2025
Tobias Bein; Ulf Bergmann; Oliver Antons; Julia C. Arlinghaus
Experience-Integrated Product Family Formation Using Clustering Algorithms Proceedings Article
In: Mizuyama, Hajime; Morinaga, Eiji; Nonaka, Tomomi; Kaihara, Toshiya; Cieminski, Gregor; Romero, David (Hrsg.): Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond, S. 311–325, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-032-03546-2.
Abstract | Links | BibTeX | Schlagwörter: Clustering, Human Decision-making, Production Planning, Production planning and control
@inproceedings{10.1007/978-3-032-03546-2_21,
title = {Experience-Integrated Product Family Formation Using Clustering Algorithms},
author = {Tobias Bein and Ulf Bergmann and Oliver Antons and Julia C. Arlinghaus},
editor = {Hajime Mizuyama and Eiji Morinaga and Tomomi Nonaka and Toshiya Kaihara and Gregor Cieminski and David Romero},
url = {https://link.springer.com/chapter/10.1007/978-3-032-03546-2_21},
doi = {10.1007/978-3-032-03546-2_21},
isbn = {978-3-032-03546-2},
year = {2025},
date = {2025-08-30},
urldate = {2026-01-01},
booktitle = {Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond},
pages = {311\textendash325},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {To ensure qualitatively sufficient results for the analysis of complex and diverse production programs, suitable analysis approaches must be utilized. As computer-based cluster algorithms become more widely used in this context, and as the demand for improved communication and coordination with the plant's stakeholders increases, there is an opportunity to integrate operator experience into clustering algorithms for production programs. This paper investigates whether and when the integration of operator experience is beneficial for this analysis. A single case study approach is utilized for this purpose, gaining insight and deriving general recommendations for integrating operator experience. While the operator's experience can enhance planning efficiency through tacit knowledge and insights in the form of inputs or feedback loops, it is susceptible to biases and must be checked by statistical analysis.},
keywords = {Clustering, Human Decision-making, Production Planning, Production planning and control},
pubstate = {published},
tppubtype = {inproceedings}
}
To ensure qualitatively sufficient results for the analysis of complex and diverse production programs, suitable analysis approaches must be utilized. As computer-based cluster algorithms become more widely used in this context, and as the demand for improved communication and coordination with the plant's stakeholders increases, there is an opportunity to integrate operator experience into clustering algorithms for production programs. This paper investigates whether and when the integration of operator experience is beneficial for this analysis. A single case study approach is utilized for this purpose, gaining insight and deriving general recommendations for integrating operator experience. While the operator's experience can enhance planning efficiency through tacit knowledge and insights in the form of inputs or feedback loops, it is susceptible to biases and must be checked by statistical analysis.