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An analytical framework for handling production time variety at workstations of mixed-model assembly lines

: Keckl, Stefan; Kern, Wolfgang; Abou-Haydar, A.; Westkämper, Engelbert

Volltext urn:nbn:de:0011-n-3798556 (100 KByte PDF)
MD5 Fingerprint: 112a432861f833a5fc4450fd35de57da
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Erstellt am: 8.3.2016

Procedia CIRP 41 (2016), S.201-206
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <48, 2015, Ischia>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
GSaME; Variantenmanagement; Produktivität; Fertigung; Variantenfertigung; Variantenvielfalt

In recent years market demands have shifted towards customized products. As a result many manufacturing companies face an increasing variety of their product range. As it is not profitable to install new assembly lines for each product, assembly lines have to be able to handle different products in batch size one. In literature these lines are called mixed-model lines. They follow the logical principal of flow production but are capable of producing different products while needing minimal modification of assembly processes at the workstations.
While mixed-model lines help manufacturing companies handling product differences profitably, they result in a number of challenges for the production process. One major challenge is related to the varying assembly times at a single workstation due to different products.
Actions have to be taken to cope with assembly time that is over cycle time, in order to avoid stops in a flow production. For economical reasons manufacturing companies have to be able to work at a high workload utilization on average. Therefore it is necessary to have a detailed look at the workstations’ situation regarding production time variety.
To address this, an analytical framework for assembly lines, based on a mixed-model line principle, is given to identify workstations that face high complexity regarding production time variety. This analytical framework contains several aspects focusing on production time variety as drifting probability, utilization and statistical dispersion. By using this framework, companies can apply their actions and line balancing more precisely to the situation at a workstation. Thus, manufacturing companies are able to handle complexity effectively and to reach a high workload utilization in their mixed-model assembly line.