Conference Agenda

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Session Overview
Session
21-AM-06: ST7.3 - Industrial Modernisation in European Industry - Diffusion, Adoption, and Effects of Advanced Production Technologies
Time:
Friday, 21/Jun/2019:
8:30am - 10:00am

Session Chair: Giacomo Copani, STIIMA-CNR
Session Chair: Bernhard Dachs, Austrian Institute of Technology
Session Chair: Lawrence Dooley, University College Cork
Session Chair: Henning Kroll, Fraunhofer ISI, Germany
Session Chair: Christian Lerch, Fraunhofer
Session Chair: Oliver Som, Management Center Innsbruck, Austria
Location: Room PC 17

Session Abstract

Manufacturing is among the key driving forces of the European economy. It provides about 20% of all jobs in Europe and generates a turnover of about €7 000 billion in 25 industrial sectors and over 2 million companies. Nowadays, as never before, manufacturing firms need to be able to continuously offer flexibility combined with a high quality/price ratio. One of the crucial prerequisites for achieving and maintaining competitiveness is the adoption and effective usage of a broad range of advanced manufacturing technologies (AMT). Across the board, much of the existing literature recognises these technologies as drivers of competitive advantage, improving productivity, production speed, operating precision as well as energy and material consumption. Moreover, they are innovation multipliers applied to facilitate development of new products. Nonetheless, Europe’s position with respect to ATM performance and the uptake of related technologies in large sections of its industrial sector has remained less than satisfactory. With Asia catching up fast and recovery in the United States, Europe is not able to rest on its laurels. While various relevant AMT are developed by European firms, far too few of them have become commonly adopted.

But the understanding of diffusion mechanisms (e.g. prerequisites, motives of firms) as well as the effects and impacts of industrial modernization (e.g. economic, ecologic performance, development of new business models, impacts on skills) is still scarce, particularly in terms of generalizable, quantitative user-level data. Given the economic importance of the manufacturing sector in the EU, also policymakers need new insights and approaches of monitoring and promoting the diffusion of AMT.

Our proposed special track wants to address these needs by offering an arena for internationally leading researchers in this field to exchange on their newest research findings as well as to strengthen the collaboration network amongst them.

Due to the multidimensional subject of AMT, the track is based on the premise of a holistic approach which is targeted on integrating research, particularly in the fields of economics, management, industry dynamics, technology assessment, decision-making, and technology policy to allow for mutual learning, exchange, and thereby deepen our understanding of AMT. This interdisciplinarity is also reflected in the team of chairs which are ready to activate their research networks to attract 8-12 high-quality contributions. Additionally, the chairs themselves have outstanding expertise in the field of AMT and authored several recent studies and reports about the determinants, dynamics, effects, and policy requirements of AMT (references are available upon request) which could serve as a basis for submissions.

Thus, we cordially invite researchers from all disciplines (see above) to present and discuss their latest findings. Ideally, the contribution within the track sessions will lead into a special issue of a renowned journal providing an integrative overview of the interdisciplinary state-of-the-art research and future research agendas on AMT.

The session format will be based on paper presentation. However, to stimulate fruitful discussions each author(s) will be asked to take the role of a discussant of one of the session’s papers. Additionally, we will ask the presenters to limit their slide decks to seven slides at max to leave enough room discussion.


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Presentations

Additive Manufacturing Technology in European Manufacturing Industry – Empirical Insights on Diffusion and Determinants of Use

Oliver Som1, Bernhard Dachs2, Christian Lerch3, Henning Kroll3, Jan Kraner4

1Management Center Innsbruck, Austria, Austria MCI; 2Austrian Institute of Technology AIT; 3Fraunhofer Institute for Systems and Innovation Research ISI; 4University of Applied Science Luzern

Context

Additive Manufacturing (AM) is a set of technologies that creates objects from layered 3D model data. AM now reaches production status. The EU is increasingly setting its sights on AM, because it could give firms the means to re-shore production from low-wage regions and create sustainable growth in the EU.

