Industry 4.0 - Potentials for Creating Smart Products: Empirical Research Results

Industry 4.0 combines the strengths of traditional industries with cutting edge internet technologies. It embraces a set of technologies enabling smart products integrated into intertwined digital and physical processes. Therefore, many companies face the challenge to assess the diversity of developments and concepts summarized under the term industry 4.0. The paper presents the result of a study on the potential of industry 4.0. The use of current technologies like Big Data or cloud-computing are drivers for the individual potential of use of Industry 4.0. Furthermore mass customization as well as the use of idle data and production time improvement are strong influence factors to the potential of Industry 4.0. On the other hand business process complexity has a negative influence.

[1]  Michael Möhring,et al.  Strategic Alignment of Cloud-Based Architectures for Big Data , 2013, 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops.

[2]  Ken Kwong-Kay Wong,et al.  Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS , 2013 .

[3]  Michael R. Mullen,et al.  Structural equation modelling: guidelines for determining model fit , 2008 .

[4]  Markus Hammer,et al.  How big data can improve manufacturing , 2022 .

[5]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[6]  Tiffany Hale.  Empire of cotton: a global history , 2016 .

[7]  M. Blanchet,et al.  Industrie 4.0: the new industrial revolution. How Europe will succeed , 2014 .

[8]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[9]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[10]  Jan Mendling,et al.  A Discourse on Complexity of Process Models , 2006, Business Process Management Workshops.

[11]  F. Kiebitz.  Nikola Tesla zum fünfundsiebzigsten Geburtstage , 2005, Naturwissenschaften.

[12]  Alois Ferscha,et al.  Constructing Ambient Intelligence - AmI 2007 Workshops Darmstadt, Germany, November 7-10, 2007 Revised Papers , 2008, AmI Workshops.

[13]  Melanie Hartmann,et al.  Core Services for Smart Products , 2009 .

[14]  Mikell P. Groover,et al.  Automation, Production Systems, and Computer-Integrated Manufacturing , 1987 .

[15]  Edward A. Lee.  Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[16]  Paul Hofmann,et al.  Cloud computing and electricity , 2010, Commun. ACM.

[17]  Cheng Leong Ang,et al.  Technical planning of factory data communications systems , 1987 .

[18]  Jorge S. Cardoso,et al.  Service Engineering for the Internet of Services , 2008, ICEIS.

[19]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[20]  Michael Marien,et al.  Book Review: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , 2014 .

[21]  Werner Siemens.  Ueber telegraphische Leitungen und Apparate , 1850 .

[22]  Alexandra Köves,et al.  A gépek korszaka - újratöltve. Erik Brynjolfsson-Andrew McAfee: The Second Machine Age Work, Progress, and Prosperity in a Time of Brilliant Technologies W. W. Norton and Company, New York, 2014, 320 o , 2015 .

[23]  Michael Möhring,et al.  Big Data as Strategic Enabler - Insights from Central European Enterprises , 2014, BIS.

[24]  Max Mühlhäuser,et al.  Smart Products: An Introduction , 2007, AmI Workshops.

[25]  Gianfranco Walsh,et al.  Preventive Product returns Management Systems - a Review and Model , 2014, ECIS.

[26]  Jr. Mikell P. Groover.  Automation, production systems, and computer-aided manufacturing , 1980 .

[27]  Peter Fettke,et al.  Industrie 4.0 , 2018, Handbuch Innovationsforschung.

[28]  John Zahorjan,et al.  The challenges of mobile computing , 1994, Computer.

[29]  J. Ramalho-Santos,et al.  Cronbach's alpha: a tool for assessing the reliability of scales , 1999 .

[30]  O. Williamson.  The Economics of Organization: The Transaction Cost Approach , 1981, American Journal of Sociology.

[31]  Earl R. Babbie,et al.  The practice of social research , 1969 .

[32]  R. Shapiro,et al.  From supply chain to demand chain: the role of lead time reduction in improving demand chain performance , 2004 .

[33]  Dean Abbott,et al.  Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst , 2014 .

[34]  Kevin Ashton,et al.  That ‘Internet of Things’ Thing , 1999 .

[35]  Peter C. Evans,et al.  Industrial Internet: Pushing the Boundaries of Minds and Machines , 2012 .

[36]  Selmin Nurcan,et al.  Introduction to the First International Workshop on Business Process Management and Social Software (BPMS2 2008) , 2008, Business Process Management Workshops.

[37]  R. Florida.  A New Industrial Revolution , 1991, New Electronics.

[38]  Clayton M. Christensen.  The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail , 2013 .

[39]  Jeffrey D. Shulman,et al.  Optimal Reverse Channel Structure for Consumer Product Returns , 2010, Mark. Sci..

[40]  A. Picot,et al.  Information, Organization and Management: Expanding Markets and Corporate Boundaries , 1997 .

[41]  Stuart Hannabuss,et al.  The Big Switch: Rewiring the World, from Edison to Google , 2009 .

[42]  Péter Horváth,et al.  Industrie 4.0 - Volkswirtschaftliches Potenzial für Deutschland , 2015 .

[43]  Alexandre Megretski,et al.  NSF Workshop in Cyber-Physical Systems , 2006 .