Abstract
The topic of this study is approaches for implementing Statistical Process Control (SPC) in a low-volume manufacturing environment which has difficulty in applying conventional SPC techniques centered around high-volume, repetitive processes. To make up for the weaknesses of SPC in low-volume production, a new method to test data independence, a new plot to describe the similarity between two charts, and two modified graphical tools are proposed. In low-volume production, it is frequently hard to analyze processes due to the lack of data. This study focuses on an environment in which there are not enough time-related data to apply conventional SPC techniques, but a large number of data from the same tyke of processes in a part or product are available for monitoring and diagnosis. The proposed test for data independence is useful in terms of the descriptive statistics analysis. The similarity plot and graphical tools are designed to make up for these difficulties of low-volume production by visual representation methodology. The graphical tools are color-coded for easy recognition and focus on presenting the trend and distribution of defect occurrence within the same part or product.
Chin, Chang-Ho (1999). Dimensional SPC for low-volume production. Master's thesis, Texas A&M University. Available electronically from
https : / /hdl .handle .net /1969 .1 /ETD -TAMU -1999 -THESIS -C457.