2022
Konstantin Büttner; Oliver Antons; Julia C. Arlinghaus
Applied Machine Learning for Production Planning and Control: Overview and Potentials Proceedings Article
In: 10th IFAC Conference on Manufacturing Modelling, Management and Control, pp. 6, Elsevier, 2022.
Abstract | Links | BibTeX | Tags: Control, Machine learning, Production control, Production Planning
@inproceedings{Buettner2022,
title = {Applied Machine Learning for Production Planning and Control: Overview and Potentials},
author = {Konstantin B\"{u}ttner and Oliver Antons and Julia C. Arlinghaus},
url = {https://www.sciencedirect.com/science/article/pii/S2405896322021152},
doi = {https://doi.org/10.1016/j.ifacol.2022.10.106},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
booktitle = {10th IFAC Conference on Manufacturing Modelling, Management and Control},
volume = {55},
number = {10},
pages = {6},
publisher = {Elsevier},
abstract = {Manufacturing companies are under constant pressure to increase efficiency and to achieve logistical objectives. Improving production planning and control (PPC) has significant impact on these efforts. At the same time, increasing complexity and dynamics of PPC environments make PPC more difficult. One way to cope with this situation is the application of machine learning (ML) methods. In this article, we therefore address the current state of PPC-ML research and show, based on the Aachen PPC model, in which PPC tasks and subtasks ML is already applied and to what degree the task is covered by ML. The analysis is limited to core and cross-sectional tasks of the Aachen PPC model, procurement and network tasks are not included. Furthermore, a broad analysis of the targeted data mining, business and logistic objectives is conducted. In addition, we also identify motivations which prompted researchers to apply ML in PPC. },
keywords = {Control, Machine learning, Production control, Production Planning},
pubstate = {published},
tppubtype = {inproceedings}
}
Manufacturing companies are under constant pressure to increase efficiency and to achieve logistical objectives. Improving production planning and control (PPC) has significant impact on these efforts. At the same time, increasing complexity and dynamics of PPC environments make PPC more difficult. One way to cope with this situation is the application of machine learning (ML) methods. In this article, we therefore address the current state of PPC-ML research and show, based on the Aachen PPC model, in which PPC tasks and subtasks ML is already applied and to what degree the task is covered by ML. The analysis is limited to core and cross-sectional tasks of the Aachen PPC model, procurement and network tasks are not included. Furthermore, a broad analysis of the targeted data mining, business and logistic objectives is conducted. In addition, we also identify motivations which prompted researchers to apply ML in PPC.