ADA3 5 ANEANCE METRICS TO FORECAST RESOURCE DEMANDS OF 1/1APO AN TTE M)BEN G EOPC OSATEM

0HINGES ET AL. NOV 83 AFHRL-TR-83-9 F33615-77-C-0075

UNCLASSIFIED FOG 155 NL

NEESEhEEhEE

11112 112.36

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MICROCOP'Y RESOLUTION TEST CHART

NA I I NA. HUrA AL I 01 TAN LAR[), A

AFHRL-TR.83.9

AIR FORCE LMAINTENANCE METRICS TO FORECAST

H RESOURCE DEMANDS OF WEAPON SYSTEMS

UM By

ADonald K. Hne

David H. WilsonN Boeing Aerooac CompanyP.O. Box 3999

Seattle, Wasitington 98124

R Frank Malier

LOGISTICS AND HUMAN FACTORS DIVISIONE Wright-Patterson Air Force Base, Ohio 45433

Novem~ber 1983

U Finai Report

RC Approved for public relese, distribution unlimited.

ES LABORATORY

C"b

AIR FORCE SYSTEMS COMMANDBROOKS AIR FORCE BASETEXAS 78235

8401 05 002

84A

NOTICE

When Government drawings, specifications, or other data are used for any purpose other thanin connection with a definitely Government-related procurement, the United StatesGovernment incurs no responsibility or any obligation whatsoever. The fact that theGovernment may have formulated or in any way supplied the said drawings, specifications,or other data, is not to be regarded by implication, or otherwise in any manner construed,as licensing the holder, or any other person or corporation; or as conveying any rights orpermission to manufacture, use, or sell any patented invention that may in any way be relatedtheretL

The Public Affairs Office has reviewed this report, and it is releasable to the NationalTechnical Information Service, where it will be available to the general public, includingforeign nationals.

This report has been reviewed and is approved for publication.

FRANKA. MAHERContract Monitor Acce!7sion For

NTiz ' ~& I

JOSEPH A. BIRT, Lt Col, USAF U.Technical Director J ___.

Logistics and Human Factors Division

ALFRED A. BOYD, JR., Colonel, USAFCommander _

¢ "eS

Di"

~ ". ._ ..._______... . .-___. .____.__,__._,__-_"_ _"_. .__,_-_..

UnclassifiedSECURITY CLASSIFICATION OF THIS PAGE (Whem Dow EAered)

READ INSTRUCTIONSREPORT DOCUMENTATION PAGE BEFORE COMPLETING FORM

1. REPORT NUMBER 2. GOVT ACCESSION NO. 3. RECIPIENTS CATALOG NUMBER

4. TITLE (and Subtitle) 5. TYPE OF REPORT & PERIOD COVEREDFinal

MAINTENANCE METRICS TO FORECAST RESOURCEDEMANDS OF WEAPON SYSTEMS 6. PERFORMING ORG. REPORT NUMBER

7. AUTHOR s) 8. CONTRACT OR GRANT NUMBERIs)

Donald K. Hindes David H. Wilson F33615-77-C-0075Gary A. Walker Frank Maher

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT, PPOJECT, TASKBoeing Aerospace Company AREA & WORK UNIT NUMBERS

P.O. Box 3999 62205F

Seattle, Washington 98124 17100022

II.CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATEHQ Air Force Human Resources Laboratory (AFSC) November 1983Brooks Air Force Base, Texas 78235 13. NUMBER OF PAGES

7t14. MONITORING AGENCY NAME & ADDRESS (if dit ,ent from Controling Office) 15. SECURITY CLASS (of this report,

Logistics and Human Factors Division UnclassifiedAir Force Human Resources LaboratoryWright-Patterson Air Force Base, Ohio 45433 15.a. DECLASSIFICATION/DOWNGRADING

SCHEDUIE

16. DLTRIBUTION STATEMENT (of this Report)

Approved for public release; distribution unlimited.

17. DISTRIBUTION STATEMENT (of this abstraci entered in Block 20. if different from Report)

18. SUPPLEMENTARY NOTES

19. KEY WORDS (Continue on reverse side if necessary and identify by lock number)

avionics equipment maintenance resource demandsenvironmental parameters METRICSequipment parameters multiple regressionmaintenance forecasting operations parametersmaintenance requirements weapon system support

20. ABSTRACT (Continue on reverse side if neceuary and identift by block number)

""his report describes the methodology and results of a IS-month effort to develop maintenance metrics to forecastresource demands of weapon systems. Increased concern with the rising cost to support weapon systems currentlyin operation, as well as those in development, has created the need for more accurate methods of projectingmaintenance requirements. The objective of the research was to alleviate this need by identifying, determining, andintegrating those measurable weapon systems parameters that are necessary and sufficient to predict and quantifythe drivers of maintenance resource demands. ,

DD Forn 1475 EDITION OF 1 NOV 6.5 IS OBSOLETE UnclassifiedI Jan 73 SECURIT CLASSFICATI N OF T IS PAGE(When )see E______

SECURITY CLASSIFICATION OF THIS PAGE (When Date Enteredi

V Unclassified

SECURITY CLASSIFICATION OF THIS PAGE (WUmn Dow Eimd-

Item 20 (Continu.ed)

The study was accomplished as three separate but similar efforts. The data base was divided into separate databases for cargo/transport, bomber, and fighter aircraft. Separate sets of metric- were then developed for each of thethree classes of aircraft from these separate data bases.

This final report is intended to be a summary overview of the study project. Study descriptions and findingsinclude ) study aircraft/base case selection, (10 critical equipment selection, JA maintenance impact parameterselection, W) data base acquisition and integration, (f maintenance impact analysis, Jff maintenance metric modeldevelopment, and ( maintenance demand causal analysis.

The most important end products of this study are new maintenance metrics that can increase the accuracy withwhich the maintenance demand rates of the various aircraft subsystems are predicted.

Unclassified

SECURnY CLASSW1CATION OF ThIS PAGE (V em Aw Coed)

|, H .. . . . .. . . . ... .. ...,

SUMMARY

Objective

The objective was to isolate significant relations between maintenance resource demands and selected candidatemaintenace impact parameters for three classes of Air Force aircraft (bombers, transports, and fighters). The relationswere to be (a) based on useful historical field experience data, (b) determined through multiple correlation analyses andverifications and (c) structured in a form that could be used to estimate maintenance demand in new aircraft or new basingsituations for use in projecting manpower or other resource requirements.

Background/Rationale

Increased concern with the rising cost to support weapoin systems that currently are in operation, as well as thosein development, has created the need for more accurate meth, Is of projecting maintenance requirements. This researchand development (R&D) effort identifies metric relations for weapon system analyses that can predict and quantify thedrivers of maintenaci rmource demands.

Approach

The approach was to collect, review, and catalog a large data base containing a great variety of design, operations,and environmental factors that might conceivably influence maintenance demands. This data base was then analyzedto identify those factors that appeared to be strongly correlated with the expenditure of maintenance resources. Thesemaintenance impacts were structured parametrically and cataloged for future use. The detected maintenance impactswere then verified and combined into mathematical maintenance metric regression models for each item of equipmentstudied. The resulting models predict maintenance action demand based on selected design, operational, andenvironmental factors that impact the maintenance of each equipment item.

Specifics

This study was accomplished in three phases over 48 months. Each phase was accomplished as an eight-task effortcomprising (a) literature search, (b) aircraft/base and study equipment selection, (c) maintenance impact parameteridentification, (d) data base acquisition and integration, (e) maintenance impact parameter analysis and prioritization,(f) maintenance metrics model development, (g) maintenance weightings development, and () model verification andcausal linkage analysis.

Phase I explored the feasibility of developing general maintenance impact estimating statistical models for theavionic and engine subsystems of the Air Force aircraft. Phase If extended the feasibility study to the rest of the commonaircraft/base cases. Phase Ill expanded the statistical sample to 62 aircraft/base cases and developed separatemathematical model sets for bomber, fighter, and cargo/transport aircraft.

An example of a statistical relation is that wheel and tire maintenance demands were found to be related onlyindirectly with sortie rate, but directly with such maintenance impact factors as average landing weights, landing speeds,and number of landings. Such relations were used with actual data in a Logistics Composite Model (LCOM) simulationof C-135 operations at a specific base as a verification. These and other results were further verified by experts in thefield.

Conelslons/Recommendations

The useful products consisted of (a) an extensive data base on the common subsystems of Air Force aircraft, (b)an extensive catalog of parametric maintenance impact estimating relationships, (c) maintenance metric mathematicalmodels, and (d) causal explanations (i.e., verified by experts in the field) of the factors underlying the maintenancedemands of aircraft equipment.

Most of these improved metrics can be used now by the Air Force in prediction and estimation efforts for (a)manpower determination studies, (b) cost-of-ownership studies, (c) new basing and deployment planning, and (d) designtrade studies for future aircraft. A few require further investigation of causal relations. The major future effort is to derivesimilar relations for combat environments to supplement the peacetime data bases and relations developed in the study.

