Presentation on theme: "President, Greater Alabama SCEA Chapter"— Presentation transcript:
1 President, Greater Alabama SCEA Chapter Cost Risk – An OverviewDr. Christian SmartPresident, Greater Alabama SCEA ChapterFebruary 17th, 2005
2 Agenda Introduction to Society of Cost Estimating and Analysis (SCEA) Cost Risk OverviewUpcoming Cost Risk Seminar
3 Society of Cost Estimating and Analysis (SCEA) Who we areOrganization for cost analysis professionals, a highly diverse community that includes people who work in areas such as:Budget analysisEarned value managementCost estimationStatistical analysisOperations researchAccountingEtc.Overlap with other professional organizations such as INCOSE and PMIHuntsville 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 offerNational OrganizationHosts an annual conference and educational workshop2005 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 publicationProvides the Certified Cost Estimator/Analyst professional credentialWeb site (http://www.sceaonline.net)
5 Society of Cost Estimating and Analysis (SCEA) What we offerLocal ChapterMonthly luncheons with presentations on topics of interest to the cost analysis communitySome recent presentation topics include:Decision Making Using Cost Risk AnalysisNonparametric Regression in Cost AnalysisSchedule Risk AssessmentEarned Value Management Systems ConceptsChapter web siteincludes full presentations dating back to 2003 and other links of interestFree training and materials for certification preparationSeminars and other forms of training
6 SCEA CertificationCertified Cost Estimator/Analyst (CCE/A) professional credentialRecognized credential throughout the professionGovernment procurements often require CCE/AsCertification by examinationTwo years of professional experience in cost analysis/estimating required to take the examRecertification (every 5 years) byRetaking the examCombination of experience, education, and service to the profession
7 SCEA CertificationSCEA 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 February7,14,21, and 28 March 20055:30 - 7:30 ELMCO Facilities6000 Technology DriveDefinitions andconceptsAccounting principlesapplied to cost analysisContract administrationand pricingLearning curvesManufacturingTime value of moneyEconomics andStatisticsSociety of Cost Estimating and Analysis (SCEA)Greater Alabama ChapterDr. H. Samuel CookeCertified 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 email@example.com if you are interested in attending the trainingSessions.Linda Adams atto be added to the SCEA distribution list to receivethe 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 TerminologyRisk 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 ProbabilityProbability is the branch of mathematics used for the quantification of cost riskBasic termsProbability Density Function (PDF) : describes a range of values and their associated probabilitiesCumulative Distribution Function (CDF) : describes a range of value and their associated cumulative probabilities; also called an “S-curve”PDFCDF
13 PercentilesPercentiles for an example Lognormal distribution:
14 Why Risk Analysis?There is uncertainty about each cost element, and it is usually not symmetricCost elements are correlatedFor 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 amountPoint 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$)
18 Modeling Sources of Cost Risk Two common sources of uncertainty explicitly addressed in cost risk estimates are technical risk and estimation riskTechnical risk is associated with uncertainty in model inputsWeight, Technical and Management Parameters, etc.Estimation risk is associated with uncertainty in the estimation toolsCost 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 uncertaintyCERs are equations that relate a variable or a set of variables that drive the cost (or define the scope of a project) to costClassic example:where Y represents cost and X represents weight
20 Estimation RiskSince CERs are typically based on historical data, there is uncertainty associated with the equation’s goodness-of-fit to the historical dataThis can be measured by the standard deviation of the actuals versus the estimates for the data used in developing the CER
22 Technical RiskDefine 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 valueWLWMWHDL = DMDHWeightNew Design
24 Correlation Among Cost Elements There is correlation between hardware elementsA problem that results in an increase in structures costs may cause an increase in thermal control costsThere is correlation between hardware elements and systems level costsSystems 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 riskNot accounting for correlation will underestimate the total cost standard deviationFor 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 whencorrelation assumed to be 0 instead of r given n WBSelements
27 Aggregation + + . . . At Summation Levels WBS Element 1 Total Hardware CostWBS Element 2+...
28 Risk – The Big Picture . . X X X Correlation Matrix Technical Risk CER RiskTechnical RiskSubsystem 1XCER RiskTechnical RiskSubsystem 2..XCER RiskTechnical RiskSubsystem N
29 Methods for Aggregation Monte CarloUses simulationComputationally complexMust be careful to calculate correlations correctlyMethod used in ACE-ITAnalytic ApproximationUses method of momentsComputationally simpleAccuracy similar to Monte CarloMethod 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)
33 Cost Risk Seminar Location Date Teledyne Brown Engineering, Huntsville, ALDateMarch 15, 2005, 8:00 AM to 5:00 PMPrice is $150 for SCEA and INCOSE members, and Teledyne Brown employees, $200 for othersPayment due February 28thLunch and refreshments will be provided at no additional chargeCEU credit will be awarded for attending the seminar
34 Cost Risk SeminarSeminar is self-contained, presupposes no special knowledge of cost analysis or riskAll necessary background material is covered in the courseWhat you will gain from attending the seminarUnderstanding of the basics of cost risk analysisSome advanced topics covered also treated, material is broad in scopeAll 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 riskAuthor 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. IntroductionPart I. Introduction and Historical PerspectivePart II. The Problem SpacePart III. Presenting Cost as a Probability DistributionPart IV. Developing Cost as a Probability DistributionPart V. Using the Cost Probability DistributionPart VI. Issues and ConsiderationsPart VII. Benefits of Cost Uncertainty Analysis
37 Cost Risk Seminar – Outline (continued) Section B. TheoryFundamentalsPart I. Concepts of Probability TheoryPart II. Random Variables, Distributions, and the Theory of ExpectationPart III. Special Distributions for Cost Uncertainty AnalysisSpecial Technical TopicsPart IV. The Central Limit Theorem and a Cost Analysis PerspectivePart V. Correlation in Cost Uncertainty AnalysisPart VI. Distribution Function of a System’s Total Cost
38 Cost Risk Seminar – Outline (continued) Section C. ApplicationsSystem Cost Uncertainty AnalysisPart I. Work Breakdown StructuresPart II. An Analytical Framework and Monte Carlo SimulationPart III. Case StudyModeling Cost and Schedule UncertaintiesPart IV. IntroductionPart V. Joint Probability Models for Cost-SchedulePart VI. Case Study
39 Cost Risk Seminar – Sample Chart Treating Cost as a Random VariableThe 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 occurringThis 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 StudyRight-Most CDF: A 24 month schedule has an 80% chance of overrun; 36 month schedule has a 30% chance of overrunLeft-Most CDF: Mean cost is 7.7 ($M) for a 24 mos. scheduleMiddle CDF: Mean cost is 9.8 ($M) unconditioned scheduleRight-Most CDF: Mean cost is 10.7 ($M) for a 36 mos. schedule
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