A Leveling Production Strategy To Automotive Assembly Using

University of Texas at El Paso
[email protected]
Open Access Theses & Dissertations
2018-01-01
A Leveling Production Strategy To Automotive Assembly Using Queuing Systems Software Through Simio Simulation
Ivan Arturo Renteria Marquez
University of Texas at El Paso, [email protected]
Follow this and additional works at: https://digitalcommons.utep.edu/open_etd Recommended Citation
Renteria Marquez, Ivan Arturo, "A Leveling Production Strategy To Automotive Assembly Using Queuing Systems Software Through Simio Simulation" (2018). Open Access Theses & Dissertations. 153. https://digitalcommons.utep.edu/open_etd/153 This is brought to you for free and open access by [email protected] It has been accepted for inclusion in Open Access Theses & Dissertations by an authorized administrator of [email protected] For more information, please contact [email protected]

A LEVELING PRODUCTION STRATEGY TO AUTOMOTIVE ASSEMBLY USING QUEUING SYSTEMS SOFTWARE THROUGH SIMIO SIMULATION
IVAN ARTURO RENTERIA MARQUEZ Master’s Program in Manufacturing Engineering
APPROVED: Tzu-Liang (Bill) Tseng, Ph.D., Chair Amit Lopes, Ph.D., Co-chair Yirong Lin, Ph.D.
Charles Ambler, Ph.D. Dean of the Graduate School

Copyright ©
by Ivan Arturo Renteria Marquez
2018

Dedication
This work is dedicated to my family.

A LEVELING PRODUCTION STRATEGY TO AUTOMOTIVE ASSEMBLY USING QUEUING SYSTEMS SOFTWARE THROUGH SIMIO SIMULATION
by IVAN ARTURO RENTERIA MARQUEZ, PhD ECE
THESIS Presented to the Faculty of the Graduate School of
The University of Texas at El Paso in Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE
Department of Industrial, Manufacturing and Systems Engineering THE UNIVERSITY OF TEXAS AT EL PASO December 2018

Acknowledgements
I would like to thank my master thesis adviser Dr. Tzu-Liang (Bill) Tseng for all his support, both financial and scientific.
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Abstract
The automotive manufacturing industry is challenged with an unpredictable costumer demand with considerable fluctuation. In addition, the number of products (vehicles) and the complexity of them are constantly growing. These characteristics make very difficult to plan and schedule production. This thesis presents a methodology to model with a high degree of accuracy the production floor, warehouse and material handling system of an automotive assembly facility through Simio simulation software; resulting in a dynamic model that combines all the critical variables of the system. The presented methodology includes an algorithm developed with the goal of modeling the automotive facility Kanban system. A hypothetical case study of an automotive manufacturing assembly plant is used as an example to show the method. In order to plan the production batch size, a Heijunka analysis was conducted. The presented model could be used by the automotive manufacturing assembly industry as a tool to develop the planning and scheduling strategy.
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Table of Contents
Acknowledgements ..........................................................................................................................v Abstract .......................................................................................................................................... vi Table of Contents .......................................................................................................................... vii List of Tables ............................................................................................................................... viii List of Figures ................................................................................................................................ ix Chapter 1: Introduction ....................................................................................................................1 Chapter 2: Description of the automotive manufacturing assembly plant and solution approach..6 Chapter 3: Numerical results and conclusions...............................................................................19 References ......................................................................................................................................36 Vita.................................................................................................................................................40
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List of Tables
Table 3.1: Results of the experiment. ............................................................................................20 Table 3.2: Lead time, units manufactured and WIP for both vehicle models. ..............................35
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List of Figures
Figure 2.1: Automotive manufacturing assembly plant...................................................................8 Figure 2.2: Logical simulation model. ...........................................................................................10 Figure 2.3: Properties of workstation object..................................................................................12 Figure 2.4: Properties of conveyor object. .....................................................................................12 Figure 2.5: Kanban based material handling algorithm.................................................................16 Figure 2.6: 3D view of the generated automotive manufacturing assembly plant. .......................17 Figure 2.7: ExcelWrite Step. ..........................................................................................................18 Figure 2.8: Properties of ExcelWrite Step......................................................................................18 Figure 3.1: Transient and steady-state period analysis for the average WIP.................................20 Figure 3.2: Average lead time SMORE plot..................................................................................21 Figure 3.3: Average WIP SMORE plot. ........................................................................................21 Figure 3.4: WIP for a batch size of 7200 vehicles. ........................................................................23 Figure 3.5: Lead time for a batch size of 7200 vehicles. ...............................................................23 Figure 3.6: WIP for batch sizes of 20 vehicles. .............................................................................24 Figure 3.7: WIP for batch sizes of 30 vehicles. .............................................................................24 Figure 3.8: WIP for batch sizes of 40 vehicles. .............................................................................25 Figure 3.9: WIP for batch sizes of 50 vehicles. .............................................................................25 Figure 3.10: WIP for batch sizes of 80 vehicles. ...........................................................................26 Figure 3.11: WIP for batch sizes of 90 vehicles. ...........................................................................26 Figure 3.12: WIP for batch sizes of 100 vehicles. .........................................................................27 Figure 3.13: WIP for batch sizes of 150 vehicles. .........................................................................27 Figure 3.14: WIP for batch sizes of 200 vehicles. .........................................................................28
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