PREFACE

This technical report documents work performed under Contract No.F33615-77-C-0075, Development of Maintenance Metrics to ForecastResource Demands of Weapon Systems.

The study provided statistically valid samples of cargo/transport,bomber, and fighter aircraft equipment maintenance demands, designcharacteristics, operational factors, and environmental factors.These data were used to develop more accurate maintenance metricsfor each class of aircraft (cargo/transport, bomber, fighter) fortransition to the user community. The details of the statisticalda .., maintenance impacts, maintenance demand causality, and theresulting maintenance metrics mathematical models are contained inthree supplemental volumes to this TR; (1) Cargo/Transport Users'Guide, (2) Bomber Users' Guide, (3) Fighter Users' Guide.

This study contract was performed by the Boeing Aerospace CompanyProduct Support/Experience Analysis Center (PS/EAC), Seattle,Washington. USAF Contract F3615-77-C-0075 was initiated underExploratory Development Area PMS 77-43 (1124). Work wasaccomplished under the direction of the Logistics and Human FactorsDivision of the Air Force Human Resources Laboratory with Mr. FrankMaher as the Work Unit Scientist and Air Force Contract Monitor.

Experience Analysis Center program technical leader was George R.Herrold. Principal program analysts were Donald K. Hindes, Gary A.Walker, and David H. Wilson.

The Boeing Aerospace Company wishes to express their appreciationfor the technical assistance and data provided by (a) Air ForceLogistics Command Headquarters, Aeronautical Systems Division, andAir Force Maintenance and Supply Management Engineering Team,Wright-Patterson AFB, Ohio, (b) Military Airlift Command (MAC)Headquarters, and Air Weather Service (MAC) Environmental TechnicalApplications Center, Scott AFB, Illinois, (c) Air Force EuropeHeadquarters, Ramstein AB, Germany, (d) Strategic Air CommandHeadquarters, Offutt AFB, Nebraska, (e) Tactical Air CommandHeadquarters, Langley AFB, Virginia, (f) 36th TFW, Bitburg AB,Germany, (g) 58th TTW, Luke AFB, Arizona, (h) 60th MAW, Travis AFB,California, (i) 92nd BW, Fairchild AFB, Washington, Cj) 354th TFW,

-d~ig-

Myrtle Beach AFB, South Carolina, (k) 355th TFW, Davis-Monthan AFB,Arizona, (1) 380th BMW, Plattsburgh AFB, New York, (w) 63rd MAW,Norton AFB, California, (n) 22nd BW, March AFB, California, (o) 35thTFW, George AFB, California, (p) 320th BW, Mather AFB, California,(q) 23rd TFW, England AFB, Louisiana, (r) 31st TFW, Homestead AFB,Florida, (s) 33rd TFW, Eglin AFB, Florida, (t) 27th TFW, Cannon AFB,New Mexico, (u) 49th TFW, Holloman AFB, New Mexico, (v) 347th TFW,Moody AFB, Georgia, (w) 19th BW, Robins AFB, Georgia, (x) 437th HAW,Charleston AFB, South Carolina, (y) 317th TAW, Pope AFB, NorthCarolina, (z) 68th BW, 4th TFW, Seymour Johnson AFB, North Carolina,(an) 366th TFW, Mountain Home AFB, Idaho, (bb) 388th TFW, Hill AFB,Utah, (cc) 438th HAW, McGuire AFB, New Jersey, (dd) 436th MAW, DoverAFB, Delaware, (ee) 1st TFW, Langley AFB, Virginia, (ff) 435th TAW,Rhein-Main AB, Germany, (gg) 50th TFW, Hahn AB, Gerviany, (hh) 86thTFG, Ramatein AB, Germany, (ii) 52nd TFW, Spangdahlem AB, Germany,(jj) 32nd TFS, Camp New Amsterdam, Netherlands, (kk) 20th TFW, RAFUpper Iteyford, England, (II) 10th TRW, RAF Alconbury, England, (mm)81st TFW, RAF Bentwaters, England, (nn) 513th TAW, RAF Mildenhall,England, (oo) 48th TFW, RAF Lakenheath, England, (pp) 96th BW, Dyesa

AFB, Texas, (qq) 7th BW, Carswell AFB, Texas, (rr) 314th TAW, LittleRock AFB, Arkansas, (sa) 416Lh BW, Giffiss AFB, New York, (tt) 509thBW, Pease AFB, New llampshire, (uu) 42nd BW, Loring AFB, Maine, (vv)28th BW, Ellsworth AFB, South Dakota, (ww) 5th BW, Minot AFB, NorthDakota, (xx) 319th BW, Grand Forks AFB, North Dakota, (yy) 62nd MAW,

McChord AFB, Washington.

2

I

TABLE OF CONTENTS

SECTION PAGE

1.0 Introduction 7

1.1 Objective 8

1.2 Overview of Study 12

2.0 Data Base 17

2.1 Phase III Aircraft/Base Study Sample 18

2.2 Aircraft Subsystem/Equipment Sample 18

2.2.1 Develop Subsystem/Equipment 18Selection Criteria

2.2.2 Identify Subsystem/Equipment 22

Applications by Aircraft

2.2.3 Select Subsystem Equipment for Phase III 23

2.3 Parameter Identification 24

2.4 Data Base Acquisition 26

2.5 Statistical Sufficiency of the Data Sample 30

3.0 Analyzing and Prioritizing Parameters - 32(Maintenance Impact Analysis)

3.1 Input Data Preparation 34

3.1.1 Master File Creation 34

3.2 Detection and Screening of Maintenance 38Impact Estimating Relationships (MIERs)

3.3 Special Findings From the Data Base Analysis 40

3.3.1 Fighter Usage Factor Investigation 41

3

A~77* -77 - , r

TABLE OF CONTENTS (Continued)

SECTION PAGE

3.3.2 Aircraft Tire Failure Data Investigation 42

4.0 Development of Maintenance Metrics Models 45

4.1 Model Development 46

4.2 Model Rationale 49

4.3 Special Findings From Maintenance Metrics 50Model Development

5.0 Maintenance Demand Causal Analysis 52

5.1 Causal Analysis Approach 52

5.2 Special Findings From Causal Analysis 56

6.0 Summary Conclusion 58

6.1 Assumptions and Uncertainties 58

6.2 Overall Findings and Recommendations 59

From the Study

References 65

Glossary of Abbreviations 67

1 4

LIST OF ILLUSTRATIONS

FIGURE PAGE

1. System Funds Committed by Initial Planning Process 10

2. Sequence of Major Tasks 13

3. Study Activities and Products by Major Tasks 16

4. Study Sample Selection Process 20

5. Air Force Bases Visited by Survey Team 28

6. Parameter Analysis and Prioritization Process 33

7. Preliminary Input Data Processing 35

8. Typical Maintenance Impact Estimating 39Relationship (MIER)

9. Maintenance Metrics Predictor Model 47Development Process

10. Example of Composite Maintenance 48Metrics Model

11. Causal Analysis Approach 53

12. Relationships Between Maintenance Causal 54Factors and Underlying Stressors

13. Example Qualitative Correlation of Metrics 55Model Parameters With Field SurveySupporting Evidence

14. Example of Cold Adverse Weather Effects on 61Peacetime Maintenance Demand

15. Example of Climatic Temperature Effects on 62Peacetime Maintenance Demand

16. Example of Corrosive Environment Effects on 63Peacetime Maintenance Demand

5

, _" . . . . . . . . - ., - .

LIST OF TABLES

TABLE PAGE

1. List of Candidate Study Aircraft 19

and Bases by Generic Class

2. Subsystem/Equipment Sample 2]

3. List of Equipment, Operations, and 25

Environmental Parameters for Analysis

4. Equipment Characteristics Parameters 37

(Propulsion) Example

I

6

t4

1.0 INTRODUCTION AND BACKGROUND

This technical report describes work accomplished and findings forthe and Logistics and Human Factors Division study effort,"Development of Maintenance Metrics to Forecast Resource Demands ofWeapon Systems."

Phases I and II of the maintenance metrics study were intended toexplore the feasibility of developing maintenance impact estimatingstatistical models for the various subsystems of Air Force aircraft.The statistical data base utilized for this effort was composed ofhistorical field data on selected maintenance, equipment design,operational, and environmental characteristic parameters.

The research subjects on which these data were obtained consisted ofselected common aircraft equipment within a nine-case sample of U.S. Air Force aircraft/base combinations. This sample spanned thetypes of aircraft used by the Air Force (bomber, fighter, transport,and trainer) and the various ground base environments experienced.

