Illustrations for Joint Agency CSRUH

Illustrations for Joint Agency CSRUH
As of 08 September 2014

Tool independent process Flow for the handbook and presentation
Figure 1-1 Performed in Parallel Uncertainty Distributions 2.3 Risk Register 2.7 Point Estimate 2.2 Subjective 2.5 Objective 2.4 Special Considerations 2.8 Run Simulation 3.1 Measure then Apply Correlation 3.3 Other Influences 3.4 Interpret Results 3.5 Allocate and Phase PA Dollars 3.6 Report Results 4.0 For Decision 4.2 For Technical Review 4.1 Tool independent process Flow for the handbook and presentation 5.0 Alternatives to CISM Portfolio Considerations

Figure 1-2 * if available * CSRUH focus

Figure 1-2 (alternate) * if available * CSRUH Focus

Risk Uncertainty Budget Opportunity
Figure 1-3 Favorable Outcomes Unfavorable Outcomes Spectrum of Outcomes Risk Uncertainty Budget Opportunity

Figure 2-1 Point Estimate
Measure correlation created by the model Apply additional correlation as required Compare Results To Guidelines Run the Simulation Assign Uncertainty Link distributions to the point estimate by defining their parameters as factor of the point estimate where ever feasible Unsatisfactory Results Collect All Uncertain Elements In One Location Estimating Methods Parametric Equation Factor Analogy Build-up Throughput 3rd Party Tools Drivers CER Inputs Labor Rates Effort Hours Durations Anything else that is not “certain” Risk Register Risk Consequence and Opportunity Savings Probability of Occurrence Satisfactory Results Measure For Convergence Generate Reports

Figure 2-2 Estimate WBS EMD Variables Production Variables
 Drives EMD Schedule Risk Register 1 Variables  Risk Register 2 Variables  Risk Register 2 Impact Risk Register 1 Impact  Figure 2-2

Table 2-2 Note: Low/high are defined with an associated percentile
DISTRIBUTION TYPICAL APPLICATION KNOWLEDGE OF MODE NUMBER OF PARAMETERS REQUIRED RECOMMENDED PARAMETERS Lognormal Log-t Default when no better info. Probability skewed right. Replicate another model result. Power OLS CER uncertainty. Log-t when < 30 data points Mean or median known better than the mode 2 3 Median, high (some tools have a 3rd parameter : “Location” . By default, it is zero. Used to “shift” the lognormal left or right (even into negative region) Add Degrees of Freedom Triangular Expert opinion. Finite min/max. Probability reduces towards endpoints. Skew possible. Labor rates, labor rate adjustments, factor methods Good idea Low, mode, and high BetaPert Like triangular, but mode is 4 times more important than min or max. Very good idea Beta Like triangular, but min/max region known better than mode. Not sure 4 Min, low, high, and max Normal Student’s-t Equal chance low/high. Unbounded in either direction Linear OLS CER uncertainty. t when < 30 data points Good idea, but unbounded in either direction Mean/Median/Mode and high value Uniform Equal chance over uncertainty range. Finite min/max. No idea Low and High (some tools require min and max) Empirical Fit Unable to fit a distribution to the data Not required N/A Enter source data and estimated probability for each data point Note: Low/high are defined with an associated percentile Min/Max are the absolute lower/upper bound (also known as the 0/100)

Intentionally left blank

Figure 2-6

Figure 2-7 Mode Median (124.3) Mean (129.5) Max High (78 percentile)
Low (8 percentile) Max (232.7) Mode (100) Median (124.3) Mean (129.5) Min (55.8)

Triangular Uniform Beta, BetaPERT
Figure 2-8 Triangular Uniform Beta, BetaPERT Point Estimate Point Estimate - Mode Point Estimate - Median Point Estimate – Mean/Median/Mode Left/Negative Skew Not Skewed Right/Positive Skew Normal Lognormal Always Right/Positive Skew

Figure 2-9 Additive Error Multiplicative Error Y Y X X
Reference: H.L. Eskew and K.S. Lawler, “Correct and Incorrect Error Specifications in Statistical Cost Models,” Journal of Cost Analysis, Spring 1994, page 107.

