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Analysis of Manufacturing Complexity for Optimal Resource Allocation in Mixed Model Production of the Automotive Industry

Author(s)
Lee, Wooyeol
Advisor
Kim, Nam Hun
Issued Date
2015-02
URI
https://scholarworks.unist.ac.kr/handle/201301/71830 http://unist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001925619
Abstract
With the ever increasing product variety in the automotive industry, the majority of automobile manufacturers have been forced to produce mixed models in smaller volumes per product with limited manufacturing resources, such as production lines, storage and facilities. Consequently, a mixed model assembly line causes complexities in its manufacturing processes and material flows, which drives manufacturers to investigate opportunities to maximize production flexibility in order to mitigate the complexity. The quantitative analysis of the manufacturing complexity level, however, has become an essential issue prior to the investigation of flexibility opportunities due to the lack of absolute indices to defined manufacturing complexity in reality. In this research, we aim to present the manufacturing complexity measures for the automotive industry associated with product variety in the mixed model assembly line, and illustrate how to utilize the complexity measures to organize more flexible opportunities.
This study begins by presenting a quantified complexity measuring framework based on the information entropy approach. In the proposed framework, the information entropy modeling handles uncertainties of the manufacturing process information, with configurations of mixed model assembly lines. The information comprises tasks, options and sequence to name a few. The entropy measure is then converted into a process reliability value, which represents the severity in the health of the manufacturing processes. The complexity model in this work incorporates the variability in production volumes, models and options, which are hypothesized as having critical impacts on the manufacturing complexity.
The last part of this thesis includes a case study on the manufacturing complexity, with production data and verification of the proposed complexity measuring framework. The automotive manufacturing process consists of a number of sub-process lines such as a press shop, body shop and general assembly. In this work, we specifically focus on the assembly line, which is among the processes most affected by complexity, and present the implications of the analyzed complexity and flexibility measures, as well as the potential applicability of the proposed framework.
Publisher
Ulsan National Institute of Science and Technology (UNIST)
Degree
Master
Major
Department of Human and Systems Engineering

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