This data base was then analyzed for possible causal factors for theexpenditure of maintenance resources. These maintenance impactswere structured parametrically and cataloged for future use. Thedetected maintenance impacts were then combined into mathematicalmaintenance metric models for each item of equipment studied. Thesemodels predict maintenance action demand based on significantdesign, operational, and environmental factors which impact themaintenance of each equipment item. Validation of the models wasperformed through testing within the context of LCOM simulations.

The Phase 1/I study and findingst demonstrated the feasibility otdeveloping credible maintenance demand estimators to augment presentmethodology.

The Phase 111 study built on the findings of Phases I and II. Itwas based directly on the approach taken and the experience 6aineddurinig those initial study efforts.

1.1 RATIONALE AND OBJECTIVE

The rising cost to support weapon systems currently in operation, aswell as those in development has created the need for more accuratemethods of projecting maintenance requirements. There are two costdriver variables that are generally understood by all. These arethe manpower and material or resources to maintain the weaponsystem. In a study conducted on the life cycle cost of the C-130E

1. Donald K. Hindes, et. al., Development of Maintenance Metricsto Forecast Resource Demands of Weapon Systems. Five VolumeBoeing Aerospace Company document:

D194-I0089-1, Revision A, Analysis and Evaluation, February 1980D194-10089-2, Revision A, Parameter Prioritization, October 1980D194-10089-3, Revision A, Maintenance Metrics and Weightings,

Uctober 1980D194-10089-4, Revision A, Analysis and Results of Metrics and

Weightings, November 1980D194-10089-5, Final Report, October 1980

8

aircraft it was determined that labor accounted for 70% of the 15year cumulative operational and ;support cost, resources (material)approximately 18%. with the remaining being att~ributed to fuel andbase support. The C-130% experience is typical of the other systemsin Air Force inventory.

The major proportion of total operating and support cost incurredfor labor and material has developed considerable concern for themanpower and resources required to support weapon systems currentlyin operation, as well as those in development. A study ofmaintenance and reliability impact on system support costs showedthat some 707 of the life cycle cost funds of a new weapon system

are essentially committed in the concept phase by initial planningdecisions (Figure 1).

This semi-predetermined expenditure has created the need for moreaccurate methods of projecting maintenance and manpower requirementsearly in the design process so that trades can be made to reduce

long term resource demands. Meeting this need requires thedevelopment of realistic predictive measures of maintenance ratesfor all of the diverse equipment that makes up a weapon system.

in addition, the impact of operations and environmental conditionsneed to be identified to ensure the accuracy of the newly developedmaintenance metrics under the diverse conditions met by fieldedweapon systems.

9

CU I1PERCENT ACOUISTION 6

OPERATION 6%

DEVELOPMENT10I

SYSTEM LIFECYCLE COSTS

1 2 3 412YEARS AND PHASES OF SYS7CM EVOLUTION

SYSTEMS FUNDS COMMITTED BYINITIAL PLANNING DECISIONS*

FIGURE 1

*1lani n< P. in.n~t'r inen'an~t. ll if-nifl0 Fan1LY imPact

10

oil= q

To date, the manpower and other resource requirements essential tothe operations and support (O&S) of a weapon system have beendetermined using the traditional "flying hours" and "sortie rate"measures. The deficiencies of these traditional measures are wellknown and such measures frequently are found to be totallyirrelevant (e.g., maintenance on a gun subsystem is generated byfactors like the number of rounds fired, and is not affected by thenumber of flying hours or sorties). These traditional measures arealso insensitive to variations in operations and environmentalconditions (for example, many avionics equipments may operate or arecycled on the ground greatly in excess of related flying hours ornumber of sorties). The present difficulties then lie in the factthat the currently used metrics do not consider the inherentdifferences between the individual subsystems of a weapon system andare relatively insensitive to operational and environmentalconditions.

Therefore, the objective of this subject research was to alleviatethe above deficiencies by identifying, determining, and integratingthose measurable weapon system parameters which are necessary andsufficient to form more accurate metrics and weightings with whichto predict system maintenance demands.

The objectives of Phases I and II of the overall study were todevelop the research data base acquisition and integrationmethodologies; to develop the analysis and metrics developmentmethodologies; and to demonstrate the feasibility of obtaining theoverall research objective through the experimental derivation andvalidation of general maintenance action demand predictor models forAir Force aircraft subsystems.

The objective of Phase III for this study was to refine and expandthe results of Phases I and II to increase the usability andcredibility of the maintenance metrics and weightings developed fromthem. Phase III refines the maintenance action demand predictormodels from the the general models for all Air Force aircraftdeveloped during Phases I and II to three sets of class-specificmodels, one each for bombers, transports, and fighters. At the sametime, the statistical validity of the resulting models is improvedby expanding the data case sample size of each aircraft class beyond

the threshold of "sparse" statistical samples. This greatlyincreases confidence that the developed models are in accordancewith the rules and conventions of statistical analysis. In

11

addition, model credibility is enhanced by investigation and

explanation of any counterintuitive logic and/or anomalies thatappear within the developed models.

1.2 OVERVIEW OF STUDY

The motivation behind the maintenance metrics development effort wasthe id, a that much can be learned about predicting maintenancedemand rates by looking at actual historical maintenance data onselected items of hardware and how and why these rates vary withchanges in design, operational, and environmental parameters. Thisidea continued to serve as the central theme through all threephases of this study. Phase III was a logical extension of Phases Iand I for improving the efficiency and user value of theexploratory work already accomplished. Phase III expanded the database to cover additional aircraft moaels and aircraft/base datacases. The data base could then be used to develop maintenancedemand predictor models that were derived from as much of the AirForce aircraft inventory and basing situations as study resourcesand time would allow.

Exploration of the basic underlying causal factors of maintenancedemand was also a goal of the maintenance metrics development effortduring Phase 11I.

The general task-oriented approach taken to accomplish themaintenance metrics development effort is illustrated in Figure 2and summarized as follows:

1. Search and Review Literature, Develop BibliographyReview related studies and research dealing withmaintenance rates and causes. Developed and documenteda bibliography of relevant references.

2. Select Aircraft/Base Study SampleThe 62 aircratt/bases that were selected for studyincludes the major aircraft in the Air Force inventoryand most of their operating environments.

12

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13

3. Select Subsystem EquipmentThe matrices of equipment by current Air Force aircrafttype were developed and used to select a representativecross-section of subsystem equipment for the 19 commonaircraft subsystems.

4. Identify Maintenance Impact ParametersThis effort identified the equipment, operational,

and environmental parameters that might significantlyimpact maintenance resource demand on the selectedsubsystem equipment. Phases I and II identified andtested a broad range of potential maintenance impactparameters. Phase III utilized only those parameterswith significant maintenance impact based on theexperience of Phases I and 11.

5. Identify and Integrate Data SourcesThis task identified, acquired, and integrated thedata base on the equipment selected in (3) for the

related parameters being considered in (4). ForPhase 11, separate data bases were assembled

for bombers, cargo/transports, and fighters.

6. Analyze and Prioritize ParametersThese analyses prioritized the collected aircraftdata to define and test relationships between thestudy parameters and maintenance demand rates.

Parameters with significant impact on maintenance

demand rates were used as source data for Task 7.

7. Develop Maintenance Metric ModelsThese metrics quantified maintenance demand ratesin accordance with variations in equipment design

parameters. Weighting factors were incorporatedin Lhe metrics models to adjust operational andenvironmental impacts on maintenance demand rates.Four maintenance demand rate estimating modelswere developed for each aircraft subsystem; i.e.,equipment, operations, environmental, and composite.

14

8. Validate Maintenance Metrics MethodologyDemonstrated validity of methodology through seriesof LCOM uiwuiation comparative and sensitivityanalysis experiments.

9. Analyze Developed Metrics ModelsThese analyses investigated counterintuitive logicwithin the resulting metrics models. Underlyingcauses for model anomalies were defined where possible.

10. Prepare DocumentationUsers manuals (cargo/transport, bomber, and fighter) and thisfinal report provide the material necessary totransition the program to an operational status.In addition, the study data base and Phase I and IIinterim documentation is ovailable for examinationby th! Air Force user community at the Logistics andHuman Factors Division, Wright-Patterson Air ForceBase, Ohio.

Figure 3 presents an overview of the study activities and resultingproducts that are available and useful for the user community.

15

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2.0 STUDY DATA BASE

The data base from which the Phase Ill maintenance metrics weredeveloped is composed of three major blocks:

1. Data on related research, which provided historicinput for study planning, aircraft/equipment

and relevant study parameter selection.This information was gathered during studyPhase I and is documented. 2

2. Data on maintenance results for aircraft equipment.These data were obtained from the existingcomputerized Air Force data systems in accordancewith Air Force Regulation (AFR) 66-1 MaintenanceData (D056E data tapes) and AFR 65-1.10 VehiclePerformance Data (C033B datas tapes), and aredescribed in paragraph 2.4.

