Use-Case-Driven Architectures for Data Platforms in Manufacturing

Since the term “Industry 4.0” was coined in 2011, machine data retrieval, storage and processing has been one of the major drivers for process optimization and factory management. Data platforms have been introduced as a key resource to process, align and enhance data from machines, sensors and other sources. At the same time, different use cases and applications vary greatly in their technical demands towards data amounts, formats, retrieval rate, scalability, latency and many more. Thus, holistic data platforms are often a compromise between these requirements. This contribution thus looks into the requirements and needs of different use cases and applications for data usage in manufacturing—from factory management, to process control and auxiliaries monitoring. From these use cases, specific requirements will be deducted to propose architectures that reflect the needs of these distinctive applications. From a structured literature analysis with more than 100 scientific publications, archetypes for data management platforms in manufacturing will be condensed and explained in detail. Overall, eight distinctive archetypes have been identified and clustered, with the major distinguishing feature being their reliance on extensive models or digital twin. This contribution closes with application examples for some of these archetypes.