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Slide 1 of 26. Agenda. The ProblemVariability
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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. Slide 1 of 26 Agenda The Problem
Variability & Standard Deviation
Briefing Goals
An Uncertainty Primer
Definitions
Distributions and Simulation…
Excel Prowess
Benefits, Headlines, Other Disciplines
3. Slide 2 of 26 My Premise…
4. Slide 3 of 26 The Problem
5. Slide 4 of 26 “Variability” and “Standard Deviation” 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.
6. Slide 5 of 26 Briefing Goals The “threshold” is for you to think in terms of risk, uncertainty, and variability…
7. Slide 6 of 26 An Uncertainty Primer… (Slide 1 of 4) 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!
8. Slide 7 of 26 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 An Uncertainty Primer… (Slide 2 of 4)
9. Slide 8 of 26 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
10. Slide 9 of 26 An Uncertainty Primer… (Slide 4 of 4) 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. Slide 10 of 26 “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
12. Slide 11 of 26 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.
13. Slide 12 of 26 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
14. Slide 13 of 26 Triangular Distributions…
15. Slide 14 of 26 …Triangular Distributions…
16. Slide 15 of 26 Monte Carlo Simulation
17. Slide 16 of 26 Monte Carlo Output
18. Slide 17 of 26 …Other Common Distributions…
19. Slide 18 of 26 With a little Excel prowess…
20. Slide 19 of 26 With a little Excel prowess…
21. Slide 20 of 26 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
22. Slide 21 of 26 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. Slide 22 of 26 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. Slide 23 of 26 MS Project Monte Carlo Analysis
25. Slide 24 of 26 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. Slide 25 of 26 And if all else fails…..,re-define the word “outlier”…
27. Slide 26 of 26 Questions / Comments