Abstract
Over the past few decades, ‘digital technology’ has shaped the so-called Third Industrial Revolution—the first in the nineteenth century being characterized by steam and water, and the second at the beginning of the twentieth century being based on electricity and the emergence of mass production. In his book, The Fourth Industrial Revolution, Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, suggests that it will be a further step in human production based on a complete integration between the cyber and physical dimensions. The fourth revolution has the potential to transform not only the way we produce and distribute things but also the dynamics of customer engagement, value creation, management, and regulation (Kagermann et al., 2013; Schwab, 2017a, 2017b). An historical account of the origins, history, and impact of cybernetics is beyond the scope and goals of this contribution (Ampère, 1843; Wiener, 1948a, 1948b; Simon, 1968). However, the idea of the new cyber-physical revolution or ‘Industry 4.0’ has been introduced, inspired by the transformations made in German manufacturing (Kagermann et al., 2013). Industry 4.0 has also been described as digital manufacturing, industrial Internet, smart industry, and smart manufacturing (Hermann et al., 2016; Nuccio & Guerzoni, 2019).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The research combines proprietary firm-level databases with publicly available information from company press releases, news articles, peer-reviewed journals, and trade and industry reports.
- 2.
GPTs are technologies characterized by the potential of pervasive use in a wide range of sectors and are the ultimate trigger of technical-driven long-run growth (Bresnahan & Trajtenberg, 1995).
- 3.
Respectively, about 194,000 fewer firms and 800,000 fewer workers than before the onset of the last crisis (ISTAT, 2017).
- 4.
- 5.
Based on an analysis of the characteristics of technologies as described by the IPC system, the authors provide a conversion table mapping a set of key enabling technologies to the IPC codes. Robotics/automation technologies are identified by IPC codes: B03C, B06B 1/6, B06B 3/00, B07C, B23H, B23K, B23P, B23Q, B25J, G01D, G01F, G01H, G01L, G01M, G01P, G01Q, G05B, G05D, G05F, G05G, G06M, G07C, G08C, except for co-occurrence with sub-classes directly related to the manufacture of automobiles or electronics. Additional information, i.e. the list of IPC codes related to the manufacture of automobiles or electronics, is from Van Looy and Vereyen (2015).
- 6.
For both periods, we consider the first year for which data are available. Moreover, we calculate a three-year moving average to smooth annual fluctuations.
References
Acemoglu, D., & Restrepo, P. (2017). Robots and jobs: Evidence from US labor markets.
Ambrosetti, F., & Bruschera, C. L. (2017). Analisi di ciclo di vita di uno stack di celle a combustibile per applicazione automobilistica.
Ampère, A. M. (1843). Essai sur la philosophie des sciences. Bachelier.
Antonelli, C., Krafft, J., & Quatraro, F. (2010). Recombinant knowledge and growth: The case of ICTs. Structural Change and Economic Dynamics, 21(1), 50–69.
Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis. OECD Social, Employment, and Migration Working Papers, 189, 0_1.
Aschhoff, B. Crass, D., Kremers, K., Grimpe, C., Rammer, C. (2010). European competitiveness in key enabling technologies: Summary Report to the European Commission. Retrieved from https://www.researchgate.net/profile/Carlos_Montalvo2/publication/271521098_European_Competitiveness_in_Key_Enabling_Technologies_Summary_Report/links/54cb3bf30cf2c70ce5252c15/European-Competitiveness-in-Key-Enabling-Technologies-Summary-Report.pdf
Babiceanu, R. F., & Chen, F. F. (2006). Development and applications of holonic manufacturing systems: A survey. Journal of Intelligent Manufacturing, 17(1), 111–131.
Baldwin, R. E. (2006). Globalisation: The great unbundling (s). Economic Council of Finland. https://doi.org/10.1017/S174413310600404X
Baldwin, R. E. (2016). The great convergence: Information technology and new globalization. Harvard University Press.
Blinder, A. S., & Krueger, A. B. (2013). Alternative Measures of Offshorability: A Survey Approach. Journal of Labor Economics, University of Chicago Press, 31(S1), S97–S128.
Boston Consulting Group. (2015). The robotics revolution: The next great leap in manufacturing. BCG Perspectives. Retrieved from BCG perspectives website: https://www.bcgperspectives.com
Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies ‘Engines of growth’? Journal of Econometrics, 65(1), 83–108.
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Industrial Science and Engineering, 8(1), 37–44.
Brzeski, C., & Burk, I. (2015). Die Roboter kommen. Folgen der Automatisierung für den deutschen Arbeitsmarkt. INGDiBa Economic Research.
Calligaris, S., Del Gatto, M., Hassan, F., Ottaviano, G., & Schivardi, F. (2016). Italy’s productivity conundrum European Economy.
Calvino, F., & Virgillito, M. E. (2018). The innovation-employment nexus: A critical survey of theory and empirics. Journal of Economic Surveys, 32(1), 83–117. https://doi.org/10.1111/joes.12190
Carlisle, B. (2000). Robot mechanisms. Adept Technology, Inc. In 2000 IEEE International Conference on Robotics & Automation, San Francisco, California.
