discrete event simulation in automotive final process system vishvas patel john ma throughput...
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Discrete Event Simulation in Automotive Final Process
System
Vishvas PatelJohn Ma
Throughput Analysis & Simulations
General Motors 1999 Centerpoint ParkwayPontiac, MI 48341, U.S.A.
James Ashby
Engineering Performance Improvement
General Motors585 South Blvd.
Pontiac, MI 48341, U.S.A.
Presented by Milan Harris, CS Dept., MWSU 4/11/2007
Introduction
The Final Process System is an important part of the entire quality assurance system in an automotive manufacturing process.
Operators and machines perform a series of crucial testing procedures before shipping a vehicle: Dynamic Vehicle Test. Visual Inspection and Repairs. Alignment Tests.
The objective is to use Discrete Event simulation to develop an efficient and effective process to ensure the system maximum performance.
Introduction
What is dynamic vehicle Test? DVT is a functional verification of the vehicle,
performed on a roll test machine.
Examples: Emission controls, engines, transmission, cruise
control
Alignment testing: Wheel alignment, head lamp aim, vehicle audio
system testsOther Tests
Visual inspection, water leak, squeak and rattle audit
Introduction
Many factors add to the complexity of the system Percentage repair rates Repair and service routing logic First time success rate
Hence, analysis is conducted to answer the following: What is the impact of percentage repairs on the throughput? What is the best layout of the system? How many repair stations are required to meet the
throughput? What are the requirements of the driver and operator staff?
Introduction
Reasons to use simulation: Experiment before implementation. Cost is a major
factor! Numerous factor involved making the process very
analytical and complex Simulation model can easily accommodate changes
such as location of testing centers, conveyer line vs stand alone station.
Discrete event simulation has successfully been used in the design and implementation of numerous automotive manufacturing systems.
Methodology
How to simulate:Determining the scope and objectivesCollection of DataModel construction, Verification and
ValidationOutput Analysis
Methodology
Scope and Objective: Analyze the capacities of the elements which possess the
most direct impact on the system performance Process layout Testing station Repair stations Operator staffing
Evaluation of the different process options and utilizations of equipment
Data Collection: Engineers supplied routing logic, process data and layout
Repair rates, pick up and drop off times, equipment breakdown frequencies, capacities of testing equipment, repair times, etc.
Data record from other similar manufacturers and previous simulations
Methodology
Model Construction and Validation Develop base model, which depicted a system without
process variation. Verification and validation achieved through model logic
and extensive use of execution traces Developed secondary model implementing
stochastic variation Included rejection probabilities, randomness of vehicle
and equipment repair times, unscheduled downtime occurrences
Comparison of results Rockwell’s Arena (automation simulation software)
was used for model construction and analysis
Methodology
Output Analysis: Microsoft Visio template was utilized to accurately
and properly document each simulation project Development of a set of standardized documentation
to be used throughout the cooperation in vehicle development process.
Project objectives Scope Assumptions Input data and sources Experiment designs and results Conclusions and recommended actions
Experimental Analysis and Results
Determine the desired level of capacities Experiments were conducted by varying the parameter
for which you seek optimization Optimal number of heavy repair stations required in order
to handle the system first time success rate of 70% or higher is (9)
The same experiment was done for optimizing operators (9) and repair stations in the Paint Repair area (5)
Experimental Analysis and Results
Determine the impact of routing logic Scenario 1: vehicle is routed to designated DVT
station Scenario 2: vehicle can go to any DVT station as it
become availableResult
No significant difference in either scenario with regards to their impact on the overall system performance
Experimental Analysis and Results
Identifying Potential Resource Constraints Is it necessary to adjust the capacities of testing
equipment and repair stations when production volumes increase?
Keep all parameters of the FPS the same Increase the final throughput by a fixed percentage(2.5,
5…)
Result: The FPS is able to handle up to 12% volume increase
without changing configurations of any element in the system.
If production increases past 12%, then the Alignment area will become the system bottleneck
Conclusion
This paper discusses the methodology involved with modeling and studying Final Process System using Discrete Event Simulation.
The focus is to determine the best system, which in reality means that it should be capable of handling a first time success rate of 70% or higher.
The project demonstrates the ability to use simulation for optimizing resources and identifying constraints.
Conclusion
Discrete Event Simulation is the perfect tool! Highly effective for manufacturing system Able to meet objectives within constraints of
operational complexity
References
Discrete Event Simulation in Automotive Final Process System by Vishvas Patel, James Ashby, and John Ma , Winter Simulation Conference 2002
Secrets of Successful Simulation Projects by Robinson and Bhatio.1995