Authors:
Richard May
1
;
Leonard Cassel
2
;
Hashir Hussain
1
;
Muhammad Talha Siddiqui
1
;
Tobias Niemand
3
;
Paul Scholz
4
and
Thomas Leich
1
Affiliations:
1
Harz University of Applied Sciences, Wernigerode, Germany
;
2
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
;
3
Siemens Mobility GmbH, Brunswick, Germany
;
4
Hilti AG, Thüringen, Austria
Keyword(s):
Artificial Intelligence, Machine Learning, Manufacturing, Industry 4.0, Added Value, Challenges, Survey.
Abstract:
Artificial-Intelligence Systems (AIS) are reshaping manufacturing by optimizing processes, enhancing efficiency, and reducing costs. Despite this potential, their adoption in practice remains challenging due to limited understanding of technological complexities and practical hurdles. In this study, we present findings of a survey involving 26 manufacturing AIS practitioners, highlighting key challenges, strategies for implementing AIS more effectively, and perceived added value. Data preparation, deployment, operation, and change management were identified as the most critical phases, emphasizing the need for robust data management and scalable, modular (i.e., configurable) solutions. Predictive maintenance, driven by supervised learning, dominates current AIS, aligning with industry goals to reduce downtime and improve productivity. Despite the benefits, broader applications, such as real-time optimization and advanced quality control, seem to remain underutilized. Overall, the stu
dy aims to provide insights for both practitioners and researchers, emphasizing the importance of overcoming these barriers to facilitate the adoption of AIS in advanced manufacturing.
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