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Flowsimulation of Manufacturing Process
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

National Electric Vehicle Sweden (NEVS) is a manufacturer of electric vehicles. New facilities are to be developed where an electric car is to be fully assembled. Due to quality assurance car bodies need to be reodred in the Main painted body buffer (MPBB). To be able to investigate this on a non-exiting process discrete event simulation is used (DES). DES improves the understanding of the area of interest (AOI). A simulation study also builds consensus within team members improving decision making. Though simulation requires expertise in both discrete event simulation and statistics, it’s a time consuming process which not guarantee a useful result and requires lots of data. Typical dangers in discrete event simulation is, inaccurate input data, overconfidence of simulation response or the modeller do not fully understand the underlying process. The validation and verification of the model is important to create a credible model bus is also a danger since it’s possible to conduct in inaccurate manners. The simulation study consists of a number of phases in the following order;•Study of real world – Identification of the problem/process to attain understanding of the area of interest that is to be simulated.•Conceptual model – Develop a non-software specific description of how the area of interest is to be abstracted as a model. Contains simulation objective, experimental factors, scenario, content, level of detail and required input data. •Data management – Gather or estimate data that are required by the conceptual model. Translate the data from raw form such it can be used in the simulation software. •Modelling – Build the model stepwise. Start with smaller simple models ensuring key features. Then increase the level of details and merge the smaller models to a final simulation model. •Experimentation – During this phase the experimentation of the model is conducted. The solution space is explored to attain knowledge of the area of interest.•Implementation– The result from the study can be implemented as findings or using the model for future simulation or learning purposes. The simulation showed that the max length of MPBB is influenced by jobs per hour (JPH), amount of quality assurance (QA) and uptime. No major change of max length was identified for different amount of car variants. Neither does the number of variants change the sensitivity of quality assurance. An excessive limitation of the MPBB affects the upstream processes due to car bodies are hindered to enter the buffer. The simulation model does not present an optimal value; thus it can be used testing how parameters relatively affects the system.

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Keyword [en]
Keyword [sv]
URN: urn:nbn:se:ltu:diva-51891Local ID: 9105bf92-0ca5-473d-9259-5a440f4f184dOAI: diva2:1025255
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Subject / course
Student thesis, at least 30 credits
Educational program
Mechanical Engineering, master's level
Validerat; 20160610 (global_studentproject_submitter)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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Sandblom, Jakob

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