Upcoming SlideShare
×

# William.jarvis

13,957 views
13,904 views

Published on

0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total views
13,957
On SlideShare
0
From Embeds
0
Number of Embeds
9
Actions
Shares
0
10
0
Likes
0
Embeds 0
No embeds

No notes for slide

### William.jarvis

1. 1. Cost and Schedule Integration A Practical Perspective Will Jarvis NASA PA&E/IPAOPresented at the PM Challenge 2010 Conference, February 9-10, 2010, Galvelston, TX
2. 2. Cost-Schedule IntegrationA common wisdom in cost estimating is that “one needsa good schedule in order to do a good cost estimate”. Good cost and schedule estimates are, in turn,conditional upon the baseline technical definition of the Program or Project for which the estimates are being performed. Cost = F (Technical, Schedule)
3. 3. Cost-Schedule IntegrationTechnical definition influences cost and schedule. schedule technical costTechnical definition is uncertain; therefore, cost and schedule are dependent.
4. 4. Cost-Schedule Integration Variables cost and schedule are dependent. Independent Dependent Correlated Uncorrelated… and they are usually correlated.
5. 5. 70% JCL Frontier 0% Correlation GPM Core Observatory Total Cost With - RY\$ vs Launch Date Costs in RY \$M, 5000 Iterations 1400.000GPM Core Observatory Total Cost With - RY\$ 1200.000 1000.000 JCL=70% 800.000 SRB ICE Conditional Prob = 60.9% GPM Project (With 600.000 Reserves) 400.000 26-Jan-13 17-Mar-13 6-May-13 25-Jun-13 14-Aug-13 3-Oct-13 22-Nov-13 11-Jan-14 2-Mar-14 21-Apr-14 Launch Date
6. 6. 70% JCL Frontier 80% Correlation GPM Core Observatory Total Cost - RY\$ vs Launch Date Costs in RY \$M, 5000 Iterations 1400.000GPM Core Observatory Total Cost - RY\$ 1200.000 1000.000 JCL=70% 800.000 SRB ICE Conditional Prob = 69.2% GPM Project (With 600.000 Reserves) 400.000 26-Jan-13 17-Mar-13 6-May-13 25-Jun-13 14-Aug-13 3-Oct-13 22-Nov-13 11-Jan-14 2-Mar-14 21-Apr-14 Launch Date
7. 7. Cost-Schedule IntegrationBy treating cost and schedule as joint random variables, we can reduce overall estimating risk by leveraging the dependency between them.
8. 8. Cost-Schedule IntegrationTargets c and s are chosen to achieve a certain confidencelevel. For example 70 percent where the cumulative jointprobability, P(C c and S s) 0.7We know, P(C c and S s) P(C c|S s) P(S s) Why? Resource-loaded Schedule Model
9. 9. Cost-Schedule Integration P(Cost<c) P(Schedule<s) P(C c and S s) P(C c|S s) P( S s )The cost estimate is improved by making it conditional on the schedule estimate.
10. 10. Cost-Schedule IntegrationIf cost and schedule are treated as independent then, P(C c and S s) P(C c) P(S s)In other words, Carl and Sally work independently then combinetheir results at the end.For example, Carl finds c so that P(C c) 0.7 Sally finds s so that P( S s) 0.7Then, P(C c and S s) 0.49
11. 11. Cost-Schedule IntegrationIf cost and schedule are not assumed independent, then P(C c and S s) P(C c | S s) P(S s)In other words, Carl and Sally work together on a integratedcost-loaded schedule model.For example, Together, Carl and Sally find that P(C c | S s) P(C c)For the same values of c and s, the joint probability isincreased (i.e., estimating confidence is increased).
12. 12. GPM Conditional Cost Distribution • Two plots of P (Cost < x | Schedule < y) the symbol, | , means “given” • The blue curve is the original Cost S-curve, i.e., P (Cost < x | Schedule < ) • The pink curve is the modified Cost S-curve, given that we know that the launch will occur before 01 Oct 13.P TY\$M• Cost S-curve becomes steeper with increased certainty of the project’s duration.
13. 13. Case Studies• Constellation Program (CxP) Ground Operations Project (GOP)• Global Precipitation Measurement (GPM)  Analytic Method  Simulation Method  Cost Loaded Schedule Method• Radiation Belt Storm Probe (RBSP)
14. 14. Constellation Program (CxP)Ground Operations Project (GOP)