Additional Considerations When Process Yields Unsatisfactory Results
Figure 2-11 Investigate alternate hypothesis tests, etc. Select Most Appropriate Adjusted Fit Goodness- of-Fit Test Investigate alternate hypothesis tests (When using Chi-squared alternate bin sizes) Examine lower-ranked distributions Investigate outliers Fit Distributions to Data Fail Constrain standard deviation Change Objective Function (e.g. SSPE vs SSE) (remove standard deviation constraint) Pass None Accept Best Fit Accept Lesser Fit Select the top-ranked distribution Start End Do not use. Consider an Empirical Distribution in lieu of a fitted distribution. Additional Considerations When Process Yields Unsatisfactory Results Accept the best fit obtained from the original settings. Use cautiously fully aware of its shortcomings.

Figure 2-12

Figure Not Used

Figure 2-13

Figure 2-14 Mode Max High (85 percentile) Mode SME Max Min SME Min
Low ( 15 percentile) Max (214.7) Mode (100) Min (40.6) SME input = absolute bounds, skew = 0.25 SME input = 15/85% bounds SME bounds covers 70% possible range Results in DIFFERENT skew = 0.34 Further adjustment needed to match SME skew SME Max (160) Mode (100) SME Min (80) SME input = absolute bounds, skew = 0.25

Figure 2-16 Mode Max High (78 percentile) Min
Low ( 8 percentile) Max (232.7) Mode (100) Min (55.8) SME input = absolute bounds, skew = 0.25 SME input = 8/78% bounds SME bounds covers 70% possible range Results in SAME skew = 0.25 Figure 2-16

Figure 2-17

Probability of Occurrence
Figure 2-18 Probability of Occurrence Yes/No Cost Impact

Uncertain EMD Start Date Retired Sunk Duration
Figure 2-19 Uncertain EMD Start Date Retired Sunk Duration Point Estimate of Remaining Months Remaining Months Scaled Uncertainty Bounds Risk Register Item Retired Sunk Costs Throughput To-go Cost Cell Formulas Unchanged Change Element Structure to Sum of Sunk and To-go

Figure 2-20

Figure 3-7

From Model before link between EMD and Prod was broken
And Production Duration Uncertainty Removed

Figure 3-8

Figure 3-11 U N C E R T A I N T Y Point Estimate Selected Estimate
Probability Adjustment

Need^2 Adjusted for Correlation = Need*MMULT(CorrRow, EMD Need)
Figure 3-12 Need^2 Adjusted for Correlation = Need*MMULT(CorrRow, EMD Need)

Correlation Enabled Correlation Disabled

Figure 4-8 Figure 4-9 Cost Uncertainty Drivers SW Months
obtained from the S-Curve Utility and the “PA Dollar Allocation - Std Dev” sheet in the CB model Figure 4-8 Cost Uncertainty Drivers SW Months Design Cost/Mth SW labor Rate EMD Duration Figure 4-9

ESLOC Growth Actual Count / Initial Count Fig 5-2 ≤ 0.50 0.75 1.0 1.50
1.75 1.25 ≥ 2.0 20 10 Frequency

Fig 5-4

Also Excel Graphics Fig A-10
Figure A-10 Also Excel Graphics Fig A-10 A B C D E

Table A-14 Mann-Wald/2 and 5% significant level version Sturges
ROUND(4*(2*ObsCount^2/(NORMSINV(ChiSigLvl))^2)^0.2,0) Sturges (performs poorly for n<30) Scott’s Choice ROUNDUP((n^(1/3)*SampleRange)/(3.5*Stdev(Sample)),0) Freedman-Diaconis ROUNDUP((n^(1/3)*SampleRange)/(2*SampleInnerQuad),0) Square Root choice

Need^2 Adjusted for Correlation = Need*MMULT(CorrRow, EMD Need)
Figure A-13 Need^2 Adjusted for Correlation = Need*MMULT(CorrRow, EMD Need)