Coffano, M., Tarasconi, G. (2014). Crios InnoS&T Database: Sources, contents and access rules.. CRIOS Working paper N.1. Retrieved from SSRN http://ssrn.com/abstract=2404344
Dedrick, J., Gurbaxani, V., & Kraemer, K. L. (2003). Information technology and economic performance: A critical review of the empirical evidence. ACM Computing Surveys (CSUR), 35(1), 1–28.
Dengler, K., & Matthes, B. (2015). Folgen der Digitalisierung für die Arbeitswelt: Substituierbarkeitspotenziale von Berufen in Deutschland (No. 11/2015). IAB-Forschungsbericht.
Deutsche Bank Research. (2014). Industry 4.0: Huge potential for value creation waiting to be tapped. Retrieved from: http://www.dbresearch.com
Dorn, D., Katz, L. F., Patterson, C., & Reenen, J. V. (2017). The fall of the labor share and the rise of superstar firms. CEP Discussion Papers, (1482). https://doi.org/10.1016/S2213-2600(17)30448-4.
Frey, C. B., & Osborne, M. A. (2013). The future of employment. How susceptible are jobs to computerisation.
Gartner. (2012). Hype cycle for Big Data.
Gorecky, D., Schmitt, M., Loskyll, M., & Zühlke, D. (2014, July). Human-machine-interaction in the industry 4.0 era. In Industrial Informatics (INDIN), 2014 12th IEEE International Conference (pp. 289–294). IEEE.
Helpman, E., & Trajtenberg, M. (1994). A time to sow and a time to reap: Growth based on general purpose technologies (No. w4854). National Bureau of Economic Research.
Hermann, M., Pentek, T., & Otto, B. (2016, January). Design principles for industrie 4.0 scenarios. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 3928-3937). IEEE.
ISTAT. (2016). Rapporto sulla competitività dei settori produttivi (Edizione 2016). ISTAT.
ISTAT. (2017). Rapporto sulla competitività dei settori produttivi (Edizione 2017). ISTAT. https://doi.org/10.1002/2016GL072259
Jazdi, N. (2014, May). Cyber physical systems in the context of Industry 4.0. In Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on (pp. 1-4). IEEE.
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 working group. Forschungsunion.
Lee, J., Bagheri, B., & Kao, H. A. (2014). Recent advances and trends of cyber-physical systems and big data analytics in industrial informatics. International Proceeding of Int Conference on Industrial Informatics (INDIN), 1–6.
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.
Leitão, P. (2009). Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, 22(7), 979–991.
McKinsey Global Institute (2017) A future that works: Automation, employment, and productivity.
Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17, 9–13.
Nesta, L., & Schiavo, S. (2017). International competition and imperfect markets firm level evidence from French manufacturing firms.
Nuccio, M., & Guerzoni, M. (2019). Big data: Hell or heaven? Digital platforms and market power in the data-driven economy. Competition & Change, 23(3), 312–328.
Nuccio, M., Guerzoni, M., Cappelli, R., & Geuna, A. (2020). Industrial pattern and robot adoption in European regions. Department of Management, Università Ca'Foscari Venezia Working Paper, 18, 3.
Pajarinen, M., & Rouvinen, P. (2014). Computerization threatens one third of Finnish employment. ETLA Brief, 22(13.1), 2014.
Pearson, J. (2015). The sheer difficulty of defining what a robot is. Vice Magazine. Retrieved from http://motherboard.vice.com/
Pĕchouček, M., & Mařík, V. (2008). Industrial deployment of multi-agent technologies: Review and selected case studies. Autonomous Agents and Multi-Agent Systems, 17(3), 397–431.
Perlongo, K. (2016). Robohub roundtable: Why is it so difficult to define ‘robot’?. Retrieved from http://robohub.org/
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 48(3), 567–572.
Schwab, K. (2017). The fourth industrial revolution. Crown Business.
Shen, W., Hao, Q., Yoon, H. J., & Norrie, D. H. (2006). Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, 20(4), 415–431.
Simon, H. A. (1968). The sciences of the artificial. The MIT Press.
Van Looy, B.,Vereyen, C. (2015). Patent statistics: Concordance IPC V8 – NACE REV.2. Retrieved from https://circabc.europa.eu/webdav/CircaBC/ESTAT/infoonstatisticsofsti/Library/methodology/patent_statistics/IPC_NACE2_Version2%200_20150630.pdf
Wang, L., Törngren, M., & Onori, M. (2015a). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37(Part 2), 517–527.
Wang, S., Wan, J., Li, D., & Zhang, C. (2015b). Implementing smart factory of industrie 4.0: An outlook. International Journal of Distributed Sensor Networks.
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158–168.
Wiener, N. (1948a). Cybernetics. Scientific American, 179(5), 14–19.
Wiener, N. (1948b). Cybernetics: Control and communication in the animal and in the machine. The MIT Press.
Wilson, H. J. (2015). What is a robot, anyway? Harvard Business Review. Retrieved from http://hbr.org/
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Geuna, A., Guerzoni, M., Nuccio, M., Pammolli, F., Rungi, A. (2021). Digital Technologies and Industrial Transformations. In: Resilience and Digital Disruption. SpringerBriefs in Business. Springer, Cham. https://doi.org/10.1007/978-3-030-85158-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-85158-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85157-6
Online ISBN: 978-3-030-85158-3
eBook Packages: Economics and FinanceEconomics and Finance (R0)