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Towards a data science platform for improving SME collaboration through Industry 4.0 technologies

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  • Han, Hui
  • Trimi, Silvana

Abstract

Industry 4.0 (I4.0) is about realizing digital transformation by linking machines to plants, fleets, and humans through sensors and control elements in order to create smart networks, smart factories, smart manufacturing, and smart value chains. By leveraging I4.0 technologies, a small and medium enterprise (SME) can increase its organizational agility, adaptability, and resilience to cope with today's competitive environment by becoming a valuable and innovative partner in the power dynamics with its large buyer counterparts. However, SMEs face technology, trust, and big data challenges when they adopt I4.0 technologies. This study provides new solutions for SMEs to overcome these three challenges in implementing I4.0. Specifically, the paper proposes the following: (1) a roadmap for the application of I4.0 technologies to enhance the collaboration capabilities of SMEs; (2) a structure for I4.0 standardization to develop and sustain trust among partners; and (3) an improved data science platform for systematizing big data to extract critical information for collaboration solutions for SMEs. Additionally, the solutions are evaluated based on an application case of a Greek SME, demonstrating their potentials for practical implementation.

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  • Han, Hui & Trimi, Silvana, 2022. "Towards a data science platform for improving SME collaboration through Industry 4.0 technologies," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521006752
    DOI: 10.1016/j.techfore.2021.121242
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    References listed on IDEAS

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    2. Cannavacciuolo, Lorella & Ferraro, Giovanna & Ponsiglione, Cristina & Primario, Simonetta & Quinto, Ivana, 2023. "Technological innovation-enabling industry 4.0 paradigm: A systematic literature review," Technovation, Elsevier, vol. 124(C).
    3. Luqman, Adeel & Wang, Liangyu & Katiyar, Gagan & Agarwal, Reeti & Mohapatra, Amiya Kumar, 2024. "Unpacking associations between positive-negative valence and ambidexterity of big data. Implications for firm performance," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    4. Grego, Marica & Magnani, Giovanna & Denicolai, Stefano, 2024. "Transform to adapt or resilient by design? How organizations can foster resilience through business model transformation," Journal of Business Research, Elsevier, vol. 171(C).
    5. Battistoni, Elisa & Gitto, Simone & Murgia, Gianluca & Campisi, Domenico, 2023. "Adoption paths of digital transformation in manufacturing SME," International Journal of Production Economics, Elsevier, vol. 255(C).
    6. Delke, Vincent & Schiele, Holger & Buchholz, Wolfgang & Kelly, Stephen, 2023. "Implementing Industry 4.0 technologies: Future roles in purchasing and supply management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    7. Ganotakis, Panagiotis & Angelidou, Sofia & Saridakis, Charalampos & Piperopoulos, Panagiotis & Dindial, Miguel, 2023. "Innovation, digital technologies, and sales growth during exogenous shocks," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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