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President, Greater Alabama SCEA Chapter

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Presentation on theme: "President, Greater Alabama SCEA Chapter"— Presentation transcript:

1 President, Greater Alabama SCEA Chapter
Cost Risk – An Overview Dr. Christian Smart President, Greater Alabama SCEA Chapter February 17th, 2005

2 Agenda Introduction to Society of Cost Estimating and Analysis (SCEA)
Cost Risk Overview Upcoming Cost Risk Seminar

3 Society of Cost Estimating and Analysis (SCEA)
Who we are Organization for cost analysis professionals, a highly diverse community that includes people who work in areas such as: Budget analysis Earned value management Cost estimation Statistical analysis Operations research Accounting Etc. Overlap with other professional organizations such as INCOSE and PMI Huntsville chapter is one of the largest and most active in the nation (over 150 members)

4 Society of Cost Estimating and Analysis (SCEA)
What we offer National Organization Hosts an annual conference and educational workshop 2005 conference will be held in Denver, Colorado, June 13th-17th, jointly with the International Society for Parametric Analysts (ISPA) (http://www.ispa-cost.org/conf.htm) Publishes National Estimator magazine and Journal of Cost Analysis and Management, a refereed technical publication Provides the Certified Cost Estimator/Analyst professional credential Web site (http://www.sceaonline.net)

5 Society of Cost Estimating and Analysis (SCEA)
What we offer Local Chapter Monthly luncheons with presentations on topics of interest to the cost analysis community Some recent presentation topics include: Decision Making Using Cost Risk Analysis Nonparametric Regression in Cost Analysis Schedule Risk Assessment Earned Value Management Systems Concepts Chapter web site includes full presentations dating back to 2003 and other links of interest Free training and materials for certification preparation Seminars and other forms of training

6 SCEA Certification Certified Cost Estimator/Analyst (CCE/A) professional credential Recognized credential throughout the profession Government procurements often require CCE/As Certification by examination Two years of professional experience in cost analysis/estimating required to take the exam Recertification (every 5 years) by Retaking the exam Combination of experience, education, and service to the profession

7 SCEA Certification SCEA certification exam will be given in Huntsville in mid-April and also at the national conference in June

8 Cost Estimating and Analysis CERTIFICATION Training Sessions
21 and 28 February 7,14,21, and 28 March 2005 5:30 - 7:30 ELMCO Facilities 6000 Technology Drive Definitions and concepts Accounting principles applied to cost analysis Contract administration and pricing Learning curves Manufacturing Time value of money Economics and Statistics Society of Cost Estimating and Analysis (SCEA) Greater Alabama Chapter Dr. H. Samuel Cooke Certified Cost Estimator/Analyst, Society of Cost Estimating and Analysis (SCEA) Professional Designation in Cost and Price Analysis, Air Force Institute of Technology (AFIT) Honor Graduate, Cost Analysis for Decision Making, Army Logistics Management Center, Ft. Lee, VA

9 SCEA Points of Contact E-mail Sam Cooke at cooke@westar.com if
you are interested in attending the training Sessions. Linda Adams at to be added to the SCEA distribution list to receive the local chapter’s free monthly newsletter.

10 Cost Risk “The only certainty is uncertainty” Pliny the
Elder (Gaius Plinius Secundus), AD 23-79, Roman Senator, Imperial Fleet Commander, Historian; died at the Mt. Vesuvius Eruption

11 Basic Terminology Risk is the chance of uncertainty or loss. In a situation that includes potentially favorable and unfavorable events, risk is the probability that an unfavorable event occurs. Uncertainty is the indefiniteness about the outcome of a situation. Uncertainty includes both favorable and unfavorable events. Cost Risk is a measure of the chance that, due to unfavorable events, the planned or budgeted cost of a project will be exceeded. Cost Uncertainty Analysis is a process of quantifying the cost estimating uncertainty due to variance in the cost estimating models as well as variance in the technical, performance and programmatic input variables. Cost Risk Analysis is a process of quantifying the cost impacts of the unfavorable events.

12 Probability Probability is the branch of mathematics used for the quantification of cost risk Basic terms Probability Density Function (PDF) : describes a range of values and their associated probabilities Cumulative Distribution Function (CDF) : describes a range of value and their associated cumulative probabilities; also called an “S-curve” PDF CDF

13 Percentiles Percentiles for an example Lognormal distribution:

14 Why Risk Analysis? There is uncertainty about each cost element, and it is usually not symmetric Cost elements are correlated For these and other reasons, a point estimate is likely to be much less than the median risk estimate and thus underestimate cost by a large amount Point estimate is often less than the 30th percentile, which means that the probability that the actual cost will exceed that estimate is at least 70%

15 Point Estimates Vs. Risk Estimates – Example*
Estimated Total Cost, in Millions (2004$)

16 Sources of Cost Risk

17 Inflation Risk

18 Modeling Sources of Cost Risk
Two common sources of uncertainty explicitly addressed in cost risk estimates are technical risk and estimation risk Technical risk is associated with uncertainty in model inputs Weight, Technical and Management Parameters, etc. Estimation risk is associated with uncertainty in the estimation tools Cost estimating relationship standard errors, for example

19 Estimation Risk Estimating methods usually involve some uncertainty
For example, cost-estimating relationships (CERs) that are based on historical data involve a high degree of uncertainty CERs are equations that relate a variable or a set of variables that drive the cost (or define the scope of a project) to cost Classic example: where Y represents cost and X represents weight

