What is Robust Quality Engineering(RQE)?

　Companies and public organizations seek to develop and deliver products and services that meet or exceed the needs and expectations of customers and society. Variability in the functions of products and services can lead to functional failures, defects, and quality problems, ultimately resulting in losses to society. Robust Quality Engineering (RQE), also known as Robust Engineering or the Taguchi Methods, aims to minimize such losses to society. These losses may be tangible or intangible, short-term or long-term, and direct or indirect. Losses to society include, but are not limited to, the following:
　・Costs and time incurred by customers and users for repair, maintenance, and
　　 replacement
　・Customer and user dissatisfaction, discomfort, anxiety, injury, and loss of life
　・Waste of global resources
　・Losses due to environmental pollution
　・Warranty and recall costs (short-term losses for producers)
　・Decreases in repeat customers (long-term losses for producers)
　・Damage to corporate reputation and brand image (long-term losses for producers)
　・Reduced resale value of products
　・Declines in competitiveness and market share (long-term losses for producers)
　・Resources expended on warranty analysis and reporting

The quality of products and services is the loss to society after they are shipped to the market. The quality is evaluated in terms of the loss to society. The smaller the loss, the better the quality is.        

　Since costs are largely driven by labor, Dr. Genichi Taguchi emphasized that reducing losses to society ultimately increases individuals' free time. Robust Quality Engineering (RQE) seeks to minimize such social losses, thereby contributing to the long-term sustainability of corporate competitiveness.
　RQE focuses on robustness as a strategic approach to preventing functional variability and minimizing defects and failures in products and services. A robust design is one in which product or service performance is minimally affected by noise factors such as variations in operating environments or aging. By optimizing for robustness, functional variability is reduced, thereby minimizing losses to society.
　RQE offers methodologies applicable to a wide range of functions. It provides a versatile framework for optimizing the robustness of hardware, software, integrated hardware–software systems, chemical and biological processes, diagnostic, pattern recognition, and predictive functions. Since 1993, more than 2,400 case studies on robust optimization across these domains have been presented at RQES and are available in a reference database.
　We invite you to join us in advancing efforts to minimize losses to society.