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Engineering risk methods and tools account for and make decisions about risk using an expected-value approach. Psychological research has shown that stakeholders and decision makers hold domain-specific risk attitudes that often vary between individuals and between enterprises. Moreover, certain companies and industries (e.g., the nuclear power industry and aerospace corporations) are very risk-averse whereas other organizations and industrial sectors (e.g., IDEO, located in the innovation and design sector) are risk tolerant and actually thrive by making risky decisions. Engineering risk methods such as failure modes and effects analysis, fault tree analysis, and others are not equipped to help stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions. This article presents a novel method for translating engineering risk data from the expected-value domain into a risk appetite corrected domain using utility functions derived from the psychometric Engineering Domain-Specific Risk-Taking test results under a single-criterion decision-based design approach. The method is aspirational rather than predictive in nature through the use of a psychometric test rather than lottery methods to generate utility functions. Using this method, decisions can be made based upon risk appetite corrected risk data. We discuss development and application of the method based upon a simplified space mission design in a collaborative design-center environment. The method is shown to change risk-based decisions in certain situations where a risk-averse or risk-tolerant decision maker would likely choose differently than the expected-value approach dictates.
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... In engineering design, risk plays an integral role. While innovative design firms embrace risk as an essential feature for their success, some other industries are very risk-averse [6]. Although risk management is a critical part of the supply chain management, the impacts of the decision maker's risk attitude have not been considered in the literature. ...
... The objectives considered in the GP model are the minimization of risk score (Equation (1)) and the minimization of the SC costs (Equation (2)). First, an integer linear programming (ILP) model is solved independently for the minimization of the SC risk score (SCRS) (using Equation 1 as the objective function) and constraints (6) to (18). Similarly, the model is solved independently for the minimization of SC cost (SCC) using Equation (2) as the objective function and constraints (6) to (18). ...
... First, an integer linear programming (ILP) model is solved independently for the minimization of the SC risk score (SCRS) (using Equation 1 as the objective function) and constraints (6) to (18). Similarly, the model is solved independently for the minimization of SC cost (SCC) using Equation (2) as the objective function and constraints (6) to (18). ...
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... Van Bossuyt et. al. [42], Van Bossuyt et. al. [43], and Van Bossuyt et. ...
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