Global Challenges of Digital Transformation of Markets: Collaboration and Digital Assets

Cyber-physical system (CPS) and digital twin (DT) technologies are the key enablers of smart manufacturing. The main idea of CPS is to build bi-directional interaction channels between the physical and cyber worlds. The research gap is ontological consideration of the concept of the digital object (DO) as a representation of a physical object (PO) in the digital space/world. The objective of this study is an ontological analysis of the digital object (DO). This object is fairly well-understood from a technical point of view; although there are many options for its definition, its basic composition and functionality are defined clearly, but currently in the economic science DO has not yet been enough considered. The DO, which first appeared as a digital twin has not been properly explored by economic science. Authors attempt to determine whether all the properties and characteristics of the DO are described by modern economic language or whether there is a need to introduce new concepts and categories to describe such objects. The ontological analysis of the DO within the existing conceptual framework of economic science is presented. The result of the research is comprehensive study of DO which allows the consideration of the additional benefits that economic actors can gain from using the DO. We propose to analyze the DO in terms of such economic categories as goods; innovation process; the system of division of labor; the role of market participants in the creation and use of the DO; intellectual property; etc.

[1]  Jun Ota,et al.  Development of an education program for digital manufacturing system engineers based on ‘Digital Triplet’ concept , 2019 .

[2]  Jing Dai,et al.  The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model , 2020, International Journal of Production Economics.

[3]  Wernher Behrendt,et al.  An open source approach to the design and implementation of Digital Twins for Smart Manufacturing , 2019, Int. J. Comput. Integr. Manuf..

[4]  M. Kenney,et al.  The platform economy: restructuring the space of capitalist accumulation , 2020, Cambridge Journal of Regions, Economy and Society.

[5]  Sreekumar Muthuswamy,et al.  Identification and classification of materials using machine vision and machine learning in the context of industry 4.0 , 2019, Journal of Intelligent Manufacturing.

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

[7]  L. Robbins,et al.  An Essay on the Nature and Significance of Economic Science. , 1934 .

[8]  Zhong Fan,et al.  Digital Twin: Enabling Technologies, Challenges and Open Research , 2020, IEEE Access.

[9]  Vladimir Badenko,et al.  As-built BIM in real estate management: the change of paradigm in digital transformation of economy , 2020, IOP Conference Series: Materials Science and Engineering.

[10]  Dimitris Kiritsis,et al.  Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems , 2019, ArXiv.

[11]  Vladimir Badenko,et al.  Multicriteria analysis and information modelling in management of built environment , 2020, E3S Web of Conferences.

[12]  Moncef Hammadi,et al.  Integrating model-based system engineering with set-based concurrent engineering principles for reliability and manufacturability analysis of mechatronic products , 2019, Concurr. Eng. Res. Appl..

[13]  Manas Bajaj,et al.  Simulation-Based Design Using SysML Part 1: A Parametrics Primer , 2007 .

[14]  Daniel Pakkala,et al.  A service requirements engineering method for a digital service ecosystem , 2015, Service Oriented Computing and Applications.

[15]  Elena De La Poza Plaza,et al.  The impact of corporate social responsibility transparency on the financial performance, brand value, and sustainability level of IT companies , 2020, Corporate Social Responsibility and Environmental Management.

[16]  F. Tonelli,et al.  Food industry digitalization: from challenges and trends to opportunities and solutions , 2018 .

[17]  G. B. Benitez,et al.  The expected contribution of Industry 4.0 technologies for industrial performance , 2018, International Journal of Production Economics.

[18]  Vladimir Badenko,et al.  Digital twins of complex technical systems for management of built environment , 2020, IOP Conference Series: Materials Science and Engineering.

[19]  John P. Wilson,et al.  Financial functional analysis: a conceptual framework for understanding the changing financial system , 2016 .

[20]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[21]  Fernando Romero,et al.  A review of the meanings and the implications of the Industry 4.0 concept , 2017 .

[22]  Mark Girolami,et al.  Construction with digital twin information systems , 2020, Data-Centric Engineering.

[23]  Helmut Krcmar,et al.  Leveraging industry 4.0 – A business model pattern framework , 2020, International Journal of Production Economics.

[24]  S. Barykin,et al.  Digital Logistics Approach to Energy Service Socio-economic Mechanisms , 2021 .

