Booz & Company Through-Life Cost of Ownership Project Overview, OCT 12 RIZZO REFORM PROGRAM.
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Transcript of Booz & Company Through-Life Cost of Ownership Project Overview, OCT 12 RIZZO REFORM PROGRAM.
Booz & Company
Through-Life Cost of Ownership Project
Overview, OCT 12
RIZZO REFORM PROGRAM
Booz & Company
Objectives
Provide overview of Rizzo reform program and the Through Life Cost of Ownership Project
Outline current work being undertaken and potential applications to the management of the Navy fleet
Identify points for future consideration and focus
2
Booz & Company
Through Life Cost of Ownership is part of the broader Rizzo reform program – focus on three recommendations
3
Through Life Cost of Ownership Project
Addressing three Rizzo Recommendations
– Rec 4: Plan for Aging vessels
– Rec 22: Quantify the Engineering and Maintenance Backlog
– Rec 23: Confirm Maritime Resources (both budget and workforce)
Three phase approach, currently in Phase 2
– Phase 1 (to JUL 12): establish interim cost model, initial ‘bathtub’ studies review, quantify backlog
– Phase 2 (to MAR 13): further refine and extend the cost model across the fleet, refine the UUC, commence costing tool development
– Phase 3 (to SEP 13): implementation
The Rizzo Review
Booz & Company
With particular focus on the ‘bathtub’ effect and associated budgeting approaches as identified in the Rizzo report
4
The ‘Bathtub’ Effect Fleet Management Budgeting
Rizzo report identified impact of the ‘bathtub’ effect
Reflects the impact of ‘ageing vessels’
Budgeting process does not necessarily account for the impact of ‘ageing’
Booz & Company
Cost modelling focused on understanding future sustainment costs – focus on maintenance costs
5
Six platforms in phase 1: ANZAC, FFG, ACPB, Collins, LHD, AWD
Tailored approach across each platform including ‘bottom-up’ analysis for ANZAC and FFG
Understood cost baseline across the fleet– Direct costs– Indirect costs
Driver analysis informs future cost – Fuel, EO, Personnel largely variable costs– Sustainment costs (maintenance and inventory)
informed by bottom up analysis– Focus on future scheduled maintenance and
engineering change costs; other sustainment costs modelled consistent with DMFP (e.g.: ISS)
Leverage broad spectrum of work underway across Navy
Approach Comments
Focus on sustainment costs – other costs largely variable
Future sustainment cost estimates assume current practices and reflect an inconsistent system
Engineering changes span a spectrum of types and criticality – these are currently being classified in more detail to develop a clearer view of “true” criticality
Comparison to current budget undertaken
Booz & Company
Maintenance cost has been our major focus; current and historical practices have informed forward projections
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What this enables
Limitations and Constraints
Limitations and Constraints
Initial estimate of future maintenance and engineering change costs
Reflects historical and current maintenance practices
Does not optimise maintenance
Limited obsolescence understanding
Data integrity challenges e.g., unknown coding, impact of FFG-up, etc
~75%
Indicative % Maintenance
Cost
~5-10%
~15-20%
Source: TCOP analysis
Booz & Company
Third-party research indicates ship maintenance costs increase with age -- this informs our approach to RAN costs
7
US Navy Study
DSTO Studies
Cost vs. Avg. Age
$-
$2
$4
$6
$8
$10
$12
0.0 5.0 10.0 15.0 20.0 25.0 30.0
Avg. Age
Co
st (
FY
03$M
)
CG 16
CG 26
CG 47
DD 963
DDG 51
DDG 993
FFG 7
2004 paper by Grinnell, Summerville, et al
Reviewed data from 1984 – 2003 across scheduled overhaul, repair parts, POL, centrally provided materials
Cross platform evaluation, weight normalised
Concluded an age-driven effect for scheduled overhaul
Applied both fifth order polynomial and linear regression due to lack of data after year 28
Illustrative DSTO Sustainment Cost Curve
Co
st
Age
Range of studies focused on aircraft and initial review of submarines – no research into surface ships
Aircraft research is cross-platform in nature Research indicates similar ‘s’ shaped curve
indicated in the USN study above Unable to access due to no contractor
access allowed
Informing Our Approach
Test range of functional forms– Apply log-linear model– Hypothesising an ‘S’
shaped curve for scheduled maintenance costs with an age effect present, or at least the exponential portion of the ‘S’ curve
Studies indicate cross-platform comparisons are valid when investigating cost relationships – enables combination of ANZAC and FFG analysis
Tested our approach with DSTO
Third-Party Research
RAND
Reviewed aircraft maintenance costs versus age Applied log-linear regression model Identified age impact on airframe but not as clear for engines Compared wide range of aircraft type in some analysis, e.g. Boeing 737 and F111
Others
Bitros and Kavussanos study analysing commercial ship maintenance costs applying semi-log linear regression
Octeau study – Cost of Battlefield Deployments modelling ships and aircraft maintenance costs as a multi-variable regression based on use and age
Source: DSTO, RAND, Grinnell, Summerville, et al, Bitros and Kavussanos
Booz & Company
Third-party research indicates ship maintenance costs increase with age -- this informs our approach to RAN costs
8
US Navy Study
DSTO Studies
2004 paper by Grinnell, Summerville, et al Reviewed data from 1984 – 2003 Cross platform evaluation, weight normalised Concluded an age-driven effect for scheduled overhaul Applied both fifth order polynomial and linear regression due
to lack of data after year 28
Range of studies focused on aircraft, cross-platform in nature
Research indicates similar ‘s’ shaped curve indicated in the USN study above
RAND Reviewed aircraft maintenance costs versus age Applied log-linear regression model Identified age impact on airframe but not as clear for engines Compared wide range of aircraft type in some analysis, e.g.
