out of the four industries addressed, the automotive industry – and
other similar industries – will be those in which could benefit most of AM operations due to high capital
investments in factors of production, complex webs of distribution, and high inventory costs due to extreme cases
of modularization and postponement strategic emphasis.
6.3 Building-block 4: Discussion on Implications for AM-adapted Supply Chains
Much research suggests that the true disruptive force of AM is actually related to the potential impact it may have
on SC performance. Thus, the following discussion look at academic contributions that challenge the established
perception of how organizations consider the strategic purpose boundaries of PT’s, and how they evaluate related
performance trade-offs.
6.3.1 The Re-distribution of Manufacturing
As also mentioned in the problem formulation, research contributions on recent development and contemporary
issues in SC’s, suggests an increase in spatial distance of value adding activities due to decentralization of
production, and dispersion of other activities in locations where production factors are cheap (labor, land,
materials). This ever-increasing fragmentation of value adding activities from a supply chain perspective include
longer and more complex SC’s, that place higher resource requirements on mitigation of risks and uncertainties
through increased coordination, communication, and monitoring (Manuj & Mentzer, 2008).
As both raw materials and finished goods has to travel long distances between where value addition happens –
ultimately ending up at the customer –, conventional SC’s have many cost andtime-incurringelements. AM-driven
SC’s may change, and even reverse this trend through a shift recognized by Dr. Tim Minshall at Institute for
Manufacturing at Camebridge University as the re-distribution of manufacturing(Video link in reference list). Re-
distribution of manufacturing is further enable by two game changing features offered by AM discussed in the
following section.
6.3.1.1 Key Features of AM Driving Operations & Supply Chain Regionalization
First, as indicated by the analysis in BB-2, AM processes employ raw materials as factors of production rather than
parts and components. This means that applicable AM raw materials – in which does not need to be transformed
into parts or components prior to assembly – can be sourced from local sources, rather than from locations in
which facilities are located basedontheir proximity of access to cheaper factors of production. Accordingto Reeves
(2008) the commercial application and business benefits offered by AM through regionalized supply chains and
manufacturing closer to point of consumption are far reaching, and include reduction in lead times, and increased
SC responsiveness. The models in figure 19a and 19b proposed by Lee (2013), illustrate the differences between a
regionalized and conventional SC scenario for consumer electronics.
75

Figure 23a – Digital supply chain scenario. Source: Own creation,
adapted from Lee, 2013.
Figure 23b – Conventional Supply Chain Scenario. Source: Own
creation, adapted from Lee, 2013.
Figure 23a and 23b suggest all activities inAM-adaptedSC’s to be regional, consequently removingthe necessity for
stockholding of semi-finished parts and components. Second, it is suggested that AM may enable an additional
delivery option, either through conventional SC channels, or by digital object transportationmeaningthat a design
may be developed in one corner of the world and sent as a digital file to a recipient in a different location in which
prints out the purchased product (Barnatt, 2013). Both of these features have further implications for SC
performance and strategy.
6.3.2 Performance Implications for Re-distributing the Geographical Landscape of Manufacturing
It is noted in literature that SC performance measures traditionally have been focused around two particular
performance measures, including: “(1) cost; and (2) a combination of cost andcustomer responsiveness” (Beamon,
1999, p. 277). In line with these prevalent variables, much research on AM-adapted SC’s propose that the ability to
transfer physical ideas and product designs across the value chain through non-physical digital channels,
consequently cutting the number of value-adding activities, involve reduction of time, costs, and risks.
6.3.2.1 Lead times & Customer Responsiveness
Figure 24a – Lead times for AM scenario. Source: Mashhddi
et al., 2015, p 8.
Figure 24b – Lead times for TM scenario. Source: Mashhadi et
al., 2015, p. 8.
Local raw material
supplier
Raw materials Raw materials
Local manufacturer
Finished goods Finished goods
Local
retailer
Local
retailer
Local
retailer
Global supplier Global supplier Global supplier
Semi-finished parts
& components
Semi-finished parts
& components
Semi-finished parts
& components
Centralized
manufacturing site
Global distribution
network
Global distribution
network
Global distribution
network
Finished goodsFinished goods
76

The large difference in processingspeedaccountedfor inBB-2 are basedonproductionthroughput andcycle times,
and does not necessarily provide a clear picture of the real difference AM can make interms of leadtimes (Lee IBM,
2013). Mashaddi et al (2015) presented a system dynamics simulation study on lead times for AM adapted SC’s in
which where fabrication of products happen at the retailer location instead of at the manufacturing site (see
appendix for 19 for value stream maps). As figure 24a and 24b indicate, the different scenarios hold very different
prospects for lead times; suggesting enhanced customer responsiveness for AM-adapted SC’s.
Considering the value stream map in appendix 19, AM has the potential ability to facilitate a delivery scenario in
which where personal designs can be directly uploadedby the customer, andfurther be manufacturedandshipped
from local facilities to the desiredlocation, while simultaneously trigger a shift from what the EC call resource-based
manufacturing to a knowledge-based manufacturing paradigm (EC, 2004).
6.3.3 Impact on Cost Performance
From a theoretical perspective, SC cost performance measures are embedded in what Beamon (199) refers to as
resource performance includes: (1) total cost, (2) distributioncosts, (3) manufacturingcosts, (4) inventory costs, and
(5) return on investment. Figure 25 illustrate the different areas aspects of cost savings followed by a compressed
SC scenario.
Figure 25 - Net benefit comparison of traditional vs. digital manufacturing supply chain. Own creation, adapted from Lee, 2013.
6.3.3.1 Local Distribution – Reduced Cost of Inventory & Assembly
According to Walter et al (2004) a more centralized production oriented SC solution enabled by AM implies the
elimination of high cost incurring SC elements suchas the needto keepsafety stock, andthe possibility of increased
inventory turnover for fast-moving items as productionplanningcantailoredfor made-to-order (MTO) production.
Tuck et al., (2006) complimented this research and noted that repercussion effects of raw material utilization in
manufacturing, at the expense of parts and components, leads to: “reductions in stock levels, logistics costs,
component costs (through reduction in assembled components) and increase the flexibility of production, through
the ability to produce products to order in a timely and cost effective fashion” (Tuck et al., 2006, p. 10).
AM Distribution cost benefits:
• Local sourcing Reduced transport costs
• Local Assembly Reduced logistics costs
Localized AM Assembly benefits:
• Improved assembly cycle times
• Reduced risk and impact of part/components stock-outs
AM Inventory cost benefits:
• Reduced cost of inventory due to lower material carrying
costs
AM component cost benefits:
• Reduced cost associated with procured, finished parts
from suppliers.
• Reduced cost related to scrap, material waste
77

