The FROST Method is presented which improves the efficiency of long-term sustainment of hardware systems. The FROST Method makes sustainment and scheduling decisions based on the minimization of the expected value of current and future costs. This differs from current methods which tend to base decisions not on the expected value of costs, but on the expected inventory demand found through projections using data which is often inaccurate.
Distributions are used to account for randomness and inaccuracy in inputs such as failure rates and vendor-claimed dates for end of production. A Monte Carlo technique is then used to convert these distributions into a statistically relevant set of possible futures. Finally, these futures are analyzed to determine what combination of actions will result in the system being sustained for least cost.
Simulations show that, for a realistic range of system parameters, the FROST Method can be expected to reduce the cost of sparing and sustainment engineering between 21.1% and 69.1% depending on the situation, with an average of 43.6%. Implementation involves a slightly increased burden over current methods in terms of the amount of data that must be collected and provided as inputs.
","abstract_html":"<p>The FROST Method is presented which improves the efficiency of long-term sustainment of hardware systems. The FROST Method makes sustainment and scheduling decisions based on the minimization of the expected value of current and future costs. This differs from current methods which tend to base decisions not on the expected value of costs, but on the expected inventory demand found through projections using data which is often inaccurate.</p> <p>Distributions are used to account for randomness and inaccuracy in inputs such as failure rates and vendor-claimed dates for end of production. A Monte Carlo technique is then used to convert these distributions into a statistically relevant set of possible futures. Finally, these futures are analyzed to determine what combination of actions will result in the system being sustained for least cost.</p> <p>Simulations show that, for a realistic range of system parameters, the FROST Method can be expected to reduce the cost of sparing and sustainment engineering between 21.1% and 69.1% depending on the situation, with an average of 43.6%. Implementation involves a slightly increased burden over current methods in terms of the amount of data that must be collected and provided as inputs.</p>","abstract_has_math":false,"creators":["Gaintner, Michael Ashton"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Engineering Management & Systems Engineering","degree_department":null,"school":null,"contributors":["Shannon Bowling","Rafael Landaeta","C. Ariel Pinto","V. Dale Bloodgood"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-04-01T07:00:00Z","date_published":"2011-04-01T07:00:00Z","updated_at":"2026-07-24T03:34:18Z","subjects":["Consequences","Inventory support","Optimization","Probability","Sustainment","Business Administration, Management, and Operations","Operational Research","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9781124696669"],"render_values":[{"text":"9781124696669","href":null,"code":true}]}]},"links":{"outbound_url":"https://digitalcommons.odu.edu/emse_etds/69","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shannon Bowling","Rafael Landaeta","C. Ariel Pinto","V. Dale Bloodgood"]},{"key":"dc:creator","label":"Author","values":["Gaintner, Michael Ashton"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-03-14T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Management & Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Consequences","Inventory support","Optimization","Probability","Sustainment","Business Administration, Management, and Operations","Operational Research","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9781124696669","https://digitalcommons.odu.edu/emse_etds/69"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The FROST Method is presented which improves the efficiency of long-term sustainment of hardware systems. The FROST Method makes sustainment and scheduling decisions based on the minimization of the expected value of current and future costs. This differs from current methods which tend to base decisions not on the expected value of costs, but on the expected inventory demand found through projections using data which is often inaccurate.
Distributions are used to account for randomness and inaccuracy in inputs such as failure rates and vendor-claimed dates for end of production. A Monte Carlo technique is then used to convert these distributions into a statistically relevant set of possible futures. Finally, these futures are analyzed to determine what combination of actions will result in the system being sustained for least cost.
Simulations show that, for a realistic range of system parameters, the FROST Method can be expected to reduce the cost of sparing and sustainment engineering between 21.1% and 69.1% depending on the situation, with an average of 43.6%. Implementation involves a slightly increased burden over current methods in terms of the amount of data that must be collected and provided as inputs.
"]},{"key":"dc:title","label":"Title","values":["A Method to Improve the Sustainment of Systems Based on Probability and Consequences"]}]}],"canonical_facts":{"dc:contributor":["Shannon Bowling","Rafael Landaeta","C. Ariel Pinto","V. Dale Bloodgood"],"dc:creator":["Gaintner, Michael Ashton"],"dc:date.available":["2019-03-14T07:00:00Z"],"dc:description.abstract":["The FROST Method is presented which improves the efficiency of long-term sustainment of hardware systems. The FROST Method makes sustainment and scheduling decisions based on the minimization of the expected value of current and future costs. This differs from current methods which tend to base decisions not on the expected value of costs, but on the expected inventory demand found through projections using data which is often inaccurate.
Distributions are used to account for randomness and inaccuracy in inputs such as failure rates and vendor-claimed dates for end of production. A Monte Carlo technique is then used to convert these distributions into a statistically relevant set of possible futures. Finally, these futures are analyzed to determine what combination of actions will result in the system being sustained for least cost.
Simulations show that, for a realistic range of system parameters, the FROST Method can be expected to reduce the cost of sparing and sustainment engineering between 21.1% and 69.1% depending on the situation, with an average of 43.6%. Implementation involves a slightly increased burden over current methods in terms of the amount of data that must be collected and provided as inputs.
"],"dc:identifier":["9781124696669","https://digitalcommons.odu.edu/emse_etds/69"],"dc:subject":["Consequences","Inventory support","Optimization","Probability","Sustainment","Business Administration, Management, and Operations","Operational Research","Systems Engineering"],"dc:title":["A Method to Improve the Sustainment of Systems Based on Probability and Consequences"],"thesis:degree_discipline":["Engineering Management & Systems Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:34:18Z"}