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regression analysis is the regression equation and correlation coefficients. The model
parameters are estimated from the data using the method of least squares. The model
also should be checked for adequacy by reviewing the quality of the fit and checking
residuals.
8.7.4
Sample Verify and Validate Phase DFSS Tools
FMEA can provide an analytical approach when dealing with potential failure
modes and their associated causes. When considering possible failures in a soft-
ware design—like safety, cost, performance, quality, and reliability—a team can get
a lot of information about how to alter the development and production process, in
order to avoid these failures. FMEA provides an easy tool to determine which risk has
the greatest concern, and therefore, an action is needed to prevent a problem before it
develops. The development of these specifications will ensure the product will meet
the defined requirements.
Capability analysis is about determining how well a process meets a set of spec-
ification limits, based on a sample of data taken from a process. It can be used to
establish a baseline for the process and measure the future state performance of the
process for comparison.
It graphically illustrates the relationship between a given outcome and all the
factors that influence the outcome. This type of diagram is sometimes called an
Ishikawa diagram (a.k.a. Fishbone or cause-and-effect). A cause-and-effect diagram
is a tool that is useful for identifying and organizing the known or possible causes of
quality, or the lack of it. The structure provided by the diagram helps team members
think in a very systematic way. Some of the benefits of constructing a cause-and-effect
diagram are as follows:
Helps determine the root causes of a problem or a CTQ using a structured
approach
Encourages group participation and uses group knowledge of the process
Uses an orderly, easy-to-read format to diagram cause-and-effect relationships
Increases knowledge of the development process by helping everyone to learn
more about the factors at work and how they relate
Identifies areas where data should be collected for further study
For many engineered systems, it is necessary to predict measures such as the sys-
tem's reliability (the probability that a component will perform its required function
over a specified time period) and availability (the probability that a component or
system is performing its required function at any given time). For some engineered
systems (e.g., processing plants and transportation systems), these measures directly
impact the system's throughput: the rate at which material (e.g., rocks, chemicals,
and products) move through the system. Reliability models are used frequently to
compare design alternatives on the basis of metrics such as warranty and mainte-
nance costs. Throughput models typically are used to compare design alternatives in
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