20 Estimation Risk Since CERs are typically based on historical data, there is uncertainty associated with the equation’s goodness-of-fit to the historical data This can be measured by the standard deviation of the actuals versus the estimates for the data used in developing the CER

21 CER Risk

22 Technical Risk Define a triangular distribution about each estimate with the minimum, most likely, and maximum values

23 Technical Risk – at the WBS Level
For each WBS element (D&D and flight unit cost): For each CER input, define a triangular distribution using minimum value, most likely value, and maximum value WL WM WH DL = DM DH Weight New Design

24 Correlation Among Cost Elements
There is correlation between hardware elements A problem that results in an increase in structures costs may cause an increase in thermal control costs There is correlation between hardware elements and systems level costs Systems level costs are often a function of hardware cost

25 Importance of Correlation
Cost risks are often measured by the standard deviation of the total risk Not accounting for correlation will underestimate the total cost standard deviation For a 10-element WBS, not accounting for correlation will underestimate risk by as much as 70%

26 The Importance of Correlation - Illustration
Percent that Total-Cost Sigma is underestimated when correlation assumed to be 0 instead of r given n WBS elements

27 Aggregation + + . . . At Summation Levels WBS Element 1
Total Hardware Cost WBS Element 2 + . . .

28 Risk – The Big Picture . . X X X Correlation Matrix Technical Risk
CER Risk Technical Risk Subsystem 1 X CER Risk Technical Risk Subsystem 2 . . X CER Risk Technical Risk Subsystem N

29 Methods for Aggregation
Monte Carlo Uses simulation Computationally complex Must be careful to calculate correlations correctly Method used in ACE-IT Analytic Approximation Uses method of moments Computationally simple Accuracy similar to Monte Carlo Method used in the NASA/Air Force Cost Model (NAFCOM)

30 NAFCOM Example - Inputs
The capability to define triangular distributions for all cost driver inputs and for cost thruputs is made available when Risk is turned On.

31 NAFCOM Example - Outputs
The final result is uncertainty distributions for DDT&E, Flight Unit, Production and Total Cost. Result data includes summary statistics, probability densities, and cumulative distributions for each major estimating element (i.e. stage, bus)

32 Risk Management

33 Cost Risk Seminar Location Date
Teledyne Brown Engineering, Huntsville, AL Date March 15, 2005, 8:00 AM to 5:00 PM Price is $150 for SCEA and INCOSE members, and Teledyne Brown employees, $200 for others Payment due February 28th Lunch and refreshments will be provided at no additional charge CEU credit will be awarded for attending the seminar

34 Cost Risk Seminar Seminar is self-contained, presupposes no special knowledge of cost analysis or risk All necessary background material is covered in the course What you will gain from attending the seminar Understanding of the basics of cost risk analysis Some advanced topics covered also treated, material is broad in scope All attendees receive a hard-copy of the training materials, 200+ pages of Powerpoint charts

35 Cost Risk Seminar Presenter is Paul Garvey
Chief Scientist, Mitre Corp. Internationally recognized expert in cost risk Author of Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective (http://www.dekker.com/servlet/product/productid/8966-0) Winner of several best paper awards at the annual Dept. of Defense Cost Analysis Symposium

36 Cost Risk Seminar - Outline
Section A. Introduction Part I. Introduction and Historical Perspective Part II. The Problem Space Part III. Presenting Cost as a Probability Distribution Part IV. Developing Cost as a Probability Distribution Part V. Using the Cost Probability Distribution Part VI. Issues and Considerations Part VII. Benefits of Cost Uncertainty Analysis

37 Cost Risk Seminar – Outline (continued)
Section B. Theory Fundamentals Part I. Concepts of Probability Theory Part II. Random Variables, Distributions, and the Theory of Expectation Part III. Special Distributions for Cost Uncertainty Analysis Special Technical Topics Part IV. The Central Limit Theorem and a Cost Analysis Perspective Part V. Correlation in Cost Uncertainty Analysis Part VI. Distribution Function of a System’s Total Cost

38 Cost Risk Seminar – Outline (continued)
Section C. Applications System Cost Uncertainty Analysis Part I. Work Breakdown Structures Part II. An Analytical Framework and Monte Carlo Simulation Part III. Case Study Modeling Cost and Schedule Uncertainties Part IV. Introduction Part V. Joint Probability Models for Cost-Schedule Part VI. Case Study

39 Cost Risk Seminar – Sample Chart
Treating Cost as a Random Variable The cost of a future system can be significantly affected by uncertainty. The existence of uncertainty implies the existence of a range of possible costs. How can a decision-maker be shown the chance a particular cost in the range of possible costs will be realized? The probability distribution is a recommended approach for providing this insight. Probability distributions result when independent variables (e.g., weight, power-output, staff-level) used to derive a system’s cost randomly assume values across ranges of possible values. For instance, the cost of a satellite might be derived on the basis of a range of possible weight values, with each value randomly occurring This approach treats cost as a random variable. It is a recognition that values for these variables (such as weight) are not typically known with sufficient precision to perfectly predict cost, at a time when such predictions are needed

40 Cost Risk Seminar – Sample Chart 2
Part VI. Case Study Right-Most CDF: A 24 month schedule has an 80% chance of overrun; 36 month schedule has a 30% chance of overrun Left-Most CDF: Mean cost is 7.7 ($M) for a 24 mos. schedule Middle CDF: Mean cost is 9.8 ($M) unconditioned schedule Right-Most CDF: Mean cost is 10.7 ($M) for a 36 mos. schedule


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