Share your ideas for the JMP Scripting Unsession at Discovery Summit by September 17th. We hope to see you there!
Choose Language Hide Translation Bar
Chrismtoy
Staff (Retired)

New Methods for Developing Limited Data Sets for Predicting US Marine Corps Combat Losses

 New Methods for Developing Limited Data Sets for Predicting US Marine Corps Combat Losses

 

Aaron D. Burciaga, Director; Mario L. Solano, Operations Analyst, Installations & Logistics, Headquarters Marine Corps (HQMC)
Andrea Ferris, Consultant; John Stocker, Lead Associate, Booz Allen Hamilton Inc.

With the planned decreases to the overall Department of Defense budget, there is an increasing emphasis on achieving more efficient net asset management throughout all services. The recalculation of key variables used for the development of the US Marine Corps War Reserve Materiel Requirement provides an opportunity to reduce total Marine Corps ground equipment liabilities while still ensuring the enterprise has the combat-essential equipment necessary to conduct operations. Predictive modeling of ground equipment combat losses observed in Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) is explored to develop replacement rates or Combat Active Replacement Factors (CARF). The resulting interactive predictive modeling capability, called CARF Statistical Analysis Tool (CARF-STAT), is implemented in JMP Pro. CARF-STAT utilizes a recursive partitioning model with parameters that have been optimized using JMP scripts to predict CARF values by considering various ground equipment attributes. As equipment evolves and new observations on combat losses are made, CARF-STAT provides the Marine Corps with a traceable and repeatable capability to update CARF value predictions with the simple click of a button that executes a multifaceted JMP script. A highly interactive analyst dashboard allows the user to review and evaluate total cost and materiel quantity impacts, CARF distributions and model fit statistics. With CARF-STAT, the Marine Corps is able to more accurately and consistently predict CARF values and effectively develop long-term plans for budget and equipment replacement.

Article Labels
Article Tags
Contributors