15. 15. GOP Case Study• Independent Cost Estimate (ICE) & subsequent analysis (2007-2008)• Initial Attempt to Integrated Cost and Schedule Risk Analysis• Analysis focused on the Ground Systems Development for the IOC phase due to the lack of available detailed schedules for future phases• Tools  Cost Model developed in ACEIT  Schedule Model developed in GOLDPAN  Cost Schedule Interactions Implement in ACEIT• Method  Cost Risk Analysis adjusted for impact of Schedule Uncertainty  No Inefficiency Penalty for schedule slips
16. 16. GOP Case Study Process• Cost Estimate  Facilities Hardware Estimate  Fixed Price Construction Contracts and GSE Acquisition/Installation  A Category of Cost Now Referred to as “Time Independent” Costs  Government Labor Estimate  FTE and WYE Labor and Related Costs  Project management, system engineering, acceptance, and activation activities  A Category of Cost Now Referred to as “Time Dependent” Costs• Schedule Estimate  Durations for Completion of Major Facilities  Baseline Durations (B) From Deterministic Schedule used for Critical Path Analysis  CDF for Days of Deviation (D) from Baseline  Transfer Schedule CDF to ACEIT• Cost Schedule Adjustment Factor CSAF=(B+D)/B  Factor applied to Labor or “Time Dependent” Costs  Calculated on each iteration of Simulation  Adjustment  For D>0  CSAF>1 increases costs  For D<0  CSAF<1 decreases costs  Straight Line Adjustment – No penalties for inefficiencies caused by schedule slips
17. 17. GOP Case Study Results Initial Operational Capability (IOC) Impact of Schedule Risk 100% 90% 80%Confidence Level (CDF) 70% 60% 50% 40% 30% 20% 10% 0% 4,000 4,500 5,000 5,500 6,000 6,500 7,000 7,500 8,000 TY \$M Discrete Risk Case (cdf) Point Estimate Discrete Risk No Schedule Risk Case (cdf)
18. 18. GOP Case Study Evaluation• Strengths  Incorporates schedule uncertainty into the cost risk analysis  Can be implemented at detailed levels of WBS  Implemented at the Major Facility Level (Pad, MLP, VAB, etc.)• Weaknesses  Does not display Joint Cost/Schedule results  Cost S-curve impacted by schedule  No visibility into schedule  Limited Schedule Scope – Did not include the complete program  Limited WBS Implementation – Schedule Impacts Only included for Facilities
19. 19. Global Precipitation Measurement (GPM) Analytic Approach
20. 20. GPM Case Study• Analysis focused on the Core Observatory Satellite due to the lack of available detailed schedules for the Low Inclination Satellite• Tools  Cost Model developed in ACEIT  Schedule Model developed in MS Project and Pertmaster  Cost Schedule Interactions Implement in EXCEL  NASA Cost-Schedule Integration Spreadsheet (MCR, Inc.)• Method  Analytical Calculation of Bivariate Log-Normal Distribution  Cost mean and standard deviation – per GPM analysis (ICE)  Schedule mean and standard deviation – per GPM analysis (ISA)  Cost/Schedule correlation coefficient of 0.8 (based on analysis by Aerospace, Corp.)  Performed at top level for total Core Observatory Satellite  Calculated Joint Distribution and Conditional Probability Curves P(Cost<x|Schedule<y)
21. 21. SRB ICE S-Curve CORE Spacecraft GPM Project NASA Costs (Includes Discrete Risks) CORE Observatory Mission Calculated with 5000 iterations 100% 90% 80%Confidence Level (CDF) 70% 60% 50% 40% 30% 20% 10% 0% \$350 \$450 \$550 \$650 \$750 \$850 \$950 \$1,050 \$1,150 \$1,250 TY \$MCORE CDF (includes Discrete Risks) SRB ICE CORE - Parametric Point Estimate50% Confidence Level 70% Confidence LevelGPM Budget - Core Mission (Excluding Reserves) GPM Budget - Core Mission (Including Reserves)
22. 22. SRB ISA S-Curve CORE Spacecraft GPM Schedule PDR Model 000260 - Launch Readiness Date : Finish Date 100% 31/Mar/14 95% 26/Nov/13 140 90% 28/Oct/13 85% 03/Oct/13 80% 18/Sep/13 120 75% 06/Sep/13 70% 26/Aug/13 Cumulative Frequency 65% 19/Aug/13 100 60% 08/Aug/13 55% 03/Aug/13Hits 80 50% 29/Jul/13 45% 22/Jul/13 40% 16/Jul/13 60 35% 10/Jul/13 30% 03/Jul/13 40 25% 25/Jun/13 20% 17/Jun/13 15% 07/Jun/13 20 10% 30/May/13 5% 17/May/13 0 0% 20/Mar/13 06/May/13 14/Aug/13 22/Nov/13 02/Mar/14 Distribution (start of interval)