6.3.3.2 Cost of Transportation & Logistics
A supporting consequence of local distribution is a reduction in transportation and logistics costs (Huang, 2012).
Although there are – at present time – significant differences in energy consumption on a single process level
between AM and TM techniques exemplified in BB-2 analysis, it is suggestedthat AM energy consumptionbenefits
is related to reduced transport and logistics costs due to localized production (Tuck et al, 2006). A hypothetical
estimate suggests that energy consumption can be reduced by as much as 40-65 % due to elimination of
transportation costs (Frost & Sullivan, 2013)
6.4 Supply Chain Design & Configuration Strategies
One of the most influential theoretical benchmarks for SC design and configuration is that of Christopher et al
(2006), in which propose a taxonomy for global SC pipeline selection made up of the following three characteristic
constituents: (1) product characteristics (standard or special), (2) demand requirements (stable or volatile), and(3)
replenishment lead times (short or long).
Supply
Characteristics
Long
Lead
Time
Short
Lead
Time
Predictable Unpredictable
Demand Characteristics
Figure 26 – New supply chain pipeline selection paradigm. Source: Own creation, a revised version of the original by Christopher et
al., 2006, p. 9.
Combining what has been established in BB-2 analysis, suggestingthat AM offer higher levels of material utilization
through waste reduction in manufacturing – consequently fitting into what is recognized as lean SC principles –
with what has been established in BB-3 analysis, and further discussed in this section regarding the ability to offer
on-demand production – consequently fitting into what is recognizedas agile SC principles –, one canconclude that
overcoming of lead time variabilities through regionalization of SC activities could cause the pipeline selection
strategy taxonomy to be re-evaluated (see figure, as a SC paradigm where the need for modularization and
postponement strategies to accommodate leagile strategies becomes obsolete. Hence: “the first truly flexible, and
JIT supply chain paradigm” (Tuck et al., 2006, p.11), may be conceivable. Figure 24a and24billustrate this potential
shift where quick response is achievable even when lead times are long.
LEAN: PLAN AND
EXECUTE
LEAGILE:
POSTPONEMENT
AM adapted SC
LEAN: CONTINUOUS
REPLENISHMENT
AGILE:
QUICK RESPONSE
f
78

Figure 27 - Strategic roadmap for AM purpose boundaries. Source: own creation, inspired and adapted from Kotha & Orne (1989).
Demonstrated from a manufacturing perspective in figure 27, it can be proposed that the strategic scope of AM
application is currently located between “point 2 – segment, differentiation strategy” and “point 6 – industry-wide
differentiation strategy”.
This can be exemplified by fragmented application in some industries (medical), and more extensive use in others
(aerospace). Although it is suggested previously in this thesis that AM currently suffer drawbacks related to low
process structure complexity (low dependability performance), developments intechnology maturity will allow AM
to find further application in “point 6 – industry-wide differentiation”, in which will automatically qualify AM for
purposes in the strategic orientation in “point 8 – industry wide, cost and differentiation”. This can be explainedby
the fact that the organizational scope in the synthesis of Kotha & Orne (1989) must be considered global through
regionalization as the ability of covering a global market is not negatively influenced, but the manner in which it is
covered is just different. Thus, cross-border facilitation of manufacturing activities becomes obsolete, and the
necessity to organize multiple facilities across the globe – suggested by the authors to be embedded in a global
organizational scope – also becomes excessive.
Hence, reconciliation between the SC perspective’s supply characteristics of long/short lead times with the
manufacturing perspective’s definition of organizational scope, suggests that; as the definition of long lead times
will need to be revised – as it becomes relative from a regionalizationperspective comparedto today’s SC paradigm
–, leagile strategies will become increasingly feasible for more industries, as postponement and stock/inventory
strategies becomes less influential due to this redefinition of organizational scope.
6.5 Concluding implications from BB4-Discussion
Despite the fact that most research found on the impact of AM introduction on SC performance and strategy is
driven by hypothetical scenarios, the disruptive potential advocated by several researchers is difficult to not
find logical and prospective.
In addition to potentially facilitate a SC structure that has the ability to enhance performance through
reduction of lead times and increasing customer responsiveness, a closer operational proximity to end-users –
disrupting global manufacturing hubs such as those found in Southeast-Asia (Frost & Sullivan, 2013) – also
7
5 6
8
3 4
21
Low
High
HighLow
Low
High
Process Structure
Complexity
Product Line
Complexity
Organizational
Scope
79

