Systematic AI Potential Analysis for Sustainable Rough Factory Planning
D. Kürpick, J.-P. Disselkamp, J. Lick, A. Hovemann, R. Dumitrescu, in: Lecture Notes in Mechanical Engineering, Springer Nature Switzerland, Cham, 2025.
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Kürpick, Dominik;
Disselkamp, Jan-Philipp;
Lick, Jonas;
Hovemann, Aschot;
Dumitrescu, RomanLibreCat
Abstract
<jats:title>Abstract</jats:title>
<jats:p>Current megatrends are influencing industrial production and leading to ever shorter innovation cycles. The resulting fast pace of production requirements requires an accelerated development of production systems and an associated increase in efficiency in factory planning. Due to its knowledge-intensive activities, rough factory planning promises great potential to be supported in its activities by innovative technologies such as artificial intelligence. However, industrial companies face the challenge to recognize the potential of artificial intelligence (AI) in rough planning and to evaluate possible applications in their business context. As a result, a systematic approach for analyzing AI potential in rough factory planning was developed as part of this work. The system includes a procedural model and several artefacts used in it, which support the identification and evaluation of AI potential in organizations. This approach not only streamlines the planning process but also aligns with sustainable manufacturing principles by enhancing resource efficiency, promoting intelligent system design, and fostering innovation in product development and manufacturing processes.</jats:p>
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Lecture Notes in Mechanical Engineering
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Kürpick D, Disselkamp J-P, Lick J, Hovemann A, Dumitrescu R. Systematic AI Potential Analysis for Sustainable Rough Factory Planning. In: Lecture Notes in Mechanical Engineering. Springer Nature Switzerland; 2025. doi:10.1007/978-3-031-93891-7_84
Kürpick, D., Disselkamp, J.-P., Lick, J., Hovemann, A., & Dumitrescu, R. (2025). Systematic AI Potential Analysis for Sustainable Rough Factory Planning. In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-93891-7_84
@inbook{Kürpick_Disselkamp_Lick_Hovemann_Dumitrescu_2025, place={Cham}, title={Systematic AI Potential Analysis for Sustainable Rough Factory Planning}, DOI={10.1007/978-3-031-93891-7_84}, booktitle={Lecture Notes in Mechanical Engineering}, publisher={Springer Nature Switzerland}, author={Kürpick, Dominik and Disselkamp, Jan-Philipp and Lick, Jonas and Hovemann, Aschot and Dumitrescu, Roman}, year={2025} }
Kürpick, Dominik, Jan-Philipp Disselkamp, Jonas Lick, Aschot Hovemann, and Roman Dumitrescu. “Systematic AI Potential Analysis for Sustainable Rough Factory Planning.” In Lecture Notes in Mechanical Engineering. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93891-7_84.
D. Kürpick, J.-P. Disselkamp, J. Lick, A. Hovemann, and R. Dumitrescu, “Systematic AI Potential Analysis for Sustainable Rough Factory Planning,” in Lecture Notes in Mechanical Engineering, Cham: Springer Nature Switzerland, 2025.
Kürpick, Dominik, et al. “Systematic AI Potential Analysis for Sustainable Rough Factory Planning.” Lecture Notes in Mechanical Engineering, Springer Nature Switzerland, 2025, doi:10.1007/978-3-031-93891-7_84.
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