TY  - CHAP
A1  - Schmitt, R. H.
A1  - Kiesel, R.
A1  - Buschmann, D.
A1  - Cramer, S.
A1  - Enslin, C.
A1  - Fischer, Markus
A1  - Gries, T.
A1  - Hopmann, Ch.
A1  - Hübser, L.
A1  - Janke, T.
A1  - Kemmerling, M.
A1  - Müller, K.
A1  - Pelzer, L.
A1  - Perau, Martin
A1  - Pourbafrani, M.
A1  - Samsonov, V.
A1  - Schlegel, P.
A1  - Schopen, M.
A1  - Schuh, Günther
A1  - Schulze, T.
A1  - van der Aalst, W. M. P.
A2  - Brecher, Christian
A2  - Schuh, Günther
A2  - Aalst, Wil van der
A2  - Janke, Matthias
A2  - Piller, Frank T.
A2  - Padberg, Melanie
T1  - Improving Shop Floor-Near Production Management Through Data-Driven Insights
T2  - Internet of Production - Fundamentals, Applications and Proceedings
N2  - In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.

Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis. The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed to identify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.

With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.
KW  - Predictive Quality
KW  - Parameter Prediction
KW  - Learning-based Scheduling
KW  - Process Analysis
KW  - Deviation Detection
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/2568
UR  - https://link.springer.com/referenceworkentry/10.1007/978-3-030-98062-7_16-1
SN  - 978-3-030-98062-7
N1  - Die DOI des Gesamtwerkes/The DOI of the complete work: 

https://doi.org/10.1007/978-3-030-98062-7
PB  - Springer
CY  - Cham [u. a.]
ER  -