suggests that leagile strategies, in which traditionally has been viewed as somewhat inconceivable and difficult
to accomplish may become the standard, as modularization and extensive efforts in complex postponement
strategic initiatives becomes obsolete. Hence, the future potential impact that AM holds in terms of partially
cutting warehousing costs enabled by on-demand manufacturing, could be of great value for first-mover
manufacturers exploiting the PT.
7.0 Conclusions
Figure 28 – Updated conceptual framework including key determinants for each building block. Source: Own creation.
Keeping in mind the importance of treating PT adoption-decisions as long-term oriented investment as: “(…) there
is much evidence that a process can succeed in one attempt at adoption and fail in another” (Voss, 1988, pp. 56),
the central purpose of this thesis has been the provision of a multi-perspective, adoptiondecisiontool basedonan
analysis of contemporary performance capabilities of additive manufacturing, with managerial implications for
manufacturing and business strategic appropriateness.
Through identification of different key determinants of industrial AMT adoption (figure 28), the thesis offer
implications for both the academic and managerial domains. Academic implications is presented through
demonstration of how AM disrupts established theorems in terms of strategic typologies within the context of
manufacturing and SC, while managerial implications for contemporary performance capabilities is presented
through a very technical approach to a performance analysis that represents what is contemporarily real and
The most important considerations and key
determinants for industrial AM adoption...
BB-3:
• Strategic system requirements
o Technical flexibility
o Low  TM processes
o High AM processes
o Technical complexity
o Low  AM processes
o High  TM processes
• Customization Requirements
• Capital investment
BB-4:
• Supply chain complexity
o High AM scenario Low  TM scenario
• Lead times
o Long AM scenario Short TM scenario
• Demand volatility
o High AM scenario Low  TM scenario
BB-1:
• Opportunities:
o Exponential market growth
suggesting high yield potential
o New business models
o High degree of governmental
support of sustainable
manufacturing
• Challenges
o Intellectual property rights
o Patents
o Standardizations
o Oligopolistic markets
BB-2:
• Conversion process complexity
o Low  TM High  AM
• Material costs
• Output volume
o Low  AM High  TM
• Operational dependability
o Low  AM High  TM
• Operational flexibility
80

quantifiable at this point in time. From this scientifically provenandverifiable vantage point, the thesis utilize more
creative methods of triangulating what is quantifiable (BB-2) and empirically proven through evidence from
practical application (BB-3), and test these with theoretical frameworks stimulating bothdomains withknowledge
through abductive reasoning.
Regardless of the domain of interest that appeal to the reader, the conclusions made is that the ability of AM-
adapted operations to provide close to total manufacturingflexibility – exemplifiedby total control of product-, and
process flexibility on a conversion-process level, to increasedcontrol over inventory levels that accommodate ever-
increasing fluctuations in demand, and digitalization of the transportation and distribution functions in SC
operations –,all support the potential disruptive force it might become in a near future scenario. It is of high
importance to emphasize that although AMT proved itself more flexible than TMT on an operational level; it failed
to meet the same standards within all the other performance dimensions at this point in time.
However, technology maturity should pave way for increased performance in machine dependability in whichwill
increase quality andspeedperformance. Furthermore, the fact that most of the reviewedresearchonthe impact of
AM-adapted operations and SC scenarios was – and still is – hypothetically grounded, managers should approach
the implications from BB-4 with a critical eye.
Building block 1 – Identified Key Determinants: Findings related to BB-1: Technology & Market Overview can be
directed or classified into technology opportunities and challenges. First, the strong claims by industry
professionals and academics with regards to forecasts on technology development, suggests that AM may
potentially reconcile the best of both direct and – indirect process technology development experiencedinthe last
decades.
Supported by a the PT’s additive rather than subtractive nature, close to exponential CAGR, and extensive
government initiatives, AM represents a type of technology that will create new markets and business
opportunities as well as reshape the competitive landscape in existing ones. However, legislative barriers such as
patents and IPR, and lacking of formal technologic standardizations such as dominant machine design and poor
selection of available materials constitute the most important challenges in which needs to be met.
Building block 2 – Identified Key Determinants: The analysis of findings and data from BB-2: Process Technology
Performance & Capabilities providedconclusive evidence that the contemporary performance capabilities of AMTis
limited to the extent that industrial application needs to be justified by the highest degree of conversion process
complexity, material utilization dictated by manufacturing environments, and industries where product nature
requirements involve high capital investments.
Exemplified by the analysis high processing and material costs, high variabilities in quality performance, low
machine dependability, and inability to produce high output volumes in a relatively short timeframe, suggest that
the technology still has some maturity issues before it can develop into a viable option for widespreadapplication.
Regardless of these issues, the ability of AMT to disrupt establishedtheorems withregards to “free complexity” and
“free customization” propose that once the addressed issues is resolved, AMT should find itself as a strong
competitive option compared with TMT technologies.
81

