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Phil Beenhouwer May 17, 2006

Risk Analysis & Estimating Uncertainty …and what this has to do with the price of milk in McLean. The Society of Cost Estimating & Analysis (SCEA) Greater Washington DC Chapter. Phil Beenhouwer May 17, 2006. Agenda. The Problem Variability & Standard Deviation Briefing Goals

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Phil Beenhouwer May 17, 2006

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  1. Risk Analysis & Estimating Uncertainty…and what this has to do with the price of milk in McLean.The Society of Cost Estimating & Analysis (SCEA)Greater Washington DC Chapter Phil Beenhouwer May 17, 2006

  2. Agenda • The Problem • Variability & Standard Deviation • Briefing Goals • An Uncertainty Primer • Definitions • Distributions and Simulation… • Excel Prowess • Benefits, Headlines, Other Disciplines

  3. My Premise… There are three kinds of people in the World……., …………….those who can count, and those who can’t. The lack of risk/uncertainty analysis and poor cost/schedule estimating (across all levels of costing) are the primary reasons that Programs are over-budget.

  4. Cost Estimate $ Historical data point Cost estimating relationship Standard percent error bounds Cost Driver Input variable The Problem "It's tough to make predictions, especially about the future." -- Yogi Berra. Combined Cost Modeling and Cost Driver Uncertainty Cost = a + bXc Cost Modeling Uncertainty Cost Driver Uncertainty Source: Timothy P. Anderson, The Aerospace Corporation, 2005 DoDCAS Symposium

  5. “Variability” and “Standard Deviation” “Figures lie and liars figure…” "There are lies, damned lies and statistics." • When someone says it’ll cost you $100… • The average American carries $9,000 in credit card debt.1 • In reality, most Americans owe nothing to credit card companies. • Most households that carry balances owe $2,000 or less. • Only about 1 in 20 American households owes $8,000 or more on credit cards. 1 CardWeb.com, a service that tracks credit card trends.

  6. Hmmm…, this Contractor’s saying that the kitchen remodel will cost twenty grand, but… Briefing Goals Timmy’s saying that the soccer trip to Europe will only cost three grand, but… • The “threshold” is for you to think in terms of risk, uncertainty, and variability… You know, Phil….., we may have to place a sensor suite anywhere from 1.2 to 3.1 miles… Hey Phil..., there’s a good chance that we’ll have to replace the hardware every two to three years… Phil…, I think we should plan for a worst-case of 2.5 million transactions per year… • The “objective” is for you to speak in terms of risk, uncertainty, and variability…, and then to apply it…

  7. SDLC Phase Concept and Business Case Initiation and Authorization Project Definition System Design Construction Acceptance Operational Readiness Action Resolve the lack of milk at home Spousal approval/funding Buy a gallon of milk Find merchant along route home Drive to store and purchase milk Did you have enough money? Get a clean glass! An Uncertainty Primer… (Slide 1 of 4)

  8. TRUNCATED An Uncertainty Primer… (Slide 2 of 4) • Gallon of Milk Data Collection • Convenience Store: $3.49 • Warehouse Club #1: $2.59 • Warehouse Club #2: $2.69 • Grocery Store #1: $2.69 • Grocery Store #2: $3.19 • Grocery Store #3: $2.89 • Grocery Store #4: $2.79 • Specialty Store #1: $3.09 • Specialty Store #2: $2.99 • Specialty Store #3: $3.29

  9. An Uncertainty Primer… (Slide 3 of 4) • So, if you budgeted $3.00 for milk (the mean), you can go to… • Warehouse Club #1: $2.59 • Warehouse Club #2: $2.69 • Grocery Store #3: $2.89 • Grocery Store #1: $2.69 • Specialty Store #2: $2.99 • Grocery Store #4: $2.79 • But you can’t go to… • Convenience Store: $3.49 • Specialty Store #3: $3.29 • Grocery Store #2: $3.19 • Specialty Store #1: $3.09 I could go here with 21 cents more… 80% of the stores sell milk for less than $3.21 per gallon.

  10. An Uncertainty Primer… (Slide 4 of 4) • But you can’t go to… • Conv. Store: $3.49 • Spec. Store #3: $3.29 • Grocery Store #2: $3.19 • Spec. Store #1: $3.09 • What can you afford to buy at these stores? • Is a half-gallon acceptable? • Will you even leave with milk? • How will ‘Operational Readiness’ go when you get home? • Can you use ‘legacy’ orange juice in tomorrow’s cereal instead? • Will you need to reduce the number of cereal users? • Will you need to cut all cereal training from the budget? • Will there be a GAO report on your pillow in the morning? • “Sure”, you say, “if I end-up at the Convenience Store with only $3.21, I can find the 28 cents I need from under the driver’s seat…”. • But what if these weren’t dollars, but billions of dollars…? • Could you find $28M under the Program Manager’s seat?

