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Balancing Lifecycle Sustainment Cost with Value of Information during Design Phase
- Sam Kassoumeh - CCDC Ground Vehicle Systems Center ,
- Monica Majcher - CCDC Ground Vehicle Systems Center ,
- James Ealy - CCDC Ground Vehicle Systems Center ,
- David Gorsich - CCDC Ground Vehicle Systems Center ,
- Paramsothy Jayakumar - CCDC Ground Vehicle Systems Center ,
- Vijitashwa Pandey - Oakland University
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Citation: Kassoumeh, S., Majcher, M., Ealy, J., Gorsich, D. et al., "Balancing Lifecycle Sustainment Cost with Value of Information during Design Phase," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(5):2451-2458, 2020, https://doi.org/10.4271/2020-01-0176.
The complete lifecycle of complex systems, such as ground vehicles, consists of multiple phases including design, manufacturing, operation and sustainment (O&S) and finally disposal. For many systems, the majority of the lifecycle costs are incurred during the operation and sustainment phase, specifically in the form of uncertain maintenance costs. Testing and analysis during the design phase, including reliability and supportability analysis, can have a major influence on costs during the O&S phase. However, the cost of the analysis itself must be reconciled with the expected benefits of the reduction in uncertainty. In this paper, we quantify the value of performing the tests and analyses in the design phase by treating it as imperfect information obtained to better estimate uncertain maintenance costs. A multi-attribute decision framework for military ground vehicles acquisition is employed to illustrate the methodology and the value of performing the analysis early in the system’s lifecycle. Attributes considered are maintenance cost and operational availability, while the utility is calculated for a risk averse decision maker. Numerical methods are employed to calculate the value of sample information and reflect an increase in expected utility (EU) after collecting the information. While less than the value of perfect information that completely eliminates outcome uncertainty, results demonstrate a positive value for testing. This value determines the maximum amount that should be spent on testing given the anticipated benefits.