Building Block 3 – Identified Key Determinants: The analysis in BB-3: Strategic Purpose Areas & Paradigmatic
Appropriateness provided conclusive evidence that when combining process technology performance capabilities
with manufacturing and business strategic appropriateness AM hold contemporary limitations with regards to
pursuing cost-leadership oriented strategies.
Moreover, the analysis of system type classification – complimented by the analysis in BB-2: Process Technology
Performance & Capabilities – uncovered that AM maturity will move from its current position as supportive of
intermittent manufacturing systems, to a position more familiar to concurrent manufacturing system. This leap
across typologies further enables a shift in business strategic purpose from satisfying pure differentiationstrategic
purposes offered through high flexibility, to also accommodate operational performance requirements related to
cost-efficiency and speed.
The repercussion effects of this technological development on strategic purpose boundaries of AM application can
be argued to include a redefinitionof internal operational requirements, inwhichenable MO’s to more aggressively
pursue a new type of mass-customization strategy where demand-driven business models recognized by a shift in
which where “technology push” may adapt more easily to “customer pull” and replace the current need for
modularization and extensive resource allocation and efforts in S&OP.
Building block 4 – Identified Key Determinants: The conclusions drawn from the discussion-based BB-4:
Implications for AM-adapted Supply Chains can be argued to provide a perfect representation of the
“disruptiveness” that AM may pose on established theoretical models.
AM adapted SC’s may enable MO’s of highly customized products, with long lead times operating in volatile
markets to not just pursue differentiation strategic positions, but also aim for cost leadership oriented ones (also
discussed in BB-3 analysis). As the re-distribution of manufacturing disrupts the relative understanding of lead-
times, directly influencing cost incurring SC activities, the importance of thoroughestimations of total landedcosts,
rather than just looking at each value adding stage individually, should therefore be in focus by management as
cost savings in transport and logistics may make up for higher conversion process costs for AM in manufacturing
activities.
Moreover, the discussion conclude that while leagile SC pipeline selections traditionally has been associated with
employment of strategies such as modularization and postponement, coupled with IT integration of customer
involvement platforms on the internet to meet contemporary challenges of increasingcustomizationrequirements
and move the decoupling point closer to the customer, AM-adapted operations may disrupt the “pipeline selection
taxonomy” by Christopher et al (2006) as regionalization of value chains through re-distribution of manufacturing
activities should increase agility and customer responsiveness, while simultaneously allowing MO’s to leverage on
lean principles and cost leadership due to the natural reduction of the “organizational scope”.
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7.1 Additional Considerations & Future Research Proposals
The introduction of new process technology poses numerous additional considerations in which the adopting
organization needs to address. Especially in the case of unproven areas of application – as in the case of AM as a
direct process technology –, related risks and uncertainties are particularly difficult to account for. For instance,
determining changes in the value of non-observable and intangible assets such as brand strength, and impact on
supplier relationships followed by AM adoption is especially difficult, as no benchmarks to measure the impact of
these events currently exist.
Moreover, as noted in the section 1.5 – Delimitations, implementation-specific suggestions or plans of actionis not
considered in this thesis, and involve a very different set of assessments with regards to the impact on the
dynamics within the adoptingorganization. Inadditionto humanresource-specific considerations suchas extensive
investments in employee skills and training to operate new manufacturing systems, different process flow
requirements posed by AM will certainly also influence structural dimensions suchas communicationflows. Future
areas of proposed research beyond these usage- and managerial-specific areas of research, the following
operational and supply chain related areas of research is recommended.
First, as AM introduces a new material state from which products are fabricated (powders and liquids), a thorough
assessment of how a new upstream sourcing paradigm may influence the adopting organization, and further
compare it to the current model should be in focus. Associatedactivities inthis evaluationinclude the assessingthe
alternative costs of shifting from a part and component-based supplier network, to one in which the supplier
extracts raw materials, where considerations such as the necessity for, and influence of a re-evaluation of the
entire network of suppliers in order to identify their ability to provide the requiredquality levels of a different state
of the materials should be given precedence in future research.
Second, the introduction of AM shop floors also influences the manner in which downstream operational process
flows are managed. Case study research on how these processes should be organized sequentially to optimize the
time and costs associated internally should be given attention. One way to go about this is through value stream
mapping (VSM) of material flows in AM-driven operations, and further measure the real time business effect on
cost and time aspects.
Last, the effects of the proposition that AM-adapted supply chains could leverage on JIT delivery and lean
principles, while simultaneously confine with agile principles should be explored to a greater extent than just
hypothetically discussed.
83

7.2 Closing Remarks
After reflecting upon all the different domains of knowledge that has had to be investigated in the process of
writing this thesis, there are many takeaways, as well as unanswered questions with regards to the future of
AM that only time can provide answers to.
Perhaps the most fascinating discovery with regards to the future potential of AM proposed in this thesis is the
true disruptive force that shortening of the spatial frame of value adding activities through re-distribution of
manufacturing may pose on supply chain operations. Such a change may even facilitate a true paradigm shift
that meets the longstanding goal noted in the European Commission’s report: “A Vision for 2020”, to be a shift
from resource-based- to a knowledge-based manufacturing (EC, 2004, p. 13) that could re-balance the
concurrent structural challenges posed by developments in macro-trends and the increasing geographical
dispersion between production, distribution and consumption (Rodrigue, 2012).
In that sense, the future of AM is an exciting prospect, as the technology could offer an improved business
operational situation in an economically, sustainable way, that supports organizational growth without
compromising the ability to achieve cost efficiency. However, the efforts that have to be put into a paradigm
transition at such a big scale are so great that MO’s might not consider it a feasible option. In the end it might
just be all hype, but who knows, one day we might be able to purchase an item online, push print and receive
the item on our own consumer printer on an instant demand basis. Again, who knows, as even the greatest
minds agree that: “Prediction is very difficult, especially about the future” – Niels Bohr (1885-1962).
84

