Obsolescence occurs when system elements become outdated, and it leads to operational, logistical, reliability, and cost implications. In the U.S. military, this problem is a result of the U.S. Department of Defense's (DoD) departure from Military Specification (MILSPEC) standards in 1994 and transition to the use of Commercial Off the Shelf products. Obsolescence costs the DoD more than $750 million annually. The current risk management tools for obsolescence are based on a quantitative approach that uses cost optimization, and expert judgment is not used as a critical criterion. A review of the literature has revealed that during the design phase of technological systems, there is limited knowledge and a lack of training associated with mitigating obsolescence, and multicriteria decision-making (MCDM) methods are not currently used to mitigate the risk of obsolescence. Thus, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS, which is a MCDM method) and Monte Carlo simulations are proposed as the foundation for this work. This paper adds to the methodology by introducing an expert judgment criterion. A case study was conducted using military and civilian experts. Expert validation showed that the TOPSIS model successfully identified the best system for mitigating obsolescence. This model can be used by system designers and other decision makers to conduct trade studies in obsolescence management. K E Y W O R D SAS02 government, defense and security, SEE11 decision analysis/management, SEE17 logistics/supportability

In designing a system, multi-dimensional obsolescence design criteria such as Scheduling; Reliability, Availability, Maintainability; Performance and Functionality; and Costs affect its overall lifespan. This work examines the impacts of these factors on systems during the design phase using a new application called the Simple Additive Bayesian Allocation Network Process (SABANP). The application uses a combination of Multi-Criteria Decision Making (MCDM) methodology and a Bayesian Belief Network to address the impact of obsolescence on a system. Unlike the requirement of weights that are prevalent in the analysis of MCDM, this application does not require weights. Moreover, this application accounts for functional dependencies of criteria, which is not possible with the MCDM methodologies. A case study was conducted using military and civilian experts. Data were collected on systems’ obsolescence criteria and analyzed using the application to make trade-off decisions. The results show that the application can address complex obsolescence decisions that are both quantitative and qualitative. Expert validation showed that SABANP successfully identified the best system for mitigating obsolescence.