Discrete Event Simulation in Automotive Final Process System Vishvas Patel John Ma Throughput Analysis & Simulations General Motors 1999 Centerpoint Parkway Pontiac, MI 48341, U.S.A. James Ashby Engineering Performance Improvement General Motors 585 South Blvd. Pontiac, MI 48341, U.S.A. Presented by Milan Harris, CS Dept., MWSU 4/11/2007

Overview Introduction Methodology Experimentation and Results Conclusion

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 tests Other 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 objectives  Collection of Data  Model construction, Verification and Validation  Output 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

Model Design

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 available Result  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

Experimental Analysis and Results

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

Questions?

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