COST ESTIMATION AND ANALYSIS CAPSTONE PROJECT

i

NAVAL POSTGRADUATE

SCHOOL

MONTEREY, CALIFORNIA

COST ESTIMATION AND ANALYSIS CAPSTONE PROJECT

HISTORICAL REVIEW OF DOD PRIME

CONTRACTOR DIRECT LABOR RATES TO DEVELOP BUDGET FORECASTING

By

Josh Frazier, Majonka Herlikofer,

Larry Mattivi, Phillip Ramon, and Anne Ryan Advisor: Joe Cardarelli, NAVAIR-4.2 Professor: CAPT Douglas E. Otte, USN (Ret.)

MCEA CAPSTONE HISTORICAL REVIEW OF DOD CONTRACTOR TEAM

FINAL REPORT DIRECT LABOR RATES NAVAIR

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Historical Review of DOD Prime Contractor

Direct Labor Rates

To Develop Budget Forecasting

Team Members:

Josh Frazier

B.S., Mathematics

Certificate, Cost Estimating & Analysis, NPS

Majonka Herlikofer

B.S., Admin Studies, M.S., Technology

Certificate, Air Command and Staff College


Larry Mattivi

B.A., Labor Studies & Industrial Relations,

M.S., Business – Marketing Concentration


Phillip Ramon

B.A., Physics

A.S., Engineering


Anne Ryan

B.S., Marketing, M.B.A., Marketing Concentration


Advisor:

Joe Cardarelli

NAVAIR-4.2.1

Submitted in partial fulfillment of the

requirements for the degree of

MASTERS OF COST ESTIMATION AND ANALYSIS

from the

NAVAL POSTGRADUATE SCHOOL

March 2013



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TABLE OF CONTENTS

I. EXECUTIVE SUMMARY................................................................................................................... 1

A. INTRODUCTION ............................................................................................................................ 1

B. RESULTS ......................................................................................................................................... 2

C. RECOMMENDATIONS .................................................................................................................. 4

II. STUDY INTRODUCTION .............................................................................................................. 5

A. BACKGROUND .............................................................................................................................. 5

1. PURPOSE ..................................................................................................................................... 5

2. SCOPE .......................................................................................................................................... 6

B. ASSUMPTIONS AND CONSTRAINTS ......................................................................................... 7

1. GROUND RULES ........................................................................................................................ 7

2. ASSUMPTIONS ........................................................................................................................... 7

3. STUDY LIMITATIONS............................................................................................................... 8

4. STUDY CONSTRAINTS ............................................................................................................. 8

III. ANALYTICAL APPROACH............................................................................................................... 9

A. METHODOLOGY ........................................................................................................................... 9

1. DATA COLLECTION ................................................................................................................. 9

2. DATA NORMALIZATION ......................................................................................................... 9

3. DATA ANALYSIS ....................................................................................................................... 9

a. ESTIMATING TOOLS ................................................................................................................ 9

b. ESTIMATING TECHNIQUES .................................................................................................. 10

c. LEVEL OF ANALYSIS ............................................................................................................. 10

d. DATA RULES ............................................................................................................................ 11

e. COST MODEL ........................................................................................................................... 11

f. FORMAT OF RESULTS............................................................................................................ 12

B. MEASURES OF EFFECTIVENESS ............................................................................................. 12

C. ALTERNATIVES ........................................................................................................................... 12

1. OSD INDICES ............................................................................................................................ 12

2. DOD CONTRACTOR HISTORY.............................................................................................. 14

3. BUREAU OF LABOR STATISTICS (BLS) ............................................................................. 16

4. GLOBAL INSIGHT (GI)............................................................................................................ 19



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IV. ANALYSIS........................................................................................................................................... 22

A. SUMMARY .................................................................................................................................... 22

B. SENSITIVITY ANALYSIS ........................................................................................................... 28

1. ENGINEERING ......................................................................................................................... 28

2. MANUFACTURING ................................................................................................................. 29

V. SUMMARY .......................................................................................................................................... 30

A. CONCLUSIONS .............................................................................................................................. 30

B. RECOMMENDATIONS ................................................................................................................... 30

ANALYSIS OF RESULTS ................................................................................................................ 30

C. FURTHER ANALYSIS ..................................................................................................................... 34

BIBLIOGRAPHY: .................................................................................................................................. 36

APPENDICES ........................................................................................................................................ 37

APPENDIX A. STUDY ISSUES AND CONCERNS .......................................................................... 37

APPENDIX B. STUDY DATA ............................................................................................................. 39



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LIST OF TABLES

Table 1: OSD Indices by Appropriation ................................................................................... 13 Table 2: DOD Prime Contractor Data Collection ................................................................... 15 Table 3: Engineering Rates of Growth ..................................................................................... 28 Table 4: Manufacturing Rates of Growth ................................................................................ 29 Table 5: DLR Growth Factors using BLS, GI, Industry Averages, & Highest History ...... 33

Table 6: DLR Growth Factors using DOD Contractors and Industry Averages ................. 33 Table 7: DLR Growth Factors using DOD Contractor Labor Categories ............................ 34

LIST OF FIGURES

Figure 1: Example: Compounding Effect of Inflation............................................................... 1 Figure 2: DOD Contractors vs. All Indices (Engineering) ....................................................... 3

Figure 3: DOD Contractors vs. All Indices (Manufacturing) ................................................... 3 Figure 4: Study Results Compounded ........................................................................................ 4

Figure 5: Example: Compounding Effect of Inflation............................................................... 5 Figure 6: Cost Model Structure ................................................................................................. 11 Figure 7: DOD Contractor Locations ...................................................................................... 14

Figure 8: Example of BLS Occupational Groups and Wages Rates ...................................... 17 Figure 9: BLS Occupational Groups and Labor Categories Used in Study ......................... 17

Figure 10: BLS Occupational Averages (Engineering) ........................................................... 18 Figure 11: BLS Sample Labor Categories (Engineering) ....................................................... 18

Figure 12: BLS Occupational Average (Manufacturing) ........................................................ 19 Figure 13: BLS Sample Labor Categories (Manufacturing) .................................................. 19

Figure 14: GI (NAVAIR) Indices for Aircraft Labor.............................................................. 21 Figure 15: Current 2012 Navy Inflation Indices % Change by Year .................................... 22 Figure 16: Current DOD Indices (Base Year 2005) ................................................................. 23

Figure 17: OSD Indices: Navy Forecasted by Official Release Date ...................................... 23 Figure 18: DOD Contractors vs. OSD Indices (Engineering) ................................................. 24

Figure 19: DOD Contractors by Industry Sector (Engineering) ............................................ 24 Figure 20: DOD Contractors vs. OSD Indices (Manufacturing) ............................................ 25 Figure 21: DOD Contractors by Industry Sector (Manufacturing) ....................................... 25 Figure 22: BLS vs. OSD Indices (Engineering) ........................................................................ 26 Figure 23: BLS vs. OSD Indices (Manufacturing) ................................................................... 26

Figure 24: GI (NAVAIR) vs. OSD Indices (Engineering) ....................................................... 27

Figure 25: GI (NAVAIR) vs. OSD Indices (Manufacturing) .................................................. 27

Figure 26: DOD Contractor History vs. All Indices (Engineering)....................................... 28 Figure 27: DOD Contractor History vs. All Indices (Manufacturing) .................................. 29 Figure 28: Study Results Compounded .................................................................................... 31 Figure 29: Potential DLR Growth Factor Options .................................................................. 32 Figure 30: DOD Contractor Example: Design Engineering (2005-2020) .............................. 35



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ACRONYMS AND ABBREVIATIONS

• AFCAA - Air Force Cost Analysis Agency

• AHE - Average Hourly Earnings

• AMMO - Ammunition Procurement, Army

• APA - Aircraft Procurement, Army

• APN - Aircraft Procurement, Navy

• BLS - Bureau of Labor Statistics

• CCDR - Contractor Cost Data Report

• CER - Cost Estimating Relationship

• CSDR - Cost and Software Data Report

• CY - Constant Year

• DCAA - Defense Contract Audit Agency

• DCMA - Defense Contract Management Agency

• DLR - Direct Labor Rates

• DOD - Department of Defense

• DRI - Data Resources, Inc.

• DW - Defense-Wide

• ECI - Employment Cost Indices

• FPRPs - Forward Pricing Rate Proposals

• FPRRs - Forward Pricing Rate Recommendations

• FPRAs - Forward Pricing Rate Agreements

• GI - Global Insight

• JCARD - Joint Cost Analysis Research & Database

• JIC - Joint Inflation Calculator

• IPR - In Progress Review

• KTR - Contractor

• MILCON - Military Construction

• MIPA - Missile Procurement, Army

• MS - Microsoft

• NCCA - Naval Center for Cost Analysis

• OMN - Operations and Maintenance, Navy

• OPA - Other Procurement, Army

• OPN - Other Procurement, Navy

• OSD - Office of the Secretary of Defense

• PMC - Procurement, Marine Corps

• PAO - Public Affairs Office

• POA&M - Plan of Action and Milestones

• PPI - Producer Price Index

• RDT&E,A - Research, Development, Test & Evaluation, Army

• RDT&E,N - Research, Development, Test & Evaluation, Navy

• SCN - Ship Procurement, Navy

• SLR - Simple Linear Regression

• TDD - Target Detection Device

• TY - Then Year

• UAVs - Unmanned Air Vehicles



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ACRONYMS AND ABBREVIATIONS

• USA - United States Army

• USAF - United States Air Force

• WEFA - Wharton Economics Forecasting Associates

• WPI - Wholesale Price Index

• WPN - Weapons Procurement, Navy

• WTV - Weapons and Track Vehicles Procurement, Army

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I. EXECUTIVE SUMMARY

A. INTRODUCTION

The NAVAIR Cost Department prepares budget estimates for all major weapon system programs

at NAVAIR. The Office of the Secretary of Defense (OSD) Comptroller guidance requires the

use of the OSD Inflation Indices to generate all budgetary estimates. The NAVAIR-4.2 Cost

Department, through analysis of Department of Defense (DOD) contractor labor rates, has seen

differences in the rates of growth on Direct Labor Rates (DLR) than those predicted by the OSD

Inflation Indices. This study will analyze historical DOD contractor direct labor rates for

engineering and manufacturing labor and compare to OSD Indices.

When analyzing rates of growth, it is important to understand the compounding effect of

inflation and how it affects cost estimates. Seemingly small differences in the inflation rate can

make significant differences as costs are projected over time. Figure 1 below is an example that

shows the impact of changing the rate of growth in one percent increments over a twenty year

timeframe.

Figure 1: Example: Compounding Effect of Inflation

The implication is that small differences in the rate of growth between OSD indices used and the

contractor historical performance can have a great impact on our program budgets.

The goals of this study are:

- To determine if OSD Indices are sufficient to capture true economic conditions

experienced by defense contractors;

- To determine if there are any discernible patterns from Direct Labor Rates (DLR) to use

for budget forecasting;

- To quantify and recommend DLR growth factors that capture economic conditions

experienced by the Aerospace Industrial sector to use for budget forecasting;

- To improve the quality of labor estimates for Naval Aviation budgets.

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The overall methodology for analysis included analyzing current alternatives for forecasting

inflation and comparing and contrasting these alternatives against the OSD indices. The current

methods used for inflation in NAVAIR cost models include:

- OSD Indices for Navy appropriations,

- NAVAIR defense contractor DLR history,

- Industry standards for inflation forecasting: Global Insight (GI) indices and Bureau of

Labor Statistics (BLS) wage rates.

