No abstract

A Japanese ceramic tile manufacturer knew in 1953 that is more costly to control causes of manufacturing variations than to make a process insensitive to these variations. The Ina Tile Company knew that an uneven temperature distribution in the kiln caused variation in the size of the tiles. Since uneven temperature distribution was an assignable cause of variation, a process quality control approach would have increased manufacturing cost. The company wanted to reduce the size variation without increasing cost. Therefore, instead of controlling temperature distribution they tried to find a tile formulation that reduced the effect of uneven temperature distribution on the uniformity of tiles. Through a designed experiment, the Ina Tile Company found a cost-effective method for reducing tile size variation caused by uneven temperature distribution in the kiln. The company found that increasing the content of lime in the tile formulation from 1 % to 5% reduced the tile size variation by a factor of ten. This discovery was a breakthrough for the ceramic tile industry.A technique such as this that reduces variation by reducing the sensitivity of an engineering design to the sources of variation rather than by controlling these sources is called parameter design. This example, which appeared in a Japanese book, "Frontier Stories in Industry" published by Diamond Sha Publishing Company in Japan, illustrates that parameter design is a cost-effective technique for improving manufacturing processes.

No abstract

The traditional method for estimating or predicting linear combinations of the fixed effects and realized values of the random effects in mixed linear models is first to estimate the variance components and then to proceed as if the estimated values of the variance components were the true values. This two-stage procedure gives unbiased estimators or predictors of the linear combinations provided the data vector is symmetrically distributed about its expected value and provided the variance component estimators are translation-invariant and are even functions of the data vector. The standard procedures for estimating the variance components yield even, translation-invariant estimators.1249

In this paper we describe the off‐line quality control method and its application in optimizing the process for forming contact windows in 3.5‐μm complementary metal‐oxide semiconductor circuits. The off‐line quality control method is a systematic method of optimizing production processes and product designs. It is widely used in Japan to produce high‐quality products at low cost. The key steps of off‐line quality control are: (i) Identify important process factors that can be manipulated and their potential working levels; (ii) perform fractional factorial experiments on the process using orthogonal array designs; (iii) analyze the resulting data to determine the optimum operating levels of the factors (both the process mean and the process variance are considered in this analysis; (iv) conduct an additional experiment to verify that the new factor levels indeed improve the quality control.