Effective Use of Cost Risk Reports
Cost estimates are generated in Constant Year (CY), Then Year (TY) and something called cost risk-allocated TY dollars. Several papers have been published describing various ways to allocate risk dollars from a selected level in the Work Breakdown Structure (WBS) to ensure WBS elements sum at the percentile of interest. Cost risk-allocated results are typically used as the basis for estimating the projects budget, but, rarely used as the basis for the subsequent analysis to identify cost and uncertainty drivers in the model (cost risk reports).
Analysts use several common cost risk reports to better understand the models behavior and to obtain important information for the decision makers. For example, a Pareto chart identifies the WBS elements with the highest cost. A Tornado chart identifies the variables that have the greatest influence on total cost. A Variance Analysis chart (also called a sensitivity chart) identifies the uncertain WBS elements or input variables that contribute the most to total uncertainty. These reports/charts are available in several risk tools and are often used to supplement cost risk presentations. However, standards regarding their use are hard to find. Consequently, both analysts and decision makers are often confused about their purpose and how to interpret of their results.
Cost risk report results are heavily influenced by user decisions such as how many trials to use to establish a stable simulation result, which elements to test, how and how much to vary the elements tested, and how to address inflation. Cost risk reports can be generated in CY, TY or cost risk-allocated TY dollars, yet they are routinely performed and reported on the statistical CY (or sometimes TY) results. The disconnect between the units of the estimates that define budgets (TY risk-allocated results) and the cost risk reports (CY or TY statistical results) can go unnoticed. Consequently, the objective of the cost risk reports may not be achieved. This presentation makes a case for using a TY risk-allocated result as the basis for Pareto, Tornado and identifying the WBS elements that contribute most to total uncertainty. The analysis to identify the most important cost drivers (input variables) that contribute to total uncertainty can be performed on CY dollar results.
The missile cost model used throughout the Air Force Cost Analysis Agencys (AFCAA) Cost Risk and Uncertainty Handbook (CRUH) was created in ACEIT, Crystal Ball and @Risk to demonstrate that the risk analysis results are the same regardless of the tool selected if users follow the same uncertainty analysis guidance. The AFCAA CRUH missile cost model is used in this study to explore the impact of several user-selected choices when developing key cost risk reports. Example risk charts are generated to clarify the purpose of each report and explain their relationship to one another. In addition, standard approaches for developing each cost risk report are proposed.
Tecolote Research, Inc.
Alfred earned a Bachelor Mechanical Engineering degree from the Canadian Royal Military College and a Master of Science with Distinction in naval architecture from the University College, London, England. He spent 21 years in the Canadian Navy driving submarines (Navigator, Operations Officer) and ten years as a naval architect. He has over 20 years experience leading, executing or contributing to life cycle cost model development and cost uncertainty analysis for a wide variety of military, Coast Guard, NASA and foreign projects. He has been with Tecolote since 1995 and since 2000 has been the General Manager for Tecolote’s Software Products/Services Group, responsible for the development, distribution and support of a variety of web and desktop products including ACEIT. Alfred has delivered numerous papers on cost risk analysis topics and was the lead writer of the AFCAA Cost Risk and Uncertainty Handbook. He is a SCEA Certified Cost Estimator/Analyst (CCEA).