A separate excursion was conducted to determine the change in the Navy OSD Indices over time.

Historical Navy inflation indices published over the past twelve years (2000-2012) were

analyzed for trends. All data was converted to a 2005 base year index to normalize the indices

and labor rate data and to mask proprietary information. The dataset time horizon, based on the

limitation of available contractor history, includes indices from 2005 through 2020 and historical

contractor data for 2005 through 2011, with rate projections for 2012-out.

B. RESULTS

OSD Indices for All Services

Graphical results of the current 2012 Navy OSD indices (dated 2/10/12) show a consistent

pattern of rate of change by year except for the Weapons Procurement, Navy (WPN) index.

Further investigation is needed to determine the reason for the large variation in this index.

OSD Inflation Indices for each of the other services (Army, Air Force, and Defense-Wide) were

indexed to a 2005 base year and plotted to determine any service-unique patterns. While all

services use the same starting point for year-to-year rate of change, unique outlays are applied by

each service based on their service-unique experience to define Then Year (TY) dollars. Indices

for all services show similar patterns of growth. If OSD Indices are not sufficient for Navy

contractors, all other services have a similar problem because of the same patterns of growth in

the OSD indices. Therefore, results of this study could be applied to the other services’ cost

estimates.

OSD Indices: Navy Excursion The historical analysis of the Navy’s OSD indices forecasts over the past twelve years (2000-

2012) indicates that the Navy indices have been lowering over time and there is significant

volatility in current 2012 indices. There also appears to be a pattern in the data associated with

the timing of change in the Executive Branch Administration. This may indicate a political

influence on the OSD forecasts.

DOD Contractor History Historical DLR data was collected from six NAVAIR prime contractors across a total of fifteen

sites. Analysis of DOD contractor DLR history shows rates of growth for individual labor

categories are outpacing the OSD indices for both engineering and manufacturing.



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Bureau of Labor Statistics and Global Insight

Research indicates that BLS wage rates and GI indices are also outpacing OSD indices. GI

projections fall closer to the industry average for engineering and manufacturing, while BLS

wage rates are closer to OSD indices for both engineering and manufacturing. The comparisons

of all alternatives analyzed for engineering are shown in Figure 2 and for manufacturing in

Figure 3.

Figure 2: DOD Contractors vs. All Indices (Engineering)

Figure 3: DOD Contractors vs. All Indices (Manufacturing)

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BLS ENG AvgOSD (RDT&E)

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BLS MFG AvgBLS A/C Structure

BLS Machinists

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GI HelosGI Missiles



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For both engineering and manufacturing, OSD indices are lower than contractor DLR growth. It

was also found that OSD Indices are lower than BLS & GI indices. This analysis shows OSD

Indices alone are insufficient to capture true economic conditions experienced by defense

contractors. Also, DOD contractor DLR history does show a discernible pattern that could be

used for budget forecasting for engineering and manufacturing labor.

C. RECOMMENDATIONS

Analysis confirms that in some areas, the DOD contractor history and the contractor’s current

projections are outpacing the OSD inflation indices that are directed to be used for budget

forecasting.

Figure 4: Study Results Compounded

DLR growth factors have been quantified and are recommended to be used in lieu of OSD

indices to provide a more accurate representation of the Aerospace Industrial sector for

budgeting. Factors were calculated from DOD contractor DLRs (individual labor categories,

contractor averages, and industry averages), from BLS indices, and from GI indices. BLS factors

run closest to the 2% OSD indices, followed by Global Insight and industry averages ~3.8%, and

at the top were the highest DOD contractor engineering and manufacturing rates of growth

~5.7%. These DLR growth factors can be applied at different levels: total costs, total labor

estimates, and to direct labor estimates for different estimate purposes (negotiations vs.

budgeting vs. planning) to account for the true economic effects experienced by DOD

contractors on NAVAIR contracts.

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II. STUDY INTRODUCTION

A. BACKGROUND

When analyzing rates of growth, it is important to understand the compounding effect of

inflation and how it affects cost estimates. Seemingly small differences in the inflation rate can

make significant differences as costs are projected out over time. Figure 5 below is an example

that shows the impact of changing the rate of growth in one percent increments over a twenty

year timeframe.

Figure 5: Example: Compounding Effect of Inflation

The implication is that small differences in the rate of growth between OSD indices used and the

contractor historical performance can have a great impact on our program budgets.

1. PURPOSE

Problem Statement:

The NAVAIR Cost Department prepares budget estimates for all major weapon system programs

at NAVAIR. OSD Comptroller guidance requires the use of OSD Inflation Indices are required

to generate all budgetary estimates and convert Constant Year (CY) dollar estimate to Then Year

(TY) dollar budget forecasts.

In the process of analyzing DOD contractor labor rates, the Cost Department has seen higher

rates of growth on direct labor rates. This study has answered the following questions:

• Are OSD Indices adequately capturing the true economic conditions experienced by the

defense industry for labor?

• Is the growth of labor rates across the Aerospace Industrial sector outpacing the OSD

Escalation Indices?

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• If OSD Escalation Indices alone are not adequate, what labor rate growth factor can be

used?

• What is industry using to determine economic conditions for labor?

Study Approach:

The study approach was to conduct a historical review of DOD contractor cost data and compare

to OSD Indices. The goal of the study was three-fold:

(1) To determine if OSD Indices are sufficient to capture true economic conditions

experienced by defense contractors,

(2) To determine if there is a discernible pattern from Direct Labor Rates that could

be used for budget forecasting, and

(3) To quantify and recommend DLR growth factors that capture economic

conditions experienced by the Aerospace Industrial sector to use for budget

forecasting and to improve the quality of labor estimates for Naval Aviation

budgets.

2. SCOPE

The Study Plan included four main alternatives for analysis:

(1) OSD Inflation Indices

(2) DOD Contractor History (NAVAIR prime contractors)

Industry Standards:

(3) Bureau of Labor Statistics (BLS)

(4) Global Insight (GI)

These four methods are currently used for accounting for inflation in cost models. The baseline

for this study was the OSD inflation indices used for Navy acquisition appropriations

(Development and Procurement). Data on NAVAIR defense contractors DLRs was collected for

engineering and manufacturing labor categories. To safeguard proprietary DLR data, contractor

names were masked using letter designations.

GI forecasts and the data from the BLS are used across the Aerospace Industrial sector by

contractors to forecast labor and inflation. These industry standards were investigated to

determine if they can provide support for a budgeting forecast position for NAVAIR budgets in

lieu of OSD indices in addition to the DOD contractor history.

The data was analyzed on multiple levels and compared to each alternative to determine patterns.

This analysis was used to determine the sufficiency of using the OSD Indices for budgeting. If

OSD indices are found insufficient, DLR growth factors could be calculated to provide another

methodology for applying inflation in budget estimates.



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B. ASSUMPTIONS AND CONSTRAINTS

The following section defines the ground rules, assumptions, limitations and constraints for the

study.

1. GROUND RULES

Overall

1. All datasets were consistent (i.e., same timeframe).

2. All data was indexed to 2005 to enable comparisons between rates & indices.

3. Government labor rates were not included.

OSD Indices

4. 2012 OSD Indices for the Navy are the baseline for comparison.

5. Current & historical Navy indices were obtained from the NAVAIR-4.2 in-house

inflation tool.

6. All other services’ OSD Indices were derived from the Naval Center for Cost

Analysis (NCCA) Joint Inflation Calculator model.

DOD Contractor History

7. The changes in contractor historical DLRs year-over-year capture a rate of growth.

8. DLR data was obtained from the Defense Contract Management Agency (DCMA)

and contractor rate proposals.

2. ASSUMPTIONS

Overall:

1. Historical trends can be used to project future trends.

2. A seven year historical dataset (2005-2011) is sufficient to develop conclusions.

3. Contractor engineering and manufacturing labor categories are cost drivers for growth.

OSD Indices 4. Navy inflation indices are representative of all other services indices.

DOD Contractor History 5. Historical rates reported by contractors are complete and accurate.

6. Regression techniques are used to fill in data gaps in contractor historical data.

7. Contractor historical DLRs capture contractor rates of growth.

BLS 8. Historical wage data reported are complete and accurate.

9. BLS Occupations (Engineering, Engineering Technicians, & Production) are

comparable to DOD contractor engineering & manufacturing labor categories.

GI 10. NAVAIR-tailored GI indices are representative of contractor growth.

11. Commodity indices are comparable to DOD contractor engineering & manufacturing

labor categories.



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3. STUDY LIMITATIONS

Overall: 1. Historical data was collected from years 2005-2011.

2. Projections for contractor history and indices were collected for years 2012-2020.

OSD Indices

3. Navy inflation indices for Development and Procurement appropriations were used.

DOD Contractor History 4. DLR data for six contractors covering fifteen sites were collected and reviewed.

5. Only engineering and manufacturing labor categories were reviewed.

BLS 6. Engineering, Engineering Technicians, and Production occupations were used.

7. Average wage rates from The National Occupational and Wage Estimate Survey were

used for comparison.

GI 8. NAVAIR-tailored GI indices used.

9. Three commodities were reviewed including Aircraft, Helicopter, and Missile indices.

4. STUDY CONSTRAINTS

Overall:

1. All data was indexed to one year (2005) to mask proprietary cost data.

2. Direct labor rates (without fringe benefits) were analyzed.

3. Investigate two Industry Standards for inflation: BLS and GI.

OSD Indices

4. Indices for Navy, Air Force, Army, and Defense-Wide were reviewed for trends.

5. Navy historical inflation forecasts were analyzed.

DOD Contractor History

6. The focus was on NAVAIR prime contractors.

7. Data analysis and recommendations focused on contractors’ historical trends.

8. The analysis of contractor DLR projections is recommended for a follow-on study.

Bureau of Labor Statistics (BLS)

9. The focus was only on engineering and manufacturing wage rates for comparison.

Global Insight (GI)

10. Only NAVAIR-tailored GI indices were analyzed.

11. Analysis was limited to fixed wing, helicopter, and missile commodity indices.

12. Commodities are broken out into airframe, engines, and electronics.



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III. ANALYTICAL APPROACH

A. METHODOLOGY

The overall methodology for analysis was to look at the current alternatives for forecasting

escalation and to compare and contrast these alternatives to the OSD indices (current budget

direction) to determine the best forecasting methodology. The next step was to determine the

methodology for data collection, data normalization, and data analysis.

1. DATA COLLECTION

Data collection included defining data to be collected and identifying potential data sources.

Data definition determined the appropriate time horizon of cost data to be collected, specific

labor categories sought, and defining any supporting data needed. Data collection was an

iterative process that took place throughout the study as adjustments and refinements were made

to the study plan.

For each of the alternatives, the study team collected historical data and current projections for

the same timeframe. This timeframe was determined to be from 2005 through 2020 based on the

availability of DOD Contractor data. The data collected included: indices for OSD and Global

Insight, and direct labor wage rates for DOD Contractors and BLS survey data.

2. DATA NORMALIZATION

To normalize the data so contractor proprietary rate information is masked, a data masking

algorithm was first defined for all of the collected data. Next, all contractor data was indexed to

a 2005 base year.

