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Framework for data analytics in data-driven product planning

 
: Massmann, M.; Meyer, M.; Frank, M.; Enzberg, S. von; Kühn, A.; Dumitrescu, R.

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Volltext ()

Procedia manufacturing 52 (2020), S.350-355
ISSN: 2351-9789
International Conference on System-Integrated Intelligence (SysInt) <5, 2020, Online>
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IEM ()

Abstract
While many companies are currently trying to use data generated and enabled by Industry 4.0 and networked systems to create data-based services, this also results in new possibilities and potentials for product planning and product engineering. Product life cycle analytics plays an essential role in data-driven product planning. In addition to the actual analysis, analytics projects must always take into account the use case, the data collection and acquisition. In this paper we propose an extended framework for successful realization of data analytics solutions in product planning. Starting from a thorough analysis of challenges in data-driven product planning, we derive requirements for structured data analytics solutions in product planning. The proposed framework is based on standard models as CRISP-DM [1], the four-layer model for Analytics Use Cases, and the Analytics Canvas [2] and offers an overview of structured solutions to fulfill the specialized requirements of data-driven product planning. It consists of four phases “use cases”, “data sources”, “data acquisition & integration”, and “data analysis”, each presenting corresponding approaches and methods. Based on a specific application example, we illustrate the application potential of using the framework.

: http://publica.fraunhofer.de/dokumente/N-639580.html