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Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

May, Marvin Carl ORCID iD icon 1; Fang, Zeyu 1; Eitel, Michael B. M. 1; Stricker, Nicole 1; Ghoshdastidar, Debarghya; Lanza, Gisela 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

Abstract:

Key performance indicators (KPIs) are widely used to monitor and control the production in industry. On an aggregated level, often represented as graphs or interrelated KPI systems, a comprehensive overview is given. However, missing or inaccurate sensor data and KPIs, as well inconsistencies in KPI based management are a major hurdle disturbing operations. To counter the impact of such missing KPIs, we propose a value optimization based approach to reconstruct the values of missing KPIs within a KPI system. While the approach shows successful reconstruction in the case study, the value optimization can be sped up through a random forest prediction of the initial optimization set. Thus, the inclusion of previous knowledge about the system behavior proves beneficial and superior to the pure optimization based approach, as validated by both randomized and simulation-based measurement data.


Verlagsausgabe §
DOI: 10.5445/IR/1000156127
Veröffentlicht am 20.02.2023
Originalveröffentlichung
DOI: 10.1007/s11740-022-01179-y
Scopus
Zitationen: 2
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 0944-6524, 1863-7353
KITopen-ID: 1000156127
Erschienen in Production Engineering
Verlag Wissenschaftliche Gesellschaft für Produktionstechnik e.V. (WGP)
Band 17
Heft 2
Seiten 211–222
Vorab online veröffentlicht am 22.12.2022
Nachgewiesen in Dimensions
Scopus
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