Indexing the collected data served two purposes: (1) it masked the contractor proprietary wage

rates, and (2) it allowed a direct comparison between the wage rates against the indices. The

year 2005 was chosen as the base year because it is a common year for which DOD contractor

history was available.

3. DATA ANALYSIS

Data analysis included determining what estimating tools and techniques would be used defining

the level of detail to be analyzed, and establishing rules for addressing any problems with the

data (including data gaps or outliers) . Cost model structure and formatting of the results were

also defined.

a. ESTIMATING TOOLS

Microsoft Excel was chosen to host the cost model and perform analysis on the data. Excel was

chosen for several reasons:



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(1) Excel was accessible to all team members (and would be compatible for future analysis

as it is readily-available software).

(2) It supports data sorting, graphical analysis, and statistical analysis.

Microsoft Project was selected to develop the Plan of Actions and Milestones (POA&M) to plan

the study in detail. Microsoft PowerPoint was selected to develop briefing slides for the study

presentations.

b. ESTIMATING TECHNIQUES

After the data was normalized, it was plotted to determine if any patterns exist. Simple Linear

Regression (SLR) analysis was applied to the historical data to develop trend line. This trend

line was projected out to the year 2020. Forecasts from 2012 to 2020 were plotted against the

historical trend line for comparison. Where there were data gaps, the existing data collected was

regressed to develop a trend line for the historical actuals. This trend line was used to fill in data

gaps.

Estimating techniques for data collection and normalization used included:

1. Ensuring data source consistency (using historical actuals only),

2. Ensuring the same timeframe (2005-2011),

3. Ensuring a consistent baseline for comparison (OSD indices),

4. Normalizing to a 2005 index for indices and rates,

5. Applying Simple Linear Regression (SLR) analysis.

c. LEVEL OF ANALYSIS

Once the data was graphed and trend lines developed, the trend lines were compared to

determine patterns in the data. The data was analyzed on multiple levels to identify patterns.

Levels of data analysis included the following:

For OSD indices, the data was grouped by service (Army, Navy, Air Force, and Defense-

Wide) and by appropriation (Aircraft Procurement, RDT&E, Other Procurement, etc.).

For DOD contractor data, the data was analyzed for each contractor by individual labor

category, and then by grouping engineering and manufacturing labor categories by

contractor site to develop a composite trend lines. Composite trend lines for engineering

and manufacturing were plotted for each contractor to determine the range of industry

data. Then an average of all the contractor trend lines was calculated to determine an

industry average for engineering and manufacturing labor. Data for each labor type was

then graphed and averaged for different commodity types (i.e., helicopter vs. fixed wing

manufacturers).

For GI indices, the indices were grouped into three commodity types: Fixed Wing,

Helicopter, and Missiles with each index broken out into three separate labor categories.



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For BLS data, only direct labor wages were analyzed. No fringe benefits were included.

The datasets were grouped into three comparative occupational codes: engineering,

engineering technician, and manufacturing. Labor categories that were similar to

aerospace design and manufacturing were extracted from the overall occupation. These

extracted categories were then averaged for an occupational average. For each

occupation code, three specific representative labor categories were identified and

analyzed.

d. DATA RULES

See Measures of Effectiveness section for data rules used in this study.

e. COST MODEL

The following is a pictorial summary of the overall model structure:

Figure 6: Cost Model Structure

The following is the structure of the Overall Cost Model by individual spreadsheet:

Overall Cost Model:

• OSD 2012 Indices: indices & graphs for each service and appropriation for 2012

• OSD 2001-2011 Navy Indices: indices & graphs for historical Navy indices

• KTR A: DLR data, indexed, averaged, and trend lines graphed for Contractor A

• KTR B1: DLR data, indexed, averaged, and trend lines graphed for Contractor B1


COST MODEL

Baseline:

Navy Indices

Historical

look at Navy

Forecasts

OSDIndices

(All

Services)

KTR F2

KTR F1

KTR E

KTR D

KTR C

KTR B3

KTR B2

KTR B1

KTR A

Contractor

DLR

Production

Eng Tech

Engineering

BLS Occupations

Missiles

Helicopter

Aircraft

GI Commodities

Model

Summary

- Occupation averages

- Avg by labor category- Commodity averages

- Avg by labor grouping

- Mfg & Eng labor category

- Contractor Mfg & Eng averages

- Industry avgs for Mfg & Eng

- Dev & prod appropriations

- Indices for Navy appropriations

- History of Navy indices



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• KTR C: DLR data, indexed, averaged, and trend lines graphed for Contractor C

• KTR D: DLR data, indexed, averaged, and trend lines graphed for Contractor D

• KTR E: DLR data, indexed, averaged, and trend lines graphed for Contractor E

• KTR F1: DLR data, indexed, averaged, and trend lines graphed for Contractor F1

• KTR F2: DLR data, indexed, averaged, and trend lines graphed for Contractor F2

• KTR Summary: Summary of indexed and averaged data graphed for all contractors

• GI AC: Data and graphs for Global Insight Aircraft Labor

• GI Helo: Data and graphs for Global Insight Helicopter Labor

• GI Missiles: Data and graphs for Global Insight Missiles Labor

• BLS All Years: Data and graphs for BLS Eng., Eng. Tech., & Mfg. labor wage rates

• BLS Summary: Summary of BLS Data and graphs

• BLS Charts: Data and graphs for BLS individual labor categories

• Graph Data: Summary of all data used for graphing (slopes, y-intercepts, indices)

• ENG: Graphs for all engineering data

• MFG: Graphs for all manufacturing data

• Labor Cats: Summary of labor category definitions

f. FORMAT OF RESULTS

The initial results were in the form of the index values based on the 2005 base year. Results

were also developed by graphing the indexed data and developing trend lines from them. Slopes

defining the rates of growth were defined for each category.

B. MEASURES OF EFFECTIVENESS

The main forecasting technique used was simple linear regression to determine the best fit for the

datasets. The measure of effectiveness used for the regression trend lines was the R2 parameter.

R2 measures the amount of variation that exists between the Cost Estimating Relationship (CER)

and the historical data. R2 was used to determine that strength of the trend line for the historical

data. Where R2 values were below .70, we analyzed the data to determine if there were any

outliers in the datasets. If an outlier was present, it was deleted to improve the R2 value. Where

there were poor R2 values (< .70) or negative slopes in the trend lines, the slope was adjusted to

1.0 and then averaged into the other labor categories.

C. ALTERNATIVES

1. OSD INDICES

Background:

The OSD Indices are the budgeting standard used for inflation by DOD. The study plan for this

alternative was to research how these indices are calculated and to define trends in these

forecasts over time.



13

The Department of Defense (DOD) inflation policies are set by the Council of Economic

Advisors (CEA), the U.S. Treasury, and the Office of Management and Budget (OMB). They

also issue inflation rates for all federal agencies and departments which are used in budgeting

and planning. The Office of the Undersecretary of Defense (Comptroller) (OUSD(C)) uses these

inflation rates in order to develop inflation guidance, including determining the amount of price

escalation for procurement items, in the branches of the military.

The OUSD (C) determines the price index or rate of growth for each appropriation by taking the

OMB price indices for five categories of spending: Military Pay, Civilian Pay, Fuels, Medical

Expenditures, and Other Purchases. Each category is then weighted by the percentage of the

appropriations spending and then summed.

The OSD Procurement Index is a special case of price indices. The price index for procurement

is derived solely from the OMB category, Other Purchases. The OMB category Other Purchases

is based on projections of the Bureau of Economic Analysis (BEA) rate of growth for the Gross

Domestic Product (GDP). Using this methodology, the OUSD (C) inflation guidance assumes

that the prices for DOD procurement items have the same rate of growth as the economy as a

whole. OSD indices are driven by the Executive Branch as a challenge to cost growth. They are

a political position on the economy.

Data Collection:

Data for the OSD indices was collected from multiple sources. For the Navy indices, the

information was obtained from an internal NAVAIR Cost Department database. For Army and

Defense-Wide indices, the Joint Inflation Calculator (JIC) database hosted by the Naval Center

for Cost Analysis (NCCA) website was used to collect indices. Air Force indices were obtained

from an inflation calculator database obtained from the Air Force Cost Analysis Agency

(AFCAA).

As the focus of the study was on acquisition budget forecasting, the data collection for inflation

indices was limited to Research, Development, Test & Evaluation (RDT&E) and Procurement

appropriation indices. Table 1 shows the list of indices collected for each service and

appropriation for years 2005 through 2020.

Table 1: OSD Indices by Appropriation

Navy Indices Excursion

The historical forecasted Navy indices published from 2000 through the current 2012 indices

were collected and analyzed to identify any trends in the inflation forecasting over the twelve

year timeframe.

Navy RDT&E(Purchases), RDT&E(Composite), APN, OPN, WPN, PMC

Army RDT&E,A, APA, OPA, WTCV, MIPA, AMMO

Air Force RDT&E (3600), AP(Other) (3010), AP(Special) (3010), MP(Other)

(3020), MP(Special) (3020), OP(Other) (3080), OP(Special) (3080)

Defense-Wide RDT&E,DW, DW (Procurement), DW (Other Procurement)



14

Methodology:

After the Navy, Air Force, Army and Defense-Wide indices and historical forecasts for Navy

indices were collected, a regression was applied to develop trends for each service’s indices.

Trend lines were projected out to 2020 and graphed for comparison.

2. DOD CONTRACTOR HISTORY

Background:

Growth experienced in the DLR directly influences the wrap rate growth that is used to convert

labor hour forecasts into labor dollars.

The study plan included collecting direct labor rates from several DOD prime contractors for

multiple years. The team collected data from multiple locations for each of the contractors. Then

the team analyzed data from the contractors’ current FPRPs that contains both historical data and

forecast projections. Engineering and manufacturing labor categories were the focus of this

alternative. The study plan also included collecting and defining inflation methodologies used

by DOD contractors to determine which industry standards are being used across the Aerospace

Industry.

Data Collection:

Data was collected and analyzed from six DOD prime contractors covering fifteen sites that span

the US geographically. The following is a map of the DOD contractor locations by state used in

this study:

Figure 7: DOD Contractor Locations

WA

UT

GA

AZ

CA

VA

NY

MO

TX

MD

FL

AL

OK

IL

PA

SD

ND MT

ID

ME

OH

KY

WI

NE

WY

IN

IA

MI

TN NC

AR

LA

MN

NM

CO

NV

OR

MS

MA

RI

NJ

WV

KS

SC

VT

NH

DC

DE

AK

HI

CT



15

Datasets from DOD contractors were collected from multiple sources. Direct labor rates were

collected for multiple years with the goal of obtaining direct labor rates from multiple locations.

Historical data was obtained from current FPRPs that also included projections out for two to

five years.

Data was collected for multiple engineering and manufacturing categories at each site. Labor

category definitions were also collected to determine differences between contractors for the

same type of effort. Each contractor has unique labor definitions. The definitions were used to

determine groupings into the engineering and manufacturing categories. Rates data requests

were sent to several DCMA points of contact. Contractor data collected included: FPRPs,

Estimating Manuals, Disclosure Statements and DCMA published reports and analysis. Table 2

summarizes the data collection effort for the six prime contractors.

* Historical actuals for 2007-2011 used to fill in data gaps for 2005-2006 (Contractor C)

Table 2: DOD Prime Contractor Data Collection

Methodology:

The methodology was to analyze multiple engineering and manufacturing categories after using

labor category definitions to determine grouping into engineering or manufacturing.