Literature

Generally, the adoption of manufacturing technologies by companies is influenced by a range of drivers and barriers (Tornatzky and Fleischer, 1990; Martínez Sánchez, 1991; Rogers, 2003; Del Rio Gonzales, 2005; Darbanhosseiniamirkhiz and Ismail, 2012). The existing literature on the diffusion of AM however mainly focuses on the technological potential of AM and new fields of application (e.g. (EC, 2014, 2016; Gebhardt, 2000, 2013; Hague, 2006; Tuck and Hague, 2006; Hotza et al., 2014; Bopp, 2010; Yan et. al., 2009). Studies that focus on the user perspective of adoption of AM and its related determinants and barriers, however, are very scarce. To the best of the authors’ knowledge, the study presented by Som et al., 2016 is the only one that investigates the uptake of AM from the user side with a qualitative, case-study based approach.

Literature Gap

Existing literature focuses on its technological potential and new fields of application. The only Study which focuses on the uptake of AM from the user perspective in Germany by using a qualitative research design is provided by Som et al. 2016. So there is need for EU-wide quantitative studies.

Research Questions

The paper addresses this research gap by the following research questions:

(1) To which extent has AM for rapid prototyping and rapid manufacturing diffused across European manufacturing firms?

(2) What are firm-level determinants related to the adoption of AM (rapid prototyping and/or rapid manufacturing) in European manufacturing firms?

Methodology

The paper is based on a quantitative analysis of firm-level data. Descriptive statistics are used to analyse the diffusion of AM among European manufacturing firms in terms of usage, year of introduction, planned use and re-investments of existing equipment. Subsequently, determinants and drivers of AM’s adoption by firms are analysed by a series of multivariate regression models. The selection of the independent variables is based on the diffusion model by Rogers (2003) and includes different technology-related, organisational and external factors that could influence the adoption of AM.

Empirical Material

The secondary analyses are based on firm-level data from the European Manufacturing Survey (EMS) which is conducted by a consortium of European research organisations and coordinated by the Fraunhofer Institute for Systems and Innovation Research ISI. The latest survey in 2015/2016 was carried out in 9 European countries and covers data from more than 3,300 manufacturing firms.

With regards to the diffusion of AM, the dataset includes the following variables:

1) Use of additive manufacturing technologies for rapid prototyping (yes/no, year of introduction, planned used, re-investments since first use)

2) Use of additive manufacturing technologies for mass production (yes/no, year of introduction, planned used, re-investments since first use)

3) Position in the value chain

4) Competitive strategy

5) Use of other advanced manufacturing technologies

6) Innovation activities & R&D

7) Type of product development

8) Production characteristics

9) Age

10) Qualification structure of workforce

11) Country

12) Industry

13) Firm size

14) Vertical range of manufacturing

Results

Firstly, the paper shows to what extent AM has been adopted by European Manufacturing firms over time, and whether the diffusion is related to firms’ specific structural characteristics. The results will show whether and in which respect early adopting firms of AM differ from late-comers or non-users in terms of technology-related, organization-related or external characteristics. Additionally, the results will also highlight possible path dependencies of technology usage. More specific, results will show whether firms who have used rapid prototyping in the past, have a higher propensity to use AM in series production as well.

Contribution to Scholarship

The paper contributes to the research on the diffusion of advanced manufacturing technologies in Europe in general, and for the case of AM in particular. It is expected to close the addressed research gap by providing current empirical insights on the adoption and its internal and external determinants of AM on the firm level. Based on this, it will be discussed whether existing theories and models of technology adoptions can be applied to the field of AM. The findings also provide new insights into the diffusion mechanism of AM on the firm level by looking into the path dependencies and cross-enabling role of existing technological and organisational frameconditions that influence the uptake.

Contribution to Practice

By providing novel insights on the adoption of advanced manufacturing technologies by European manufacturing firms, policy maker will gain a better understanding of the diffusion mechanisms as a basis to design/improve support schemes in the field of innovation and technology policy.

Business practitioners can use the findings to get a comprehensive and reliable picture about the technological maturity of AM especially in the field of series production as well as assess their own company in comparison to other firms in the European industry. Finally, managers’ awareness about technological and organisational antecedents of adopting AM is increased.