[25]  Suk-Hwan Suh,et al.  Design Considerations and Architecture for Cooperative Smart Factory: MAPE/BD Approach , 2018 .

[26]  Manoj Kumar Tiwari,et al.  Digital Twin Driven Inclusive Manufacturing Using Emerging Technologies , 2019, IFAC-PapersOnLine.

[27]  Fei Tao,et al.  Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey , 2018 .

[28]  Detlef Zühlke,et al.  Future Modeling and Simulation of CPS-based Factories: an Example from the Automotive Industry , 2016 .

[29]  Endrit Hoxha,et al.  BIM and LCA Integration: A Systematic Literature Review , 2020, Sustainability.

[30]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[31]  Kevin I-Kai Wang,et al.  Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues , 2020, Robotics Comput. Integr. Manuf..

[32]  Andreas Rauber,et al.  Evaluating Emulation and Migration: Birds of a Feather? , 2012, ICADL.

[33]  Elisa Negri,et al.  Review of digital twin applications in manufacturing , 2019, Comput. Ind..

[34]  Norbert Gronau,et al.  A factory operating system for extending existing factories to Industry 4.0 , 2020, Comput. Ind..

[35]  G. B. Benitez,et al.  Influence of Open Innovation Variables on the Competitive Edge of Small and Medium Enterprises , 2020, Journal of Open Innovation: Technology, Market, and Complexity.

[36]  Ercan Öztemel,et al.  Literature review of Industry 4.0 and related technologies , 2018, J. Intell. Manuf..

[37]  Isabel Praça,et al.  A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future , 2020, Applied Sciences.

[38]  Fei Tao,et al.  Digital Twin Service towards Smart Manufacturing , 2018 .

[39]  G. B. Benitez,et al.  Industry 4.0 innovation ecosystems: An evolutionary perspective on value cocreation , 2020 .

[40]  Sheryl Staub-French,et al.  Developing owner information requirements for BIM-enabled project delivery and asset management , 2017 .

[41]  Itxaro Errandonea,et al.  Digital Twin for maintenance: A literature review , 2020, Comput. Ind..

[42]  F. Valero,et al.  Impact of digital transformation on the automotive industry , 2020, Technological Forecasting and Social Change.

[43]  Barbara Aquilani,et al.  The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework , 2020, Sustainability.

[44]  N. Kryvinska,et al.  Scenario-Based Analysis of IT Enterprises Servitization as a Part of Digital Transformation of Modern Economy , 2020, Applied Sciences.

[45]  Kun Dai,et al.  Electrically conductive polymer composites for smart flexible strain sensors: a critical review , 2018 .

[46]  Luigi Atzori,et al.  The Virtual Object as a Major Element of the Internet of Things: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[47]  Felix T.S. Chan,et al.  Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors , 2019, Int. J. Prod. Res..

[48]  S. Wolfert,et al.  Big Data in Smart Farming – A review , 2017 .

[49]  Herwig Winkler,et al.  Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process , 2018 .

[50]  Monique de Jong McKenzie Micro-assets and portfolio management in the new platform economy , 2020, Distinktion: Journal of Social Theory.

[51]  Carlos Eduardo Suprinyak,et al.  The nature and significance of Lionel Robbins’ methodological individualism , 2017 .

[52]  Joakim Björkdahl Strategies for Digitalization in Manufacturing Firms , 2020 .

[53]  G. Seliger,et al.  Opportunities of Sustainable Manufacturing in Industry 4.0 , 2016 .

[54]  Thomas Friedli,et al.  The smart factory as a key construct of industry 4.0: A systematic literature review , 2020 .

[55]  Noël Crespi,et al.  Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models , 2020, Proceedings of the IEEE.

[56]  S. Barykin THE PLACE AND ROLE OF DIGITAL TWIN IN SUPPLY CHAIN MANAGEMENT , 2021 .

[57]  N. Guadalajara,et al.  MODELLING IT BRAND VALUES SUPPLIED BY CONSULTANCY SERVICE COMPANIES: EMPIRICAL EVIDENCE FOR DIFFERENCES , 2020, Technological and Economic Development of Economy.

[58]  Stefano Riemma,et al.  Digital Twin Models in Industrial Operations: A Systematic Literature Review , 2020, Procedia Manufacturing.