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97

9.0 Table of Appendices
APPENDIX 1: FORECASTED GROWTH IN WORLD POPULATION & CONSUMPTION ..................................................
APPENDIX 2: OVERVIEW OF TRADITIONAL MANUFACTURING PROCESS TECHNIQUES ............................................
APPENDIX 3: THE GENERIC AM PROCESS .................................................................................................................
APPENDIX 4: DIFFERENT TYPES OF AM PROCESSES..................................................................................................
APPENDIX 5 TYPES OF MATERIALS USED IN AM AND ACCORDING AM PROCESSES .................................................
APPENDIX 6: OVERVIEW AND DESCRIPTION OF DIFFERENT POWDER BED FUSION (PBF) PROCESSES......................
APPENDIX 7: PBF APPLICABLE AM MATERIALS ........................................................................................................
APPENDIX 8: THE 3DP/AM VALUE CHAIN.................................................................................................................
APPENDIX 9: TECHNOLOGY HYPE-CYCLE DEVELOPMENT FOR 3DP/AM 2012-2015 ..................................................
APPENDIX 10: INFLUENCE FACTORS FOR DIMENSIONAL VARIATION IN PLASTIC INJECTION MOLDING ..................
APPENDIX 11: QUALITY DIFFERENCES FOR AM ........................................................................................................
APPENDIX 12: EVALUATION MODEL & TOTAL COST COMPARISON BETWEEN SLS AND PIM TECHNOLOGIES FOR
PLASTIC LAMP HOLDER ...........................................................................................................................................
APPENDIX 13: TOTAL COSTS BREAKOUT FOR SLS & PIM .........................................................................................
APPENDIX 14: COST EVALUATION MODELS FOR SLS AND HPDC MANUFACTURING TECHNOLOGIES .......................
APPENDIX 15: AM STRUCTURAL DESIGN COMPOSITION..........................................................................................
APPENDIX 16: AM FABRICATED JET ENGINE NOZZLE................................................................................................
APPENDIX 17: AM APPLICATION IN CONSUMER GOODS INDUSTRIES......................................................................
APPENDIX 18: DATASET FOR PROCESS TECHNOLOGY PERFORMANCE AND OPERATIONAL CAPABILITIES................
APPENDIX 19: ALTERNATIVE SUPPLY CHAIN CONFIGURATION FOR AM VS. TM SCENARIOS....................................
98

Appendix 1: Forecasted growth in world population & consumption
Population
(millions)
Major area 2015 2030 2050 2100
World 7 349 8 501 9 725 11 213
Africa 1 186 1 697 2 478 4 387
Asia 4 393 4 923 5 267 4 889
Europe 738 734 707 646
Latin America and the
Carribean 634 721 784 721
Northern America 358 396 433 500
Oceania 39 47 57 71
Forecasted developments in world population. Source: Own creation inspired by data found in: The 2015 Revision,
Department of United Nations.
Indicator Brazil India China Mexico Indonesia Nigeria Turkey
Total GDP in billion USD 2245,67 1876,8 9240 1260,91 868,3 820,21
GDP growth rate in % 0,3 1,6 1,3 0,7 0,18 0,7
GDP annual growth rate in
% 0,2 7,5 7 2,6 4,71 2,6
GDP per capita in USD 5823,04 1165 3583,38 8519 1810,31 8716,68
GDP per capita (PPP) in
USD 14555,08 5238,02 11524,6 16290.81 9254,42 18646,78
Gross Net Income (GNI)
per capita 2005 10240 2880 4920 11750 5510 3240 11390
Gross Net Income (GNI)
per capita 2014 14750 5350 11850 16020 9270 5360 18570
Change in GNI per capita
2005-2014 in % 44,00% 86,00% 141,00% 36,00% 68,00% 65,00% 63,00%
Human Development
Index rank(HDI) 2014 79 135 91 71 108 152 69
Change in Rank 2012-
2013(latest data available) 1 0 2 -1 0 1 0
Overview of forecasted growth in consumption. Source: Own Creation, inspired by data from World Bank & IMF,
2015
99

Appendix 2: Overview of traditional manufacturing process techniques
Figure a: Injection molding – is the most commonly used
manufacturing process forthe fabrication ofplasticparts.
Figure b: Die casting – is a manufacturing process that can
produce geometrically complex metal parts through the
use of reusable molds, called dies.
100

Appendix 3: The generic AM process
(1) The CAD Software
First, it all starts with the CAD-software in which all the features of the
object that are being manufactured are defined prior to the
automated printing process. A secondary approach is reversed-
engineering, where the desired object is scanned and directly printed,
or modified in the CAD-software to configure the material properties
in line with the object, or one might even enhance them.
(2) File type Conversion
Second, as all the features of the object are specified, the CAD-
software file is converted into an STL (Stereolithography) file type in
which is compatible with almost every 3DP/AM machine regardlessof
brand or technology applied. File manipulation may include
verification of the given parts geometric properties, selection of
orientation and location, addition of potentially needed support
structures, the scale of the object, number of duplications etc.
(Wohlers, 2014).
(3) File type Manipulation
The third step involves the transfer of the STL file to the printer. This
step may involve a manipulation of the file in order for it to
correspond with the specifications of the applied printer such as size,
position and orientation for building.
(4) Printer Setup
The fourth step involves setting up the printer in accordance with the
build/process parameters of the object being manufactured. These
parameters include material constraints, energy source, layer
thickness, timings, etc.
(5-6)Process Execution & Post Processing
On the fifth step, the fabrication process starts. This step is mainly
automated and is not very different than any other subtractive CNC
machining as it can be carried out without any supervision.
In the sixth step, the printed object is completed and removed from
the printer manually by the user. As the object may be weak at this
point and have support features that need to be removed,
considerable amount of manual manipulation and time consuming
post-processing may be necessary.
(7) Process Evaluation
The seventh and final step involves the comparison of the AM object
with a subtractive manufactured substitute before the object can be
applied as a direct part in the assembly of a final product, or used as a
finished product.
101