The data was analyzed on multiple levels:

• Individual contractor labor categories.

• Composite contractor trends (using average of multiple categories).

• Averaged trend lines across industry.

• By Sector: Helicopter vs. fixed wing contractors/sites.

Contractor Location Actuals ForecastData

Requested

Data in

spreadsheet

Actuals

graphed

Forecast

Graphed

Excludes

Fringe

Have labor

category

defini tions

Contractor A Sites 1, 2 & 3 2001-2011 2012-2018 Yes Yes Yes Yes Yes Yes

Contractor B1 Site 1 2005-2011 2012-2020 Yes Yes Yes Yes Yes Yes



Contractor C Site 1 2007-2011 2012-2017 Yes Yes Yes Yes Yes Yes



Contractor D Site 1 2004-2011 2012-2015 Yes Yes Yes Yes Yes Yes

Contractor D Site 2 2004-2011 2012-2015 Yes Yes Yes Yes Yes Yes

Contractor E Site 1 2004-2011 2012-2016 Yes Yes Yes Yes Yes Yes

Contractor F1 Site 1 2004-2011 2012-2015 Yes Yes Yes Yes Yes Yes





16

3. BUREAU OF LABOR STATISTICS (BLS)

Background:

BLS is one of the two industry standard investigated in this study. BLS publishes information on

the wages, earnings, and worker’s benefits. The information is categorized by one of the

following ways:

Geographic area (national, regional, state, metropolitan area, or county data),

Occupation (such as teacher or carpenter),

Industry (such as manufacturing or retail trade),

Additional categories such as age, sex, or union membership may also be used,

Wage data available by occupation for nation, region, by state, and many cities.

Wage data by area and occupation are collected in the following surveys:

The National Occupational and Wage Estimates Survey.

The Employment Cost Trends Index.

Occupational Employment Statistics Survey.

The Current Population Survey.

The study plan included investigating BLS information to determine if comparisons can be made

to wage rates from DOD contractor information. Research showed that the data from the

National Occupational & Wage Estimate Survey was the most useful comparison. It was

determined that fringe benefits were not included in the average rates used.

Data Collection:

Average wage data from the National Occupational & Wage Estimate Survey was used in this

study for comparison. Estimates in the survey are calculated with data collected from employers

in all industry sectors. The consolidated mean wage rates from the following three occupations

were used:

• Engineering (17-0000)

• Engineering Technician (17-0000)

• Production (51-0000)



17

Figure 8: Example of BLS Occupational Groups and Wages Rates

Figure 9: BLS Occupational Groups and Labor Categories Used in Study

Methodology:

Labor categories for each occupation were reviewed to determine a set of categories that were

similar to the defense Aerospace Industry. These selected categories were indexed and the

averages were calculated for each occupation. In addition, individual “representative” wage

Major Occupational Groups Mean and Median

Wage RatesEngineering Wage Rates

Major Occupational Groups

Engineering Labor

Engineering Technician Labor

Production / Manufacturing LaborNational Occupational and Wage Estimates



18

rates were chosen from each occupation to determine how specific wage rates compared to the

occupational averages. The results were graphed for years 2005 to 2011.

Shown in Figure 10 is the average for the two engineering occupations: BLS Engineering and

BLS Engineering Technicians. Figure 11 shows the trend lines and rates of growth for the

representative labor categories for the two engineering occupations.

Figure 10: BLS Occupational Averages (Engineering)

Figure 11: BLS Sample Labor Categories (Engineering)

Shown in Figure 12 is the average for the production (equivalent to manufacturing) occupation,

followed by the trend lines and rates of growth for two of the representative labor categories

Figure 13 for the production occupation.

y = 0.0312x + 0.9799R² = 0.9758

1.00

1.05

1.10

1.15

1.20

1.25

2005 2006 2007 2008 2009 2010 2011

Gro

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m 2

00

5

Year

BLS - AVG ENGINEERS (2005-2011)

y = 0.023x + 0.9773R² = 0.9956

1.00

1.02

1.04

1.06

1.08

1.10

1.12

1.14

1.16

2005 2006 2007 2008 2009 2010 2011

Gro

wth

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m 2

00

5Year

BLS - AVG ENG TECHNICIANS (2005-2011)

Occupational Averages

y = 0.0327x + 0.9735R² = 0.9779

1.00

1.05

1.10

1.15

1.20

1.25

2005 2006 2007 2008 2009 2010 2011

Gro

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m 2

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5

Year

17-2011 Aerospace Engineers Index (2005-2011)

Sample Labor Categories

y = 0.0255x + 0.9688R² = 0.9713

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

2005 2006 2007 2008 2009 2010 2011

Gro

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m 2

005

Year

17-3024 Electro-Mechanical Technicians Index (2005-2011)



19

Figure 12: BLS Occupational Average (Manufacturing)

Figure 13: BLS Sample Labor Categories (Manufacturing)

4. GLOBAL INSIGHT (GI) Background:

Global Insight is the other industry standard investigated in this study. GI indices are one of the

industry standards for inflation used by DOD contractors for their inflation methodologies. IHS

Global Insight, Inc. is a consultant that “provides 10-year forecasts for up to 1,300 commodity

prices and wages, as well as access to thousands of historic price and wage series from around

the globe.” (ihs.com, 2012) Formerly known as Data Resources, Inc. (DRI) and Wharton

Economics Forecasting Associates (WEFA), GI is one of the world’s largest repositories of

economic, financial, and industry data.

GI provides Price, Wage, and Employment Cost indices by commodity within sectors of the

economy. For Price, GI reports the Producer Price Index (PPI), the Wholesale Price Index

(WPI), and market prices. “The PPI measures the average change over time in the selling prices

received by domestic producers for their output.” (BLS.gov) The Wholesale Price Index is an

index reported monthly “that measures and tracks the changes in price of goods in the stages

before the retail level…and show the average price changes of goods sold in bulk…they are a

Occupational Average

y = 0.0237x + 0.9694R² = 0.9909

0.98

1.00

1.02

1.04

1.06

1.08

1.10

1.12

1.14

2005 2006 2007 2008 2009 2010 2011

Gro

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5

Year

BLS - AVG PRODUCTION (2005-2011)

Sample Labor Categories

y = 0.0262x + 0.9629R² = 0.9789

0.95

1.00

1.05

1.10

1.15

2005 2006 2007 2008 2009 2010 2011

Gro

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m 2

00

5

Year

51-4041 MachinistsIndex (2005-2011)

y = 0.0172x + 0.9802R² = 0.7411

0.95

1.00

1.05

1.10

1.15

2005 2006 2007 2008 2009 2010 2011

Gro

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m 2

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5

Year

51-2011 Aircraft Structure, Surfaces, Rigging, and Systems Assemblers

Index (2005-2011)

http://www.investopedia.com/terms/w/wpi.asp



20

group of the indicators that follow growth in the economy. Although some countries still use the

WPIs as a measure of inflation, many countries, including the United States, use the producer

price index (PPI) instead. (Investopedia.com) For wages, GI reports the Average Hourly

Earnings (AHE). They also report and track the Employment Cost Index (ECI) which is “a

quarterly economic series detailing the changes in the costs of labor for businesses in the United

States economy. The ECI is prepared by the Bureau of Labor Statistics (BLS.gov), in the U.S.

Department of Labor.” (Wikipedia.com)

GI also publishes two quarterly publications: (1) Cost Planner and Supply Manager that included

forecast tables and written analysis, and (2) Cost Analyzer, which is a MS Excel Based software

tool with its own database that includes historical & forecasted indices for more than 1,200

commodity prices & wages. The software tool allows users to create customized composite

indices. GI Indices are weighted versions of the BLS codes.

Data Collection:

GI data was collected for several indices from different labor categories for multiple years. The

GI indices used across the NAVAIR Cost Department are “Flyaway” indices, customized into

composite indices that GI created specifically for NAVAIR. They were tailored by GI for

NAVAIR using weightings based on NAVAIR Contractor Cost Data Reports (CCDRs) and

budget data provided by NAVAIR.

The data is grouped into three commodity indices: Aircraft, Helicopter, and Missile data. Each

commodity index is further broken out into multiple labor areas including: Airframe, Engines,

and Electronics labor for both Aircraft and Helicopter commodities, and Propulsion, Target

Detection Device (TDD), and Warhead labor for the Missile commodity index.

Methodology:

GI (NAVAIR) indices were indexed to 2005. SLR was applied to the data to develop trend lines.

The data was analyzed on multiple levels: Composite trends were created from averages of each

commodity. Using these composite trends, contractors for the Fixed Wing sector were compared

to Aircraft indices and Rotary Wing sector contractors were compared to the Helicopter indices.

Figure 14 shows an example of the GI indices trends.

http://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

http://en.wikipedia.org/wiki/U.S._Department_of_Labor

http://en.wikipedia.org/wiki/U.S._Department_of_Labor



21

Figure 14: GI (NAVAIR) Indices for Aircraft Labor

All GI indices used in this study follow similar patterns across the three commodities.

y = 0.011x + 0.9611R² = 0.4295

y = 0.0603x + 0.9278R² = 0.9787

y = 0.0462x + 0.9531R² = 0.9853

y = 0.0392x + 0.9473R² = 0.9893

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

2005 2006 2007 2008 2009 2010 2011

Gro

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from

200

5

Year

GI: AIRCRAFT LABOR (Index 2005-2011)

Airframe

Engine

Electronics

Average Aircraft

Linear (Airframe)

Linear (Engine)

Linear (Electronics)

Linear (Average Aircraft)



22

IV. ANALYSIS

A. SUMMARY

OSD INDICES

Results analyzing the current Navy weighted indices (dated 2/10/12) show a consistent rate of

change by year except for the Weapons Procurement, Navy (WPN) index (See Figure 15 below).

Figure 15: Current 2012 Navy Inflation Indices % Change by Year

OSD Indices for All Services

OSD Inflation Indices for each of the other services were plotted to see if there were any service-

unique patterns. All services use the same starting point for year-to-year rate of change (the raw

index). Unique outlays are applied by each service based on their service-unique experience to

define the Then Year (TY) dollar values (weighted index). Figure 16 shows the indices for each

service indexed to a 2005 base year.

Trend lines for all services inflation indices have R2 values in the 99% range. All services trend

line slopes (or rates of growth) were from 2.0% - 2.2%.

OSD Indices Conclusions:

All indices show similar patterns for each service. If the OSD Indices were shown to be

inadequate for the Navy, then all other services have a similar problem

-1.00%

-0.50%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

% Change

Year

NAVY Weighted Index % Change By Year

APN

OPN

WPN

RDTENPur



23

Figure 16: Current DOD Indices (Base Year 2005)

Navy Excursion: A separate excursion was conducted by gathering historical Navy inflation indices to determine

how the forecasts have been changing over time. Navy indices released from 2005 through 2012

were collected and plotted by release date as shown in Figure 17. All Navy inflation indices

(forecasts) have been lowering since 2009, with significant volatility being seen in the current

2012 indices.

