@article{KieferGrimmStraubetal.2024,
  author    = {Kiefer, Daniel and Grimm, Florian and Straub, Tim and Bitsch, G{\"u}nter and Van Dinther, Clemens and H{\"o}llig, Jacqueline},
  title     = {Speeding up CNC tool manufacturing: implementing explainable AI for setup time reduction and production agility},
  journal   = {Procedia CIRP},
  volume    = {130},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2024.10.195},
  institution = {ESB Business School},
  pages     = {982 -- 987},
  year      = {2024},
  abstract  = {Long setup times in CNC tool production significantly hinder operational efficiency, characterized by reduced machine utilization, increased planning efforts, and subsequent delivery delays and production bottlenecks. These inefficiencies not only escalate production costs but also tie up capital, compromise order flexibility, augment storage expenses, and prevent the capitalization on market opportunities. This paper explores the application of explainable AI to analyze process data within CNC setups, aiming to identify and elucidate patterns that contribute to prolonged setup durations. By implementing AI models with explanation methods, this research transparently highlights critical improvement points, facilitating targeted interventions to enhance production agility. The outcome is a dual advantage of reducing setup times and operational costs, thereby speeding up overall manufacturing processes. This approach emphasizes innovative manufacturing systems and provides practical insights on using artificial intelligence to enhance efficiency in CNC tool production.},
  language  = {en}
}