Fitness

By focusing on the diffusion of AM into the European industry and by providing a better understanding of how such innovative manufacturing technologies are adopted by firms, the paper directly addresses the bridge between applied research on manufacturing technologies and industry.

Bibliography

Bopp, F. (2010): Rapid Manufacturing: Zukünftige Wertschöpfungsmodelle durch generative Fertigungsverfahren. Hamburg: Diplomica Verlag.

Darbanhosseiniamirkhiz, M. Ismail; W. K. W. (2012): Advanced Manufacturing Technology Adoption in SMEs: An Integrative Model, Journal of Technology Management & Innovation, Vol. 7, No. 4, pp. 112-120.

Del Rio Gonzales, P. (2005): Analysing the factors influencing clean technology adoption: a study of the Spanish pulp and paper industry, Business Strategy and the Environment, Vol. 14, No. 1, pp. 20-37.

EC (2014): Additive Manufacturing in FP7 and Horizon 2020. Report from the EC Workshop on Additive Manufacturing held on 18 June 2014. Brussels.

EC (2016): Identifying current and future application areas, existing industrial value chains and missing competences in the EU, in the area of additive manufacturing (3D-printing). Final Report, Brussels, 15th of July, 2016.

Gebhardt, A. (2000): Rapid Prototyping – Werkzeuge für die schnelle Produktentstehung. München: Carl Hanser Verlag.

Gebhardt, A. (2013): Generative Fertigungsverfahren. Additive Manufacturing und 3D Drucken für Prototyping - Tooling – Produktion. München: Carl Hanser Verlag.

Hague, R. (2006): Unlocking the Design Potential of Rapid Manufacturing. In: Hopkinson, Neil; Hague, Richard; Dickens, Phill (ed.): Rapid Manufacturing – An Industrial Revolution for The Digital Age. Chichester: John Wiley & Sons, S. 5-18.

Martínez Sánchez, A. (1991) "Advanced Manufacturing Technologies: An Integrated Model of Diffusion", International Journal of Operations & Production Management, Vol. 11 Issue: 9, pp.48-63.

Rogers, E. M. (2003): Diffusion of innovations, New York: Free Press.

Som, O., Thielmann, A., Schnabl, E., Daimer, S., Berghäuser, H., Rothengatter, O. (2016): Anwendungs- und Entwicklungsperspektiven der additiven Fertigung für den Wirtschaftsstandort Deutschland, Gutachten an den Deutschen Bundestag, Büro für Technikfolgenabschätzung beim Deutschen Bundestag TAB, Berlin.

Tornatzky, L., Fleischer, M. (1990): The process of technology innovation, Lexington, MA: Lexington Books.

Tuck, C., Hague, R. (2006): Management and Implementation of Rapid Manufacturing. In: Hopkinson, Neil; Hague, Richard; Dickens, Phill (ed.): Rapid Manufacturing – An Industrial Revolution For The Digital Age. Chichester: John Wiley & Sons, 2006, S. 5-18.

Yan, Yongnian et. al. (2009): Rapid Prototyping and Manufacturing Technology: Principle, Representative Techniques, Applications, and Development Trends. In: Tsinghua Science and Technology 14, Heft 1, S. 1-12.



Analysis of barriers limiting advanced manufacturing innovation in the European Regional ecosystem

Golboo Pourabdollahian, Giacomo Copani

CNR-STIIMA, Italy

Context

Efficient manufacturing innovation ecosystems provide external resources to SMEs to collaborate with external agents to transfer research results in the ecosystem -including Industry 4.0 technologies- to their products, processes and services. This paper analyses the cooperation barriers in manufacturing innovation ecosystems through in-depth analysis of four EU regional ecosystems.

