Posted by Inflation Cost Risk Analysis to Reduce Risks in Budgeting
Risk Track
RSK12_Presentation_InflationCostRiskAnalysistoReduceRisksinBudgeting_DeCarlo
RSK12_Paper_InflationCostRiskAnalysistoReduceRisksinBudgeting_DeCarlo
Abstract:
For any project there is a danger of unanticipated cost growth because inflation rates are extremely difficult to estimate. This presents a significant challenge to estimators. Predicting future inflation rates with some precision is possible, however, when the appropriate analysis is implemented. Even with previous recommendations from a major federal and commercial consulting firm, there is evidence indicating that the government has not been making adequate assessments of inflation rates for future budgeting. Without applying proper attention and techniques to the analysis and prediction of inflation rates, budgets run a higher than necessary risk for increased cost growth due to inflation prediction error. This is the inflation risk. Historically, the government has been overly optimistic in its predictions resulting in insufficient initial allocation of funding and therefore causing significant setbacks for projects. In this paper the authors will describe a seven-step, statistical analysis, which is a technique to reduce inflation risk by providing more precise estimates for future inflation rates based off of historical research. This report includes a correlational study performed alongside of the analysis methodology. The study showed that as the predicted rate’s year moved farther into the future, the fidelity of the predicted rate decreased. Using additional statistical techniques – distribution fitting tests and Monte Carlo Simulation – key components describing the behavior of inflation, such as statistics about inflation prediction error and general prediction error behavioral patterns, were obtained and used to observe inflation risk. The full analysis that this report demonstrates consists of seven steps – inflation data collection, data normalization, assessment for erroneous data, obtaining descriptive statistics about prediction error, fitting inflation prediction error with appropriate distributions, using historical standard deviations (from the descriptive statistics) with fitted distributions to form error models, make budget decisions or adjustments based on prediction error results. Within the methodology the appropriate goodness of fit tests are used to determine the best fit distribution for the inflation prediction error and Monte Carlo simulation is used to build a model for future inflation prediction error that can then be applied to predicted inflation rates giving more precision to funding allocation. By using this inflation prediction error analysis the government and other organizations will be able to construct budgets with greater confidence in real time.
Author(s):
Michael DeCarlo
Booz Allen Hamilton
Michael J. DeCarlo graduated from the University of Maryland, Baltimore County, with a B.S. in Applied Statistics in 2011. He is now employed by Booz Allen Hamilton as a Consultant in the Business Analytics Center of Excellence. Within this division, Mr. DeCarlo leverages his knowledge in statistical analysis and research methodology in the utilization, marketing and development of an internally developed statistical modeling tool. Specifically, he has worked on cost models and analysis for the consideration of the Military Health System, Centers for Medicare & Medicaid Services, and Accountable Care Organizations Program.
Eric Druker
Booz-Allen Hamilton
Eric Druker has 6 years experience performing cost and schedule risk analysis for a varied range of clients across the DoD, Intelligence and Civil Arena. He currently serves as lead for Booz Allen’s RealTime Analytics capability and technical lead for Booz Allen’s Cost Estimating support to NASA Headquarters. A recognized industry expert in cost estimating and risk analysis, in 2009 Eric was named National Cost Estimator/Analyst of the Year for Technical Achievement by the Society of Cost Estimating & Analysis (SCEA). As an author of over a dozen industry papers, including one best paper award, he is an internationally recognized expert in cost estimating and risk analysis having been an invited speaker or panelist at conferences including, but not limited to, Department of Defense Cost Analysis Symposium (DoDCAS), Naval Postgraduate School’s Acquisition Research Symposium (NPS-ARS), Space Systems Cost Analysis Group (SSCAG) Meeting, Australian Department of Defense Cost Analysis Symposium and NASA’s Project Management Challenge. Eric’s works are also cited in the GAOs Cost Estimating & Assessment Best Practices guide.