23. 23. GPM Cost-Schedule Correlation While Significant Variability is Evident, for Every 10% of Schedule Growth, there is a Corresponding 12% Increase in Cost 200% %Cost Growth = 1.2348 * %Schedule Growth = 0.8 R2 = 0.6124 150% Cost Growth 100% 50% 0% TRMM -50% 0% 20% 40% 60% 80% 100% Schedule Growth for Non-Restricted Launch Window Projects © 2008 The Aerospace Corporation 4Debra Emmons, Bob Bitten, Claude Freaner, Using Historical NASA Cost and Schedule Growth to SetFuture Program and Project Reserve Guidelines, Presented at the IEEE Aerospace Conference, March 3-10,2007, Big Sky, Montana
24. 24. GPM Joint Cost Schedule Distribution 70-80% Confidence Band 1.0 0.9-1 0.9 66 0.8 0.8-0.9 0.7 63 0.7-0.8 0.6 60 0.5 0.6-0.7 0.4 57 0.5-0.6 0.3 54 0.4-0.5 0.2 0.1 51 0.3-0.4 0.0 0.2-0.3 66 48 1750 60 1550 550 650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1350 54 0.1-0.2 1150 950 48 750 0-0.1 550 The Point Estimate Cost BY2009\$M A 70% Confidence Solution Schedule Months from PDR. . . stated that the essence of the new policy is that programs and projects are to be baselined, rebaselined, andbudgeted based on a joint cost and schedule probabilistic analysis; that programs must have a confidence level of 70%or the level approved by the decision authority, projects must have a confidence level consistent with the program’sconfidence level, and as a minimum, projects are to be funded at a level that is equivalent to a confidence level of 50% oras approved by the decision authority.
25. 25. GPM Conditional Cost Distribution • Two plots of P (Cost < x | Schedule < y) the symbol, | , means “given” • The blue curve is the original Cost S-curve, i.e., P (Cost < x | Schedule < ) • The pink curve is the modified Cost S-curve, given that we know that the launch will occur before 01 Oct 13.P TY\$M• Cost S-curve becomes steeper with increased certainty of the project’s duration.
26. 26. GPM Analytic Approach Evaluation• Strengths  Joint Cost and Schedule Results P(C<c and S<s)  Conditional probability of Cost Given Schedule P(C<c|S<s)• Weakness  Assumption of Bivariate Log-Normal Model for cost and schedule variables  Assumption on Cost and Schedule Correlation parameter for model  Aerospace Study Related Cost Growth and Schedule Growth  Limited Schedule Scope – Did not include the complete program  Included Only Core Observatory  Limited WBS Implementation  Analysis performed at total Satellite Level
27. 27. Global Precipitation Measurement (GPM) Simulation Approach
28. 28. GPM Simulation Approach• Analysis focused on the Core Observatory Satellite due to the lack of available detailed schedules for the Low Inclination Satellite• Tools  Cost Model developed in ACEIT  Schedule Model developed in MS Project and Pertmaster  Cost Schedule Interactions Implemented in ACEIT and EXCEL  Risk Analysis Performed in ACEIT  Simulation Draws are Extracted into EXCEL for Analysis and Display• Method  Simulation of unconstrained Cost and Schedule distributions  Requires Assumption for Correlation between Cost and Schedule  Performed at top level for total Core Observatory Satellite  Calculates Joint Distribution
29. 29. GPM Simulation Results GPM Core Observatory Total Cost - RY\$ vs Launch Date Costs in RY \$M, 5000 Iterations 1400.000GPM Core Observatory Total Cost - RY\$ 1200.000 1000.000 JCL=70% 800.000 SRB ICE Conditional Prob = 69.2% GPM Project (With 600.000 Reserves) 400.000 26-Jan-13 17-Mar-13 6-May-13 25-Jun-13 14-Aug-13 3-Oct-13 22-Nov-13 11-Jan-14 2-Mar-14 21-Apr-14 Launch Date
30. 30. GPM Simulation Approach Evaluation• Strengths  Joint Cost and Schedule Results P(C<c and S<s)  Conditional probability of Cost Given Schedule P(C<c|S<s)  No Assumption Required for form of Joint Distribution• Weaknesses  Assumption of Cost Growth and Schedule Growth Correlation  Limited Schedule Scope – Did not include the complete program  Included Only Core Observatory  Limited WBS Implementation  Analysis performed at total Satellite Level
