数字化赋能工业能源效率与灵活性的商业模式：九项案例研究综述
Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies
AI总结：
本综述通过九项工业案例和商业模式画布分析，提出VBM评估指标，发现数字化方案多聚焦能效与灵活性，并给出六项发展建议。
摘要：
数字化在重工业领域面临挑战，许多试点项目难以复制和规模化。案例研究是学习和分享经验与知识的强有力的教学工具，但文献中很少提供。因此，本文进行了一项调查，收集了九个不同的工业案例，随后使用商业模式画布（BMC）对其进行分析。这些案例基于九个BMC组成部分进行总结和比较，并提出了商业模式价值（VBM）评估指标，以评估工业数字化解决方案的商业潜力。结果表明，主要合作伙伴是行业利益相关者、IT公司和学术机构。他们针对数字化解决方案的关键活动包括大数据分析、机器学习算法、数字孪生和物联网开发。大多数案例的价值主张是提高能源效率和实现能源灵活性。此外，六项工业数字化解决方案的技术就绪水平低于7级，表明它们需要在真实环境中进一步验证。基于这些见解，本文为未来工业数字化解决方案的发展提出了六项建议：促进跨部门合作、优先进行全面的测试和验证、扩展价值主张、增强产品适应性、提供用户友好的平台以及采用透明的推荐机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
