Predictive End-to-End Enterprise Process Network Monitoring

• Published in: Business & information systems engineering Vol. 65; no. 1; pp. 49 - 64
• Main Authors: Oberdorf, Felix, Schaschek, Myriam, Weinzierl, Sven, Stein, Nikolai, Matzner, Martin, Flath, Christoph M.
• Format: Journal Article
• Language: English
• Published: Wiesbaden Springer Fachmedien Wiesbaden 01-02-2023; Springer Nature B.V
• Subjects: Artificial neural networks; Business and Management; Case studies; Company structure; Data processing; Deep learning; Departments; Information systems; IT in Business; Logistics; Machine learning; Manufacturing; Mathematical analysis; Methods; Monitoring; Monitoring systems; Neural networks; Organizations; Research Paper; Value creation; Deep learning; Process mining; Machine learning; Business process anagement; Neural network; Predictive process monitoring; Predictive process analytics
• ISSN: 2363-7005; 1867-0202
• DOI: 10.1007/s12599-022-00778-4

Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method’s utility for organizations.