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Präventive Schwachstellenanalytik Mit Methodenzuweisung Zur Produktivitätsoptimierung Von Fertigungsbetrieben Der Automobilzulieferindustrie

Präventive Schwachstellenanalytik Mit Methodenzuweisung Zur Produktivitätsoptimierung Von Fertigungsbetrieben Der Automobilzulieferindustrie

          
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About the Book

Unternehmen jeder Branche und Größe bieten hohes Potenzial zur Produktivitätssteigerung in den Fertigungsbereichen. Im Laufe dieser Arbeit wird aufgezeigt, wie mit einer ganzheitlichen Schwachstellenanalytik und passender Zuweisung von anzuwendenden Methoden zur Schwachstellen-beseitigung, Potenziale der Produktivitätssteigerung identifiziert und erreicht werden können. Digitalisierung und vornehmlich Künstliche Intelligenz helfen dabei als unterstützende Kraft.

Im Aufbau startet diese Dissertation zunächst mit Begriffsdefinitionen zum detaillierteren Verständnis der Schwachstellenanalytik. Im weiteren Verlauf wird eine Struktur betrieblicher Schwachstellen erarbeitet, ergänzt durch einen entsprechenden Kennzahlenkatalog sowie Methodenkatalog. Dabei wird ein erhebliches Mengengerüst erkennbar: Die Erarbeitung einer grundlegenden Struktur betrieblicher Schwach-stellen zeigt einen Schwachstellenkatalog mit 297 potenziellen Schwachstellen, der Kennzahlenkatalog beinhaltet 264 bekannte Kennzahlen und der Methodenkatalog enthält 551 verschiedene Methoden. Die Erforschung und Evaluation der Schwachstellenanalytik erfolgte anhand eines exemplarischen Stanzkontaktes. Die grundlegende Prozessfähigkeit wurde bestätigt. Anschließend wurden gezielt Korrelationen untersucht und eine Ampelprognose entwickelt. Die Verifizierung erfolgte mittels eines erneuten Datensets desselben Produktes. Die Schwachstellenanalytik wurde in ihren Grundzügen mathematisch formuliert. Die Erprobung anhand eines Montage-Prozesses bestätigte die Reproduzierbarkeit und Funktionalität der Schwachstellenanalytik. Letztlich können erhebliche Produktivitätspotenziale belegt und so der Mehrwert der Schwachstellenanalytik zur Modell-verfeinerung von Machine Learning in Fertigungsbereichen bestätigt werden.


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Product Details
  • ISBN-13: 9783662687680
  • Publisher: Springer
  • Publisher Imprint: Springer Vieweg
  • Height: 244 mm
  • No of Pages: 259
  • Spine Width: 15 mm
  • Width: 170 mm
  • ISBN-10: 3662687682
  • Publisher Date: 05 Apr 2024
  • Binding: Paperback
  • Language: German
  • Returnable: Y
  • Weight: 458 gr


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Präventive Schwachstellenanalytik Mit Methodenzuweisung Zur Produktivitätsoptimierung Von Fertigungsbetrieben Der Automobilzulieferindustrie
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Präventive Schwachstellenanalytik Mit Methodenzuweisung Zur Produktivitätsoptimierung Von Fertigungsbetrieben Der Automobilzulieferindustrie
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