Fig B-1 TD = Time-Dependent Cost, e.g. ‘marching army’ cost
Uncert Uncert Uncert Project Start Uncert Duration Uncertainty Project End Uncert Task Duration Uncert Burn Rate Uncertainty TD = Time-Dependent Cost, e.g. ‘marching army’ cost

Fig B-2 TI $ TI $ TD $ = Segment Duration X Burn Rate TI $ TI $ TI $
TI = Time-Independent Cost, e.g., Materials Uncert TI $ Uncert Uncert TI $ Uncert Hammock Task TD $ = Segment Duration X Burn Rate Uncert Uncert Project Start TI $ Uncertainty Uncert Duration Uncertainty TI $ Uncert Project End TI $ Uncert Uncert Task Duration TI $ Hammock Task TD $ = Segment Duration X Burn Rate Uncert Burn Rate Uncertainty TD = Time-Dependent Cost, e.g., ‘marching army’ cost

Fig B-3 TI $ TI $ TD $ = Segment Duration X Burn Rate TI $ TI $ TI $
TI = Time-Independent Cost, e.g., Materials Uncert TI $ Uncert Uncert TI $ Uncert TD $ = Segment Duration X Burn Rate Uncert Uncert Project Start TI $ Uncertainty Uncert Duration Uncertainty TI $ Uncert Project End TI $ Uncert Probability of Occurrence Uncert Uncert Task Duration TI $ Uncert TI $ Risk Register TD $ = Segment Duration X Burn Rate Uncert Burn Rate Uncertainty TD = Time-Dependent Cost, e.g. ‘marching army’ cost

Assess Event Cost and Duration Uncertainty
Figure B-4 The FIC/SM Process Risk Schedule Cost Collect Schedule Data Create Analysis Collect Assign Likelihood, Estimate Impact Map to Activities Update Identify as TD or TI Assess Duration Uncertainty Validate File Run Assess Assess Event Cost and Duration Uncertainty Apply Correlation

Figure B-6

Figure B-7 Hammock Start Hammock End

Figure B-10

Figure B-16

Figure 8-17

Figure B-19 Start Uncertainty PEs Mean Activity Durations
Finish Uncertainty

Figure B-20

Figure B-21

Figure B-22

Figure B-23

Figure B-24

BACKUP

Figure 2-11 Fit Distributions to Data Accept Best Fit Pass
Investigate alternate hypothesis tests, etc. Select Most Appropriate Adjusted Fit Goodness- of-Fit Test Investigate alternate hypothesis tests (When using Chi-squared alternate bin sizes) Examine lower-ranked distributions Investigate outliers Fit Distributions to Data Fail Constrain standard deviation Change Objective Function (e.g. SSPE vs SSE) (remove standard deviation constraint) Pass None Accept Best Fit Accept Lesser Fit Do not use or accept best fit from original settings and use cautiously fully aware of its shortcomings. Compare selected fit’s mean, std dev and CV to the sample. If any are notably different than the sample note this possible weakness. Select the top-ranked distribution Consider an Empirical Distribution Consider lognormal, triangular, normal or uniform if similar to best fit

Time-Independent Cost Uncertainty
TI vs TD Time Dependent Burn Rate Uncertainty “Cost as a function of schedule duration” Cost = Burn Rate * Duration Duration Uncertainty Cost Time Independent Duration Time-Independent Cost Uncertainty “Cost independent of schedule duration” Cost Duration Uncertainty Duration

Point Estimate Simulation Non Simulation Inputs-Based Analysis
Outputs-Based Analysis Scenario Based Method of Moments Objective Uncertainty Subjective Uncertainty Identify Factors By Cost Element CER Inputs Other Cost Drivers Schedule (Durations) CER Adjustments Correlation Distribution Shape Skew Bound Selection Bound Interpretation Risk Register CERs Factors CER Inputs Correlate Factors TOTAL ESTIMATE UNCERTAINTY Allocate, Phase, Report

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