Posted by Parametric Joint Confidence Level Analysis: A Practical Cost and Schedule Risk Management Approach
From the Journal of Cost Analysis and Parametrics: Volume 10 | Issue 1 | October 2021
Downloadable file: JCAPv10i1-ParametricJointConfidenceLevelAnalysis-Smart
Abstract: The use of Joint Confidence Level (JCL) analysis at NASA has proven to be a successful policy. Bottom-up resource-loaded schedules are the most common method for jointly analyzing cost and schedule risk. However, high-level parametrics and machine learning for JCL have been used successfully by one of the authors. This approach has some advantages over the more detailed method. In this paper, we discuss the use of parametrics and machine learning methods, especially as they apply to JCL analysis. The parametric and machine learning approach involves the development of mathematical models for cost and schedule risk. Parametric methods for cost typically use linear and nonlinear regression analysis. These methods applied to schedule often do not provide the high R-squared values seen in cost models. We discuss the application of machine learning models, such as regression trees, to develop higher-fidelity schedule models. We then introduce a bivariate model to combine the results of the cost and schedule risk analyses, along with correlation, to create a JCL using models for cost and schedule as inputs. We provide a previous case study of the successful use of this approach for a completed spacecraft mission and apply the approach to a large data set of cost, schedule, and technical information for software projects.
Authors:
Sara Jardine is a Senior Cost Analyst for Galorath Federal with over 16 years of financial management experience. She has served a broad variety of federal agencies including the Army, Navy, OUSD AT&L, DAU, Veterans Affairs, and the Department of Homeland Security. She is skilled in Cost Analysis and course development, Project Management, Requirements Analysis, Contract Management, and Budget Management. Sara earned a MS in Project Management from George Washington University and a BS in Mathematics from the University of Michigan.
Kimberly Roye is a Senior Data Scientist for Galorath Federal. Starting her career as a Mathematical Statistician for the US Census Bureau, Kimberly transitioned to a career in Cost Analysis over 10 years ago. She has supported several Department of Defense hardware, software and vehicle programs, as well as NASA and the Department of Homeland Security (DHS). She is currently a lead developer of Machine Learning training for the Army and DHS. Kimberly earned a MS in Applied Statistics from Rochester Institute of Technology and a dual BS in Mathematics/Statistics from the University of Georgia.
Dr. Christian Smart is the Chief Data Scientist with Galorath Federal. He is author of the book Solving for Project Risk Management: Understanding the Critical Role of Uncertainty in Project Management. Dr. Smart is the VP for Professional Development with ICEAA. He regularly presents at conferences and has won several best paper awards. Dr. Smart received an Exceptional Public Service Medal from NASA in 2010 and has a PhD in Applied Mathematics.