3. Data on possible causes for aircraft maintenancedemand. These data were obtained from on-sitesurveys at the individual Air Force bases in thePhase Tll study sample. These data are

described further in paragraph 2.4.

The data base was developed around the selected aircraft/basecombination (described in paragraph 2.1), the selected subsystemequipments (described in paragraph 2.2), and the selected studyparameters (described in paragraph 2.3).

2. Donald K. Hindes, et. al., Development of Maintenance Metricsto Forecast Resource Demands of Weapon Systems. BoeingAerospace Company document, D194-10089- 1, Revision A, Analysisand Evaluation, February 1980.

17

.... T- - -.. ..

2.1 AIRCRAFT/BASE STUDY SAMPLE

For Phase I, a sparse sample of 9 aircraft/Air Force basecombinations was selected to explore the feasibility of maintenancemetrics development. This sample covered the range of aircrafttypes in current use and the range of basing environments in whichthe Air Force operates.

For Phase III, a representative sample of cargo/transportaircraft/Air Force base combinations was selected for each of thegeneric aircraft classes studied (bomber, cargo/transport, andfighter) to include sufficient cases for valid statistical analysistest and inference. In addition, the selected sample encompassed arepresentative range of operational environments. Table I lists theaircraft/base data cases from which the Phase III maintenancemetrics were developed. Note that the Phase III sample includes thecase sample selected for the Phase I/Il feasibility study. Figure 4illustrates the aircraft/base selection process, as well asequipment selection process discussed in paragraph 2.2.

2.2 AIRCRAFT SUBSYSTEM/EQUIPMENT SAMPLE

During Phase I/Il, 30 representative, "high driver" pieces ofequipment within 19 common aircraft subsystems were selected formaintenance metric development. These same items were used for thePhase III study. These common subsystems generate the major portionof aircraft maintenance demands exclusive of aircraft-model-uniquesubsystems such as ordnance stores. Table 2 lists the selected

subsystem/equipment sample.

2.2.1 DEVELOP SUBSYSTEM/EQUIPMENT SELECTION CRITERIA

The initial subsystem/equipment selection criteria were developedearly in the study and were expanded during the accomplishment ofthe literature review. The selection criteria utilized during theactual subsystem equipment selection process are as follows:

1. Equipment selected should be functionally representativeof a wide cross-section of aircraft applications and useenvironments.

18

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TABLE 2 SUBSYSTEM/EQU[PMENT SAMPLE

(Air Force Work Unit Code Nomenclature)

SYSTEM NO. SUBSYSTEM EQUIPMENT

11 Airframe RadomeWindshieldWing

12 Interior Fittings Crew Seat

13 Landing Gear Wheel & TireBrakes

14 Flight Controls StabilatorRudderFlaps

23 Propulsion Complete Engine

41 Environ. Control Moisture Separator

42 Electric Power Generator

44 Lighting Anti-Collision LightsLanding/Taxi Lights

45 mydrauliCS Pump

46 Fuel Internal Tanks

47 Oxygen Oxygen RegulatorLOX Converter

49 Mtscellaneo 4 Engine Fire Detector

51 Instruments Flight IndicatorsAir Data SystemHorizontal-Situation Indicator

52 Autopilot Autopilot System

63 UHF Comm. System R/T Units

65 1ff System IFF System

71 Radio Navigation Inertial Navigation JetInstrument Landing Set

TACAN Set

73 Bomb Nay. System Attitude Heading Ref. Set

74 Radar Nay. System R/T Units

21

2. Equipment selected should represent a wide variation intype; i.e., design technology (new-old), electrical/mechanical, parts count/complexity, maturity states,testability, and usage.

3. Packaging and design technology must be projectable intothe future to prevent obsolete technology from undulybiasing statistical relationships which will be used forfuture predictions.

4. Equipment must be mature enough for data samples to betaken beyond the learning curve period, yet include

relatively new as well as old equipment.

5. Equipment must have a statistically valid populationof operational units in use.

6. The equipment must have sufficient historical dataavailable for valid analysis.

7. Equipment selected should represent a significant

percentage of the total maintenance resourcesexpenditure demands; i.e., maintenance manhours,failures, removals, costs, etc.

8. Equipment should be of a nature such that factors

other than just flying hours may contribute to theirreliability/maintainability characteristics.

2.2.2 IDENTIFY SUBSYSTEM/EQUIPMENT APPLICATIONS BY TYPE AIRCRAFT

The next logical process was to develop an aircraft versus subsystem

application matrix identifying the aircraft subsystems. This wasaccomplished by detailed review of each system in the applicableaircraft work unit code (series -06) technical orders. During this

review for Phase I/Il, 663 individual equipment items were examined

22

and hundreds more were examined for the added aircraft modelsstudies in Phase III (the B-52D, B-52H, C-130E, C-5A, A-7D, F-4E,F-SE, F-16A, and F-lilA, D, E, and F aircraft were new items forPhase III).

2.2.3 SELECT SUBSYSTEM EQUIPMENT FOR PHASE III

Utilizing the equipment identified in Phases I and II, the followingsequential step-by-step subsystem/equipment selection process wasaccomplished for the Phase III refined study.

I. Eliminated those systems/subsystems that showed up on lessthan five of the study aircraft.

2. Utilized the subsystem set (30 items) from 19 subsystemsidentified in Phase I/II.

3. Identified the functionally equivalent subsystems orsimilar equipment groupings within the new study aircraft.

4. Identified and listed all work unit codes (at the four orfive-digit level as appropriate) for each of the subsystem/equipment functional groupings identified in 2 and 3 above.

5. Determined the number of failures reported against each ofthe work unit codes within each of the subsystem functionalgroupings from 2 and 3 above.

6. Totaled the number of failures within each subsystem

functional grouping and computed what percentage of thesubsystem functional grouping total the failures for eachwork unit code represented.

7. Selected those work unit code(s) within each subsystemfunctional grouping, for each aircraft, that representedthe top failure percentage (50% or greater) of the totalfailures within the subsystem.

23

8. Compared common functions of the subsystem equipmentsselected on each aircraft and made minor adjustments asnecessary to ensure that functional equivalent or similarsubsystem equipments were selected across each studyaircraft.

2.3 PHASE II PARAMETER IDENTIFICATION

The identification and screening process for selection ofmaintenance impact parameters associated with the subsystemequipments relied heavily on the previous work conducted during thePhase I and II study effort. In this initial effort, the literaturesearch and interviews with experienced engineering, maintenance, andoperational personnel were utilized to identify a list of over 200potential maintenance impact causal parameters comprising equipmentdesign factors, operational factors, maintenance factors andenvironmental factors. The efficiency of Phase III was enhanced byusing only those parameters that were identified as having shownsignificant maintenance impacts as a result of the Phase I and Ilanalysis.

The parameters selected passed the following selection criteria:

1. Sensitivity - The parameter is sensitive to themaintenance resource demand requirements of thesubsystem(s)/equipment(s) that are being studied.

2. Availability - The information necessary for use o1 theparameter is identifiable and available to the study teamfrom a known source, based on need-to-know requirements.

Considering these two selection criteria, a list of 54 individualparameters (Table 3) were selected within three major categories;

i.e., equipment (hardware), operations, and environmental. Theseparameters are measurable characteristics of the equipment design,and of the operational and geo-climatic environments within whichthe study equipment operates. As such, they form the body ofpossible causal information within the study data base. The data

24

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for these parameters were obtained from the on-site base surveys.

The dependent maintenance demand variable chosen as the primemeasure of maintenance expenditure was MAD. The MAD was comprisedof historic maintenance (D056E) data records (averaged over 3 years)for "on-equipment" action-taken codes as follows:

R - Remove and Replace J - Calibrated - no adjustmentP - Removed K - Calibrated - adjustment

requiredQ - Installed L - AdjustF - Repair V - CleanG - Repair/Replace small Y - Troubleshoot

parts and soft goodsE - Equipment checked - no Z - Corrosion repair

repair required

2.4 DATA BASE ACQUISITION

During Phase III of the study, the Phase I and II data base wasexpanded to include additional aircraft/base combinations asdiscussed in paragraph 2.1 and shown in Table 1. This ensured thatsufficient data were collected to achieve a statistically valid datasample for the maintenance impact analysis of each of the 30subsystems within each aircraft class.

This task was extremely critical for achieving the study objectives.Therefore, additional emphasis was placed on this task b, dividingit into three logical subtasks:

I. Data Identification - The identification of data sourcesand the types of data available within each source wasrelatively straightforward for the Phase 11I effortbecause of the experience gained by the study team duringPhases I and 11. The data required for Phase III coveredtwo main sources for collection:

a. Headquarters AFLC/ACRAM - AFR 66- Maintenance Data(D056E data tapes) and AFR 65-110 Air Vehicle

26

Performance Data (G033B data tapes).

b. Air Force Bases - Data required for equipment,operational, and environmental parameters (Table 3)used in the Phase III study were obtained from theappropriate individual Air Force Bases (Table 1)tor each study aircratt.

