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Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies

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Energy Informatics (EI.A 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14467))

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Abstract

Digitalization is challenging in heavy industrial sectors, and many pilot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and Internet of Things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.

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Acknowledgements

This paper is part of the IEA IETS Task XVIII: Digitalization, Artificial Intelligence and Related Technologies for Energy Efficiency and GHG Emissions Reduction in Industry, funded by the Danish funding agency, the Danish Energy Technology Development and Demonstration (EUPD) program, Denmark (Case no.134–21010), and the project “Data-driven best-practice for energy-efficient operation of industrial processes - A system integration approach to reduce the CO2 emissions of industrial processes” funded by EUDP (Case no.64020–2108).

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Authors and Affiliations

  1. SDU Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230, Odense, Denmark

    Zhipeng Ma, Bo Nørregaard Jørgensen & Zheng Ma

  2. CanmetMINING-Sudbury, Natural Resources Canada, 1079 Kelly Lake Road, Sudbury, ON, P3E 5P5, Canada

    Michelle Levesque

  3. CanmetENERGY Varennes, Natural Resources Canada, 1615 Lionel-Boulet Blvd., Varennes, QC, J3X 1S6, Canada

    Mouloud Amazouz

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  1. Zhipeng Ma
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  2. Bo Nørregaard Jørgensen
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  3. Michelle Levesque
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  4. Mouloud Amazouz
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  5. Zheng Ma
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Correspondence to Zhipeng Ma , Bo Nørregaard Jørgensen or Zheng Ma .

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Editors and Affiliations

  1. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  2. University of Campinas, Campinas, São Paulo, Brazil

    Luiz Carlos Pereira da Silva

  3. University of Southern Denmark, Odense, Denmark

    Zheng Ma

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Ma, Z., Jørgensen, B.N., Levesque, M., Amazouz, M., Ma, Z. (2024). Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14467. Springer, Cham. https://doi.org/10.1007/978-3-031-48649-4_15

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  • DOI: https://doi.org/10.1007/978-3-031-48649-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48648-7

  • Online ISBN: 978-3-031-48649-4

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