Boeing 737 and F111
Others Bitros and Kavussanos study analysing commercial ship
maintenance costs applying semi-log linear regression Octeau study – Cost of Battlefield Deployments modelling ships
and aircraft maintenance costs as a multi-variable regression based on use and age
Informing Our Approach
Test range of functional forms– Apply log-linear
model– Hypothesising
exponential growth in maintenance costs as vessels age
Studies indicate cross-platform comparisons are valid when investigating cost relationships – enables combination of ANZAC and FFG analysis
Tested our approach with DSTO
Booz & Company
Age-based growth in hull D/SRA maintenance costs is evident, reflecting ageing vessels; limited IMAV trend identified
Expected corrective maintenance costs by age – Hull D/SRAs
FY12 $m
Expected corrective maintenance costs by age – Hull IMAVs
FY12 $m
ANZAC ships
FFG ships
19 21 22 24 263 10 12 14 15 171 5 7 8 28 29 31 33 35 36 38 40
Corrective Maintenance Cost Model – Hull D/SRAsFY12 $
Corrective Maintenance Cost Model– Hull IMAVsFY12 $
Sample analysis: corrective maintenance costs of ANZAC hulls
Source: CIP EMA data, TCOP analysis. Excludes inventory costs
Ship age (years)
Ship age (years)
Ship age (years)
This analysis is repeated for propulsion, systems /
auxiliaries, electrical and other systems
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PRELIMINARY
3937363432302927252322201816151311 4186 942
ANZAC ships
FFG ships
Ship age (years)
Booz & Company
Corrective maintenance cost estimates accurately account for historical EMAs and align to third party studies
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Estimated EMA corrective maintenance cost of a ‘typical’ ship versus actual costs DSRA/SRA
FY12 $millions
Corrective maintenance curve
Uses regression analysis of past EMAs, combining FFG and ANZAC ships and analysed by major systems
Curve accurately accounts for historical actual EMAs with the exception of known outliers
Curve fits expected shape as per several academic studies and other third party experience (e.g., US Navy, DSTO studies on aircraft)
High ANZAC outlier
delayed DSRALow FFG
outlier result of reduced hull spend,
being compensated for in current
EMA
Ship age
Source: TCOP analysis
PRELIMINARY
Booz & Company
Historical corrective maintenance costs conform to third-party research
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Analytic Findings Implications for Cost Modeling
Corrective Major Availability Strong evidence of exponential maintenance cost growth with age
Statistically significant regression coefficients
Strongest results for Hull and systems/ auxiliaries
Consistent with third-party research Modelling consistent with historical costs
Apply modelled cost to estimate future maintenance costs
Need to apply pragmatic ‘capping’ of cost to reflect management practice as approach planned withdrawal date
Assume that cost impact of extended EMA durations reflected in cost escalation
Minor Availability Limited evidence of cost growth with age across major systems
Apply historical average IMAV cost by system
Preventative Evidence of cost growth, but potentially more reflective of maintenance practice differences
Apply average preventative cost on class-specific basis
Requires further investigation
Project Management EMA duration and total EMA cost as significant overhead cost drivers – reflects hull
Apply modelled cost to expected EMA durations
Allow for extended EMA durations
Major Analytic Findings
Booz & Company
Consistent with the research, scheduled maintenance costs follow a ‘bathtub’ curve, driven by hull costs
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Estimated Corrective and Preventative EMA Cost by System, ANZACs
