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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 in new product development. However, much indicate 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 adoption decision tool for managers exploring the 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 provided a 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 utilizing five 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 linked with technology 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 an expansion of the purpose boundaries for strategic areas of application. It is further suggested that these advances hold the potential to support a new, more sustainable manufacturing operations and supply chain paradigm without compromising the ability to achieve cost efficiency. 1

Transcript of Thesis incl. appendices

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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 in new product development. However, much indicate 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 adoption decision tool for managers exploring the 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 provided a 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 utilizing five 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 linked with technology 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 an expansion of the purpose boundaries for strategic areas of application. It is further suggested that these advances hold the 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, and friend throughout 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 surrounding 3D printing (3DP), Additive Manufacturing (AM), Rapid Prototyping (RP), Rapid Manufacturing (RM) or Stereolithography (SL) – as it initially was named by 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, leading to an explosion in the field of research on the 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 and challenges that have motivated this thesis. Each sub-section constitutes a natural phenomenological order in which the 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, creating a 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 noted in Hesse & 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 growth by 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 population growth will come from developing and emerging economies (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 urbanization growth in 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 becoming a 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 in the highest final energy-consuming transport 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, meaning a phenomenon in which where organizations find it beneficial to outsource non-core value adding activities resulting in cross-border physical separation of different parts of the production process (Feenstra, 1998, Arndt & Kierzkowski, 2001).For instance, predicted forecasts on production and 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 increased global competition, posing challenges 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.

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3. Increased focus on sustainability: such as 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 argued that 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 and disruptive force embedded in the emergence of industrial AM. The manner, in which the author goes about this task, is through identification and addressing of 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 with regards to the following levels of strategic influence: (1) manufacturing operations, (2) general business, and supply chain strategic orientations.

(4) Implications for operations & supply chains: Identification of 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 systems thinking 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 distinguish between adoption decisions and implementation 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 following the decision to adopt an innovation and ending 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 which serves 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 existing research 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 section therefore takes an exploratory approach in order to identify patter of opportunities and challenges noted by academics, practitioners and industry 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 distinguishing characteristics 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 identified through: “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 with overall business - and operations strategy is considered an important determinant in the adoption-decision process. Hence, the knowledge and insight found in BB-1 and BB-2 with regards to AM´s distinguishing features, characteristics, technology readiness and conversion-process performance capabilities, will coupled with observations on contemporary 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, in which the researcher approaches the latter, is less constrained.

As theoretical frameworks is supposed to drive the answering of the proposed research question(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 found in section 2.1. Each BB – with the exception of BB-1, in which is restricted to exploration and description – has its own specific 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 and must support corporate strategy in order 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

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3.2 Manufacturing Strategy Operations strategy is concerned with successful 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 on its 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 design and variety. 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 build the process structure based on internal resource capabilities such as plant and equipment, production planning and control, labor and staffing, product design and engineering, and organization and management (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 through benchmarking of best industry practices and continuous 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 pursuing MO. Given its contingency-based approaches, as for instance embedded in Hill (1993) – in which emphasize 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.

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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 satisfying different 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 which organizations utilize their resources and dictate what types of practices the organization choose to employ. Tan et al (2007) refers to these operating characteristics as manufacturing capabilities.

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3.2.2.3 Manufacturing Capabilities According to Größler & Grübner (2006), the two aforementioned constituents of manufacturing strategy 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 in use” within the manufacturing operation. In addition to 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 Much contemporary work on business and manufacturing strategy 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) provided a generic taxonomy for manufacturing strategies. Through development of capability profiles based on competitive capabilities identified in the work of skinner (1969) and Hayes & Wheelwright (1984), they pooled manufacturers into three strategic categories, including: (1) caretakers – are predominantly focused on 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.

