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Development of an Integrated Data-Driven Process to Handle Uncertainties in Multi-Variant Production and Logistics. A Survey

: Dürr, Simon; Lamprecht, Raphael; Kauffmann, Matthias; Winter, Jörg; Alexy, Heinz; Huber, Marco

Postprint urn:nbn:de:0011-n-6364394 (173 KByte PDF)
MD5 Fingerprint: 5857ddd6ea735cc298ac0c0e7d5519dd
Erstellt am: 3.9.2021

Weißgraeber, Philipp:
Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), 9th and 10th of November 2020
Wiesbaden: Springer Vieweg, 2021 (ARENA2036)
ISBN: 978-3-662-62961-1 (Print)
ISBN: 978-3-662-62962-8 (Online)
Stuttgart Conference on the Automotive Production (SCAP) <1, 2020, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
Künstliche Intelligenz; Auftragsmanagement; Auftragsplanung

A key differentiator in customer satisfaction in the automotive industry is offering a choice of high-dimensional possibilities to customize an individual vehicle. In the luxury segment this equals more than one billion variants. Innovative data-driven processes are necessary for the planning and handling of vehicles in production and distribution in order to guarantee indispensable factors such as stability, flexibility and transparency across the entire supply chain and to deliver the right vehicle to the right place at the promised time. Highly complex business environments, multi-variant products, trends with effects on distribution networks and future mobility concepts confront manufacturers with new challenges. This paper provides a survey of currently used methods and technologies to handle the previously mentioned challenges in the area of the customer order management of an automotive manufacturer. A specific field of research is the concept of planned orders which are derived from early anticipated customer requirements and their utilization throughout the entire planning and order management process. In addition to the generic approaches for achieving integrated planning of sales and production programs as well as the resulting material requirements, artificial intelligence methods are investigated with regard to the concept of the planned orders. For this purpose, various existing approaches to anticipate planned orders and to effectively manage the supply chain are examined. In conclusion, this paper provides a survey of state of the art methods regarding artificial intelligence linked to current research on agile production systems. Despite existing uncertainty in the upcoming years the implementation of stable data-driven processes is crucial. In this context, the possibility to prepare an automotive manufacturer for the upcoming challenges in digitalization and globalization is evaluated.