FY12 $million
Cost Growth
Growth in maintenance cost driven by corrective hull cost growth
Consistent with third party research
Other cost drivers need to be incorporated and understood e.g., Usage/OpTempo
S/D
SR
A22
S/D
SR
A23
Systems /
AuxillariesAuxiliaries
Propulsion
Other
Electrical
S/D
SR
A21
S/D
SR
A20
S/D
SR
A19
S/D
SR
A18
S/D
SR
A17
S/D
SR
A16
S/D
SR
A15
S/D
SR
A14
S/D
SR
A13
S/D
SR
A12
S/D
SR
A11
S/D
SR
A10
S/D
SR
A09
S/D
SR
A08
S/D
SR
A07
S/D
SR
A06
S/D
SR
A05
S/D
SR
A04
S/D
SR
A03
S/D
SR
A02
S/D
SR
A01
Hull
Source: CIP data, EMA Schedule - Financial
PRELIMINARY
Booz & Company
Estimated Cost vs. Actuals – ANZAC and FFGs, SRAs and DSRAsFY12 $M
Source: CIP EMA data, TCOP analysis. Excludes inventory , engineering change and non-EMA URDEF costs
Modelling aligns to actual EMA costs, with a 30% confidence interval applied
Estimated Cost vs. Actuals – ANZAC and FFGs, IMAVsFY12 $M
13
Ship age
40383635333129282624222119171514121087531
Ship age
Range between historic min / max
-30%
+30%
Actual average
Predicted cost
4139363730 3427 322925232220181615131198642
Confidence Interval
Statistical analysis has standard errors of 4.3 - 7.1%
However, there are a range of high level assumptions that introduce uncertainty into the estimates– Historical maintenance practices– Impact of usage patterns
Therefore broad confidence intervals of 30% have been applied
These will be further refined as Project 4 progresses
PRELIMINARY
Outlier: HMAS Darwin SRA9 – significant
underreporting of Hull related defects
Booz & Company14
Estimated EMA costs are consistently higher than DMFP assumptions in later years, driving increased cost
IMA
V14
IMA
V15
IMA
V08
IMA
V07
IMA
V06
IMA
V09
IMA
V09
IMA
V12
IMA
V11
IMA
V10
IMA
V13
Estimated
DMFP
Estimated ANZAC EMA Cost Profile versus DMFP Assumptions
FY12 $million
FY22FY21FY20FY19FY18FY17FY16FY15FY14FY13
Estimated
DMFP
Maintenance Cost for Single Ship FY12 $million
S/D
SR
A06
S/D
SR
A05
S/D
SR
A09
S/D
SR
A08
S/D
SR
A11
S/D
SR
A04
S/D
SR
A10
S/D
SR
A13
S/D
SR
A16
S/D
SR
A15
S/D
SR
A07
S/D
SR
A12
S/D
SR
A14
DSRAs and SRAs
$million
IMAVs$million
Source: CIP data, DMFP, TCOP analysis
PRELIMINARY EXAMPLE
Booz & Company
This analysis enables future funding requirements to be estimated
15
Funding Scheduled Maintenance
Non-EMA URDEFs
Engineering Change
Inventory Total
Estimated FY13-FY22 Maintenance and Inventory Funding Requirement$m
ILLUSTRATIVE
Booz & Company
There have been a range of challenges in developing this approach
Limited central data availability
– Most of the data resides locally in the SPOs
– Inconsistent approaches across different vessel classes
Mixed data quality
– Coding of maintenance tasks and alignment to costs
– Level of detail that can be captured and incorporated
– Limited historical data for some classes
Understanding the cost that should have been incurred versus the cost that was actually incurred
16
Booz & Company
Future considerations
17
Ensuring Data Integrity and Availability
Understanding Cost Drivers
Linking cost understanding to decision
making
Improve consistency across classes to enable more rapid analysis and comparability
Improve data integrity to ensure more accurate estimation Improve data ‘depth’ to better understand cost drivers – linkage to
operational data
Driving a generic ‘maritime’ cost understanding
Drive understanding of cost drivers, leveraging greater data depth e.g., OpTempo
Understanding impact of delayed maintenance
Future Considerations and Focus
Build understanding of a ‘normalised’ maritime scheduled maintenance cost estimate
Linkage of cost understanding to future capability investment decisions
Linkage of cost understanding to future maintenance availabilities