2. Data Acquisition - Once the aircraft types and basecombinations were selected, the next logical step wasto initiate the necessary coordination to obtain therequired data not already available within the studyresources. In obtaining the specific data, thecategories could be logically separated into computer-generated information and on-site data obtainable fromthe individual operational bases.

a. Computer-Generated Data

(1) AFR b6-1 (DO56E)j- Maintenance ResourceUtilization/Hanajement Data - This systemprovided the maintenance experience for thesubsystem equipments studies for each aircraft/base combination.

(2) G033B - Standard Aerospace Vehicle Inventory,Status. and Utilization Report _ystem - Thissystem provided the operational parametersnecessary for various rates associated withseveral of the operations parameters identifiedin Table 3.

3. On-Site Survey - As in any data acquisition task of thismagnitude, some parameters were not available throughformal Air Force data systems. This required on-siteVisits to obtain the necessary equipment, operational,and environmental parameter data. An on-site survey wasconducted to collect the equipment, operation, andenvironmental parameters not available through formaldata systems, at each base depicted in Figure 5. It was

27

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necessary to visit six major areas; the first and mostsignificant was the Deputy Commander for Maintenance (DCM)office. Here a short introductory presentation was givento all functional Officers in Charge/Non-Commissioned OfficersOfficers in Charge (OICs/NCOICs) from whom data were required.This one-time meeting set the stage for a smoothtransition of data flow. The other functional areasvisited were as follows:

a. Operations - The operations or aircrew standardiza-tion organization provided the operational aircraftcharacteristic parameter data. Important operations-related causal insights were also provided by theinterviews with these people.

b. Maintenance - The applicable maintenance organizationprovided the subsystem equipment characteristic andmaintenance characteristic type data for all studyequipments; i.e., avionics, engine, and otherequipments. Also, this organization providedimportant causal insights for the causal analysisportion of the study.

c. Weather - The base weather station provided theenvironmental parameter data for that individualstation. Geo-climatic maintenance causalityinformation was also provided by this function.

d. Analysis - Monthly maintenance summaries and supportgeneral data via a Base Level Information System(BLIS) printout were obtained.

4. Data Integration - The third and final step for data basepreparation was to prepare the data for the analysisaccomplished in Task b. The AFR 66-1 maintenance records(D056E) were screened and integrated into the requiredMAD format. The raw data from the field surveyi. werequantified, normalized, and collated in data matrix formfor computer input and analysis.

29

The basic study data base in its final form consists ofMAD equipment characteristics, operational characteristics,and environmental characteristics in the selected para-meters for each of the 30 equipment items for each ofthe 62 aircraft/base data cases. The data base wasdivided into separate bomber, cargo/transport, andfighter data files for the statistical analysis andmetrics development.

2.5 STATISTICAL SUFFICIENCY OF THE DATA SAMPLE

The bomber, cargo/transport, and fighter Phase III data samples fromthe standpoint of number, base location, and aircraft models weredesigned to give the largest sample size, the broadest range ofenvironments, and the broadest range of equipment design possiblewithin the time and resources available to the study project. Thesample sizes for each aircraft class are considered sufficient forvalid statistical analysis and data fitting. There is a high degreeof confidence that the maintenance metrics developed from the studydata base are accurate and credible predictors of maintenancedemand. The statistical sutficiency of each of the three datasamples (cargo/transport, bomber, and fighter) as measured by theirnon-parametric tolerance limits (single tail or one sided test) areas follows:

1. Cargo/Transport.For the 25 case sample, there is 90% confidence that92% of the possible range of each datum variable isincluded between the low and high values recorded inthe sample for each of these variables. Conversely,there is 95% confidence that 90% of each variable'spossible range has been included in its sample range.

2. Bomber.For the 14 case sample, there is 90% confidence that86% of the possible range of each datum variable isincluded between the low and high values in thesample. Conversely, there is 80% confidence that90Z of each variable's possible range has beenincluded in the sample range of each variable.When the FB-IIIA cases were excluded, dropping thesample size to 12, the 90% confidence point drops

30

. ..... ..... ..... ... : . -F --.... -

to 84% of each variable's range, and the confidencethat 90% of each variables range has been includedwithin the 12 datum points drops to 73%.

3. Fighter.For the 23 case sample of fighter data, there is901 confidence that 91% of each variable's rangehas been included between the low and high ofeach variable's sample range. There is 92%confidence that 90% of each variable's possiblerange has been included.

31

3.0 ANALYZING AND PRIORITIZING PARAMETERS(MAINTENANCE IMPACT ANALYSIS)

Upon acquisition and integration of the data base, qualitativequantitative analyses of these data were performed. The objectiveof the analyses was the detection, testing, and ranking of possiblestatistically useful causal relationships between the candidatemaintenance impact parameters (see Table 3, Section 2) and MAD. Itnew b,. ng relationships sere detected for each equipment typestudied, then these basic two variable parametrics could be used tobuild composite maintenance demand models (maintenance metrics).

The general approach divided the analysis into subtasks as shown inFigure b. The preparation and execution of these subtasks arediscussed in the following paragraphs. This approach isdeliberately intended as a generalized step-by-step outline of themethodology involved so that other studies can duplicate and/or

expand the methodology using widely available computerizedstatistical packages. The analysis as performed by BoeingExperience Analysis Center utilized a Boeing developed computerprogram, "PKING," which automatically combined several subtasks inorder to facilitate and speed up the parametric relationshipdetection and testing process.

The procedure was applied to the quantification and normalization ofthe source data accumulated in the study data base, and thetabulation of these data into a Master Input Data File suitable forcomputer input and processing. Processing the data with the"PKING," cross-plotting and regression analysis program resulted inthe &eneration ot sLattergrams of the MAD parameter as a function ofthe various candidate maintenance impact parameters in thecategories of equipment, operations, and environmental. Thescattergraas were screened according to the criteria of either 0.25or better correlation coefficient of regression or -- visuallyapparent curvilinear relationships. Additional criteria requiredacceptable data point distribution and at least five data points,four ol which are non-zero in both ordinate and abscissa. Thescreening process resulted in the rejection of most of the trilrela itionships tested. The remaining :at Lergrams with a 0.5 orbetter correlation tuefficient were collated by aircraft class(cargo/tranuport , bomber, and lighter) and publ ished as appendicesto the resPcctivw aircralt class user guides (see supplements 1, 2,and 3 to this TIR). These si6nificant relationships plus the

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relationships with correlation coefficients between 0.25 and 0.5were used as source data for the development of the maintenancemetrics models.

3.1 INPUT DATA PREPARATION

Before maintenance action demand/maintenance impact parametervariable combination testing and screening could proceed, thepackages of data and information gathered were sorted, quantified,and/or normalized where necessary and then tabulated in numericaldata sets suitable for computer-aided cross-plotting and simpleregression analysis (as discussed in detail in section 3.2). Figure7 depicts the preliminary input data processing.

Dummy variables were created and scaled where necessary to quantifyqualitative data. Quantitative data were normalized or averagedwhere necessary. As shown in Figure 7, the individual data packagesfor the items in each functional equipment group (subsystem)selected were integrated into a composite data package for eachgroup. Subsystem equipment groups were functionally normalizedacross all sample aircraft and the parameter value data for eachequipment item integrated into subsystem group values through aweighted average process. These composite data were next entered inthe master input data records. This master file was thentransformed to proper computer input format and entered in the"master file" prior to creation of magnetic disk data input filessuitable for computer processing.

The format master file created was tailored for the PKING dataprocessing program. The general pruoess for creating the masterinput file is widely applicable, however, and could be used tocreate input files for a wide variety of data processing programs.The detailed procedure used in quantifying and integrating the "raw"

data base is discussed in paragraph 3.1.1. The processed input datagenerated by this procedure became part of the overall study database.

3.1.1 MASTER INPUT FILE CREATION

The field experience data gathered previously were divided into tour

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categories: (a) maintenance action demand parameters, (b) equipmentcharacteristics parameters, (c) base operations characteristics

parameters, and (d) base environment characteristics parameters.

Information on each parameter in the first two categories wasobtained for each equipment item selected from each study aircraftat each study base. Information was obtained on an aircraft/basebasis for the other two categories. This information was normalizedon a bsystem basis as appropriate and entered into composite datafiles. The data in first two categories were gathered on eachindividual equipment item within each functional grouping(subsystem); therefore, data on these individual equipment itemsrequired transformation into subsystem level values. This wasaccomplished by a simple weighted-average method based on therelative frequency of maintenance of the equipment items comprisinga particular subsystem within a particular study aircraft type. Forinstance, if item A and item B comprise functional subsystem C for aparticular aircraft, and the HAD for item A is twice that of item B(e.g., 10 actions/unit/year vs. 5 actions/unit/year), thenequipment characteristic parameter values for item A would beweighted twice as heavily as B values when calculating the compositevalue of subsystem C. For example, if the volume of A is 4 cubicinches and the volume of B is 7 cubic inches, the weighted averagevolume of subsystem C for maintenance resource demand purposes is --(4 + 4 + 7) 3 = 5 cubic inches. This is the value entered inthe composite data file and represents the average volume of itemsremoved from subsystem C that must be dealt with by the maintenancesystem over the course of a year's activity. This same type ofreasoning was applied to the calculation of the composite values ofthe other equipment characteristic parameters.

