Technical Baselines (CEA 02)
Understanding the technical and programmatic issues of the program/product you are estimating is key to developing a good cost estimate. This session discusses what information you need to baseline the technical and programmatic aspects of the program, the questions that need to be asked to ensure a clear understanding and how these issues translate into a cost perspective. The Cost Analysis Requirements Description (CARD), Intelligence Capability Baseline Description (ICBD), NASA’s Cost Analysis Data Requirements (CADRe), DOEs Conceptual Design Report, and DHSs Cost Estimating Baseline Documents are also discussed. Because the Technical Baseline is the foundation of any cost analysis, we will provide you with a step by step best practice to assess the realism of the Baseline (e.g., weight, software, schedule). An example will be provided to outline the Technical Baseline assessment for a satellite system.
Development of Work Breakdown Structures (CEA 03)
Neil Albert – Vice Chairman, MCR, LLC
The Work Breakdown Structure (WBS) is a critical part of managing and planning any project. The development of the WBS is critical to ensure that all members of the team, both industry and government, can communicate and coordinate their activities. The WBS is the best tool for ensuring consistency and focus on the project. This presentation will discuss the key attributes of a good WBS in scoping and planning of projects. The presentation will provide the generally accepted concepts of the use and application of the WBS as well as why it is often misunderstood and not used as it should for maximum effectiveness.
Cost and Software Data Reports (CEA 04)
This training session will give an overview of Cost and Software Data Reports (CSDRs) and how they can be employed to facilitate the development of better and more credible cost estimates. CSDRs are a means by which the Department of Defense (DoD) collects data on the actual cost and software effort of Major Defense Acquisition Programs (MDAP) or Major Automated Information Systems (MAIS). CSDRs provide insight into the nonrecurring and recurring costs, labor hours, material costs, functional data elements (engineering, manufacturing, tooling, and quality control), overhead, profit, and quantity data on every MDAP or MAIS contract over $50m and several high risk, high technical interest contracts over $20m. The reporting structure found in CSDRs is organized according to a standard, product oriented WBS based off of the MIL-STD-881C which fosters comparability of data across companies, weapon system commodity groups, and major subassemblies.
The instructors will address how CSDR data is planned, collected, stored, and made available to the DoD cost community. They will illustrate the unique benefit of CSDR data and how it can be used to predict future lot costs and how it can be leveraged by the government to make more informed decisions when building cost estimates. You will receive unique insight into the processes, forms, requirements, and applicability of CSDRs from experienced instructors who work every day to support the Office of the Secretary of Defense (OSD) Cost Assessment and Program Evaluation (CAPE) Defense Cost and Resource Center (DCARC), the office responsible for the implementation of CSDRs.
Advanced Cost Risk (CEA 06)
This session will delve into advanced topics in risk, including a short and entertaining refresher on basic cost risk analysis. We will perform an analysis of historical risk data by studying the effect of program size on cost / schedule growth and how we can use this information in our risk analysis. Schedule risk assessments will also be covered, to include examples of how network schedules impact the risk adjusted schedule durations and program cost. An approach to risk analysis for earned value management (EVM) based estimates that forecasts EACs will be introduced. This session covers the Related and Advanced Topics section of Module 09 Cost and Schedule Risk Analysis of CEBoK. Other advanced topics in risk that are covered in CEBoK Module 09 but are not covered in the session are the quantification of uncertainty about estimates based on cost-estimating relationships (CERs), with a focus on ordinary least squares (OLS) regression; the so-called self-fulfilling prophecy; and the geometry of the bivariate normal distribution and its relationship to correlation and risk. This session covers the Related and Advanced Topics section of Module 09 Cost and Schedule Risk Analysis of CEBoK.
Monte Carlo Simulation (CEA 07)
Probability Distributions for Risk (CEA 08)
Peter Braxton – Senior Cost Analyst and Technical Officer, Technomics, Inc.
Much has been written about the use of common probability distributions in risk analysis, but many risk analysts lack a deep appreciation and intimate knowledge of the mathematical properties of these distributions and the graphical, numerical, and algebraic manifestations thereof. After all, probability distributions are to risk analysts what words are to poets! This session will focus on “befriending” three distributions in particular, the normal, lognormal, and triangular, though information on many others will be provided for reference.