Figure 17: OSD Indices: Navy Forecasted by Official Release Date

1.0

1.1

1.1

1.2

1.2

1.3

1.3

1.4

Gro

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Year

Defense Wide Indices (BY2005)

DW RDT&E

DW Proc

1.0

1.1

1.1

1.2

1.2

1.3

1.3

1.4G

row

th f

rom

20

05

Year

Navy Indices (BY2005)

APN

OPN

WPN

RDT&E Pur

RDT&E Comp

Linear (RDT&E Comp)1.0

1.1

1.1

1.2

1.2

1.3

1.3

1.4

Gro

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m 2

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5

Year

Air Force Indices (BY2005)

RDT&E AF

APAF (Special)

APAF (Other)

MPAF (Special)

MPAF (Other)

OPAF (Special)

OPAF (Other)

Linear (OPAF (Other))

1.0

1.1

1.1

1.2

1.2

1.3

1.3

1.4

Gro

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m 2

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5

Year

Army Indices (BY2005)

APA

OPA

MIPA

RDTEA

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

APN Index Forecast Over Time

2005

2006

2007

2008

2009

2010

2011

20120.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

OPN Index Forecast Over Time

2005

2006

2007

2008

2009

2010

2011

2012

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

WPN Index Forecast Over Time

2005

2006

2007

2008

2009

2010

2011

2012 0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

RDT&E (Pur) Index Forecast Over Time

2005

2006

2007

2008

2009

2010

2011

2012



24

DOD CONTRACTOR HISTORY For engineering, the NAVAIR contractors’ historical rate of growth is higher than the OSD

Indices as shown in Figure18 below. For engineering, the average rate of growth for the DOD

contractors was 3.8%, with the Rotary Wing sector averaging a 3.1% rate of growth and the

Fixed Wing sector averaging 4.4%.

Figure 18: DOD Contractors vs. OSD Indices (Engineering)

Figure 19: DOD Contractors by Industry Sector (Engineering)

For manufacturing, the NAVAIR contractors’ history rate of growth is higher than the OSD

Indices as shown in Figure 20 below. The average manufacturing rate of growth for the DOD

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

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00

5

Year

DOD CONTRACTORS vs. OSD INDICESENGINEERING (Actuals 2005-2011)

Industry ENG Avg

Ktr B1(High)

Ktr D(Low)

OSD (RDT&E)

Manufacturing by Industry Sector:

Rotary Wing (RW) vs. Fixed Wing (FW)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

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5

Year

DOD CONTRACTORS (By Sector)ENGINEERING (Actuals 2005-2011)

Industry ENG Avg

Ktr B1(High)

Ktr D(Low)

RW ENG Avg

FW ENG Avg



25

contractors was 5.2% with the Rotary Wing sector averaging 3.7% and the Fixed Wing sector

averaging 3.4% as shown in Figure 21.

Figure 20: DOD Contractors vs. OSD Indices (Manufacturing)

Figure 21: DOD Contractors by Industry Sector (Manufacturing)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

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5

Year

DOD CONTRACTORS vs. OSD INDICESMANUFACTURING (Actuals 2005-2011)

Industry MFG Avg

OSD (APN)

Ktr CKtr F

Ktr AKtr B3Ktr B2Ktr DKtr E

Ktr B1

Manufacturing by Industry Sector:

Rotary Wing (RW) vs. Fixed Wing (FW)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

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5

Year

DOD CONTRACTORS (By Sector)MANUFACTURING (Actuals 2005-2011)

Industry MFG Avg

OSD (APN)

Ktr C(High)

Ktr B1(Low)

FW MFG Avg

RW MFG Avg



26

BUREAU OF LABOR STATISTICS (BLS) For engineering, the BLS Engineering average (3.2%) and the BLS Aerospace Engineer labor

category (3.3%) indices rates of growth are higher than the OSD Indices as shown in Figure 22

below. The BLS Engineering Technician Average (2.3%) and the BLS Mechanical/Electrical

Technician labor category (2.6%) rates of growth are very close to the OSD indices.

Figure 22: BLS vs. OSD Indices (Engineering)

For manufacturing, BLS Manufacturing average indices (2.4%) and the BLS Machinist labor

category (2.6%) indices rates of growth are comparable to OSD Indices as shown in Figure 23

below. The BLS Aircraft Structures labor category (1.8%) rate of growth is lower than the OSD

indices.

Figure 23: BLS vs. OSD Indices (Manufacturing)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

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5

Year

BLS vs. OSD INDICESENGINEERING (Actuals 2005-2011)

OSD (RDT&E)BLS Eng Tech Avg

BLS Mech/Elec Tech

BLS Aero Eng

BLS ENG Avg

1.0

1.1

1.2

1.3

1.4

1.5

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

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5

Year

BLS vs. OSD INDICESMANUFACTURING (Actuals 2005-2011)

OSD (APN)

BLS MFG Avg

BLS A/C Structure

BLS Machinists



27

GLOBAL INSIGHT (GI) For engineering, the rates of growth for GI are higher than the OSD Indices as shown in Figure

24. The average engineering rate of growth for the GI is 3.6% for GI Aircraft, 3.6% for GI

Helicopters, and 3.5% for GI Missiles.

Figure 24: GI (NAVAIR) vs. OSD Indices (Engineering)

For manufacturing, the same GI indices rates of growth are used for comparison to the OSD

Indices as shown in Figure 25. The average manufacturing rate of growth for the GI is 3.6% for

GI Aircraft, 3.6% for GI Helicopters, and 3.5% for GI Missiles. The only difference is the OSD

appropriation used for comparison: RDT&E for engineering and APN for manufacturing.

Figure 25: GI (NAVAIR) vs. OSD Indices (Manufacturing)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

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5

Year

GI vs. OSD INDICESENGINEERING (Indices 2005-2020)

OSD (RDT&E)

GI Missiles

GI Helos

GI Aircraft

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

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5

Year

GI vs. OSD INDICESMANUFACTURING (Indices 2005-2011)

OSD (APN)

GI Aircraft

GI HelosGI Missiles



28

B. SENSITIVITY ANALYSIS

Sensitivity analysis was derived from the range in the rates of growth for the different datasets.

These different rates of growth led to several recommendations to address different levels of

estimating.

1. ENGINEERING

Figure 26 below shows all four alternatives for engineering on the same graph. Overall, the

entire range of the DOD contractor history for engineering is higher than OSD indices. The BLS

indices are similar to the OSD indices, but are below most of the DOD contractor range. The GI

indices straddle the contractor industry average.

Table 3: Engineering Rates of Growth

Figure 26: DOD Contractor History vs. All Indices (Engineering)

DOD Contractor 2.2 - 5.6%

Industry Avg 3.8%

Rotary Wing 3.1%

Fixed Wing 4.4%

BLS Engineering 3.2%

BLS Engineering Technician 2.3%

GI Aircraft 3.6%

GI Helicopter 3.6%

GI Missiles 3.5%

Engineering

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

wth

fro

m 2

00

5

Year

DOD CONTRACTORS vs. ALL INDICESENGINEERING (Actuals 2005-2011)

Industry ENG Avg

Ktr B1(High)

Ktr D(Low)

BLS Eng Tech

BLS Mech/ElecTechBLS Aero Eng

BLS ENG AvgOSD (RDT&E)

GI Missiles

GI Helos

GI Aircraft



29

2. MANUFACTURING

Figure 27 shows all four alternatives for manufacturing on the same graph. Overall, most of the

range of the DOD contractor history for manufacturing is higher than OSD indices. The BLS

indices are similar to the OSD indices, and in some cases below, but are below most of the DOD

contractor range. The GI indices straddle the contractor industry average for manufacturing as

well.

Table 4: Manufacturing Rates of Growth

Figure 27: DOD Contractor History vs. All Indices (Manufacturing)

DOD Contractor 2.1 - 5.7%

Industry Avg 3.5%

Rotary Wing 3.7%

Fixed Wing 3.4%

BLS Production 2.4%

GI Aircraft 3.6%

GI Helicopter 3.6%

GI Missiles 3.5%

Manufacturing

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Gro

wth

fro

m 2

00

5

Year

DOD CONTRACTORS vs. ALL INDICESMANUFACTURING (Actuals 2005-2011)

Industry MFG Avg

OSD (APN)

Ktr C(High)

Ktr B1(Low)

BLS MFG AvgBLS A/C Structure

BLS Machinists

GI Aircraft

GI HelosGI Missiles



30

V. SUMMARY

A. CONCLUSIONS

Analysis of the historical pattern of the Navy’s OSD indices forecasts for the past twelve years

reveals that the Navy indices have been flattening over time and are volatile in the latest forecast.

When plotting each of the services OSD indices for their appropriations, they follow a similar

pattern despite the service-unique outlays applied to determine weighted indices. This leads to

the conclusion that findings from this study for a Navy solution would be applicable to the other

services as well.

Analysis of DOD contractor direct labor rate history shows that rates of growth for individual

labor categories are outpacing the OSD indices.

For the BLS data, the rate of growth is similar to what is forecasted from the OSD indices.

Analysis of GI commodity indices for labor for Aircraft, Helicopter, and Missile commodities

shows rates of growth are outpacing the OSD indices.

For both engineering and manufacturing, OSD indices are lower than contractor DLR growth. It

was also found that OSD Indices are lower than BLS & GI indices. This analysis shows that

OSD Indices alone are insufficient to capture true economic conditions experienced by defense

contractors and DOD contractor DLR history does show a discernible pattern that could be used

for budget forecasting for engineering and manufacturing labor.

B. RECOMMENDATIONS

ANALYSIS OF RESULTS

This step included determining the how the results can be used and defining specific

recommendations from the results, including what may be the appropriate level of a cost estimate

to apply growth factors. Slopes or rates of growth were defined for each category to be used as

the DLR growth factors for estimating.

Analysis confirms that in almost all engineering and manufacturing labor categories, the DOD

contractor history is outpacing the OSD inflation indices that are directed to be used for budget

forecasting. Industry average rates of growth for both engineering and manufacturing maintain

this same trend.



31

Figure 28: Study Results Compounded

DLR growth factors have been quantified and are recommended to be used in lieu of OSD

indices to provide a more accurate representation of the Aerospace Industrial sector for

budgeting. Factors were calculated from DOD contractor DLRs (individual labor categories,

contractor averages, and industry averages), from BLS indices, and from GI indices. BLS

factors run closest to the 2% OSD indices, followed by Global Insight and industry averages

~3.8%, and at the top were the highest DOD contractor engineering and manufacturing rates of

growth ~5.7%. These DLR growth factors can be applied at different levels: total costs, total

labor estimates, and to direct labor estimates. These factors can be used for different estimate

purposes (negotiations vs. budgeting vs. planning) to account for the true economic effects

experienced by DOD contractors on NAVAIR contracts.

Several options were determined for applying DLR Growth Factors depending on the estimate

purpose and the level of cost data available. Listed below are the potential factors that could be

used over a spectrum of cost estimates. Depending on the goal – to obtain a lower estimate (as in

negotiation positions) or a higher estimate (budgeting or planning estimates), the cost analyst has

options on what to use to capture a more accurate rate of growth for their program.

1.0

1.5

2.0

2.5

3.0

3.5

0 2 4 6 8 10 12 14 16 18 20

Cu

mu

lati

ve R

ate

of

Gro

wth

# of Years


OSD

BLS Eng Tech

BLS Mfg

GI Aircraft

GI Helo

GI Missiles

Industry Avg Eng

Industry Avg Mfg

KTR Eng

KTR Mfg2%

FYDP

3.8%

5.7%



32

Figure 29: Potential DLR Growth Factor Options

Recommendations: The DLR Growth Factors can be applied at different levels depending on the levels of the

estimate:

(1). Total Unit Cost

Using the DLR growth factors at this level captures growth at the prime contractor (labor) and

subcontractor (material) level. It is recommended that the factors be weighted between

manufacturing and engineering.