Most of the data in the data base were obtained in quantitative

form. Information on a few parameters was obtained in qualitativeform, however, and required quantification. Table 4 shows anexample list (equipment characteristic parameters - propulsion) ofthe identification developed for each of the parameter input datacategories. Table 4 shows the category of parameters, their type(real or scaled variable), their units of measure (if any), and thescaling conventions used for variables scaled from qualitative data.The complete parameter dimension tables for each aircraft categorycan be found in supplements 1, 2, and 3 to this TR.

36

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TABLE 4 EQUIPMENT CHARACTERISTICS PARAMETERS(PROPULSIOJ) - EXAMPLE

PARAMETFR NAMF TYPE UNITS

Total No. of Installed Engines Real Number/Acft.

Take-off Thrust Per Engine Real Pounds/10

Weight Per Engine Real Pounds/t0

Volume Per Engine Real Cu. Ft./1O

Density Pe.- Engine Real Lb/Cu.Ft./1O

No. Compressor Sections PerEngine Real Number

No. Compressor Blades Per Engine Real Number

Turbine Section Size Real Ft. Diam

Max Engine Combustion Temp. Real Degrees "C"

Max Engine Fuel Flow Real Lbs/Hr

Min Engine Fuel Flow Real Lbs/Hr

Engine Prime Depot Scaled Convention:I - UCALC2 - SAALC3 - Teledyne4 - Alameda

Engine AGE Availability Real % Time Available WhenRequirea

Engine AGE Unreliability Real % Time Unreliable WhenUsed

Engine Vibration Factors Real Convention:1 - Low2 a Medium3 - High

37

. ..o

3.2 DETECTION AND SCREENING OF MAINTENANCE IMPACT ESTIMATINGRELATIONSHIPS (MIERs)

After the master input data file was transformed into suitablecomputer input records, the cross-plotting and least squaresregression analysis computer program was applied to the data. A setof cross-plots and regression statistics was generated for each ofthe 30 subsystem items for each of the three aircraft classes(cargo/transport, bomber, fighter). Each set consisted of MAD as atrial function of each of the 22 equipment parameters, each of the17 operations parameters, and each of the 15 environmentalparameters. The underlying statistical characteristics assumedduring the generation and screening of these trial relationshipswere as follows:

I. The assembled data were accurate and unbiased.

2. Each data case value was a member of orcontinuous normal distribution of possiblevalues for that data case (a necessary conditionfor least squares regression).

These trial cross-plots and regression functions were screened forsignificant statistical correlation, unbiased data-pointdistribution, and causal reasonableness. The trial parameters foundto have significant maintenance impact by this screening process arelisted in this report's supplemental user guides forcargo/transports (supplement 1), bombers (supplement 2), andfighters (supplement 3). The resultant Maintenance ImpactEstimating Relationships (MIERs) detected for each of the 30subsystem items are also listed in the user guides along with areasonable causal rationale. The detected HIERs have been dividedinto two groups according to their utility for the user community.

The first group consists of those MIERs with correlationcoefficients greater than 0.5. These relationships are consideredto have "Stand-alone" usefulness for planners and designers inestimating design, operational, and environmental impacts onequipment maintenance demands. These groups of MIERs forcargo/transport, bombers, and fighters are appended to theirrespective user guides. Figure 8 illustrates a typical exampleMIER, as it is printed out from the PKING cross-plotting and

regression analysis program.

38

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The remaining group consists of those MIERs which have passed thescreening criteria for data-point distribution, visually non-randomdistribution, and causal reasonableness, and which have correlationcoefficients between 0.25 and 0.5. These MIERs are considered toindicate definite non-random relationships between the dependentvariable HAD and the subject independent variables. However, noMIER with a correlation coefficient below 0.5 explains enough of thevariance of MAD to be used alone for maintenance demand estimating.Therefore, these MIERs were not directly included in the userguides. Instead, the data from which they were derived were incudedin the input data for the multiple regression model developmenteffort discussed in the following paragraph 4.0. All cross-plotsgenerated during the course of parameter analysis and prioritizationhave been filed as part of the study data base and are available forexamination.

3.3 SPECIAL FINDINGS FROM THE DATA BASE ANALYSIS

During the course of analysis of the Phase Ill data base, two majoranomalies became apparent and warranted further itivestigatlon. Oneconcerned the sense of the correlations betweem fighter equipmentMADs and usage variables such as flying hours per year. The dataapparently indicated that the more the equipment was used, the lessit failed. Since this seemed to go against the laws of physics,this apparent anomaly in the data was thoroughly investigated, andthe results are presented in paragraph 3.3.1.

The other major anomaly affected all three classes of aircraft andconcerned the sense of the correlations between wheel and tire MADand landing gear usage variables such as landings per year. Thesedata also seemed to indicate that the more landings made, the longertire life to be expected. Since common sense and other statisticalstudies of tire life have shown the direct wear effects of landingson aircraft tires, it was apparent that something was drasticallywrong with the study data as obtained from the D056E and G033Btapes. An in-depth investigation was made of this anomaly, and thedetailed results are presented in paragraph 3.3.2.

40 I

3.3.1 FIGHTER USAGE FACTOR INVESTIGATION

When the correlation tests of fighter equipment maintenance actiondemand against operational parameters were being screened, asignificant anomaly became apparent. In general, fighter equipmentMAD was found to be negatively correlated with the usage variablessortie rate, total landings, and flying hours. Out of the 30fighter equipment items tested, 22 indicated negative correlationsbetween MAD and these variables. The data apparently indicated thatthe more the equipment was used, the less it failed. This seemed tobe against the laws of physics as well as common sense; therefore,an investigation of the data was initiated. After a detailedcross-checking of the records, both the AFR 66-1 Maintenance DataCollection (MDC) System values for MAD, and the AFR 65-110 Statusand Utilization Reporting System values for the usage variables werefound to be accurate and complete. The anomaly was due to otherunderlying causes.

To gain some insight into these causes, various aggregate tests wereperforamed on the lighter equipment data. Aggregate MAD per aircraftwas ranked by model (F-5E, A-IOA, F-4E, etc.) and plotted againstby-model rankings of operational variables 014, sortie length; 015.total sorties; 016, total landings; and 017, total flying hours.Clear aggregate functional trends emerged from these analyses asfollows:

1. Maintenance Demand - +F (Sortie Length)

2. Total Sorties Achieved - -F (Maintenance Demand)

3. Total Landings = -F (Maintenance Demand)

4. Total Flight Hours Achieved - -F (Maintenance Demand)

From these results a working hypothesis was formed:

If a key MAD causal parameter is --

"Continuous Operating Time,"

41

Then it follows that --The longer the required sortie length, thehigher the expected MAD and maintenance downtime.

The longer the maintenance downtime -

The fewer sorties that are achieved, the fewer landingsthat are made, and the fewer flight hours that are achieved.

The foregoing statements are reasonable in light of typical flyingscheduled in fighter wings. In general, commands using fighteraircraft try to fly as much as possible within the constraints ofmaintenance downtime and adverse weather. The implication of thisopeartional policy is that flight hours, sortie rate, and landingsare dependent of maintenance demand, not the other way around. Thefighter data sample givers evidence that this is true in that itreflects historic achieved flying hours and sortie rates which maypossibly be driven by historic maintenance downtime. Due toresource constraints, other hypothesis to explain this apparentanomaly were not investigated. Clearly the finding warrantsfurther study to determine the causal factors that could impactfighter aircraft MAD.

In light of the preceding working hypothesis, "Required SortieLength" will be valid predictor for fighter MAD in newaircraft/basing situations while "Sortie Rate" and "Flying Hours"based on historic values may not be reliable. Therefore, althoughthe hypothesis is unproven, it was used as a basis for deletingfighter Sortie Rates, Landings, and Flying Hours from themaintenance metrics development process, except for some systemswith overriding considerations, such as landing gear and wings.

3.3.2 AIRCRAFT TIRE FATLURF DATA TNVESTIGATION

During the data analysis effort of the Phase I7 study, it becameapparent that landing gear maintenance action demand data for allthree types of aircraft studies (cargo/transport, bombers, andfighters) were in conflict with common sense as well as previoustire life studies.

