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Published in:
2006 | OriginalPaper | Chapter
11. Statistical Methods for Product and Process Improvement
Authors : Kailash Kapur, Qianmei Feng
Published in: Springer Handbook of Engineering Statistics
Publisher: Springer London
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Abstract
The first part of this chapter describes a process model and the importance of product and process improvement in industry. Six Sigma methodology is introduced as one of most successful integrated statistical tool.
Then the second section describes the basic ideas for Six Sigma methodology and the (D)MAIC(T) process for better understanding of this integrated process improvement methodology.
In the third section, “Product Specification Optimization”, optimization models are developed to determine optimal specifications that minimize the total cost to both the producer and the consumer, based on the present technology and the existing process capability. The total cost consists of expected quality loss due to the variability to the consumer, and the scrap or rework cost and inspection or measurement cost to the producer. We set up the specifications and use them as a counter measure for the inspection or product disposition, only if it reduces the total cost compared with the expected quality loss without inspection. Several models are presented for various process distributions and quality loss functions.
The fourth part, “Process Optimization”, demonstrates that the process can be improved during the design phase by reducing the bias or variance of the system output, that is, by changing the mean and variance of the quality characteristic of the output. Statistical methods for process optimization, such as experimental design, response surface methods, and Chebyshevʼs orthogonal polynomials are reviewed. Then the integrated optimization models are developed to minimize the total cost to the system of producers and customers by determining the means and variances of the controllable factors. Finally, a short summary is given to conclude this chapter.