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Research and Consulting in Data-Driven Strategic Product Planning
Abstract
Industry 4.0 and digitalization have transformed the industrial world. Many manufacturers create additional customer value by offering data-based services. However, companies can benefit from analyzing data themselves, too. Through data, companies can learn about product usage and behavior. This enables them to systematically improve their products. But finding improvements through data analysis is not trivial.
Henceforth, we developed a method for the data-based identification of product improvements. This method was created in the joint research project DizRuPt with four companies from different industrial sectors.
The paper at hand introduces our approach of combining research and consulting in terms of a case study from our research project DizRuPt. The result is a research and consulting concept which is optimized for a two days workshop. From our point of view, there is no other way in researching methods for strategic product planning but through working together closely with companies. This is especially important as methods must be researched for practical usage. Simultaneously, it is essential to never forget that companies only participate in research projects if they clearly see a benefit. A benefit through consulting.
This contribution introduces an approach for data-driven optimization of products and their product generations through a Closed-Loop Engineering approach resulting from the German research project DizRuPt. The approach focuses on data-driven product planning by ensuring data consistency and traceability between product planning, product development, and product operation by combining aspects and functions from Product Lifecycle Management (PLM) and the Internet of Things (IoT). The presented approach is illustrated and validated by pilot applications from the research project.
Cyber-physical systems (CPS) generate huge amounts of data during the usage phase. By analyzing these data, CPS providers can systematically uncover hidden product improvement potentials for future product generations. But many companies face difficulties starting industrial data analytics projects as they cannot rely on experience and miss orientation. Following the canonical action research methodology, this study aims to investigate the definition and specification of data analytics use cases. The results show a clear need for supporting methods and tools for defining and specifying use cases in usage data-driven product planning.
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