Literature

A Regional Innovation System is a network of stakeholders within a geographical area that interact together to implement innovation [1]. Clusters are one of the key players of a regional innovation ecosystem, enabling SMEs to benefit from the available capacities of the ecosystem by accessing innovation services offered by other stakeholders [1,2]. Despite the positive impacts of Smart Specialisation Strategy policies in Europe for the empowerment of regional innovation ecosystems, there is evidence that still the provided service offerings to SMEs across European regions is far from the expected one.This indicates that there are existing barriers which prevent SMEs to receive the expected innovative support from different stakeholders in the innovation eco-system.In recent years some studies have been conducted to identify these barriers in general innovation ecosystems. They can be categorized into managerial, technological, financial, market, and knowledge barriers [3,4]. However, few studies focus on manufacturing innovation ecosystems [5].

Literature Gap

The dynamic interaction between SMEs,RTOs and clusters in manufacturing innovation ecosystems is rarely analysed within the existing body of literature.Such an interaction includes needs and requirements of SMEs, relationship challenges,the role of each of entities for transferring advanced manufacturing technologies, the type of offered services and the role of Clusters

Research Questions

How do the manufacturing innovation ecosystems differ in European regions considering the dynamic interaction between SMEs,RTOs and clusters?

What are the barriers and success factors of the ecosystem in Europe supporting the diffusion of advanced technologies (in particular Industry 4.0) and what is the role of clusters to overcome barriers?

Methodology

A mix of analytical and empirical research methods was used. Four European regions have been identified based on their significance and contribution to EU manufacturing sector and the diffusion of advanced technologies namely Lombardy, Catalonia, Nord pais-des-Calais and South Netherlands.

Empirical Material

In each of these regions, one cluster and one RTO active in advance manufacturing domain have been identified and interviewed to understand their service offering and challenges in service provision to SMEs. Moreover, 26 interviews were carried out with SMEs in each region to understand how they perceive the service offerings from RTOs and clusters, their satisfaction level and challenges.

Results

The final results of the study suggests that despite of the substantial capacities and technology infrastructure in the European manufacturing innovation ecosystem for the provision of services to SMEs, lack of awareness of the existing capacities is a major barrier for technology transfer. SMEs are not aware of possible services they can get from the ecosystem and this seriously limits the potential of technology transfer. Moreover, the results show that clusters may play an essential role to overcome this barrier as the intermediaries of the ecosystem.

Contribution to Scholarship

Besides confirming general barriers limiting the performance of innovation ecosystems, this study contributes to scholarship by Identification of the lack of awareness as a critical barrier for innovation in manufacturing innovation ecosystem of European regions. Lack of awareness of potential innovation services and of the roles and organization of the ecosystems in general has not received enough attention in the literature so far. Such a barrier can be a detrimental factor to technology innovation, since it may avoid technology transfer process to start on the one hand, and limit its success on the other. To solve this challenge, implies further multi-disciplinary research is needed

Contribution to Practice

This study provides insights for clusters to take proper actions to overcome the identified barriers barrier to technology transfer in manufacturing innovation ecosystem (in particular lack of awareness). It also provides value to companies that can have a better view of innovation ecosystems processes and organisation in order to be more conscious of regional capabilities and establish more fruitful relationships with the actors of their ecosystem

Fitness

The current paper is aligned with this year’s conference topics since it targets the issue of interaction among research, industry and clusters to boost innovation for manufacturing, with particular reference to Industry 4.0 technologies.

Bibliography

[1] Buesa M, Heijs J, Baumert T (2010) combined factorial and regression knowledge production function approach, Research Policy 39(6), 722-735.

[2] He J, Fallah M (2011)The typology of technology clusters and its evolution — Evidence from the hi-tech industries, Technological Forecasting and Social Change 78(6), 945-952.

[3] Gupta H., Barua M. (2018) A framework to overcome barriers to green innovation in SMEs using BWM and Fuzzy TOPSIS, Science of The Total Environment, 633, 122-139.

[4] Trianni A, Cagno E, Worell E. (2018) Innovation and adoption of energy efficient technologies- An exploratory analysis of Italian primary metal manufacturing SMEs, Energy Policy, 61, 430-440.

[5] Reynolds, Elisabeth B. & Uygun, Yilmaz, 2018. "Strengthening advanced manufacturing innovation ecosystems: The case of Massachusetts," Technological Forecasting and Social Change, 136(C), 178-191.



 
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