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Priorities within the marketeer cluster were conformance quality, dependable deliveries, and product performance. The Innovators – place highest emphasis on differentiation through uniqueness in design and rate 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 manufacturing strategic 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 manufacturing strategic approaches. As noted in the section above, 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 lead times (Kim & lee, 1993). According to Kotha & Orne (1989), the strategic target is usually more segmented due 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 conceived to hold any potential strategic advantage (Kim & Lee, 1993). However, as integration between process technologies and IT-solutions such as CAM, CAD and FMS have paved way for mixing cost and differentiation strategy at corporate level, but also between business units.

Pure Differentiation

High Cost & Differentiation

No Intended Strategy

Pure Cost Leadership Low

High Low Cost Efficiency

Differentiation

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In more recent times, an additional fourth strategic orientation has opened up new 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 assumed that primary dimensions and underlying variables 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 composed of three structural constituents illustrated in 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

Organizational 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 such as: (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 conversion process and indirect process technologies process technologies such as ERP systems, that facilitates high connectivity exists (Paton et al., 2011; Slack & Lewis, 2011);

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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 found in 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

2 1

Low

High

High Low

Low

High

Process Structure Complexity

Product Line Complexity

Organizational Scope

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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 should first and foremost 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

Technical Complexity

High

High

Low

Low

Job Shop

Batch FMC

FMS

Anachronistic factory Assembly

Line Flexible

Assembly Line

Transfer Line Continuous

Flow Process

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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 manufacturing strategies. Paton et 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, monitoring and decision-making geared towards fulfilling organizational 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.

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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 Lower processing costs

Speed Short delivery queuing times Fast response requests

Higher confidence in the operation Fewer contingencies needed More internal stability Lower processing costs

Dependability On-time delivery of products Knowledge of delivery times

Better response to unpredicted events Better response to variety of activities Lower processing costs

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 such as 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-span between the 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 with what 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 intend to purchase. As the

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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 focused around satisfying external customer expectations by providing on-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, an organization that pursue pure cost leadership strategy – 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 between four categories of flexibility, including: (1) product flexibility – the extent to which an operation has the ability to introduce new products or modify existing ones, (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 on price (Slack & Lewis, 2011). In an operational context, Slack & Lewis propose a broad definition of cost to be: “any financial input to the operation that enables it to produce its products and services”. They further distinguish between three 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 needed to acquire the necessary equipment used in the transformation process (e.g. facilities, systems and processing machinery), (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.

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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 guiding structure in doing 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 to improve 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 second mode, focus more on trans-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 production of knowledge is assumed not 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 both modes, as the evolution of the field of 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:

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• 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-called paradigms in research, 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 and qualitative research (Ibid).

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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 reasoning in research. A deductive reasoning paradigm aims for generalization of particular observations through demonstration of causal relationships between theory and the research findings (Gulati, 2009). The relationship between theory and research findings through deductive reasoning is characterized by testing of hypotheses that is guided by established theoretical 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 reasoning is 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: “exploration into social and technical potentials and opportunities” (Goldkuhl, 2012, p.87). Three types of pragmatism can further be identified; functional pragmatism – meaning, knowledge for actions, referential pragmatism – meaning, knowledge about actions, and methodological 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

Theory Research Findings

Deductive reasoning

Inductive reasoning

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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 in a posited structure 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 and interpretivists 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 can be exemplified by 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 view of understanding and explaining current phenomenological 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 implications of the former types of communicated knowledge with 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.

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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 and implications of AM on manufacturing and operational 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 characterized by back and forth movement between the 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

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4.4.1 Conceptual Framework The main constituent of the dynamic research process is the conceptual framework, in which is built on four pillars (building-blocks) of key adoption determinants. With the exception of BB-1, each block is dependent on a theoretical frame of reference in which interpret the evidence from reality. Hence, the abductive research approach 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 can therefore be understood to 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 assumed by 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 in achieving 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

