Quality Intelligence (QI)

德国政府的工业4.0计划侧重于提高生产力的柔性制造系统。信息物理系统（CPS）是工业4.0的核心，也是其在生产和物流过程中的技术集成的核心。CPS是复杂的系统和大量的可用数据。数据可以通过观察、测量和统计调查获得，并描述了产生知识的第一步。如果数据与特定的上下文相关联，就会生成信息。最后，知识起源于将信息与经验、概念和专业知识联系起来。知识管理为知识形式、数据和信息的生成提供了基础。由于对信息系统的需求，商业智能（BI）已经成为一种新的方法。BI结合了数据的整合、改进、转换和分析的所有活动。供应链是CPS的一种形式。在整个供应链中，会产生大量有关产品、订单、流程和质量的数据。供应链管理已经意识到这些数据的重要性。有几种方法关注供应链中数据交换的重要性。除此之外，产品、过程和系统质量的高影响在供应链管理的目标系统中是常见的。这两个方面之间的联系，即对整个供应链的质量相关数据进行分析，以支持质量管理的决策，仍然缺失。学术空白应该在研究项目质量情报（QI）中得到弥补。研究目标是开发一个供应链参考模型中质量相关不稳定性的预测模型。因此，在第一步中，应该开发与供应链质量相关的描述模型。将识别、分配与质量有关的数据，并分析其相互依赖性。通过本体的方法，将得到的结果整合到预测模型中。研究结果应能够在将来得出与质量相关的系统状态的结论。本研究有助于有效利用现有数据等资源。事前避免与质量有关的不稳定性可以减少由于对质量问题的事后反应而产生的质量成本。

The initiative Industry 4.0 from the german government focusses on flexible manufacturing systems with enhanced productivity. Cyber-Physical Systems (CPS) are the core of Industry 4.0 as well as for its technical integration in production and logistics processes. CPS are complex systems and a huge amount of available data. Data can be obtained by observations, measurements and statistical investigations and describes the first step of generating knowledge. If data get linked with a certain context, information is generated. Finally, knowledge originates by linking information with experiences, concepts and expertise. Knowledge Management provides the basics for the generation of knowledge form data and information. Business Intelligence (BI) has become a novel approach arising from the demand for informational systems. BI combines all activities of integrating, improving, transforming and analyzing data. Supply Chains are one form of CPS. Across a supply chain numerous data concerning products, orders, processes and quality are incurred. Supply chain management has realized the importance of these data. Several approaches focusing the importance of data exchange within supply chains exist. Beyond that, the high impact of product, process and system quality is common in the target system of supply chain management. A link between both aspects, that is an analysis of quality-related data across a supply chain for decision support of quality management, is still missing. The academic void should be picked up within the research project Quality Intelligence (QI). Research objective is the development of a prediction model for quality-related instabilities across a supply chain reference model. Therefore, in a first step a quality-related description model of a supply chain should be developed. The quality-related data will be identified, allocated and the interdependencies analyzed. By means of ontology, the gained results should be integrated into the prediction model. The research results should enable conclusions about quality-related system statuses in the future. This research contributes to efficient usage of resources such as existing data. The ex-ante avoidance of quality-related instabilities could cut quality costs due to ex-post reactions on quality problems.