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Modeling Manual Assembly System to Derive Best Practice from Actual Data

Abstract : In manual assembly systems, there is often little transparency and great potential for optimization, especially in assembly systems with small batch sizes. In this paper, a model is developed that supports an approach to automated assembly optimization. For this optimization, actual data is collected in manual assemblies. Based on the data, the optimized assembly sequence is derived by developing a best practice. Best practice describes a combination of assembly processes performed by the workers during the data collection. The model shows the relationships and the dependencies in the assembly systems and allows to improve it.First, the considered assembly system is defined as a socio-technical system and general modeling principles are prepared. After presenting the benchmark approach to derive the best practice, the requirements for the model are identified. Then, the model is developed in four steps: The system boundary is defined, the features are described, and the model is formalized. Finally, the model is applied and tested in an example project and its purposefulness is shown.
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Submitted on : Thursday, January 30, 2020 - 10:17:04 AM
Last modification on : Tuesday, June 16, 2020 - 1:04:02 PM


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Susann Kärcher, David Görzig, Thomas Bauernhansl. Modeling Manual Assembly System to Derive Best Practice from Actual Data. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2019, Austin, TX, United States. pp.431-438, ⟨10.1007/978-3-030-29996-5_50⟩. ⟨hal-02460513⟩



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