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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 revisited in order 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 gathering of both quantitative and qualitative data in order to best answer the research problem at hand (Creswell, 2003). The following section elaborates on the 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 and descriptive, but involve gathering of both quantitative 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 and quantitative data, is through archival literature analysis that aim to track the relatively contemporary historical developments with regards 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 gathered in BB-2 is mostly related to PT performance, and can therefore 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 related manufacturing strategies –, and an inductive 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 proposed impact AM may have on manufacturing operations and supply chains is adapted to fit into a theoretical frame of reference involving established theorems within strategy and performance. Hence, the inductive perspective involve a focal point of the research through predefined theoretical scope of what theory says about strategy and performance, in which is challenged through the deductive approach by potentially

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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 required specificity 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) further argues that secondary data gathered within an approximate time-span of its intended purpose, may overcome problems of recall (as cited in Harris, 2001). This thesis further includes both quantitative and qualitative data sources. The data sources used as 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 in selection 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 and facts, and; (c) position the 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 applied is in accordance with the 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).

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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” each represents an important 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 spanning areas such as: production and manufacturing, material science, manufacturing engineering, 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 high importance when secondary 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.

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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 approach proposed by Slack et al (2004), as it is considered the 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 aid in confirming and/or disproving what findings in “BB-1 – technology & market overview” suggests to be with regards to AM technology viability. It thereby serves as the qualifying criteria for AM, and constitutes an important element for the analytical approach in 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 selected on the basis of matching the theoretically defined primary performance dimensions proposed by Slack & Lewis (2011), and various underlying variables from the literature review, with data and observations for those performance related dimensions found through the 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.

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Base metrics Dimensional Variables Weight

(%)

Calculation method Calculation variables

Category range (label)

Quality • Geometric properties 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨

= 𝟏𝟏𝑨𝑨 (𝑨𝑨𝟏𝟏 + 𝑨𝑨𝟐𝟐+ …𝑨𝑨𝑨𝑨)

1-5 points Very low, low, satisfying, good very good

• Mechanical properties • Physical properties

Speed/Time • Cycle time/ build rates 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨

= 𝟏𝟏𝑨𝑨 (𝑨𝑨𝟏𝟏 + 𝑨𝑨𝟐𝟐+ …𝑨𝑨𝑨𝑨)

1-5 points Very low, low, satisfying, good very good

• Set-up & processing time • Assembly time

Cost • Materials costs 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨

= 𝟏𝟏𝑨𝑨 (𝑨𝑨𝟏𝟏 + 𝑨𝑨𝟐𝟐+ …𝑨𝑨𝑨𝑨)

1-5 points Very low, low, satisfying, good very good

• Machine costs o Investment costs o Machine processing

costs (pre, in, post-processing)

o Energy consumption • Labor costs

o Operator costs Flexibility • Product flexibility 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨

= 𝟏𝟏𝑨𝑨 (𝑨𝑨𝟏𝟏 + 𝑨𝑨𝟐𝟐+ …𝑨𝑨𝑨𝑨)

1-5 points Very low, low, satisfying, good very good

o Customization o Complexity

• Mix flexibility o Process flow flexibility o Variety of products

• Volume flexibility o Volume capacity o Output flexibility

Dependability • Machine automation 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨

= 𝟏𝟏𝑨𝑨 (𝑨𝑨𝟏𝟏 + 𝑨𝑨𝟐𝟐+⋯𝑨𝑨𝑨𝑨)

1-5 points Very low, low, 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.

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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 deemed necessary 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 on that particular Performance for their processes. In addition, the thesis takes into consideration that an average 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 research findings in BB-3, the analytical content and research findings 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

Anachronistic 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.

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4.8.1.1 Manufacturing System Classification The analysis of manufacturing system classification utilize the typology framework of Kim & Lee (1993) found in the 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 second step of the analysis includes determining the process technology’s fit with different generic manufacturing and business strategic orientations. Here, we draw on the manufacturing strategic frameworks proposed by Kotha & Orne (1989), typological manufacturing classifications 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 based on a discussion of 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 research question, in which where in-depth research within one 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 in OM research, as the rapid evolution in OM 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.

