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1.8 Summary and Road Map

The road map for engineering robust products with Six Sigma is shown in Figure 1-12. Chapters 2, 3, and 4 discuss in detail how to establish the Voice-of-Customer models and how to convert them into CTQs, design concepts, and design controls. CTQs represent the product or service characteristics that are defined by the customer (internal or external), which may include the upper- and lower-specification limits or any other factors related to them. A CTQ characteristic—what the customer expects of a product—usually must be translated from a qualitative customer statement into an actionable, quantitative business specification. It is up to engineers to convert CTQs into measurable terms using Six Sigma tools.


Figure 1-12 Road map for engineering robust products with Six Sigma.

Six Sigma robust design starts with the voices of the customers, which reflect their spoken and unspoken needs and/or requirements. The Kano model, which is discussed in Chapter 2, helps engineers identify VOCs systematically.

Based on VOCs, a House of Quality can be built using a Six Sigma methodology called Quality Function Deployment (QFD). Within the House of Quality, customer requirements are converted into Critical-to-Quality characteristics. QFD enables identification and prioritization of the CTQs. A case study example in Chapter 3 illustrates the six steps to construct a House of Quality. The QFD process can also identify technical contradictions, which are the basis for applying the Theory of Inventive Problem Solving (TRIZ—the Russian acronym for the theory) to generate creative design concepts that can eliminate contradictions (see Chapter 4).

Critical-to-Quality characteristics reveal a main difficulty for developing robust designs. However, being able to integrate value-added features using TRIZ enables engineers to determine the final robust design concept. As illustrated with a practical example in Chapter 4, the quality of the design concept is the design's DNA, which drives product robustness.

Starting with Chapter 5, the focus shifts from control factors to noise factors, which are the process inputs that consistently cause variation in the output measurement that is random and expected and, therefore, not controlled. Strategies to manage noise (e.g., white noise, random variations, common-cause and special-cause variations, uncontrollable variables) are discussed in detail in Chapters 5, 6, 7, 8, and 9.

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