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Special Issue Articles
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INTRODUCTION
Risk is found throughout engineering design. Engineering risk methods such as failure modes and effects analysis (FMEA), fault tree analysis (FTA), and others are used across the spectrum of complex system design to identify these risks. In particular, such methods are designed to guide decision makers to choose the least risky options, mitigate the largest risks, and create risk-averse or fault-tolerant designs. Such an approach works well for traditionally risk-averse sectors such as the aerospace and nuclear power industries. However, not all industries and enterprises thrive on risk aversion. Many of the most successful Web 2.0 companies such as Google and Facebook and product design companies such as IDEO have become successful because they take risks that traditional, risk-averse companies are not willing to take. There is no one correct level of risk attitude for all industries.
Many methods exist in engineering design to account for risk such as functional failure identification propagation (Kurtoglu & Tumer, 2008), risk in early design (Grantham-Lough, Stone, & Tumer, 2007), or function failure design method (Stone, Tumer, & Van Wie, 2005), FMEA (Stamanis, 2003). However, these methods do not account for risk appetites of enterprises or individual decision makers. Research in psychology has produced the well-respected Domain-Specific Risk-Taking (DOSPERT) test, which enables risk appetite determination in several different domains of daily life (Weber, Blais, & Betz, 2002). Recent advances created the Engineering DOSPERT (E-DOSPERT) test, which has the goal of categorizing and determining engineering-specific risk domains (Van Bossuyt, Carvalho, Dong, & Tumer, 2011). The present research seeks to find a link between the engineering risk appetite information that the E-DOSPERT test provides with traditional and widely used engineering risk methods.
Specifically, this article presents a novel way to account for risk appetite in risk-based design. A single-criterion decision-based design (DBD) approach is adapted by way of engineering risk appetite utility functions that bring risk data from the expected-value (EV) domain into a risk appetite domain appropriate to the enterprise or individual stakeholder. The risk appetite utility functions are developed via E-DOSPERT test results rather than traditional lottery methods. By viewing risk data through a risk appetite lens, stakeholders and decision makers can make risk decisions with analytic backing...