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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 second section 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) in which in 2010 was bundled together 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 with 80% 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).

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• Rapid Tooling (RT): RT is a method of RP in which bridges the purposes of RP into a manufacturing context 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 updated version building on Pham´s principal format of classification is provided in 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

Mat

eria

ls

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

Low Presicion High

High Surface Roughness Low

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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 expanded the 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 develop the dominant design that 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

Mar

ket V

alue

in

billi

on U

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olla

rs

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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 been proven to stretch far beyond private investors, as governmental initiatives such as the 2012 opening of the National Additive Manufacturing Innovation Institute (NAMII) in Youngstown, Ohio suggest governmental interest. Composed of over 40 large companies (including IBM, Boeing and Lockheed Martin), 9 research universities (including Penn State University, Carnegie Mellon University and Lehigh University), 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 manufacturing innovation institutes 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 billion funded initiative from 2014 that aim to place the EU on the map as leaders in sustainable innovative developments. Recognized by 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 build their 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 each value chain segment.

Figure 9 – 3D Printing Industry value chain. Source: Own creation, inspired by Business insider 2012, Marketline 2013, and Frost &

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5.4.4 Additive Manufacturing – Technology Life Cycle Assessment Regardless of the technological advances and industry growth experienced through the 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 on Rogers’ (1995) Technology Adoption Life Cycle, in which aim to pinpoint the technology life cycle maturity represented by 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 explanation given by Wohlers Associates (WA) (2013) for why AM adoption is currently delaying is due to the fact that there is yet to emerge a dominant design, characterized as the standard technological platform in which all improvements takes departure from until a new disruptive innovation takes its place(Paton et 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).

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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 inflated expectations”, and expected to reach the next plateau; through of disillusionment in 5-10 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.

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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 and a revenue growth of 87.7% for printer system providers in the 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 increasing the technologies value and market 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 competition in terms of price, quality and lacking competition that 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 in the 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.

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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 section is therefore dedicated to 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 distinguishing features 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 and importance of AM for metal parts, metal based AMT processes such as selective laser melting (SLM) and direct metal laser sintering (DMLS) will be compared with traditional 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 and control systems such as process parameters and process signatures cannot be overemphasized, as this is considered to be the key for future AM

Process Parameters

•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

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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 predefined parameters 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 and can therefore 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 fabricated products differ from those manufactured with TMT 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 (Gibson et al., 2010; Singh et al., 2012). The section below 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 error of 0.140% with the maximum error of 0.192%.

TM dimensional accuracy for plastic parts: According to Stan et al (2008), molding accuracy of +/- 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.

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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 in the 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 between SLS and PIM based on the above-mentioned properties collected from different SLS adapted Nylon-12 (PA) powder material providers. The data suggests that material specifications are relatively uninfluenced by variation in process-technique. In fact, 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), in which provided empricial evidence that PA-12 Nylon fabricated materials with SLS had 10% lower tensile modulus than when applied under pressure during injection molding. Moreover, Kruth et al. (2007) demonstrated by the use of notched izod-impact testing – an ASTM

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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 referred to physical property differences between AMT and TMT 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 fabricated Ti-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 on X, Y, Z axis in the build chamber had major influence on part densities of SLS fabricated objects (Appendix 11). By simultaneous fabrication of 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 cylindered blanks using stainless 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).

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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 build rate, and is measured in cubic centimeters pr. hour (cm3/h) (Lindemann et al., 2012). However, most research use build time pr. hour (T). Due to variabilities in process parameters for each build, it is noted that build time estimation is relative, and varies not only for each build set-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. In their comparative simulation of two different AM machines (EOS P730 and EOS P390), they found that EOS P390 was able to produce 2520 parts pr. job at 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 production of output volumes 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 build rates for 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 being fabricated (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.

