Ever growing data availability combined with rapid progress in the field of analytics has laid the foundation for the emergence of Business Process Management in general and Business Process Analytics in particular. Going beyond descriptive process log analysis, manufacturing companies strive to leverage predictive process analytics to generate process-related predictions. However, current implementations are limited in their practical use as they are not able to combine multiple heterogeneous data sources without loss of information. To overcome this limitation, we propose a deep learning based approach leveraging multi-headed neural networks. We develop and evaluate the proposed approach in the context of a real-world disruption type classification showcase. To this end, we cooperate with a medium-sized German manufacturing company and observe promising results in an initial evaluation.
Oberdorf, Felix; Schaschek, Myriam; Stein, Nikolai; and Flath, Christoph, "Neural Process Mining: Multi-Headed Predictive Process Analytics in Practice" (2021). ECIS 2021 Research Papers. 54.
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