For program risk analysis, risk distributions may be generally classified into three categories: (a) input distributions, which characterize the uncertainty of inputs to the cost estimating process, such as cost-driver parameters (weight, power, source lines of code, etc.); (b) intermediate output distributions, which characterize the uncertainty about estimates for individual cost elements, such as the prediction interval (PI) associated with a cost-estimating relationship (CER) and risk ranges provided by a subject matter expert (SME); and (c) final output distributions, which characterize the uncertainty of an overall cost estimate. Input distributions commonly include normal, lognormal, and triangular; intermediate output distributions commonly include t and log t (in the CER case) and triangular (in the SME case); and final output distributions commonly include normal and lognormal. Since the t and log t have been thoroughly treated in recent papers, this session will focus on the other three, as noted, with applications across all three categories. For portfolio risk analysis, there is a fourth category of risk distribution, to characterize cross-program risk, or more specifically the cost growth factors (CGFs) associated with historical programs. Cross-program risk distributions may be lognormal, triangular, or other skew-right distributions (including heavy-tailed distributions).
Topics addressed include: understanding the shape of a distribution and how it is related to that distribution’s parameters; alternative specification of distributions, such as by any two of a central point (mean, median, and/or mode), a coefficient of variation (CV), and a percentile; correction of distributions for understatement of both mean and CV; and useful rules of thumb for characterizing distributions.
Keywords: Risk, Uncertainty, Probability, Distribution, Normal, Lognormal, Triangular, Log T, CER, SME, Prediction Interval, CGF, Heavy-ailed, PDF, CDF, Excel, Mean, Median, Mode, Standard Deviation, Variance, CV, Cost Growth, Inputs Risk
Advanced Probability and Statistics (CEA 09)
This training session builds upon the Probability / Statistics Basic concepts relevant to cost estimation and uncertainty analysis. The instructors hope to answer the question of “so what?” related to the usefulness of probability / statistics in cost estimating.
The instructors will reinforce concepts introduced in the Basics course and will provide examples on the application of the concepts to your daily estimating challenges. They will provide a detailed example using only capabilities provided by MS Excel (no “special” statistical tools) to generate random variates from commonly-used probability distributions and generate a probabilistic cost estimate along with descriptive statistics. Additionally, the instructors will provide you with useful approaches to apply advanced statistical concepts to data sets used in your cost estimate generation efforts. Topics to be addressed by the instructors include: ordinary least squares, maximum likelihood estimation, and method of moments. Your instructors will provide step-by-step implementation approaches for the techniques to ensure session attendees can apply the techniques appropriately in their real-world estimating scenarios.
Keywords: Probability distributions, Random variate generation techniques, Stochastic estimating processes, MS Excel, Ordinary Least Squares, Tests for Veracity of OLS Assumptions, Maximum Likelihood Estimation, Method of Moments
Manufacturing Cost Estimating (CEA 10)
The goal of the Manufacturing Cost Estimating module is to arm the student with a set of techniques used to address issues unique to estimating in the manufacturing environment. It will be our objective in this module to raise a few of the most common general issues, considerations and concerns the estimator must be aware of in a typical major manufacturing environment and to provide techniques for addressing them.
Three estimating techniques, Analogy, Parametric and Engineering Build-Up, will be explored as a means to estimate the material and labor required for a particular manufacturing product. The course will touch on industrial engineering principles and focus on the key concepts of segregating costs into recurring and non-recurring, fixed and variable, and direct and indirect. The essential learning curve and associated concepts of production breaks and rate adjustments will be examined as well.
This section covers the Core Knowledge section of Module 11 Manufacturing Cost Estimating of CEBoK.
AIS: Cost Estimating Methods and Metrics (CEA 11)
Wilson Rosa – AIS/C4ISR Branch Head, Naval Center for Cost Analysis
Advanced Economic Analysis (CEA 12)
Economic analysis provides a system of theory and approaches to find and employ the optimum use of scarce resources. Like cost analysis, this involves comparing two or more alternatives to achieve a specific objective or objectives, subject to assumptions and constraints. The most powerful aspect of economic analysis is the ability to quantify relevant variables and concerns over time and across actors into a single compact metric, monetary value. This course dwells on particular aspects of that quantification; namely the value of money over time, the preference for actions that are more certain or occur sooner in time; the impact of inflation on decision-making, and the concept of opportunity cost. The course reviews seven steps to performing a robust economic analysis using the tools we discuss.