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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 feasible within 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 on pre-, and post-processing varies among systems 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 and direct costs such as: labor, machine costs and material costs, Ruffo et al (2006) placed more emphasis on material

4000 8000 12 000 16 000 0 Number of parts

Injection molding

Additive Manufacturing

Cost pr. part

0

8 6 4 2

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utilization and different aspects of process speed/build rates. Thus, most of the associated research includes a combination of these. However, in line with Hopkinson & 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 version end-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 on material 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

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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 utilization suggests 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 an estimated average based on three factors including: (1) standard components cost (SC), (2) mold cavities and slide 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 investment cost, representing between 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 for stainless 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 costs pr. 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 equivalent for pre-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

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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 following cost and time: (1) machine operator cost pr. hour (O), (2) set up time pr. build (A), and (3) number of pre-processed part (N). Mold costs pr. part (KP) is covered in table 21. Hence, the following equation for AM pre-processing costs is:

Additive Manufacturing 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 assembly cost at € 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). Making up the following equations:

Additive Manufacturing Traditional Manufacturing CP = 𝐶𝐶𝐶𝐶/𝑁𝑁 CP = (𝐴𝐴 + 𝐴𝐴𝑃𝑃 ∗ 𝐴𝐴𝑃𝑃) ∗ 𝑃𝑃

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

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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:

Additive Manufacturing Traditional Manufacturing BP = (O ∗ B + HT)/𝑁𝑁 AP = 𝐻𝐻𝑃𝑃 + 𝑊𝑊𝑂𝑂 + 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 SLS and PIM 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. A complete breakdown is 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.

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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 research suggests that labor costs constitute a relatively small portion of production costs for AM (Thomas & Gilbert, 2014). According to 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-processing activities, 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 often evaluated on the operations product-, mix-, and volume – 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 and degree 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 in terms of complexity costs. Elaborates on this and state that, as: “design re-iterations are cheap because they basically require changing the instructions in the CAD software” (Bianchi, 2014 & Åhlström, p. 6).

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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 enabled by 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

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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 tooling requirements 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 related to 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 would exceed that of traditional IM. This low production volume was rationalized by the high priced nature of AM raw materials, in which on a general basis is considered to 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) and Atzeni & Salmi (2012) are paving the 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 in 17b illustrate that volumes of 42 parts can be produced more 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 following sections include research on contemporary and differences in dependability 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 and processing speed for AM fabricated products, Holmström et al, (2010) notes that time consuming machine calibration – requiring high competency and manual intervention by 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 designed artificial 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 given further 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 embracing widespread adoption of 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 increased passenger 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 produced by General Electrics and Snecma. 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

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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 in 2014, AM technology adoption in 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 can be found in that 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 injection molding 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 application provided in the 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 with SLS in the construction of 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 SLS has made it possible for NB to: “manufacture on demand, fluidly adjust our process to accommodate different sizes and widths, 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 experimented with AM. In 2013, 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), and metal 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 and product qualities for each build – meaning, no set-up fits 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 fabricated parts 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 expansion cause 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 fabricated products are therefore awarded a 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 in the conversion process suggests, AMT’s can produce 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 Process technology averages 3.25 4.0

Table 27 – Outbreak of point scores for AM vs. TM process speed/time performance. Source: Own creation.

6.1.2.1 Discussion of Results Cycle times / build rates: As suggested in the findings and data (table 20), the rate and scale in which output can be 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 in 54 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 such as machine set-up and calibration for 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-up activities are not considered in the 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 which is suggested to be between weeks and even months. 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 even by 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 drop as volume increases. However, the data suggest that economies of scope can be leveraged to a greater extent than TM methods. Hence, a cost efficiency comparison on a process level between technologies departing from a scale and scope perspective proved challenging. A breakout 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 Process technology averages 2.89 3.63

Table 28 - Outbreak of point scores for AM vs. TM Cost-efficiency. Source: Own creation.