(2). Total Labor Cost

Using the DLR growth factors at this level captures prime contractor labor growth only. It is

recommended that the factors be weighted between manufacturing and engineering.

(3). Total Manufacturing Labor & Total Engineering Labor

The DLR growth factor for the industry average or contractor site specific average can be

applied at the total for manufacturing and engineering estimates.

(4). Manufacturing Labor & Engineering Labor by Labor Category

The DLR growth factor can be applied at the labor category level if the estimate is broken out to

that level and is one of the six contractors the factors were developed from.

The application of the DLR growth factor depends on the available cost data and the level of

estimating. Table 5 lists the DLR growth factors for engineering and manufacturing derived from

the study in percent form for the BLS, GI, and industry averages. An additional methodology is

to apply the highest contractor history for either manufacturing or engineering to the estimate.

Potential DLR Growth Factors: Use OSD as-is (no adjustment)

• For negotiation positions

Use BLS factors

• For negotiations, budgets, estimates

Use GI (NAVAIR) factors

• For negotiations, budgets, estimates

Use Contractor specific averages

• Applied when contractor is known, categories unknown

Use Contractor growth by labor category

• Applied when contractor, labor categories are known

Use industry composite for engineering or manufacturing

• Useful for AoAs, source selections, etc. when contractor unknown

Use highest contractor history

• Useful for AoAs, source selections, etc. when contractor unknown

Lower

estimate

Higher

estimate



33

.

Table 5: DLR Growth Factors using BLS, GI, Industry Averages, & Highest History

Table 6 lists the DLR growth factors that are specific to the contractors reviewed in this study

and the industry average.

Table 6: DLR Growth Factors using DOD Contractors and Industry Averages

Use OSD as-is (no adjustment)

Use BLS factors

Use GI (NAVAIR) factors

Use industry composite for engineering or manufacturing

Use highest contractor history

Engineering 3.15%

Engineering Technician 2.30%

Production 2.37%

Aircraft 3.61% Helicopter 3.59% Missile 3.50%

Airframe 2.55% Airframe 2.47% Propulsion 3.99%

Engines 4.55% Engines 4.53% TDD 2.39%

Electronics 3.72% Electronics 3.79% Warhead 4.17%

Engineering 3.84% Manufacturing 3.52%

Rotary Wing 3.10% Rotary Wing 3.70%

Fixed Wing 4.40% Fixed Wing 3.40%

Engineering 5.59%

Manufacturing 5.71%

Use Contractor specific growth or industry averages

ENG MFG

KTR A 3.75% 3.47%

KTR B1 5.59% 2.11%

KTR B2 4.35% 2.99%

KTR B3 3.31% 3.16%

KTR C 3.75% 5.71%

KTR D 2.86% 3.20%

KTR E 3.45% 2.91%

KTR F1 5.37% 5.29%

KTR F2 3.46% 3.17%

Industry 3.84% 3.52%

Adjustment Factors



34

Table 7 shows the DLR growth factors that were calculated by individual labor category by

contractor by site.

Table 7: DLR Growth Factors using DOD Contractor Labor Categories

C. FURTHER ANALYSIS

Future Expansion of this study can include researching the following areas:

• Additional analysis of Navy contractors including major subcontractors,

• Analysis on prime contractors for other services,

• Other Considerations:

– Geographical / regional impacts on direct labor rates,

– Contract type as a factor in rate of growth,

– Expanding analysis levels to include Unmanned Air Vehicles (UAVs),

• DOD Contractor proposed rates.

DOD CONTRACTOR PROPOSED RATES RESULTS In the analysis of the DOD contractor data collected, there was a pattern observed across several

contractors. In many engineering and manufacturing labor categories, the contractors’

projections were more than double the rate of growth from their own historical pattern. This

observation led to the question: Is a second DLR growth factor needed to account for this?

Use Contractor growth by labor categoryKTR A Factor KTR C Factor KTR F1 Factor

ENG Plant Eng 3.84% ENG Dev Flight 6.14% ENG Design Eng 6.26%

Prod Eng 4.30% A/C Eng 1.00% Flight Test 5.13%

Mod / Test 3.10% Avionics 4.96% Support Eng 4.18%

MFG Mfg/ Field Ops 3.50% Eng Test (Site 1) 7.97% R&D Eng 1.00%

Quality Assurance 3.45% Eng Tech 3.10% Tech Services 11.26%

Eng Tech Support 4.10% MFG Production 5.02%

KTR B1 Factor Eng Test (Site 2) 1.00% Mfg Support Serv 8.91%

ENG General Eng 6.05% MFG Assy (Site 1) 6.84% QA 5.62%

Test Eng 4.79% Fabrication 6.78% Tool Design 1.60%

Design Eng 6.50% Tooling 5.48%

Eng Supervision 4.72% Assy (Site 2) 9.84% KTR F2 Factor

Sys Eng 5.91% Flight Ops 10.20% ENG Eng Design 3.40%

MFG Mfg 1.66% Ops 1.73% Offsite Eng 3.65%

Mfg Support 1.88% Prod Mgt 2.19% Test & Evaluation 3.25%

QA 2.78% Prod Support 2.62% Mfg Eng 3.54%

MFG Tooling 3.64%

KTR B2 Factor KTR D Factor Mfg Support 4.02%

ENG General Eng 3.95% ENG Eng Labs 3.20% Composites 1.00%

Eng Test 4.64% Eng Design 2.93% Assy (Site 1) 1.63%

Design Eng 4.44% Flight Test 2.46% Assy (Site 2) 1.94%

MFG Production 2.99% MFG Assy 4.24% Fabrication 2.33%

QA 2.99% Program 2 Fab 2.86% QA 2.44%

Tooling 2.99% Program 1 Fab 2.86% Offsite Mfg 13.26%

KTR B3 Factor KTR E Factor

ENG General Eng 3.64% ENG Eng Tech 3.28%

Test Eng 2.83% Eng Services 3.63%

Design Eng 3.46% MFG Mfg Touch (Site 1) 2.78%

MFG Production 1.07% Site 1 Support 2.95%

Quality 4.02% Mfg Touch (Site 2) 3.10%

Tool Fab 2.37% Site 2 Support 2.83%

Tool Eng/Design 5.02%



35

Figure 30 shows an example of the data collected and normalized for one contractor for one

engineering labor category. The contractor’s historical data (or actuals) are plotted in green, with

its corresponding trend line in black. The contractor’s forecasted projection for this same labor

category is shown in red with its corresponding trend line. For this extreme case, the contractor

historical rate of growth on DLRs is 4.4%, but the contractor proposal includes a 14.8% rate of

growth. In contrast, the OSD indices for Aircraft Procurement, Navy (APN) that would be used

for budgeting is 1.8%.

Figure 30: DOD Contractor Example: Design Engineering (2005-2020)

While this is only a sample of one contractor and one labor category, the same pattern was

repeated across different contractors. A follow-on study could include investigating the gap

between the contractors’ history and contractors’ proposal rates of growth vs. OSD indices to

determine how this evidence can be applied to a budget forecasting recommendation.

y = 0.0444x + 0.9577R² = 0.998

y = 0.1477x + 0.9346R² = 0.9989

0.60

1.10

1.60

2.10

2.60

3.10

3.60

Gro

wth

fro

m 2

00

5

Year

KTR B2: Design Engineering (2005-2020)

Design Engineering (Actuals)

Design Engineering(Forecast)

Linear (Design Engineering(Actuals))

Linear (Design Engineering(Forecast) )

History: 4.44%

Projection: 14.77%



36

BIBLIOGRAPHY:

• Glossary of Compensation Terms, U.S. Department of Labor, Bureau of Labor Statistics,

Report 923, August 1998.

• Contractor A Rates Report, DCMA, 11 October 2012.

• Contractor A Disclosure Statement excerpts provided by DCMA, November 2012.

• Contractor B1 Rate Forecast Management Manual, September 2011.

• Contractor B1 Forward Pricing Rate Proposal, 23 July 2012.

• Contractor B2 Forward Pricing Rate Proposal, 27 March 2012.

• Contractor B2 DCMA Overhead Should Cost Review Report April 2012.

• Contractor B2 DCMA Rate Review Report, April 2011.



• Contractor C: Forward Pricing Rate Proposal, August 15, 2011.

• Contractor D: Forward Pricing Rate Proposal, 29 August 2102.

• Contractor E: Forward Pricing Rate Proposal, 15 February 2012.

• Contractor E: Rate Review Report, 15 June 2011.

• Contractor E: DCMA Direct Labor rate Analysis, Excel file, 1 November 2012.

• Contractor F1: 2005 Annual Direct Labor Rates Report, January 2006.

• Contractor F1: 2006 Q4 Variance Report for Direct Labor Rates, January 2007.

• Contractor F1: CY07 Direct Labor Rate Variance Report, January 2008.

• Contractor F1: 2008 Q4 Variance Report for Direct Labor Rates, January 2009.

• Contractor F1: 2009 Forward Pricing Rate Proposal, 7 July 2009.

• Contractor F1: CY10 Direct Labor Rate Variance Report, January 2011.

• Contractor F1 2011 Rate Calculations, Excel spreadsheet from DCMA, 6 May 2011.

• Contactor F2: Site 2 Direct Labor Rates Overhead Should Cost Analysis – History, Excel

spreadsheet form DCMA, 29 June 2009.

• Contractor F2: Site 2: CY08-CY-12 Direct Labor Rate History, Excel spreadsheet from

DCMA, 18 August 2011.

• DOD Inflation Handbook, Wise, Gregory A and Cochran, Charles B, February, 2006.

• IDA Inflation Study, April 2012.

• OSD Acquisition Guidebook, Volume 3, Ch. 7 - Analyzing Direct Labor Costs, 18

September 2009.

• Internal NAVAIR Cost Department briefs on Global Insight Indices, 2011, 2012.

• Introduction to Escalation Indices and Their Applications to Aircraft and Weapons,

NAVAIR Systems Team briefing, 15 July 1993.

• DCAA Contract Audit Manual, Chapter 8, Cost Accounting Standards, November 2012.

• Websites:

• http://www.acq.osd.mil/dpap/cpf/docs/contract_pricing_finance_guide/vol3_ch7.pdf Page 6 • http://en.wikipedia.org/wiki/Global_Insight • http://www.ihs.com/products/pricing-purchasing-analysis/index.aspx • www.acqnotes.com/Acquisitions/Inflation.html

http://www.acq.osd.mil/dpap/cpf/docs/contract_pricing_finance_guide/vol3_ch7.pdf%20Page%206

http://en.wikipedia.org/wiki/Global_Insight

http://www.ihs.com/products/pricing-purchasing-analysis/index.aspx



37

APPENDICES

APPENDIX A. STUDY ISSUES AND CONCERNS

The following are the list of issues and concerns that were addressed throughout the study:

1. Concern from Advisor on the PROPRIETARY NATURE OF DATA

Data masking for actual data using 2005 base year conversion.

Contractor Rosetta stone was used for contractor names.

2. Determining appropriate labor categories to use.

Collected labor category definitions from Disclosure Statements and

Contractor Estimating Manuals to classify labor categories as engineering or

manufacturing.

3. Determine the time horizon for the data to be collected.

Historical data and Forward Pricing Rate Proposal (FPRP) projections may be

different across companies. Historical data was collected from 2005-2011 and FPRP

projections were collected for 2012 and beyond (3-5 years depending on contractor).

