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

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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

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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

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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

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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