The MAD on wheels and tires were found to be negatively correlatedwith the usage indicators, such as total landings and flying hours.In other words, the data indicated that more landings resulted inlonger tire life and less failures. A likely hypothesis was thatthe HAD data were incorrect or incomplete. Consequently, a detailedinvestigation and analysis of this anomaly was initiately with a

42

twofold objective:

1. Find the cause of the anomalies in the data samples.

2. Choose the best wheel and tire MAD parameter data formaintenance metric development for each type of aircraft.

The wheel and tire MAD which showed the anomaly was composed of

maintenance actions recorded against the wheel and tire work unitcodes in the AFR 66-1 Maintenance Data Collection (MDC) system.These included the tire shop actions: "Not Repairable This Station,"and "Condemn" for each of the 46 bases visited during the Phase IIIstudy. These action records were supposed to give an accurate countof wheel and tire maintenance demand since tires are depotrepairable only. These were the data that required investigation.

As a first step, the "as received" MDC wheel and tire data wereexamined on a month-by-month basis for the 3-year historic timeslice used to normalize the study data base. It was noted that somebases did not record any tire NRTS or condemn actions for the entire3-year period. Others had only a few actions recorded against thewheel and tire work unit codes. These AFR 66-1 MDC records werethen cross-checked at the local level by contacting the individualbases' Maintenance Production Analysis offices for their locallistings of tire NRTS and condemnation actions. Their listingsconfirmted the values in the AFR 66-1 MDC listings. The data problemwas therefore assumed to be at the base tire shop level.

From the tire shops of the subject bases, we found out thefollowing: When a wheel and tire are removed from the aircraft, aRemove (P) or Remove and Replace (R) action, with appropriate "HowMalfunction" code, is documented on an Air Force Technical Order(AFTO) Form 349, Maintenance Data Collection (MDC) Record. This isusually documented against the failed item (wheel component or tirecomponent).

After the tire has been dismounted in the shop, it is either sent tosupply for recapping or condemned to salvage. All these actions andtimes are recorded on the AFTO-349 form but now under the ShopSupport General Work Unit Code (WJC) 09000. The tire has now lostits identity and there will be no NRTS actions against the tire WUIC

43

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in the AFR 66-1 data bank. According to AFR 66-1, the shop shouldhave generated another AFTO-349 form against the tire, showing theappropriate NRTS action. However, this is done inconsistently, ornot at all, because all the times and actions spent on the tire havebeen accounted for in other paperwork. What makes this problem sodifficult is that the documentation procedures are not consistentfrom base to base. Therefore, a more accurate and consistent way tocount wheel and tire maintenance actions is to tabulate P and Raction codes recorded against the wheel and tire work unit codes.Unfortunately, this method is not always consistent among thedifferent commands so an alternative, more subjective data set wasalso examined and analyzed for wheel and tire maintenance demand.

During the base visitation phase of the metrics contract, the basetire shops were asked to furnish estimates of how many tires wereNRTS condemned in an average month. These subjective experiencedata were used as a cross-check of the MDC data sample. Afterexamining these data and comparing them with the "P and R" data set,some inconsistencies were noted at several bases. These wereprobably due to the uneveness of both the way the shop estimateswere made (everything from offhand "guesstimates" to detailed shoprecord counts), and the inconsistencies in the recording of P and Ractions against the tire WUC. Both data sets had good and badcharacteristics for determining actual wheel and tire maintenanceaction demand. Further analyses and tests were performed on boththe "P and R" and "subjective" data sets in order to select the bestwheel and tire MAD parameter for each class of aircraft. For thecargo/transport and bomber types, the MDC "P and R" data set provedto be best for wheel and tire MAD; whereas for fighters, the"subjective" data were used for wheel and tire MAD.

44

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4.0 DEVELOPMENT OF MAINTENANCE METRICS AND WEIGHTINGS MODELS -

The next step in the attainment of study objective was thedevelopment of new comprehensive prediction and estimation modelsfor maintenance action rates from the field experience andanalytical data base accumulated by the first five study tasks. Theobjective of this model development eftort is the improvement of theestimation techniques currently used to predict the maintenancemetrics of emerging weapon systems and/or new basing concepts. Thiseffort was originally intended to utilize the design, packagingenvironment, and use characteristics of the equipment items studiedto develop statistical mathematic or parametric models for theestimation of the resource demands of each study subsystem separatefrom operational or environmental factors. Then it was intended todevelop statistical weighting factors with which to modify modelestimation results to compensate for specific a.rcraft basingconcepts operational dnd environmental conditions. It was found tobe more accurate and efficient to include the operational andenvironmental factors in the initial statistical analysis.Therefore, these tasks were combined into one effort.

Maintenance metrics model development was divided into threedistinct but similar subefforts. Maintenance metric models weredeveloped in separate efforts specifically tailored forcargo/transport, bomber, and fighter aircraft equipment,respectively.

Multiple regression MAD estimating models have been developed foreach of the 30 subsystem items for each of the three classes ofaircraft. Four regression equations were developed for each item to

facilitate MAD estimating efforts within the user community. Thefirst three equations utilize either equipment parameters,operational parameters, or environmental parameters, respectively.These models are intended to be used to provide the best availableMAD estimate when the potential user has access to or knowledge ofonly one or two of the three types of parameters. For instance, aplanner who needs a maintenance demand estimate in a new basingsituation and has access to the planned operational scenario and/orthe new base environment but does not know the aircraft equipmentcharacteristics can use the operations models or the environmentmodels to obtain the estimate. Conversely, a designer who knows theaircraft equipment characteristics but does not know the basing

45

I _

situation within which the aircraft is to be used can estimatemaintenance demand with the equipment model.

The fourth regression equation for each item combines the mostsignificant equipment, operational, and environmental parameterswithin one model. These composite models are preferred forcomputing MAD estimates since use of all three parameter typesyields a more well-rounded, accurate estimate of actual fieldexperience maintenance demand. It is therefore recommended that theuser community utilized the composite models whenever possible.

4.1 MODEL DEVELOPMENT

The procedure used to develop the MAD estimating models isillustrated in Figure 9. The data from which the significant MIERsfor each of the 30 items were derived (refer to paragraph 3.0) weremerged into input data sets of significant equipment, operational,and environmental parameters (three data sets for each equipmentitem). Packages of data sets were assembled for each of the threeaircraft classes. These data sets were then operated on with aninteractive, stepwise, multiple regression computer program to yiel'the three types of generic models (equipment, operational, andenvironmental). The parameters that survived in each item's genericmodels were, in turn, merged into composite data sets for input intothe stepwise program. This procedure yielded a composite model foreach of the 30 equipment items for each of the three types ofaircraft: cargo/transport, bombers, and fighters.

The form of the composite models is as follows:

MAD - A+(B I Equip Param1 +...+Bm Equip Paramm ) +

+(C IOpnl Param 1 +... C, Opnl Paramn )+..

..+(D I Environ Param I +...+D, Environ Paramp ).

The generic and composite input data sets from which the MADestimating models were derived form part of the basis maintenancemetrics data base. Figure 10 illustrates a typical example of acomposite regression model developed during this effort. Completelistings of the generic and composite models for cargo/transport,

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bombers, and fighters can be found in the respective user guides(supplementb 1, 2, and 3 to this technical report).

The statistical characteristics assumed for model development wereas follows:

1. Each major independent variable appearing in each

model equation is unrelated to other majorindependent variables in the model.

2. The range of values represented by the data samplesused encompassed essentially the full range ofpossible Air Force-wide values for equipment,operational, and environmental characteristics forcargo/transports, bombers, and fighters.

6.2 MODEL RATIONALE

The generic and composite Maintenance Metrics and Weightingsregression equations developed for the study were based on asampling of the critical equipment items in each aircraft subsystem.Critical equipments are considered to be those items (usually onlyone or two), within a subsystem, which drive the maintenanceresource demands of that subsystem and may be used to represent thetotal subsystem without serious degradation of maintenance metricsanalysts results. Critical equipments rather than total subsystemswere used for maintenance metrics development because the far

greater time and resources required for the data gathering andanalysis of each item in each subsystem could not be justified interms of the increased accuracy of the metrics developed. (Section2 of this report eisusses subsystem equipment selection and dataacquisition.) Tieretore transformation of the outputs of theregression models (partial subsystem MAD estimates) must beperformed to prepare total subsystem MAD estimates. This isaccomplished through the utilization of an actual sample ofhistorical maintenance action demand data for the subsystems (orsimilar subsystems, if equipment is new) being analyzed andsimulated. These actual data are used to calculate a ratio factorof total subsystem MAD to selected equipment sample MAD. This totalsubsystem MAD scale factor can then be applied to the partial HADestimates to yield total subsystem MAD estimates.

49

*1 I.

