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Booz & Company Through-Life Cost of Ownership Project Overview, OCT 12 RIZZO REFORM PROGRAM.

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Presentation on theme: "Booz & Company Through-Life Cost of Ownership Project Overview, OCT 12 RIZZO REFORM PROGRAM."— Presentation transcript:

1 Booz & Company Through-Life Cost of Ownership Project Overview, OCT 12 RIZZO REFORM PROGRAM

2 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 1

3 Booz & Company Through Life Cost of Ownership is part of the broader Rizzo reform program – focus on three recommendations 2 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

4 Booz & Company With particular focus on the ‘bathtub’ effect and associated budgeting approaches as identified in the Rizzo report 3 The ‘Bathtub’ EffectFleet 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’

5 Booz & Company Cost modelling focused on understanding future sustainment costs – focus on maintenance costs 4  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 ApproachComments  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

6 Booz & Company Maintenance cost has been our major focus; current and historical practices have informed forward projections 5 What this enables 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

7 Booz & Company Third-party research indicates ship maintenance costs increase with age -- this informs our approach to RAN costs 6 US Navy Study DSTO Studies  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 Cost 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

8 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  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

9 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 Corrective Maintenance Cost Model – Hull D/SRAs FY12 $ Corrective Maintenance Cost Model– Hull IMAVs FY12 $ Sample analysis: corrective maintenance costs of ANZAC hulls Source: CIP EMA data, TCOP analysis. Excludes inventory costs Ship age (years) This analysis is repeated for propulsion, systems / auxiliaries, electrical and other systems 8 PRELIMINARY ANZAC ships FFG ships Ship age (years)

10 Booz & Company Corrective maintenance cost estimates accurately account for historical EMAs and align to third party studies 9 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 DSRA Low FFG outlier result of reduced hull spend, being compensated for in current EMA Ship age Source: TCOP analysis PRELIMINARY

11 Booz & Company Historical corrective maintenance costs conform to third- party research 10 Analytic FindingsImplications 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

12 Booz & Company Consistent with the research, scheduled maintenance costs follow a ‘bathtub’ curve, driven by hull costs 11 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/DSRA22S/DSRA23 Systems / AuxillariesSystems / AuxillariesSystems / AuxillariesSystems / AuxillariesAuxiliaries Propulsion Other Electrical S/DSRA21S/DSRA20S/DSRA19S/DSRA18S/DSRA17S/DSRA16S/DSRA15S/DSRA14S/DSRA13S/DSRA12S/DSRA11S/DSRA10S/DSRA09S/DSRA08S/DSRA07S/DSRA06S/DSRA05S/DSRA04S/DSRA03S/DSRA02S/DSRA01 Hull Source: CIP data, EMA Schedule - Financial PRELIMINARY

13 Booz & Company Estimated Cost vs. Actuals – ANZAC and FFGs, SRAs and DSRAs FY12 $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, IMAVs FY12 $M 12 Ship age Ship age Range between historic min / max -30% +30% Actual average Predicted cost Confidence Interval  Statistical analysis has standard errors of %  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

14 Booz & Company 13 Estimated EMA costs are consistently higher than DMFP assumptions in later years, driving increased cost IMAV14IMAV15IMAV08IMAV07IMAV06IMAV09 IMAV12IMAV11IMAV10IMAV13 Estimated DMFP Estimated ANZAC EMA Cost Profile versus DMFP Assumptions FY12 $million FY22FY21FY20FY19FY18FY17FY16FY15FY14FY13 Estimated DMFP Maintenance Cost for Single Ship FY12 $million S/DSRA06S/DSRA05S/DSRA09S/DSRA08S/DSRA11S/DSRA04S/DSRA10S/DSRA13S/DSRA16S/DSRA15S/DSRA07S/DSRA12S/DSRA14 DSRAs and SRAs $million IMAVs $million Source: CIP data, DMFP, TCOP analysis PRELIMINARY EXAMPLE

15 Booz & Company This analysis enables future funding requirements to be estimated 14 FundingScheduled MaintenanceNon-EMA URDEFsEngineering Change InventoryTotal Estimated FY13-FY22 Maintenance and Inventory Funding Requirement $m ILLUSTRATIVE

16 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 15

17 Booz & Company Future considerations 16 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


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