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Decision Making in Engineering Design

Editor
Kemper E. Lewis
Kemper E. Lewis
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Wei Chen
Wei Chen
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Linda C. Schmidt
Linda C. Schmidt
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ISBN-10:
0791802469
No. of Pages:
400
Publisher:
ASME Press
Publication date:
2006

As W. Wulf, president of the National Academy of Engineering, emphasized during a talk at the NSF (12∕8∕1999), making decisions is a crucial aspect of engineering. At all stages of design, with almost all engineering issues, decisions must be made. They may involve a choice of materials, options, approaches, selection of members for a team and just about all aspects of daily work. Decisions are crucial for those widely discussed approaches such as Quality Function Deployment (QFD), which provide guidelines on how to schedule and coordinate decision-making. Without argument, making accurate, good decisions is a crucial component of engineering.

A way to underscore the importance of “good decisions” is to reflect on the effects of “bad decisions.” Unfortunately, bad decisions—particularly if subtle—may be recognized only after the fact as manifested by their consequences. For instance, an inappropriate or overly cautious decision made during the early stages of design can be propagated, resulting in lost opportunities (of exploring other options) and even inferior outcomes. At any stage of design or engineering, even minor decision errors contribute to inefficiencies and a decrease in effectiveness. Faulty decisions can cause products to fail to reach attainable levels, an increase in cost, a decline in customer appeal and many other problems—such as the decision-maker being fi red. Good decision making is central in our quest to achieve competitive advantage and engineering excellence.

The need for accurate decisions is manifested by our search for accurate information: We do this by performing tests, designing prototypes, carrying out extensive computer simulations, among other approaches. But even if we can assemble all of the appropriate accurate and complete information to make a decision, we can still make a bad decision. When this occurs, we tend to blame the data, such as the possibility of experimental error, the ever-present uncertainties and the effects of the many unknown variables. Rarely is blame assigned to the decision rule. What a serious mistake!

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