Digital Twins as Software and Service Development Ecosystems in Industry 4.0: Towards a Research Agenda

While research on digital twins of cyber-physical systems within industry 4.0 is emerging, the software development perspective on digital twins remains under-explored. Contemporary definitions and examples of digital twins have covered company- or product-specific solutions or discussed the use of digital twins in rather proprietary value chains. This paper addresses the importance of taking an ecosystem view on software development on digital twins for industry 4.0 and outlines a framework for building a research agenda for such ecosystems. The framework includes three dimensions: scope of the digital twin software platform (internal, value chain, ecosystem), life-cycle phases of the industry 4.0 system related with the digital twin (creation, production, operation & maintenance, disposal), and level of integration between the twin and the physical system (model, shadow, twin). As this research-in-progress addresses examples of research questions in light of the framework, further research to build a full-scale research agenda based on a systematic literature review is suggested.

[1]  A. Gawer.  Bridging differing perspectives on technological platforms: Toward an integrative framework , 2014 .

[2]  Seongjin Yun,et al.  Data-centric middleware based digital twin platform for dependable cyber-physical systems , 2017, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN).

[3]  Leonardo Bottaci,et al.  Modular production systems: a new manufacturing paradigm , 1997, J. Intell. Manuf..

[4]  A. M. M. Sharif Ullah,et al.  Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing , 2015, Journal of Intelligent Manufacturing.

[5]  Thomas Kuhn,et al.  Towards Architecting Digital Twin-Pervaded Systems , 2019, 2019 IEEE/ACM 7th International Workshop on Software Engineering for Systems-of-Systems (SESoS) and 13th Workshop on Distributed Software Development, Software Ecosystems and Systems-of-Systems (WDES).

[6]  Mark de Reuver,et al.  The digital platform: a research agenda , 2018, J. Inf. Technol..

[7]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[8]  Dimitris Mourtzis,et al.  Simulation in Manufacturing: Review and Challenges , 2014 .

[9]  Raj S Bhopal.  Go back to the future , 2010, BMJ : British Medical Journal.

[10]  Marc H. Meyer,et al.  Does Product Platforming Pay Off , 2018 .

[11]  Annabelle Gawer,et al.  The Rise of the Platform Enterprise: A Global Survey , 2016 .

[12]  Xiaohong Iris Quan,et al.  Understanding the Artificial Intelligence Business Ecosystem , 2018, IEEE Engineering Management Review.

[13]  Michal Orkisz,et al.  Integrating hundred's of products through one architecture: the industrial IT architecture , 2002, ICSE '02.

[14]  Bendik Bygstad,et al.  The Generative Mechanisms of Digital Infrastructure Evolution , 2013, MIS Q..

[15]  C. Peltz,et al.  Web Services Orchestration and Choreography , 2003, Computer.

[16]  J. Müller,et al.  Antecedents to Digital Platform Usage in Industry 4.0 by Established Manufacturers , 2019, Sustainability.

[17]  Nadja Hoßbach,et al.  Dimensions of Digital Twin Applications - A Literature Review , 2019, AMCIS.

[18]  J. West,et al.  New frontiers in open innovation , 2014 .

[19]  Bo Wang,et al.  Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry , 2019, Int. J. Inf. Manag..

[20]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[21]  Ulrich Epple,et al.  The role of the Industry 4.0 asset administration shell and the digital twin during the life cycle of a plant , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[22]  Alan J. Dix.  Human-Computer Interaction , 2018, Encyclopedia of Database Systems.

[23]  Madeleine Gibescu,et al.  Deep learning for estimating building energy consumption , 2016 .

[24]  J. Rochet,et al.  Two-sided markets: a progress report , 2006 .

[25]  Michael W. Grieves,et al.  Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems , 2017 .

[26]  Yuan He,et al.  From Surveillance to Digital Twin: Challenges and Recent Advances of Signal Processing for Industrial Internet of Things , 2018, IEEE Signal Processing Magazine.

[27]  H. Chesbrough,et al.  Open Innovation: A New Paradigm for Understanding Industrial Innovation , 2006 .

[28]  A. Gawer,et al.  Industry Platforms and Ecosystem Innovation , 2013 .

[29]  Gian Antonio Susto,et al.  Machine Learning for Predictive Maintenance: A Multiple Classifier Approach , 2015, IEEE Transactions on Industrial Informatics.

[30]  Radu Prodan,et al.  Towards Digital Twins Cloud Platform: Microservices and Computational Workflows to Rule a Smart Factory , 2017, UCC.

[31]  Wouter Joosen,et al.  Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems , 2018, 2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop (EDOCW).

[32]  Christos Koulamas,et al.  Cyber-Physical Systems and Digital Twins in the Industrial Internet of Things [Cyber-Physical Systems] , 2018, Computer.

[33]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .

[34]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[35]  Stefan Boschert,et al.  Digital Twin—The Simulation Aspect , 2016 .

[36]  Nezih Mrad,et al.  The role of data fusion in predictive maintenance using digital twin , 2018 .

[37]  Gunnar Erixon,et al.  Modularity - the basis for product and factory reengineering , 1996 .

[38]  Ralf Steinmetz,et al.  Plug-and-Play Virtual Factories , 2012, IEEE Internet Computing.

[39]  Toivo Tähemaa,et al.  DIGITAL TWIN BASED SYNCHRONISED CONTROL AND SIMULATION OF THE INDUSTRIAL ROBOTIC CELL USING VIRTUAL REALITY , 2019, Journal of Machine Engineering.

[40]  Wilfried Sihn,et al.  Digital Twin in manufacturing: A categorical literature review and classification , 2018 .

[41]  Giuseppe Landolfi,et al.  Design of a multi-sided platform supporting CPS deployment in the automation market , 2018, 2018 IEEE Industrial Cyber-Physical Systems (ICPS).