Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies
<|reference_start|>Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies: Digitalization is challenging in heavy industrial sectors, and many pi-lot 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.<|reference_end|>
@article{ma2024business,
    title={Business Models for Digitalization Enabled Energy Efficiency and
  Flexibility in Industry: A Survey with Nine Case Studies},
    author={Zhipeng Ma, Bo N{o}rregaard J{o}rgensen, Michelle Levesque, Mouloud
  Amazouz, Zheng Grace Ma},
    journal={Energy Informatics. EI.A 2023. Lecture Notes in Computer Science,
  vol 14467},
    year={2024},
    doi={10.1007/978-3-031-48649-4_15},
    archivePrefix={arXiv},
    eprint={2402.01718},
    primaryClass={cs.CY}
}