System Risk Identification methodology for cost-effective sustainment of products and services

Abstract

The Noblis Earth Observations (EO) Team has developed a System Risk Identification methodology and dashboard to assist the National Oceanic and Atmospheric Administration (NOAA) Office of Satellite Ground Services to determine the likelihood of ground system component failures and their impact to high priority NOAA Satellite products and services. The methodology maps the system information back to products, and defines product risk based on aligned systems. The methodology then uses diagnostic information (such as system component trouble reporting frequency, system age and operating system software obsolescence) to assess system component health. The Noblis EO Team then employs various sequencing algorithms to identify and replace system components to reduce the risks associated with NOAA products and services based upon balancing high priority products and budgetary constraints. While this type of risk identification is not new it has not been widely adopted at many federal civilian agencies due to the perception that it is too complex to implement on a small/medium budget. The risk identification methodology that the Noblis EO Team has developed can be implemented using a simple spreadsheet if needed or with visualizations using Tableau or similar software. The Noblis EO Team has also built customized software to pull information from a database (Microsoft Access was used as a proxy for direct database interaction) and display the analysis results as a dashboard. Initial results using real data about NOAA products and systems along with (where necessary) assumptions about priorities/proxies for risk indicate the approach modeled in the dashboard can facilitate better decision making through comparative analysis of different resource management plans/repair sequences. In particular the Noblis EO team generated a customized Product-Risk sequencing method that was able to achieve similar results (all Products at low risk) as an “Oldest-First” sequencing method while replacing one-third the assets (8 vs. 24) and spending less than one-fourth the funds allocated to replacing/fixing ($250K vs $1.1M). Additional studies showing sensitivity to initial assumptions is planned to determine where it makes the most sense to improve or eliminate the assumptions/proxies. The Noblis EO Team expect this same paradigm could be relatively easily applied to other domain areas where modeling of the relationships of varied resources to a range of products is needed but not typically conducted. This methodology could provide quality analysis to aid in system planning and decision making.