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Leveraging Assets as a Service for Business Intelligence in Manufacturing Service Ecosystems

机译:利用资产作为制造服务生态系统的商业智能服务

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When manufacturers join forces to create, manage, and offer new Product-Services in globalized markets, a huge amount of inter-organizational data on tangible and intangible assets is generated in corporate knowledge bases. This data implies new economic opportunities as well as barriers. The presented approach depicts how e-business companies can benefit from virtualized assets - namely Assets as a Service - for Business Intelligence (BI) in Manufacturing Service Ecosystems (MSE). Thanks to Assets as a Service, more valuable, reliable, and structured data are available within the MSE, ready to be further evaluated, elaborated, or visualized. In this context, BI techniques can be used to automatically deduce implicit dependencies among assets. The purpose of this paper is to advance the understanding and adoption of BI practices in MSE by applying formal semantics also to collaboratively gather, monitor, and analyze shared data about production assets. Consequently, the findings of this work empower MSE members to take better decisions while managing Product-Service innovations in e-business scenarios, hence to create more value. Results are outlined through the example of an industrial scenario.
机译:当制造商联接创建,管理和提供全球化市场的新产品服务时,在企业知识库中产生了有形和无形资产的大量组织间数据。该数据意味着新的经济机会以及障碍。本方法描绘了电子商务公司如何从虚拟化资产中受益 - 即作为服务的服务 - 为制造服务生态系统(MSE)的商业智能(BI)。由于资产作为服务,在MSE中可以使用更有价值,可靠和结构化数据,随时可进一步评估,详细或可视化。在此上下文中,BI技术可用于在资产之间自动推断隐式依赖关系。本文的目的是通过应用正式的语义,通过应用正式的语义来协作,监测和分析有关生产资产的共享数据,提高MSE的理解和采用。因此,这项工作的调查结果使MSE成员在管理电子商务场景中管理产品的创新时,以获得更好的决策,从而创造更多价值。结果是通过工业场景的例子概述的。

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