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

Overview

IntroductionMethodologyExperimentation and ResultsConclusion

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

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

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

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