“Where do I get the most bang for the buck?”
This is a question asked all-to-often by government and business leaders alike. For this client, the challenge laid in determining the “should-cost” value for the quantity, type, and length of military operations.
This challenge was compounded by the prevalence of data quality issues stemming from unstructured financial reporting practices.
ROME utilizes a custom machine-learning algorithm rooted in linear/non-linear regression applications to forecast probable budget requirements to facilitate a specified set of military deployments. It allows users to optimize resource allocation using real-time financial, risk, and organizational priority data from any data source.
Deployment and Value Statement
ROME was deployed to provide budgeting & financial planners sufficient insight and justification for budget requests to facilitate out-year financial planning.
Further, ROME enables the end user to execute various optimization operations to determine the most valuable operational-tempo given budgetary constraints and overall organizational goals.