1. Cost and Schedule Integration
A Practical Perspective
Will Jarvis
NASA PA&E/IPAO
Presented at the PM Challenge 2010 Conference, February 9-10, 2010,
Galvelston, TX

2. Cost-Schedule Integration
A common wisdom in cost estimating is that “one needs
a 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. Cost-Schedule Integration
Technical definition influences cost and schedule.
schedule
technical
cost
Technical definition is uncertain; therefore, cost and
schedule are dependent.

4. Cost-Schedule Integration
Variables cost and schedule are dependent.
Independent
Dependent
Correlated Uncorrelated
… and they are usually correlated.

5. 70% JCL Frontier
0% Correlation
GPM Core Observatory Total Cost With - RY$ vs Launch Date
Costs in RY $M, 5000 Iterations
1400.000
GPM Core Observatory Total Cost With - RY$
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. 70% JCL Frontier
80% Correlation
GPM Core Observatory Total Cost - RY$ vs Launch Date
Costs in RY $M, 5000 Iterations
1400.000
GPM Core Observatory Total Cost - RY$
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. Cost-Schedule Integration
By treating cost and schedule as joint random variables, we can reduce
overall estimating risk by leveraging the dependency between them.

8. Cost-Schedule Integration
Targets c and s are chosen to achieve a certain confidence
level. For example 70 percent where the cumulative joint
probability,
P(C c and S s) 0.7
We know,
P(C c and S s) P(C c|S s) P(S s) Why?
Resource-loaded
Schedule Model

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. Cost-Schedule Integration
If 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 combine
their results at the end.
For example,
Carl finds c so that P(C c) 0.7
Sally finds s so that P( S s) 0.7
Then,
P(C c and S s) 0.49

11. Cost-Schedule Integration
If 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 integrated
cost-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 is
increased (i.e., estimating confidence is increased).

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. 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. Constellation Program (CxP)
Ground Operations Project (GOP)

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. 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. 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. 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. Global Precipitation Measurement
(GPM)
Analytic Approach

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. 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 $M
CORE CDF (includes Discrete Risks) SRB ICE CORE - Parametric Point Estimate
50% Confidence Level 70% Confidence Level
GPM Budget - Core Mission (Excluding Reserves) GPM Budget - Core Mission (Including Reserves)

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/13
Hits
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. 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 4
Debra Emmons, Bob Bitten, Claude Freaner, Using Historical NASA Cost and Schedule Growth to Set
Future Program and Project Reserve Guidelines, Presented at the IEEE Aerospace Conference, March 3-10,
2007, Big Sky, Montana

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, and
budgeted 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’s
confidence level, and as a minimum, projects are to be funded at a level that is equivalent to a confidence level of 50% or
as approved by the decision authority.

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. 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. Global Precipitation Measurement
(GPM)
Simulation Approach

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. GPM Simulation Results
GPM Core Observatory Total Cost - RY$ vs Launch Date
Costs in RY $M, 5000 Iterations
1400.000
GPM Core Observatory Total Cost - RY$
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. 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. Global Precipitation Measurement
(GPM)
Resource Loaded Schedule Approach

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. 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. 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. Radiation Belt Storm Probe (RBSP)

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. 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 months
37

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. Cost / Schedule Integration
Results
100% ICE @ 70% CL
90%
80%
RBSP Project Adjusted ICE @ 70% CL
70% w/Reserves
60%
50% Revised RBSP Project
40% Original ICE w/Reserves
30%
20%
ICE with Cost of
10% Schedule slip
0%
450 500 550 600 650 700 750 800 850 900

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. 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. 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