Description: Team ITA proposes a model-based, decision support digital twin, capable of guiding NASA’s investment decisions at system level across the entire facility enterprise. The key elements of this innovation are investment optimization models and scenario modeling that leverage system degradation models and investment response models: Investment optimization models – a set of global prescriptive analytics optimization models, recommending: 1) the best-value use of limited funding over a time horizon and 2) the lowest level of funding that can achieve a stated condition goal across NASA. Scenario modeling – an ability to understand the full ramifications of decisions under consideration to assist decision-making. Possible scenarios relevant to the solicitation document include: How much funding is needed to meet a specified requirement? What is the best result that can be achieved, given a funding limit? Which projects should be prioritized based on a fixed budget? When budget is fixed and a prioritized group of systems or buildings must achieve a specified condition, what is the impact on the condition of non-prioritized assets across the facility enterprise? What are the systemic macro-level issues revealed in a long-term understanding of the best possible condition provided by available funding? In their words, are budgets insufficient to maintain or improve enterprise condition? Degradation models – a foundational understanding of how facility assets degrade in condition while receiving minimal investment. Degradation models can be broad or narrow in their scope, applying to specific systems in specific buildings or to similar systems across multiple buildings. Investment response models – a foundational understanding of the relationship between increased investment and system condition improvement over degraded condition. Like degradation models, investment response models also apply to specific systems or sets of systems and to specific maintenance options.

Benefits: Digital twin will enable NASA to understand the future condition of systems across the enterprise (e.g, optional condition of assets in 2031 across enterprise given funding), apply budget against mission priorities (e.g., prioritize based on mission dependency index), translate mission to budget requirements (e.g, cost of achieving desired condition level for all launch facilities), and align work plans to mission requirements (e.g, streamline maintenance planning processes). A data warehouse will enable strategic and tactical ad-hoc analysis

Digital twin will enable Federal agencies to understand the future condition of systems across the enterprise (optional condition of assets in 2031 across enterprise given funding), apply budget against mission priorities (prioritize based on MDI), translate mission to budget requirements (cost of achieving desired condition level for all facilities), and align work plans to mission requirements.