Industrial Production Process Improvement by a Process Engine Visual Analytics Dashboard

Josef Suschnigg, Florian Ziessler, Markus Brillinger, Matej Vukovic, Jürgen Mangler, Tobias Schreck, Stefan Thalmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes. This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply. Data-driven technologies promise help in learning faster by making use of the massive data volumes collected in production environments. Visual analytics approaches are particularly promising in this regard as they aim to enable engineers with their rich domain knowledge to identify opportunities for process improvements. Based on the assumption that process improvement should be connected with the process engine managing the process execution, we propose a visual analytics dashboard which integrates process models. Based on a case study in the smart factory of Vienna, we conducted two pair analytics sessions. The first results seem promising, whereas domain experts articulate their wish for improvements and future work.
Original languageEnglish
Title of host publicationProceedings of the 53rd Hawaii International Conference on System Sciences
Pages1320-1329
Number of pages10
ISBN (Electronic)978-0-9981331-3-165
Publication statusPublished - 2020
Event53rd Hawaii International Conference on System Sciences - Manoa, United States
Duration: 7 Jan 202010 Jan 2020

Conference

Conference53rd Hawaii International Conference on System Sciences
Abbreviated titleHICSS 2020
CountryUnited States
CityManoa
Period7/01/2010/01/20

Fields of Expertise

  • Information, Communication & Computing

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