Main objective of this sub-project is to provide an EFPF-enabled solution for process health self- assessment based on existing EFPF services and data, enabling manufacturing companies, esp. SMEs, a deep dive in process behaviour analysis and discovering process improvement opportunities. Such a solution is missing and has a huge market potential.
This solution closes the gap between data analytics and process analysis, which is one of the most important challenges for realizing data-driven economy

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Data is new oil for manufacturing, but the main challenge is to understand the process improvement potential from past data, what is known as process health assessment. It explains the process behaviour on a higher (health) level, contextualizing other data-driven analyses, like anomaly
detection which detects anomalies but misses their understanding in a broader process context.

 

• Main objective is to provide an EFPF-enabled solution, d2ProcessHealth, for process health self- assessment based on existing EFPF services and data, enabling manufacturing companies, esp. SMEs, a deep dive in process behaviour analysis. Such a solution is missing

• Second objective is to enable aggregation of various existing (and future) EFPF data analytics services, which categorise some process characteristics (like anomalies, trends) into a common process behaviour atlas

• Third objective is to offer this solution through EFPF marketplace to a broader manufacturing community and boost the commercialization