  11. “Program Risk” versus“Estimating Uncertainty” • Total Risk = RiskProgram + (+/- UncertaintyEstimation)1 • For the purposes of quantitative risk analysis, I have defined: • “Program Risk” – the probability and severity of loss linked to hazards2 (e.g., software development cannot begin if the environment is not ready, system testing might fail, etc.) • “Estimating Uncertainty” -- the estimated amount or percentage by which an observed or calculated value may differ from the true value3 (e.g., the number of users could be 25% less, the COTS license cost could be $1,000 more, training could take one week longer, etc.) • Should also consider benefits and schedule, in addition to cost 2Source: DoD Dictionary, www.dtic.mil/doctrine/jel/doddict/ 1Source: Keith Horenstein, The MITRE Corporation 3Source: www.answers.com

  12. Definitions • Simulation: any analytical method meant to imitate a real-life system.1 • Monte Carlo simulation: a simulation that randomly generates values for uncertain variables over and over to simulate a model.1 • xth percentile: the percentage at which x% of all outputs are at, or below, the associated cost value • i.e., the 80th percentile in the gallon of milk example means that 80% of the values are at, or below, $3.21. • Conversely, 20% of the values exceed $3.21. 1Source: Decisioneering’s website; www.decisioneering.com

  13. Description of Monte Carlo Tools • “Crystal Ball applications transform your spreadsheets into dynamic models that solve almost any problem involving uncertainty, variability and risk.”1 • “Simply by running a simulation, @RISK takes your spreadsheet model from representing just one possible outcome to representing thousands of possible outcomes.”2 2Source: Palisade websitewww.palisade.com/ 1Source: Decisioneering’s website; www.decisioneering.com

  14. Triangular Distributions… Perfectly symmetrical; used far too often Probably the most realistic…., ‘low’ and ‘most likely’ are the same!! Getting better…., skewed to the right

  15. …Triangular Distributions… 80th Percentile 80th Percentile 80th Percentile

  16. Monte Carlo Simulation

  17. Monte Carlo Output

  18. …Other Common Distributions… Uniform distribution: represents a range with no ‘most likely’ value Normal distribution: bell-curved shape represents exam. scores, height of the people in this room, etc. Discrete distribution: distinct points represents the roll of a die Another discrete distribution: represents the unique costs for four pieces of hardware

  19. With a little Excel prowess… With every iteration, the ‘number of years’ changes, and this formula computes the annual cost based on the ‘number of years’.

  20. With a little Excel prowess…

  21. Benefits to Quantifying Risk/Uncertainty& Using Monte Carlo • Identify and apply risk/uncertainty within a model where it really exists (I.e., risk/uncertainty does not really exist “+/- 10%” around software integration!) • Sensitivity analysis • Risk-adjusted estimates are included in the individual items of the model instead of as a bottom-line “tax” • Makes it harder for decision-makers to remove the “risk” line-item Just present all your numbers at the 80th percentile!

  22. Headlines • GAO Testimony: “CAPITOL VISITOR CENTER -- Results of Risk-based Analysis of Schedule and Cost”, GAO-06-440T, February 15, 2006 • GAO Report: “INFORMATION TECHNOLOGY -- Agencies Need to Improve the Accuracy and Reliability of Investment Information”, GAO-06-250, January 2006 • GAO Report: “DEFENSE MANAGEMENT -- Additional Actions Needed to Enhance DOD’s Risk-Based Approach for Making Resource Decisions”, GAO-06-13, November 2005

  23. Relationship to Other Disciplines • Budgeting / Investment Planning • Provides risk-adjusted requirements of the E300 and other planning activities • Acquisition • Cost, Schedule, and Performance are part of defining an acquisition strategy • Contracting • Provides a cost basis for negotiation • Program Management • Provides insight into Program risks; helps prioritize mitigations • It’s just good Program Management !! • Earned Value/Baseline Management • Provides an input to the management reserve level • More objective inputs to the EVMS than the typical Integrator provides • Quantification of risks and uncertainties will result in less re-baselines • Engineering • Offers an approach to incorporate uncertain aspects of the engineering design

  24. MS Project Monte Carlo Analysis

  25. Conclusion • “Think” and “speak” in terms of risk and uncertainty (and then apply it…) • Collect uncertainty inputs when you gather data • Use Monte Carlo applications (e.g., Crystal Ball, @Risk, etc.) • These are relatively inexpensive compared to other applications in a coster’s toolkit • There is even a $15 application that we are currently investigating (Excel Business Solutions’ XL Modeling: www.excel-modeling.com/index_007.htm) • Include “Program Risk” and “Estimating Uncertainty” in cost, benefit, and schedule analyses

  26. And if all else fails…..,re-define the word “outlier”…

  27. Questions / Comments

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