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Method for Data-Driven Assembly Sequence Planning

 
: Kärcher, Susann; Bauernhansl, Thomas

:

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)
S. 71-79
Stuttgart Conference on the Automotive Production (SCAP) <1, 2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer IPA ()
Montage; Fertigungsplanung; Best Practice

Abstract
In many manual assembly systems, there is great potential for optimization, especially when products in small quantities, high variants or with high complexity are produced. The more often the assembly is changed, the greater is the potential. The main reason for the optimization potential is the still high effort required for an assembly planning. Especially in today’s challenging and volatile environment, classic assembly planning often reaches its limits. As a result, assembly systems are often not planned in sufficient detail. The consequence is a lack of transparency: Workers in assembly do not get clear work instructions and planners do not get feedback from the assembly. There are approaches to reduce the effort required for assembly planning meeting the challenge from two sides: On the one hand, there are approaches to further integrate assembly planning with previous processes, such as product development. On the other hand, there are approaches that optimize the processes from an assembly perspective. This paper focuses on a method to optimize assembly sequence planning based on actual data. Data is collected, for example, via sensors in the assembly area. Afterwards, different runs of the assembly process are analyzed. Then, an algorithm derives the best practice to assemble the product. Best practice describes the assembly sequence that leads to the fastest assembly. The method fits into a methodology to transfer benchmarking to manual assembly and can be used for a one-time optimization project as well as for continuous optimization. The results generated in the algorithm are then made available to workers and planners.

: http://publica.fraunhofer.de/dokumente/N-638147.html