4. Does the study focus on cost history or current projections?

The study focused on the historical data.

Analyzing contractor DLR projections were left for a follow-on study.

5. How is variance in FPRPs, Forward Pricing Rate Recommendations (FPRRs), &

Forward Pricing Rate Agreements (FPRAs) vs. actuals accounted for?

The contractor’s completed historical actuals were used (2005-2011) with FPRPs for

forecasts (2012-2018 or available years).

6. How is forecasting error by the contractor and/or DCMA taken into account?

The study did not include any forecasted data from the contractor or DCMA.

7. Is a Public Affairs Office (PAO)/legal review required before release of reports?

The final presentation and final report do not contain any specific traceable contractor

data. All data has been masked and indexed and the letter designations to not reveal

any contractor names.



38

Concerns from Estimating Plan brief:

8. Why are Fringe Benefits not included in this study?

Fringe benefits are listed as a study limitation as they are considered overhead

expenses.

9. How will potential holes in contractor data be handled?

Holes were filled-in based on historical trends projected through any data gaps. The

dataset determination of 2005-2020 timeframe was also chosen to minimize potential

data holes.



39

APPENDIX B. STUDY DATA

OSD Indices:

Weighted Index % Change By Year

APN OPN WPN RDTENPur

2005 2.82% 4.23% 2.76% 2.63%

2006 2.77% 3.31% 2.51% 3.12%

2007 2.33% 2.18% 2.18% 2.45%

2008 1.51% 1.64% 1.59% 1.83%

2009 1.42% 1.34% 1.47% 1.29%

2010 2.21% 2.06% -0.88% 1.51%

2011 1.81% 1.85% 4.57% 1.91%

2012 1.73% 1.75% 1.73% 1.77%

2013 1.72% 1.71% 1.71% 1.66%

2014 1.77% 1.75% 1.76% 1.73%

2015 1.80% 1.80% 1.80% 1.80%

2016 1.80% 1.80% 1.80% 1.80%

2017 1.80% 1.80% 1.80% 1.80%

2018 1.80% 1.80% 1.80% 1.80%

2019 1.80% 1.80% 1.80% 1.80%

2020 1.80% 1.80% 1.80% 1.80%

Average 1.93% 2.04% 1.89% 1.92%

Change

Avg thru 2015 1.99% 2.15% 1.93% 1.97%

NAVY AIR FORCE

Year APN OPN WPN RDT&E PurRDT&E

CompYear

RDT&E

AF

APAF

(Special)

APAF

(Other)

MPAF

(Special)

MPAF

(Other)

OPAF

(Special)OPAF (Other)

2005 1.0000 1.0000 1.0000 1.0000 1.0000 2005 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

2006 1.0277 1.0331 1.0251 1.0312 1.0312 2006 1.0302 1.0267 1.0266 1.0280 1.0289 1.0312 1.0297

2007 1.0517 1.0557 1.0475 1.0564 1.0564 2007 1.0573 1.0514 1.0540 1.0617 1.0548 1.0580 1.0556

2008 1.0676 1.0730 1.0642 1.0757 1.0768 2008 1.0786 1.0653 1.0710 1.0810 1.0741 1.0804 1.0773

2009 1.0827 1.0873 1.0798 1.0896 1.0898 2009 1.0929 1.0881 1.0896 1.0896 1.0896 1.0952 1.0920

2010 1.1066 1.1097 1.0702 1.1060 1.1064 2010 1.1068 1.1175 1.1114 1.1089 1.1056 1.1060 1.1070

2011 1.1266 1.1302 1.1191 1.1272 1.1275 2011 1.1284 1.1224 1.1312 1.1299 1.1298 1.1293 1.1283

2012 1.1460 1.1500 1.1385 1.1471 1.1472 2012 1.1499 1.1424 1.1514 1.1532 1.1511 1.1504 1.1507

2013 1.1657 1.1696 1.1581 1.1661 1.1661 2013 1.1691 1.1623 1.1711 1.1726 1.1707 1.1697 1.1701

2014 1.1864 1.1901 1.1784 1.1863 1.1861 2014 1.1889 1.1830 1.1921 1.1929 1.1911 1.1892 1.1898

2015 1.2077 1.2115 1.1997 1.2076 1.2073 2015 1.2103 1.2043 1.2135 1.2143 1.2125 1.2106 1.2112

2016 1.2295 1.2333 1.2212 1.2294 1.2288 2016 1.2321 1.2260 1.2354 1.2362 1.2344 1.2324 1.2330

2017 1.2516 1.2555 1.2432 1.2515 1.2509 2017 1.2543 1.2481 1.2576 1.2584 1.2566 1.2546 1.2552

2018 1.2741 1.2781 1.2656 1.2740 1.2734 2018 1.2769 1.2705 1.2802 1.2811 1.2792 1.2772 1.2778

2019 1.2971 1.3011 1.2884 1.2970 1.2962 2019 1.2998 1.2934 1.3033 1.3042 1.3022 1.3002 1.3008

2020 1.3204 1.3246 1.3116 1.3203 1.3195 2020 1.3232 1.3167 1.3267 1.3276 1.3257 1.3236 1.3242



40

DOD Contractor History:

ARMY DEFENSE WIDE

Year APA OPA MIPA RDTEA Year DW RDT&E DW Proc

2005 1.0000 1.0000 1.0000 1.0000 2005 1.0000 1.0000

2006 1.0271 1.0266 1.0219 1.0279 2006 1.0304 1.0261

2007 1.0478 1.0514 1.0416 1.0525 2007 1.0560 1.0474

2008 1.0645 1.0691 1.0580 1.0726 2008 1.0760 1.0638

2009 1.0800 1.0834 1.0715 1.0865 2009 1.0894 1.0782

2010 1.0994 1.1038 1.0907 1.1033 2010 1.1052 1.0978

2011 1.1209 1.1249 1.1115 1.1261 2011 1.1264 1.1181

2012 1.1430 1.1441 1.1305 1.1459 2012 1.1465 1.1375

2013 1.1682 1.1729 1.1613 1.1687 2013 1.1657 1.1569

2014 1.1890 1.1937 1.1820 1.1894 2014 1.1856 1.1771

2015 1.2104 1.2152 1.2033 1.2108 2015 1.2070 1.1983

2016 1.2322 1.2371 1.2250 1.2326 2016 1.2287 1.2199

2017 1.2544 1.2594 1.2470 1.2548 2017 1.2508 1.2418

2018 1.2769 1.2820 1.2695 1.2774 2018 1.2733 1.2642

2019 1.2999 1.3051 1.2923 1.3004 2019 1.2962 1.2869

2020 1.3233 1.3286 1.3156 1.3238 2020 1.3196 1.3101

CONTRACTOR A DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast Forecast Forecast Forecast

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018Engineering (Avg Actuals) 0.86 0.90 0.92 0.94 1.00 1.05 1.10 1.15 1.16 1.19 1.24 Engineering (Avg Forecast) 1.25 1.29 1.34 1.36 1.42 1.45 1.51 Manufacturing (Avg Actuals) 0.90 0.92 0.93 0.95 1.00 1.07 1.11 1.18 1.18 1.19 1.22

Manufacturing (Avg Forecast) 1.26 1.30 1.36 1.36 1.42 1.44 1.50

= Engineering Categories

= Manufacturing Categories

CONTRACTOR B1 DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Engineering (Avg Actuals) 1.00 1.04 1.08 1.15 1.21 1.27 1.32

Engineering (Avg Forecast) 1.38 1.44 1.50 1.57 1.64 1.71 1.79 1.87 1.95

Manufacturing (Avg Actuals) 1.00 1.00 1.00 1.08 1.07 1.07 1.13

Manufacturing (Avg Forecast) 1.17 1.20 1.23 1.25 1.28 1.31 1.35 1.38 1.41

CONTRACTOR B2 DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Engineering (Avg Actuals) 0.76 0.81 0.86 0.90 0.94 0.97 1.00 1.06 1.11 1.12 1.17 1.21 1.28

Engineering (Avg Forecast) 2.20 2.32 2.51 2.67 2.82 2.96 3.11 3.26 3.42

Manufacturing (Avg Actuals) 0.74 0.77 0.87 0.90 0.93 0.96 1.00 1.03 1.10 1.15 1.14 1.16 1.18

Manufacturing (Avg Forecast) 1.20 1.25 1.30 1.38 1.46 1.53 1.61 1.69 1.77

CONTRACTOR B3 DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast Forecast

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Engineering (Avg Actuals) 0.99 1.00 1.04 1.07 1.11 1.15 1.17 1.20

Engineering (Avg Forecast) 1.25 1.31 1.36 1.41 1.46

Manufacturing (Avg Actuals) 0.98 1.00 1.06 1.08 1.13 1.15 1.18 1.19

Manufacturing (Avg Forecast) 1.23 1.29 1.36 1.41 1.45

CONTRACTOR C Direct Labor RatesCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast Forecast Forecast

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017


Engineering (Avg Forecast) 1.23 1.28 1.33 1.37 1.42 1.47


Manufacturing (Avg Forecast) 1.42 1.49 1.55 1.62 1.69 1.76



41

BLS Wage Rates Indexed:

CONTRACTOR D DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015


Manufacturing (Avg Forecast) 1.21 1.28 1.35 1.42


Engineering (Avg Forecast) 1.20 1.26 1.32 1.37

CONTRACTOR E DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast Forecast

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


Engineering (Avg Forecast) 1.25 1.29 1.34 1.38 1.43


Manufacturing (Avg Forecast) 1.24 1.28 1.33 1.37 1.42

CONTRACTOR F1 DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015





CONTRACTOR F2 DIRECT LABOR RATESCOMPOSITE GRAPHS Actual Actual Actual Actual Actual Actual Actual Forecast Forecast Forecast Forecast

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015





2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

17-2011 Aerospace Engineers 0.81 0.84 0.87 0.92 0.94 1.00 1.04 1.08 1.10 1.13 1.16 1.22

17-2061 Computer Hardware Engineers 0.80 0.85 0.87 0.94 0.96 1.00 1.05 1.08 1.15 1.16 1.17 1.16

17-2071 Electrical Engineers 0.87 0.90 0.93 0.96 0.98 1.00 1.04 1.08 1.12 1.13 1.15 1.17

17-2072Electronics Engineers, Except

Computer0.83 0.87 0.90 0.93 0.97 1.00 1.04 1.07 1.11 1.14 1.16 1.18

17-2081 Environmental Engineers 0.84 0.89 0.90 0.93 0.97 1.00 1.03 1.06 1.10 1.14 1.18 1.18

17-2111

Health and Safety Engineers, Except

Mining Safety Engineers and

Inspectors

0.84 0.88 0.89 0.95 0.98 1.00 1.02 1.06 1.10 1.13 1.16 1.17

17-2112 Industrial Engineers 0.87 0.90 0.93 0.96 0.97 1.00 1.03 1.07 1.11 1.13 1.15 1.17

17-2131 Materials Engineers 0.85 0.88 0.90 0.94 0.97 1.00 1.06 1.10 1.18 1.20 1.20 1.22

17-2141 Mechanical Engineers 0.87 0.91 0.93 0.96 0.98 1.00 1.04 1.07 1.12 1.15 1.18 1.19

