Towards a Data-Driven and Proactive Disruption Management in Production Planning and Control
- The increasing complexity of the manufacturing environment, its products, and processes is leading to a surge in disruptions for manufacturing companies. Despite the growing availability of data, companies are struggling to manage these disruptions and utilize the data for proactive production planning and control (PPC). This article describes an extensive systematic literature review, semi-structured expert interviews, and an expert workshop which were performed in order to gain a deeper understanding of disruptions and their impact on PPC. The outcome is a categorization of disruptions and their corresponding mitigation decisions. Additionally, this article presents an overview of disruption data sources that enable manufacturing companies to detect disruptions in existing IT systems or collect data in the proposed systems to proactively adapt to disruptions, optimize the PPC, and increase the production system’s resilience.
| Author: | Julian Stang, Jan JoppienORCiD, Michael F. Zaeh |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-031-86893-1_58 |
| ISBN: | 978-3-031-86892-4 |
| ISBN: | 978-3-031-86893-1 |
| Parent Title (English): | Production at the Leading Edge of Technology. Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024 |
| Publisher: | Springer |
| Place of publication: | Cham [u. a.] |
| Editor: | Welf-Guntram Drossel, Steffen Ihlenfeldt, Martin Dix |
| Document Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2025/07/24 |
| Date of first Publication: | 2025/07/22 |
| Release Date: | 2025/07/24 |
| Tag: | 03 Disruption management; Production planning and control; Resilience |
| First Page: | 533 |
| Last Page: | 542 |
| Note: | Acknowledgments: The IGF project “KIbaroP”, project number 01IF11897N, is supported by the German Federal Ministry for Economic Affairs and Climate Action on the basis of a decision by the German Bundestag. The authors appreciate the support. |
| Note: | (Project) KIbaroP: AI-based robust production planning
The aim of the 'KIbaroP' research project is to develop and validate an AI-based and robust approach to production planning with special consideration of SME-specific data.
Duration: 01.10.2023 – 30.09.2025
Funding no.: 01IF23054 N
Funding: Federal Ministry for Economic Affairs and Energy (BMWE)
Promoters: DLR Projektträger
Funding context: IGF – Förderprogramm ‚Industrielle Gemeinschaftsforschung‘
Funding information:
This pre-competitive project is funded by the Federal Ministry for Economic Affairs and Climate Action with IGF funds.
Benefits for the Target Group:
The development of AI-based and robust production planning offers companies both direct and indirect benefits. The direct benefit lies in the creation of an understanding of disruptions, which enables preventive measures to be taken in production planning. In addition, the existing knowledge of production data is further deepened and its preparation for AI applications is described. It also shows how insights can be generated from disruption data and used preventively.
The indirect benefit is the long-term increase in competitiveness through higher delivery reliability and customer satisfaction. The sustainable increase in productivity through reduced disruption enables companies to increase their profitability.
Research partners:
Institut für Werkzeugmaschinen und Betriebswissenschaften (iwb) der TUM School of Engineering and Design, Garching bei München
Project partners:
electronic service willms GmbH & Co. KG, Stolberg-Breinig
GKD – Gebr. Kufferath AG, Düren
INFORM Institut für Operations Research und Management GmbH, Aachen
Jochen Günther Training & Consulting, München
MunichRe, München
ONIQ GmbH, Köln
Phiesel GmbH, Bad Münstereifel
PSI Automotive & Industry GmbH, Berlin
Rausch Druck GmbH, Augsburg
REO AG, Solingen
Robert Josef Wolf GmbH & Co. KG, Wilnsdorf
Schoeller Werk GmbH & Co. KG, Hellenthal |
| Institute / Department: | FIR e. V. an der RWTH Aachen |
| Produktionsmanagement | |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften |
