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Figure 2.6 shows a simplified house of quality for the car door
design. The rows represent the user needs. The columns represent
system features. The task analysis identifies the importance or
weighting of each need, which is shown in the left-most column.
These weightings are often determined by asking people to assign
numbers to the importance of each user need. The rating in each
cell in the matrix represents how well each system feature satisfies
each user need. These weightings and ratings are typically defined
using the 9/3/1 rating scale, where 9 is most important, 3 ismoderately important, and 1 is least important. The importance of
any feature can then be calculated by multiplying the ratings of
each feature by the weighting of each user need and adding the
result. This result identifies the features that matter most for the
users, separating technology-centered features from user-centered
features

Cost/benefit analysis builds on the QFD analysis, which calculates
the importance of features that best serve the user needs.
This importance serves as the input to cost/benefit analysis, which
compares different designs according to their costs relative to their
benefits. Costs and benefits can be defined monetarily or by the
9/3/1 rating scale. A decision matrix similar to Figure 2.6 can support
the cost/benefit analysis. The features are listed as rows on
the left side of a matrix, and the different design alternatives are
listed as columns. Each feature is given a weight representing importance
of the feature—the result of the QFD analysis. For the
features in Figure 2.6 this would be the total importance shown
in the bottom row of the decision matrix. Then, each design alternative
is assigned a rating representing how well it addresses
each feature. This rating is multiplied by the weighting of each
feature and added to determine the total benefit of a design. The
cost for each design is divided by this number to determine the
cost/benefit ratio. The design with the lowest cost/benefit ratio
represents the greatest value.

Tradeoff analysis identifies the most promising way to implement
a design. If multiple factors are considered (e.g., effort, speed,
and accuracy), design tradeoffs might be based on the design that
has the largest number of advantages and the smallest number of
disadvantages. Alternatively, a decision matrix can be constructed.
The matrix would assess how well systems, represented as columns,
compare according to the performance criteria, represented as
rows. For example, for the design of a new car, the performance criteria
of a key-less entry system could be represented in one row and
an existing key entry system could be another row. The columns
would be time to enter the car, likelihood of errors, and ease of use.

Although the decision matrix analyses can be very useful, they
tend to consider each product’s features independently. Focusing
on individual featuresmay fail to consider global issues concerning
the interactions of each feature on the overall use of the product.
People use a product, not a set of features—a product is more than
the sumof its features. Because of this, matrix analyses should be
complemented with other approaches, such as scenario specification
and user journeys, so that the product is a coherent whole
that supports the user rather than simply a set of highly important
but disconnected features. The overall objective of these analyses
is to identify a small set of the most promising alternatives
for implementing in prototypes for further evaluation. Chapter 7
on decision making provides a more detailed discussion for the
strengths and weaknesses of decisionmatrix analysis.