0.84 0.88 0.90 0.94 0.97 1.00 1.04 1.08 1.12 1.15 1.17 1.18 AVERAGE ENGINEERS

BLS ENGINEERING

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

17-3012 Electrical and Electronics Drafters 0.83 0.89 0.91 0.95 0.97 1.00 1.02 1.07 1.11 1.13 1.16 1.18

17-3013 Mechanical Drafters 0.89 0.92 0.95 0.98 0.99 1.00 1.01 1.03 1.07 1.09 1.13 1.15

17-3021

Aerospace Engineering and

Operations Technicians 0.91 0.94 0.97 0.97 0.99 1.00 1.00 1.04 1.03 1.09 1.10 1.14

17-3023

Electrical and Electronic

Engineering Technicians 0.85 0.89 0.91 0.95 0.97 1.00 1.04 1.08 1.11 1.14 1.16 1.18

17-3024 Electro-Mechanical Technicians 0.85 0.87 0.87 0.92 0.94 1.00 1.02 1.05 1.05 1.09 1.12 1.16

17-3026 Industrial Engineering Technicians 0.90 0.89 0.92 0.94 0.96 1.00 1.03 1.04 1.02 1.00 1.03 1.05

17-3027 Mechanical Engineering Technicians 0.89 0.92 0.92 0.95 0.97 1.00 1.03 1.06 1.08 1.09 1.11 1.14

0.87 0.90 0.92 0.95 0.97 1.00 1.02 1.05 1.07 1.09 1.11 1.14 AVERAGE ENG TECHNICIANS

BLS ENGINEERING TECHNICIAN



42

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

51-1011

First-Line Supervisors/Managers of

Production and Operating Workers 0.87 0.91 0.93 0.96 0.97 1.00 1.03 1.05 1.09 1.12 1.14 1.16

51-2011

Aircraft Structure, Surfaces, Rigging,

and Systems Assemblers 0.90 0.86 0.89 0.85 0.88 1.00 1.03 1.04 1.02 1.05 1.06 1.14

51-2021 Coil Winders, Tapers, and Finishers 0.83 0.90 0.94 0.97 1.00 1.00 1.02 1.06 1.09 1.11 1.14 1.18

51-2022

Electrical and Electronic Equipment

Assemblers 0.85 0.88 0.91 0.95 0.97 1.00 1.01 1.05 1.08 1.13 1.15 1.15

51-2023

Electromechanical Equipment

Assemblers 0.86 0.91 0.93 0.96 0.97 1.00 1.01 1.05 1.07 1.11 1.14 1.15

51-2031

Engine and Other Machine

Assemblers 0.81 0.82 0.86 0.98 0.99 1.00 0.98 0.92 0.97 1.03 1.08 1.09

51-2041

Structural Metal Fabricators and

Fitters 0.91 0.93 0.96 0.98 0.99 1.00 1.00 1.03 1.08 1.12 1.14 1.17

51-2091 Figrglass Laminators and Fabricators 0.93 0.96 1.00 0.98 1.00 1.00 1.03 1.05 1.09 1.11 1.13 1.12

51-2092 Team Assemblers 0.90 0.93 0.95 0.98 0.99 1.00 1.01 1.02 1.06 1.11 1.12 1.14

51-4011

Computer-Controlled Machine Tool

Operators, Metal and Plastic 0.90 0.93 0.95 0.97 0.99 1.00 1.02 1.05 1.08 1.11 1.12 1.14

51-4012

Numerical Tool and Process Control

Programmers 0.86 0.88 0.89 0.93 0.96 1.00 1.02 1.02 1.05 1.10 1.10 1.10

51-4021

Extruding and Drawing Machine

Setters, Operators, and Tenders,

Metal and Plastic 0.87 0.90 0.93 0.95 0.98 1.00 1.02 1.04 1.08 1.12 1.18 1.18

51-4022

Forging Machine Setters, Operators,

and Tenders, Metal and Plastic 0.92 0.93 0.96 0.95 0.97 1.00 0.99 1.04 1.07 1.11 1.13 1.15

51-4023

Rolling Machine Setters, Operators,

and Tenders, Metal and Plastic 0.89 0.92 0.94 0.96 0.99 1.00 1.03 1.09 1.13 1.17 1.20 1.21

51-4031

Cutting, Punching, and Press

Machine Setters, Operators, and

Tenders, Metal and Plastic 0.89 0.93 0.95 0.97 0.99 1.00 1.02 1.04 1.08 1.12 1.12 1.14

51-4032

Drilling and Boring Machine Tool



51-4033

Grinding, Lapping, Polishing, and

Buffing Machine Tool Setters,

Operators, and Tenders, Metal and

Plastic 0.87 0.92 0.95 0.98 0.99 1.00 1.01 1.03 1.05 1.08 1.09 1.11

51-4034

Lathe and Turning Machine Tool



51-4035

Milling and Planing Machine



51-4041 Machinists 0.89 0.93 0.94 0.97 0.98 1.00 1.01 1.03 1.06 1.10 1.13 1.15

51-4051

Metal-Refining Furnace Operators

and Tenders 0.87 0.91 0.95 0.98 0.99 1.00 0.99 1.04 1.10 1.15 1.19 1.19

51-4052 Pourers and Casters, Metal 0.91 0.95 0.95 0.99 1.00 1.00 1.01 1.07 1.09 1.13 1.15 1.16

51-4061 Model Makers, Metal and Plastic 0.77 0.81 0.85 0.97 0.97 1.00 0.95 0.95 0.97 0.96 0.97 1.00

51-4062 Patternmakers, Metal and Plastic 0.90 0.94 0.96 1.05 1.03 1.00 1.03 1.06 1.07 1.05 1.07 1.12

51-4071 Foundry Mold and Coremakers 0.84 0.87 0.88 0.94 0.96 1.00 0.97 0.98 0.98 1.00 1.03 1.03

51-4072

Molding, Coremaking, and Casting


Tenders, Metal and Plastic 0.89 0.92 0.95 0.97 0.97 1.00 1.02 1.06 1.10 1.12 1.13 1.14

51-4081

Multiple Machine Tool Setters,


Plastic 0.93 0.96 0.98 0.97 0.98 1.00 1.02 1.02 1.04 1.04 1.05 1.09

51-4111 Tool and Die Makers 0.93 0.94 0.97 0.98 0.98 1.00 1.02 1.03 1.07 1.08 1.08 1.07

51-4121

Welders, Cutters, Solderers, and

Brazers 0.90 0.94 0.95 0.98 0.99 1.00 1.02 1.05 1.10 1.13 1.16 1.17

51-4122

Welding, Soldering, and Brazing


Tenders 0.92 0.95 0.98 0.97 0.99 1.00 1.03 1.03 1.04 1.06 1.09 1.12

51-4191

Heat Treating Equipment Setters,


Plastic 0.90 0.93 0.96 0.97 0.98 1.00 1.02 1.03 1.06 1.08 1.10 1.13

51-4192 Lay-Out Workers, Metal and Plastic 0.92 0.95 0.95 0.96 0.98 1.00 1.02 1.07 1.08 1.11 1.16 1.16

51-4193

Plating and Coating Machine



51-4194

Tool Grinders, Filers, and

Sharpeners 0.91 0.93 0.96 0.96 0.97 1.00 0.99 1.01 1.04 1.08 1.10 1.10

0.88 0.92 0.94 0.97 0.98 1.00 1.01 1.03 1.06 1.09 1.11 1.13 AVERAGE PRODUCTION

BLS PRODUCTION



43

GI Indices:

Average

Aircraft

Average

Helicopter

Average

Missile

2005 1.000 1.000 1.000

2006 1.017 1.015 1.034

2007 1.057 1.052 1.083

2008 1.096 1.089 1.110

2009 1.149 1.143 1.163

2010 1.188 1.182 1.211

2011 1.220 1.216 1.233

2012 1.223 1.217 1.243

2013 1.260 1.254 1.276

2014 1.296 1.289 1.309

2015 1.333 1.327 1.345

2016 1.375 1.368 1.385

2017 1.420 1.413 1.427

2018 1.463 1.456 1.468

2019 1.505 1.499 1.507

2020 1.548 1.541 1.546

GI(NAVAIR) Weighted Index Change By Year

Airframe Engine Electronics Average Aircraft

2005 12.86% 2.82% 4.20% 6.63%

2006 -0.20% 1.73% 3.42% 1.65%

2007 -3.02% 9.59% 5.22% 3.93%

2008 0.37% 4.92% 5.47% 3.59%

2009 2.90% 6.90% 4.38% 4.73%

2010 2.36% 4.06% 3.55% 3.32%

2011 5.09% 2.05% 1.36% 2.83%

2012 -0.73% 0.89% 0.37% 0.18%

2013 3.06% 3.13% 2.85% 3.02%

2014 2.96% 2.79% 2.74% 2.83%

2015 2.83% 2.95% 2.92% 2.90%

2016 2.97% 3.29% 3.15% 3.13%

2017 3.08% 3.45% 3.13% 3.22%

2018 2.93% 3.23% 2.92% 3.03%

2019 3.01% 2.97% 2.79% 2.92%

2020 2.96% 2.78% 2.74% 2.83%

Average 2.71% 3.60% 3.20% 3.17%

Change

Avg thru

2015 2.59% 3.80% 3.32% 3.24%

GI(NAVAIR) AIRCRAFT Weighted Index % Change By Year



44

Airframe Engine ElectronicsAverage

Helicopter

2005 1.00 1.00 1.00 1.00

2006 0.99 1.02 1.03 1.01

2007 0.95 1.11 1.09 1.05

2008 0.95 1.17 1.15 1.09

2009 0.98 1.25 1.20 1.14

2010 1.00 1.30 1.24 1.18

2011 1.06 1.33 1.26 1.22

2012 1.05 1.34 1.26 1.22

2013 1.08 1.38 1.30 1.25

2014 1.11 1.42 1.33 1.29

2015 1.15 1.46 1.37 1.33

2016 1.18 1.51 1.42 1.37

2017 1.22 1.56 1.46 1.41

2018 1.25 1.61 1.50 1.46

2019 1.29 1.66 1.54 1.50

2020 1.33 1.71 1.59 1.54

Average 110.00% 136.49% 129.64% 125.38%

Change

Avg thru

2015 103.02% 125.33% 120.29% 116.21%

GI (NAVAIR) HELICOPTER Weighted Index Change By Year

Year Propulsion TDD Labor Warhead Average Missile

2005 1.00 1.00 1.00 1.00

2006 1.04 1.04 1.02 1.03

2007 1.12 1.10 1.03 1.08

2008 1.16 1.16 1.01 1.11

2009 1.22 1.23 1.03 1.16

2010 1.26 1.30 1.07 1.21

2011 1.29 1.32 1.09 1.23

2012 1.30 1.32 1.12 1.24

2013 1.34 1.35 1.14 1.28

2014 1.38 1.39 1.16 1.31

2015 1.41 1.43 1.19 1.35

2016 1.46 1.47 1.22 1.39

2017 1.50 1.52 1.25 1.43

2018 1.55 1.57 1.29 1.47

2019 1.59 1.61 1.32 1.51

2020 1.63 1.66 1.35 1.55

Average 132.82% 134.26% 114.28% 127.12%

Change

Avg thru

2015 122.91% 124.01% 107.82% 118.24%

GI(NAVAIR) MISSILE Weighted Index Change By Year

Documents

COST ESTIMATION AND ANALYSIS CAPSTONE PROJECT