6.1.3.1 Discussion of Results 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 fabrication of high-cost parts can to a greater extent justify material costs production, as no relationships between part 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-processing activities accounted for € 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, and 500.000. 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 in the analysis. However, the general notion as suggested through the data comparison of AM for metals and HPDC 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 on energy consumption differences between AM and PIM (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 mega joule (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 on labor 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.

6.1.4.1 Discussion of Results 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 fabrication enable almost unconstrained geometric freedom in the manufacturing process. Consequently, prototyping activities and expensive iterations and re-iterations in the process-chain of 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, although manufacturing of products with high complexity 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: Based on the same principles of digitalization of the design phase, 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 and 16b, the shop floor process and material flow in AM

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 research suggests that product portfolios through AM 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 such as those found in TM process flows, can raise the standards for variety of products offered. Hence, AM is awarded 5-points for product variety flexibility, compared to 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 than TMT processes, is closely linked to 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 manufacturing environment for AM is dependent on the 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.

6.1.5.1 Discussion of Results 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 intervention by the operator between each build is currently holding back full automation. As for TM systems, high levels of automation are often retained through predefined sequences 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 fabricated metal parts is proven to 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 requiring many 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

012345Quality

Speed/Time

Cost efficiencyFlexibility

DependabilityAdditive Manufacturing

Traditional Manufacturing

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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 in fabricated products, while PIM and HPDC needs to be justified by low variety and high degrees of pre-determined process-structures, as new molds and dies are required for each part or component that is being produced. Flexibility performance: It is evident that the area in which AM can offer a true competitive edge for manufacturers is in terms of flexibility oriented operations. Exemplified by potential paradigmatic shifts in material 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 manufacturing companies is induced by product and process complexity (Steinhilper et al., 2012), AMT may provide a solution that help aid in reducing costs in these 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 with regards 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.

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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 classification type 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 associated with supporting high levels of differentiated products through mass-customization, while simultaneously pursuing cost leadership. These

Technical Flexibility

Technical Complexity

High

High

Low

Low

Intermittent system

Degenerate system

Continuous system

Concurrent system

AM

Technical Flexibility

Technical Complexity

High

High

Low

Low

Job Shop

Batch FMC FMS

Anachronistic factory Assembly

Line Flexible

Assembly Line

Transfer Line Continuous

Flow Process

AM

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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 and application of CAD-software, enable manufacturing of 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, and improved managerial 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 driven industry (Conner et al., 2014), the medical industry place AM into a contemporary context of consumer oriented areas of application that hold both differentiation and 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

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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 manufacturing complexity 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 differentiation strategic 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 notion of 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

Technical Complexity

High

High Low

Low

High

Low

Cost Efficiency

Different-iation

Low High

AM ”1”

AM ”1” AM

”2” AM ”2”

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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 and time-incurring elements. 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 based on their proximity of access to cheaper factors of production. According to 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.

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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 in AM-adapted SC’s to be regional, consequently removing the 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 transportation meaning that 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 and customer 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 goods Finished goods

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The large difference in processing speed accounted for in BB-2 are based on production throughput and cycle times, and does not necessarily provide a clear picture of the real difference AM can make in terms of lead times (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 uploaded by the customer, and further be manufactured and shipped from local facilities to the desired location, 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) distribution costs, (3) manufacturing costs, (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 such as the need to keep safety stock, and the possibility of increased inventory turnover for fast-moving items as production planning can tailored for 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

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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 suggested that AM energy consumption benefits 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).

