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An off-line experiment was performed to estimate a model for on-line process control which describes the empirical relationship between the design and production factors affecting the front wheel camber angle. It is shown through a case study that models for process control estimated from off-line experiments may not generate on-line minimal variance predictions for vehicles representative of those produced in the assembly plant. This occurred in a case study experiment because a characteristic of the vehicle used in the experiment was identified which could not be manipulated or controlled during experimentation. It is shown that the developed methodology for augmenting the experiment would improve the model for on-line process control by generating minimal variance predictions for vehicles more representative of those produced in the assembly plant.

A Benchmarking technique known as Data Envelopment Analysis (DEA) will be discussed as a jumpstart technique to put energy back into a company's Six Sigma program. A hypothetical example of benchmarking the production lines of a manufacturing company will be discussed using Six Sigma quality measures. Typical DEA software output will be discussed.

Recently, managers and engineers at a major Detroit area automotive company recognized the vital importance of identifying the design and production factors which significantly affect the front end camber angle in vehicles in one of their assembly plants. The goal of identifying these factors was the eventual establishment of a total process control program to maintain the overall quality of the vehicle front end. This paper contains a summary of the numerous on-line and off-line studies undertaken in the assembly plant during a one-year period to gain an understanding of the factors which affect the front end camber angle. It will be shown that commonly used analyses in both on-line and off-line studies can possess limitations which fail to meet the needs of the quality engineer. Understanding the results from these studies is important to understanding the complexity in establishing a quality control program for currently operating assembly processes.