A Data-Driven Business Model Framework for Value Capture in Industry 4.0

被引:7
|
作者
Schaefer, Dirk [1 ]
Walker, Joel [2 ]
Flynn, Joseph [2 ]
机构
[1] Univ Liverpool, Sch Engn, Liverpool, Merseyside, England
[2] Univ Bath, Dept Mech Engn, Bath, Avon, England
关键词
Industry; 4.0; Digital Manufacturing; Data-Driven Business Models;
D O I
10.3233/978-1-61499-792-4-245
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Manufacturing is undergoing a period of intense change as a result of advanced smart technologies, such as real-time sensors and the Industrial Internet of Things (IIoT). This has paved the way for a new era of digitized manufacturing known as Industry 4.0. It is anticipated that Industry 4.0 will be disruptive enough to present both new opportunities and threats to firms within a new competitive landscape. Manufacturers will be forced to adopt new business models to effectively capture value from the emerging smart technologies. A literature review revealed that few studies have addressed business models for Industry 4.0. Hence, this research addresses: What fundamental principles should companies in the manufacturing industry consider when adopting a data-driven business model? An analysis of four case studies on data-driven business models revealed significant common attributes. Through a SWOT analysis, twelve model principles for implementing a data-driven value capture framework could be identified.
引用
下载
收藏
页码:245 / 250
页数:6
相关论文
共 50 条
  • [1] Data-Driven Framework for Predictive Maintenance in Industry 4.0 Concept
    Sai, Van Cuong
    Shcherbakov, Maxim V.
    Tran, Van Phu
    CREATIVITY IN INTELLIGENT TECHNOLOGIES AND DATA SCIENCE, PT 1, 2019, 1083 : 344 - 358
  • [2] On the link between Education and Industry 4.0: a framework for a data-driven education design
    Spada, Irene
    Chiarello, Filippo
    Curreli, Alessandra
    Fantoni, Gualtiero
    PROCEEDINGS OF THE 2022 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON 2022), 2022, : 1670 - 1677
  • [3] A Systematic Framework for Assessing the Quality of Information in Data-Driven Applications for the Industry 4.0
    Reis, Marco S.
    IFAC PAPERSONLINE, 2018, 51 (18): : 43 - 48
  • [4] Data-driven business process management-based development of Industry 4.0 solutions
    Czyetko, Timea
    Kummer, Alex
    Ruppert, Tunas
    Abonyi, Janos
    CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2022, 36 : 117 - 132
  • [5] Towards an Integrative Big Data Analysis Framework for Data-driven Risk Management in Industry 4.0
    Niesen, Tim
    Houy, Constantin
    Fettke, Peter
    Loos, Peter
    PROCEEDINGS OF THE 49TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS 2016), 2016, : 5065 - 5074
  • [6] The Next Industry 4.0 Milestone: Data-Driven Safety
    Quiring, Ryan
    Manufacturing Engineering, 2021, 167 (02): : 74 - 75
  • [7] The Next Industry 4.0 Milestone: Data-Driven Safety
    Quiring, Ryan
    MANUFACTURING ENGINEERING, 2021, 166 (08): : 74 - 75
  • [8] Circular business strategy challenges and opportunities for Industry 4.0: A social media data-driven analysis
    Bui, Tat-Dat
    Tseng, Jiun-Wei
    Thi Phuong Thuy Tran
    Hien Minh Ha
    Tseng, Ming-Lang
    Lim, Ming K.
    BUSINESS STRATEGY AND THE ENVIRONMENT, 2023, 32 (04) : 1765 - 1781
  • [9] Data-Driven Business Model Innovation
    Sorescu, Alina
    JOURNAL OF PRODUCT INNOVATION MANAGEMENT, 2017, 34 (05) : 691 - 696
  • [10] (Data-driven) knowledge representation in Industry 4.0 scheduling problems
    Rossit, Daniel A.
    Tohme, Fernando
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2022, 35 (10-11) : 1172 - 1187