Supp

ly

Char

acte

ristic

s

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, suggesting that 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 recognized as agile SC principles –, one can conclude 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 and 24b illustrate 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

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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 in technology 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 explained by 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 regionalization perspective compared to 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

2 1

Low

High

High Low

Low

High

Process Structure Complexity

Product Line Complexity

Organizational Scope

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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, adoption decision tool based on an 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 o Low TM processes o High AM processes

• Capital investment o Low TM processes o High AM processes

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 o Low TM High AM

• Output volume o Low AM High TM

• Operational dependability o Low AM High TM

• Operational flexibility o Low TM High AM

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quantifiable at this point in time. From this scientifically proven and verifiable 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 both domains with knowledge 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 manufacturing flexibility – exemplified by total control of product-, and process flexibility on a conversion-process level, to increased control 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 which will increase quality and speed performance. Furthermore, the fact that most of the reviewed research on the 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 experienced in the 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 provided conclusive evidence that the contemporary performance capabilities of AMT is 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 widespread application. Regardless of these issues, the ability of AMT to disrupt established theorems with regards 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.

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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 differentiation strategic 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 redefinition of internal operational requirements, in which enable 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 thorough estimations of total landed costs, 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 increasing customization requirements 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 action is not considered in this thesis, and involve a very different set of assessments with regards to the impact on the dynamics within the adopting organization. In addition to human resource-specific considerations such as 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 such as communication flows. 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. Associated activities in this evaluation include the assessing the 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 required quality 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.

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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).

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Other: Data & Terminology

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Material properties & average values of injection molded specimens using nylon 12 PA. Available at: http://www.efunda.com/materials/polymers/properties/polymer_datasheet.cfm?MajorID=PA&MinorID=81 [Accessed: 14.10.2015]

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Technical description of High-pressure die-casting processes. Available at: http://www.custompartnet.com/wu/die-casting [Accessed: 10.04.2016]

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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 ....................................

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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

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Appendix 2: Overview of traditional manufacturing process techniques

Figure a: Injection molding – is the most commonly used manufacturing process for the fabrication of plastic parts.

Figure b: Die casting – is a manufacturing process that can produce geometrically complex metal parts through the use of reusable molds, called dies.

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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 regardless of 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.

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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). Involves resins being are 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’ industrial printer Objet1000 Plus can build large objects measuring 1000mm 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, this AM 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.

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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 material can move horizontally 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. Given the liquid properties of the materials 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 process speed is 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.

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Powder Bed Fusion The SLS powder bed fusion process is similar to that of SLA as it involves the sintering of 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 scanning mirrors over the powder layer, sintering and attaching a new layer of the part [4]. Each time 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 then separated from un-sintered powder 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 referred to 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 (Loughborough University AMRG, 2015). Existing disadvantages such as manual post-processing increase the time frame of the process and makes it unviable for industrial application.

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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

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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 considered the 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 compromising quality (Buchbinder, 2011). However, at this point SLM also suffers drawbacks in the same form as SLS including poor 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 DMLS is that a considerable amount of research with regards to technology cost performance, process speed, energy consumption and material quality properties (Herderick, 2011). Compared to its alternatives such as EBM, DMLS is considered superior in terms of surface quality (Ibid). However, current disadvantages such as slow build rate and need of support structures in the fabrication process exist.

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Appendix 7: PBF applicable AM materials

Polymers: Polymers have to the furthest extent found application 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 polymers have proven significantly applicable due to its superior ability to melt and bond compared with other polymers (Guo & Leu, 2013).

Metals: Application of metal materials holds the largest promise of AM widespread technological adoption. It is 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 times associated with Ti-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 be applicable 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).

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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 sharing communities such as Thingiverse are somewhat similar, 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 modify the designs being uploaded.

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Appendix 9: Technology hype-cycle development for 3DP/AM 2012-2015

Hype Cycle – 2012.

Hype Cycle – 2013.

Hype Cycle – 2014.

Hype Cycle – 2015.

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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

Dim

ensio

nal A

ccur

acy

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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.

.

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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

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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.

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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

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Appendix 15: AM structural design composition

Open Lattice Structured Design. Source: Maheshwaraa et al., 2007.

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Appendix 16: AM fabricated jet engine nozzle

LEAP jet engine additively manufactured jet engine nozzle. Source: GE Reports, 2014.

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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

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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

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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.

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