What is Robust Quality Engineering(RQE)?
Overview:
Robust Quality Engineering is a discipline proposed by Dr. Genichi Taguchi. Dr. Taguchi and his colleagues applied the principles of design of experiments in manufacturing to a wide range of practical problems, significantly improving industrial product quality in Japan. Building on this foundation, Dr. Taguchi continued to refine and expand his theories, leading to the development of what is now known as Robust Quality Engineering. Through this work, Dr. Taguchi established several methodologies, including modern quality engineering. In particular, Robust Quality Engineering is internationally recognized for its systematic approach to improving R&D efficiency by replacing conventional trial-and-error processes, and it is highly regarded by researchers and engineers worldwide.
In 1980, at AT&T Bell Laboratories, Dr. Genichi Taguchi emphasized the importance of upstream design, stating:
・"Whack-a-mole after whack-a-mole after whack-a-mole will not lead to essential solutions.“
・"If you don't design it right at the design stage, you'll never get out of trouble."
At Bell Laboratories, the application of Dr. Taguchi's methods to microprocessor manufacturing successfully reduced variation in the photolithography process used to create approximately 230,000 2 μm-diameter holes within a 1.5 cm square area. As a result, production time was reduced by half.
These results were published in the Bell System Technical Journal and attracted widespread attention. The methodology was subsequently adopted by major American industries, including Ford Motor Company, contributing significantly to improvements in quality and productivity.
Following these developments, Dr. Don Clausing introduced the approach at Xerox Corporation and called it the "Taguchi Method."
In his later years, Dr. Genichi Taguchi summarized his life’s work as follows:
My work can be summarized in two key contributions:
１．Making orthogonal arrays easier to use.
２．Integrating data acquisition and analysis methods into the design of experiments to reduce
functional variation.