Posted by An Intuitive Application of Cost Risk Analysis to a LRFS
Methods and Models II Track
MMT205_Presentation_AnIntuitiveApplicationofCostRisktoLRFS_Boswell
Abstract:
This presentation explains the implementation of an Uncertainty Modeling Capability (UMC) into a USMC LRFS. Similar to Life Cycle Cost Estimate (LCCE), the Logistics Requirements Funding Summary (LRFS) captures Integrated Logistics Support (ILS) related costs for a program throughout its life cycle. The LRFS provides visibility of logistics requirements for Program Objectives Memorandum and budget submissions. Additionally, it helps to plan and quantify requirements, identify and defend funding, and serve as the ILS input to the LCCE.
The purpose of including the UMC is to provide an intuitive process for logisticians to produce uncertainty adjusted LRFS estimates. The logistician can use the UMC to report uncertainty-adjusted outputs at varying confidence levels for improved budgeting and decision making. The UMC uses a simulation engine that is entirely Microsoft Excel based. All statistical analysis is performed by Excel functions and all simulation, allocation, and phasing processes are performed by Visual Basic for Applications. Using the LRFS child element point estimates and a user designated Coefficient of Variation (CV), the UMC provides the uncertainty-adjusted outputs. The default CVs are determined in the UMC based on a program’s maturity, USMC risk standards, POPS 2.0 CV Standards. The user can further customize the CVs based on qualitative confidence levels (i.e.; low, medium, or high). The UMC models each child level element’s total program cost as a lognormal distribution.
Author:
Blake Boswell
Booz Allen Hamilton
Blake Boswell is an Operations Research Analyst for Booz Allen Hamilton’s Decision Analytics Team. He has three years of experience developing tools and processes in support of analytical projects for a variety of clients in the Defense, Space, and Health industries. In 2010, Blake was recognized for Technical Achievement by the Washington D.C. Chapter of the Society of Cost Estimation and Analysis (SCEA) for his efforts in the application of numerical methods to cost risk simulation. In 2011, he was named National Estimator of the Year by SCEA for Technical Achievement in recognition of his research efforts in the development of Booz Allens RealTime Analytics service offering. Blakes research interests include applied probability, computational mathematics, and modeling & simulation. He is a frequent presenter in the Risk Track at SCEA conferences, and has published original research in a variety of journals. Blake has a B.S. in mathematics from Auburn University Montgomery and a Masters degree in applied economics from Johns Hopkins University.