Appendix 4: Different types of AM processes
Liquid-based Approaches to AM
VAT photo-polymerization (Stereolithography)
VAT photo-polymerization, more commonly
referred to as Stereolithograpy (SLA) is regarded
the most widespread use of RP technology since
its introduction in 1988 by Charles Hull
(Custompartnet, 2015). Involvesresinsbeingare
cured using a process of photo-polymerization
(Gibson et al., 2010) or UV-light to harden liquid
resin in which bonds each successive layer. As
the resin comes in direct contact with light, it
cures.
This process provides a high level of print accuracy (Leong et al., 2009), and processing speed is high even on
larger scales. For instance, Stratasys’ industrialprinter Objet1000 Pluscanbuildlargeobjectsmeasuring1000mm
x 800mm x 500mm and a maximum printed object weight of 200 kg. However, disadvantages related to process
costs, makes SL an unviable option for scale production compared with other AM processes. In addition,thisAM
approach involve long post processing time, limited material use, and is often not strong enough for structural
use by itself , hence support structures that require removal, offers additional (Lboro University AMRG, 2015).
Material Extrusion
The process of Material Extrusion involves a
stream of melted thermoplastic material being
extruded or drawn from an extrusion nozzle
that moves horizontally, while the printing
platform can move vertically. As the process is
performed the object is built layer-by-layer,
each layer bonding the previous. The process is
most commonly referred to as Fused
Deposition Modeling (FDM), a method
developed and trademarked by Stratasys. FDM
is a viable choice as a consumer printer
option(3DP),
due to its inexpensive nature compared to SLA or SLS. Material use include ABS plastic, an easily available
material in which has good structural properties. However, disadvantages such as low accuracy and speed,
ultimately make it a non-viable option for industrial application.
102

Material Jetting
The process of Material Jetting involves a somewhat
similar method to that of a two-dimensional ink-jet
printer, as it uses inkjet-printing heads to deposit
droplets of build material(Loughborough University
AMRG, 2015;Wohlers, 2013). The nozzle in which
deposits the droplets of materialcanmovehorizontally
across the build platform, and the object is built layer-
by-layer as the droplets of material deposited by the
printing head are continuously solidified by the a UV-
light Due to a high printing
accuracy of deposited droplets this AM process have the benefits of low waste in addition to the process
technology’s ability to print multiple parts and colors simultaneously. Giventheliquidpropertiesofthematerials
used (most common are polymers and waxes), limitations regarding what can be printed serves as a clear
disadvantage (Loughborough University AMRG, 2015).
Solid-based Processes
Sheet Lamination
The process of sheet lamination more commonly
referred to as Laminated Object Manufacturing
(LOM) involves a feeder that process sheets over a
build platform in which where a heated roller applies
pressure and thereby bond sheets layer by layer. A
laser is installed to the build platform to cut the
shapes in accordance with the design specified in the
3D-CAD software (custompartnet, 2015). Materials
used range from paper to metal. The processspeedis
relatively high compared to other AM technologies.
In addition, operations costs are low, and material
handling is less complicated (Krar & Gill, 2003)
However, LOM has so far
found little application so far for industrial purposes, but have been used in RP and RT. Mueller & Kochan (1999)
explains the lack of industrial applicability by the process technology´s limited accuracy and low part durability
due to its simple machine design compared to other AM machines.
103

Powder Bed Fusion
The SLS powder bed fusion process is similar to
that of SLA as it involves the sinteringof powder
materials using a laser (Senthilkumaran et al.,
2009) The laser beam thereby selectively sinter
either powdered materials such as nylon,
titanium, aluminum and polystyrene, and/or
metal composite materials into successive
cross-sections of a three-dimensional part
(custompartnet, 2015). The SLS process uses a
laser that sinters selectively a thin layer of
powder spread over a platform. A computer
directs a laser scanningmirrors over the powder
layer,
sintering and attaching a new layer of the part [4]. Eachtime a layer is finished, the platform is lowered and
a new layer of powder is spread over the previously built layer. This process is repeated sequentially until
the part is completed and the sintered product is thenseparated from un-sinteredpowder after the cooling
down stage.
Binder Jetting
The process involves the combination of two
materials; a powder based material and a
binder. Materials include metals such as
stainless steel, polymers such as ABS plastic,
and ceramics such as glass. This particular type
of AM process technology is usually referredto
as 3DP, a term in which is it trademarked
under. Advantages include a wide range of
applicable materials such as metal, polymers
and ceramics and high process speed. In
addition, this two-material method provides
many binder-powder combinations and allow
for a wide range of
mechanical properties (LoughboroughUniversity AMRG, 2015). Existingdisadvantagessuchas manual post-
processing increase the time frame of the process and makes it unviable for industrial application.
104

Appendix 5: Types of materials used in AM and according AM processes
AM Materials AM Processes Major Manufacturer
and Supplier
Applications
PLA, ABS, PC, PC/ABS
blend, PP, TPE, PMMA,
wax
FDM, SLS, Materials
Jetting, Binder Jetting
Stratasys, 3D Systems,
Solidiscape, Voxeljet
Automotive, aerospace, medical
devices, electronics housing,
packaging, seals, precision, casting
patterns, kitchen tools, HVAC, art &
fashion.
Nylon, ULTEM,
PPSF/PPSU, PS, PET, PVA
FDM Stratasys
PA, PAEK, PS, TPU SLS 3D Systems, CRP
Technology, Materialise
Acrylics, Acrylates,
epoxy, resin, rubber-like,
ABS-like
SLA, DLP, PolyJet, MJM 3D systems, Stratasys,
Solidiscape, DSM Somos,
Envision TEC
Medical & dental, packaging, seals,
investment casting patterns,
demonstrations.
Tool steel, SS 316L & SS,
Ti-6AI-4V & Ti alloy, Co-
Cr, Ni alloy, Al-MG-Sc, AA
4047, Cu alloy
SLS, SLM, DMLS, EBM,
LENS, DMD, Aerosol Jet
EOS Optomec, Arcam,
ATI Powder Metals,
Carpenter, LPW, GE
Aviation, Airbus
Automotive, aerospace, maritime,
energy, oil & gas, mining, tooling,
cladding, metal component repair,
functionally graded laminates,
electronics, injection/die casting
mould, medical/biomedical
implant, art & jewellery.
SS tool steel, bronze, Fe,
W, w/bronze infiltrant
Binder Jetting ExOne
Al, CU, Ti, SS UC, UAM Fabrisonic
Slica sand, alumina/slica Binder Jetting, SLS Voxeljet, ExOne, EOS,
Viridis3D
Sand cores/moulds for casting, art
design.
PA filled w/glass, carbon
fibre, aluminium, WC
SLS 3D Systems, EOS Automotive, aerospace, defence.
Al, Cu, SS foils w/tiNi-
fiber
UC, UAM Fabrisonic Superstructure, reinforced low-cost
matrix
AA – Aluminium Alloy PET – Polyethylene Terephtalate SS – Stainless Steel
ABS – Acrylonitrile Butadiene
Styrene
PLA – Polyactic Acid TPE – Thermoplastic Elastomer
HVAC – Heating Ventilation and Air
Conditioning
PMMA – Polymethyl Methacrylate TPU – Thermoplastic
Polyurethane
PA – Polyamide PP – Polypropylene ULTEM – amorphous
thermoplastic polyetherimide
PAEK – Polyaryletherketone PPSF/PPSU – Poly
PC – Polycarbonate PS – Polystyrene
PVA – Polyvinyl Alcohol
Source: Own creation, adapted from DNVGL Strategic Research & Innovation, 2014
105