31. 31. Global Precipitation Measurement (GPM) Resource Loaded Schedule Approach
32. 32. GPM Resource Loaded Schedule Approach• Analysis focused on the Core Observatory Satellite due to the lack of available detailed schedules for the Low Inclination Satellite• Tools  Cost Model developed in ACEIT  Schedule Model developed in MS Project and Pertmaster  Cost Schedule Interactions Implement in Pertmaster• Method  Estimated Costs/Resources Loaded on Schedule  No attempt was made to segregate fixed and variable costs  Costs are dependent on task duration (i.e. cost increases as schedule grows)  Focused on Costs-To-Complete  Calculates Joint Distribution
33. 33. Resource Loaded Schedule Results GPM Schedule PDR Model 10% 12%Entire Plan: Cost 70% 8% 17/Mar/13 06/May/13 25/Jun/13 14/Aug/13 03/Oct/13 22/Nov/13 11/Jan/14 02/Mar/14 Entire Plan: Finish
34. 34. GPM Resource Loaded Schedule Approach Evaluation• Strengths  Joint Cost and Schedule Results P(C<c and S<s)  No Assumption Required for form of Joint Distribution  Captures Schedule Logic• Weaknesses  Resource Loading Excludes Cost Estimating Risk and Technical Risk impact on Costs  Did Not Segregate Fixed and Variable Costs  Costs only scaled by schedule
35. 35. Radiation Belt Storm Probe (RBSP)
36. 36. Independent Cost and Schedule Assessment• Independent Cost Estimate  Parametric methodology using Price, NICM and SEER-SEM  ICE performed to original schedule capturing risks identified by the SRB  Adjusted ICE done to capture results of ISA• Independent Schedule Assessment and Risk Identification  Available margin was kept in the schedule  Ten risks identified from the Project Risk List  SRB assessed the potential schedule impact due to each risk
37. 37. Independent Schedule Risk Assessment Results • At the 50th % RBSP launch has a potential of slipping 6.7 months • At the 70th % schedule slip is estimated to be 7.0 months37
38. 38. RBSP Cost and Schedule Integration• ISA results applied to specific WBS items• Determined burn-rates (generally FY10) for each affected WBS• Time to dollars conversion: ISA Schedule extensions modeled as triangular distributions with the burn-rate values• Cost of schedule extension shown at the 70th percentile to be consistent with cost risk \$25,000 \$20,000 PM/SE/MA ECT RBSPICE \$15,000 EFW EMFISIS Power Distribution Thermal Control \$10,000 Flight Software System I&T Mission Ops Dev \$5,000 \$0 2008 2009 2010
39. 39. Cost / Schedule Integration Results100% ICE @ 70% CL90%80% RBSP Project Adjusted ICE @ 70% CL70% w/Reserves60%50% Revised RBSP Project40% Original ICE w/Reserves30%20% ICE with Cost of10% Schedule slip 0% 450 500 550 600 650 700 750 800 850 900
40. 40. RBSP Approach Evaluation• Strengths  Easy to implement in ACEIT  Provided a reasonable result• Weaknesses  Not a true joint probability distribution  Did not consider time independent costs
41. 41. Conclusions• Assuming cost and schedule are independent does not allow for improved estimating confidence.• Overall cost and schedule risk is reduced by observing the interaction between cost and schedule.  Cost as a function of Schedule  Conditional Probability of Cost given Schedule  Joint Cost and Schedule Probability• Correlation between cost and schedule can be modeled in different ways:  Parametric model  Resource-loaded schedule model
42. 42. Conclusions (Continued)• Parametric JCL Model  Required Information:  Cost S-curve  Schedule S-curve  Correlation between cost and schedule  Tools  ACEIT  EXCEL  Issues  Does not require Mapping of cost to task durations  Assumption on cost/schedule correlation  Phasing of costs  How much schedule slip is included in parametric data• Resource-loaded Schedule model  “JCL Experiment” demonstrated feasibility of calculating joint probability  Tools  Pertmaster  Issues  Schedule defined to IOC only  Costs scaled to schedule durations  Excludes Cost Estimating and Technical Risks