Appendix 6: Overview and description of different powder bed fusion (PBF) processes
Selective Laser Sintering (SLS)
SLS is a PBF process-technology originally developed at University of Texas, Austin (UTA) and later
commercialized by DTM Corporation (Wang et al., 2007). SLS involves the use of thermal laser energy that
fuses successive additive layers of powdered materials into the predefined shape (Gibbons et al., 2015). SLS
advantages include high tensile strength, a wide range of compatible materials and lacking need of support
structures. Although disadvantages such as relatively poor surface finish exist, it is still consideredthe most
sustainable alternative to TM technologies (Kruth et al., 2007; Telenko & Seepersad, 2012).
Selective Laser Melting (SLM)
Selective Laser melting (SLM) is derived from SLS process technology, and has found application in the
automotive, aerospace and electronics industry (Knowles et al., 2012). Similar to SLS, SLM applies PBF
technology, and the interest around the technology has been motivated by its ability to provide eco-
optimized design in manufacturing for metal parts (Aboulkhair et al., 2014).
In addition, it is noted that SLM also has the ability to help aid manufacturers achieve increased product
performance of high complex parts – specifically applicable in the aforementioned industries – as the CAD-
software allows reduction of material-usage without compromisingquality (Buchbinder, 2011). However, at
this point SLM also suffers drawbacks inthe same form as SLSincludingpoor surface finish, requirements for
post processing and slow build rates (Kruth et al., 2007; Telenko & Seepersad, 2012).
Direct Metal Laser Sintering (DMLS)
DMLS process technology is considered the most robust AM alternative, and was developed by EOS, a
German systems manufacturer in 1989. One of the major advantages of DMLSis that a considerable amount
of research with regards to technology cost performance, process speed, energy consumptionandmaterial
quality properties (Herderick, 2011). Compared to its alternatives suchas EBM, DMLSis consideredsuperior
in terms of surface quality (Ibid). However, current disadvantages such as slow build rate and need of
support structures in the fabrication process exist.
106

Appendix 7: PBF applicable AM materials
Polymers:
Polymers have to the furthest extent foundapplication
in AM. As late as in 2012, approximately 81% of the
total AM materials market was comprised of polymers
and plastics, while metals only accounted for less than
6 percent (Cotteleer et al., 2014). Nylon polymershave
proven significantly applicable due to its superior
ability to melt andbondcomparedwithother polymers
(Guo & Leu, 2013).
Metals:
Application of metal materials holds the largest
promise of AM widespread technologicaladoption.Itis
noted by Herderick, 2011) that advances in AM for
metal parts have the ability to create new
opportunities for low cost manufacturing restricted by
current manufacturing methodologies. Due to high
costs and long lead timesassociatedwithTi-6AI-4V,the
aerospace industry has showed particular interest in
R&D developments of AM fabricated Ti-6AI-4V (Xu et
al., 2015).
Ceramics:
A relatively novel material type that has found
application for AM is ceramics, particularly in SLM
processes (Wilkes, 2013). It is noted that AM powder-
based processing ceramics has the ability to remove
material property behavior such as shrinkage and
density of parts often found in injection molded parts
(Wohlers, 2015). In addition ceramics have:
“tremendous untapped potential when paired with the
freeform capabilities of AM” (Wholers, 2015, pp. 5).
Composites:
Perhaps the most anticipated material to beapplicable
in AM is composites. Due to its relatively low cost, low
weight and high tensile properties, it is increasingly
being applied in manufacturing of aerospace parts
(Wohlers, 2015).
107

Appendix 8: The 3DP/AM value chain
Material Providers
Two types of commonly used materials are offered; powder-based or liquid based, depending on what type of
materials that are applicable for the respective PT or system used.
Design and Software developers
Comprised of design and software developers that offer 3D-CAD software. As the software is essential for the
designs to make the schematics for the object that can be transferred to the hardware or printer system
(Marketline, 2013), it is a highly dependent and complementary market to the hardware printer suppliers.
System manufacturers
3D printer manufacturers is mainly comprised of commercial printers for home-based fabrication in the
consumers own living room, and printers for industrial application whether it is for prototyping purposes in new
product developments or direct digital manufacturing of finished goods.
Service Providers and user communities
The fourth and fifth segments some extent overlap each other. For instance, Shapeways allow individuals to
upload 3D-model designs and get it fabricated using highly advanced 3D printing systems offered by Shapeways
(business insider, 2012). Users can even sell their designs and products to customers, using Shapeways as their
contracting manufacturer and distributor. Online sharingcommunitiessuchasThingiversearesomewhatsimilar,
but instead of being a marketplace for 3D-CAD schematics, it focus on free sharing between users. All designs
uploaded are licensed as a creative commons license (CCL), allowing anyone to use and modifythedesignsbeing
uploaded.
108

Appendix 9: Technology hype-cycle development for 3DP/AM 2012-2015
Hype Cycle – 2012. Hype Cycle – 2013.
Hype Cycle – 2014. Hype Cycle – 2015.
109

Appendix 10: Influence factors for dimensional variation in plastic injection molding
Influence factors for dimensional variation in plastic injection molding. Source: Own creation, adapted from
Stan et al., 2008
Part Design:
Tolerance
Symmetry
Ribs Design
Reinforcement
Wall dimensions
Model Design:
Runner
Gate(s)
Flow design
Venting holes
Injection type
Operator:
Training
Experience
Supervision
Environment:
Ambient temp
Humidity
Air currents
Cooling water
temp
Machine:
Screw design
Extruder barrel
Nozzle design
Machine control
Clamp design
Measurement:
Environment
Procedure
Precision
Accuracy
Stability
Materials:
Type
Additives
Regrind
Contamination
Water
absorption
Injection:
Parameters
Boost pressure
Hold pressure
Hold time
Injection temp
DimensionalAccuracy
110

Appendix 11: Quality differences for AM
Dimensional inaccuracy of SLM. Abd-Elghany &
Bourell, 2012
Tensile specimens for SLM fabricated cylindered
blanks using stainless steel 17-4PH / AISI-630.
Source: Spierings et al., 2012.
Figure a: Photograph of different specimens
to measure S%. Singh et al., 2012.
Figure b: Photograph of universal testing
with machine sample. Singh et al., 2012.
.
111

Appendix 12: Evaluation model & total cost comparison between SLS and PIM
technologies for plastic lamp holder
Evaluation cost model comparison PIM. Source: Atzeni
et al., 2010
Evaluation cost model comparison SLS.
Source: Atzeni et al., 2010
Total cost of the lamp holder produced by AM for two EOS SLS machines. Source: Atzeni et al., 2010.
Total cost of the lamp holder produced by IM for different production volumes. Source: Atzeni et al., 2010
112

Appendix 13: Total costs breakout for SLS & PIM
Costs breakout for PIM process technologies. Source: Atzeni et al., 2010
Costs breakout for SLS process technologies. Source: Atzeni et al., 2010.
113

Appendix 14: Cost evaluation models for SLS and HPDC manufacturing technologies
Evaluation cost model comparison HPDC. Source:
Atzeni & Salmi, 2012.
Evaluation cost model comparison SLS. Source:
Atzeni & Salmi, 2012.
Costs breakout for PIM process technologies. Source:
Atzeni & Salmi, 2012
Costs breakout for PIM process technologies.
Source: Atzeni & Salmi, 2012
114

Appendix 15: AM structural design composition
Open Lattice Structured Design. Source: Maheshwaraa et al., 2007.
115

Appendix 16: AM fabricated jet engine nozzle
LEAP jet engine additively manufactured jet engine nozzle. Source: GE Reports, 2014.
116

Appendix 17: AM application in consumer goods Industries
Figure a: New Balance 3D-Printed sole. Source:
https://www.shapeways.com/wordpress/wp-
content/uploads/2014/04/new-balance-3-d-printed-
shoes-1.jpg
Figure b: Victoria’s Secret 3D-printed costume.
Source:
https://www.shapeways.com/blog/archives/2383-
victorias-secret-snow-angel-spreads-her-3d-printed-
wings-video.html
Figure c: Continuum D.Dress 3D-printed dress.
Source:
http://www.continuumfashion.com/shoes.php
Figure d: Continuum Strvct 3D-printed shoes. Source:
http://www.continuumfashion.com/D.php
117

Appendix 18: Dataset for process technology performance and operational capabilities
Quality Weight AM Plastics AM Metals TM Plastics TM Metals Avg. for AM Avg. for TM
geometric properties 0,33 2 2 4 4
mechanical properties 0,33 3 3 4 5
Physical properties 0,33 3 4 3 4
Sum weighted average 1 2,64 3 3,66666667 4,33333333 2,82 4
Speed Weight AM Plastics AM Metals TM Plastics TM Metals Avg. for AM Avg. for TM
Cycle times / build rates 0,5 2 2 5 5
Pre-processing & post-processing 0,25 4 4 3 3
Assembly times 0,25 5 5 3 3
Sum weighted average 1 3,25 3,25 4 4 3,25 4
Cost Efficiency Weight AM Plastics AM Metals TM Plastics TM Metals Avg. for AM Avg. for TM
Material costs 0,35 3 3 3,5 3,5
Machine costs 0,45 2,75 2,75 4 4
Labor costs 0,2 3 3 3 3
Sum weighted average 1 2,8875 2,8875 3,625 3,625 2,8875 3,625
Flexibility performance Weight AM TM Avg. for AM Avg. for TM
Product flexibility 0,4 5 3
Mix flexibility 0,3 4 3
volume flexibility 0,3 3,5 3,5
Sum weighted average 1 4,25 3,15 4,25 3,15
Dependability Performance Weight AM TM Avg. for AM Avg. for TM
Machine automation 0,2 2 5
Machine reliability 0,4 2 4
Machine consistency 0,4 2 4
Sum weighted average 1 2 4,2 2 4,2
118

Appendix 19: Alternative supply chain configuration for AM vs. TM scenarios
Modified supply chain configuration. Source:
Mashhadi et al., 2015.
Classic supply chain configuration. Source: Mashhadi
et al., 2015.
119

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