4.3 SPECIAL FINDINGS FROM MAINTENANCE METRICS MODEL DEVELOPMENT

During the course of metrics model development, certain general andspecific trends and findings became apparent. One of the majorobjectives of the overall study was to evaluate actual usefulness ofthe traditional sortie rate to failure and/or flying hours tofailure metrics in predicting maintenance demands of the detailedsubsystems and equipment on aircraft. The study findings in thisarea are quite interesting in that not only have the importance ofthese traditional measures diminished within the statisticallyderived math models which were developed, but also that importantdifferences have turned up between the various categories ofaircraft. These differences seem to be due largely to the differingdesign, operational, and environmental regimes within whichtransports, bombers, and fighters operate.

The major usage parameters (sortie rate, landings, and flying hours)appear in 13 or 29 cargo/transport composite MAD estimating models.Of the 13 models containing usage parameters, six are for primarilymechanical systems and seven are for primarily electrical oravionics systems.

In the bomber aircraft category, usage parameters appear in only 10of 30 composite models. Of these 10, 8 are avionics, and only 2 arefor primarily mechanical systems. This result is rather surprisingwhen the natural assumption would be that the mechcanical systemswould be more sensitive to usage than would electronics systems. One

possible explanation lies in the bomber sample used to develop themodels. Many of the avionics systems on the older B-52 modelscontain old technology electronics, which is much more sensitive towear-out than is the newer solid-state, integrated electronics.

For fighter aircraft, usage parameters were rarely found to be

legitimate MAD causal factors. The reasons are discussed in detailin Section 3.3.1. Of the 30 equipment items, only theIdentification Friend or Foe (IFF) system composite model containssortie rate as a legitimate usage factor. Another causal parameterthat also is an indication of wear-rate and stress exposure isimportant to the fighter MAD estimating models, however. Thisparameter is "sortie length," and is a factor in 11 of the fightercomposite models.

50

In summary, it can be seen that usage factors do not, in general,drive the maintenance metrics models. This does not necessarilymean that these factors do not play a role in the maintenance demandof equipment. Rather, these results should be interpreted asindicative of relative statistical importance. During thestatistical analysis of the data, other causal factors showed morestatistical strength in "fitting" the data, and the conventionalusage measures dropped out of the models.

Another lesson to be learned from the model development process isthat reliance cannot be placed on any few strong driving causalfactors to predict the demands of the diverse equipments comprisingmodern aircraft (as exemplified by the usage factors above). Eachsubsystem requires individual analysis in light of its individualdesign characteristics and use environment. For instance, themaintenance demand for the landing gear is strongly dependent onusage factors; while jet engines seem to be far more sensitive tooperating temperature, temperature cycles, and complexity factorsthan to operating time or usage alone. Modern aircraft electronicsMADs, on the other hand, tend to be more dependent on complexityfactors (part counts) than either operating time or on-off cycles(temperature excursions).

51

L

5.0 MAINTENANCE DEMAND CAUSAL ANALYSIS

During Phases I and II model development. it was found that some ofthe independent regression variables and/or the polarities of theircoefficients appeared to run counter to conventional experience andexpectations as to the causality of maintenance demand. Theappearance of this counterintuitive model logic indicated therequirement for further investigation into the actual underlyingcauses and mechanisms of maintenance action demands associated withdesigi,, operational, and environmental factors. In some cases,these anomalies in conventional logic appeared to be due to basicunderlying variables in equipment design, usage, or environmentalexposure. These causes may not be directly measurable but act toinfluence the maintenance metrics models through surrogateparameters in the model equations. In other anomalous cases, it wassuspected that certain variables entered the regressions incounterintuitive ways because of the manner in which multiple

regression analyses are mechanized.

The refined and expanded maintenance metrics and weightings modelsdeveloped during Phase III (see Section 4.0) also contain occasionalcounterintuitive anomalies. Therefore, the focus of this effort wasto explain and normalize the anomalies encountered on selected

maintenance metrics models and provide a causality rationale for theparameters entering the equations.

5.1 CAUSAL ANALYSIS APPROACH

The approach taken to the causal analysis is illustrated in Figure11. In Lhis approach, the independent variables (model parameters)explaining each equipment item's MAD are logically linked with basiccausal factors which in turn can be decomposed into elementalequipment and human stressors (underlying causes for equipmentfailure and/or human maintenance error). These logical linkages arein turn given credibility through the linking of supporting directfield evidence gathered during the on-site base surveys. Figure 12portrays the relationships between the nine identified aircraftmaintenance demand causal factors and the elemental aircraftequipment stressors. An example of a causal factor/iield evidencelinkage diagram for a typical equipment item's composite MADestimating model is illustrated by Figure 13. Complete listings ofeach equipment item linkage diagram for cargo/transport, bombers,

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and fighters can be found in the respective users guides

(supplements 1, 2, and 3 to this technical report)..

5.2 SPECIAL FINDINGS FROM CAUSAL ANALYSIS

During the course of the base surveys and the subsequent causalanaly,es, certain insights were gained to the maintenance problemsand underlying causes of maintenance demand in the field situation.These major insights are as follows:

1. Unrecorded Ground Operating Time. This is a major factorwhich escapes the present data collection systems. In thebase surveys, the intent was to capture this factor in thedata sample through interview methods. Gathering groundoperating events at the flight line, at maintenance control,and in operations permitted computing and recording totaloperating times on the study equipment that are much closerto actuals than the flying hours recorded on the G033B tapes.These ground operating times turned out to be as much as threeor four times the flying hours on some items of equipment.An example of how large amounts of ground time can gounrecorded can be seen in the case of Hahn Air Base, Germany.Hahn is in an area of many quick storms, fog, and otherobstructions to vision. Weather aborts at the end of therunway are common. The aircraft may sit with the enginerunning and all systems operating for 1.0 to 1.3 hours andnever fly. This extra ground time is not recorded in thecommon Air Force data systems. The same thing happens to alesser extent at many other bases.

2. Maintenance And Spares. Spares shortages, long depot recycle

times, and poor quality/faulty spares were common complaintsfrom base maintenance and quality control people. This has adirect bearing on overall maintenance demand and missionreadiness. An example of the sensitivity of maintenance tothe depot/supply function can be seen at RAF Lakenheat,England. Thia location has depot repair facilities andpersonnel colocated at the base for critical components.This works out very well for enhancing "fully missioncapable" readiness.

56

' I.J

3. Manning. In general, all of the bases visited have sufficient

total personnel but need skilled middle grades (primarily dueto attrition of specialist 5-levels. The available personnel

were primarily 3-levels and also 7- and 9-levels. This void

definitely impacts maintenance efficiency.

4. Adverse Weather Maintenance Demand Factors. During the courseof the causal analysis of the data, an interesting relation-

ship became apparent. In general, an inverse relationshipexists between HAD and all "bad" weather parameters, such as

rain days, snow days, snow depth, freezing days, and pre-

dominant northwest winds. This is, the worse the weather,

the fewer the maintenance action demands. This relation-ship exists for practically all equipment items regardless

of their type or their location on or in the aircraft. A

plausible explanation for this, borne out by general flying

hour trends, is that there is less flying, hence less

usage and less maintenance demand during adverse weather.

It is also possible that maintenance performance rateis slowed by adverse weather. This is, fewer maintenanceactions get done and recorded per day.

57

6.0 SUMMARY CONCLUSION

This technical report summarizes the final findings and conclusionsof the 3-phase AFHRL maintenance metrics contact. This report issupplemented by the three users' guides which document the developedmaintenance metrics and findings for (1) cargo/transport. (2)bomber, and (3) fighter aircraft respectively. It presentsdescriptions of methodologies, data, models, and findings developedduring the study effort. The methodological descriptions andfindings contained within this final report are presented in atask-oriented sequential format as follows: (a) data baseacquisition, (b) maintenance impact analysis, (c) maintenancemetrics model development, and d) maintenance demand causalanalysis. The findings represent the results of the researchapproaches and "lessons learned" during the implementation andcompletion of this research effort.

6.1 ASSUMPTIONS AND UNCERTAINTIES

Certain assumptions and uncertainties were inherent in theregression procedures used to detect the MIERs (section 3.2) anddevelop the maintenance metrics models (section 4.1).

I. The assembled data were accurate and unbiased.

2. Each data case value was a member of a continuousnormal distribution of possible values for thatdata case (a necessary condition for least squares

regression).

3. Each major independent variable appearing in eachmetrics model equation is unrelated to the othermajor independent variables in the model.

4. The range of values represented by the data samplesused encompassed essentially the full range of possibleAir Force-wide values for equipment, operational,and environmental characteristics for cargo/transports,bombers, and fighters.

5. The selections of the scalor ranges which were usedto transform the qualitative variable data such asseverity of vibration (refer to section 3.1.1) were

58

based on research and experience. As such wereassumed to be valid quantitative measures ofqualitative datum variables.

6. Many of the models, particularly in the cargo/transport and bomber categories are derived inpart from data on older technology equipment.This may limit their predictive validity whenapplied to new, high technology systems unlessthey are used in conjunction with comparabilityanalysis techniques which can bridge the gaps.

6.2 OVERALL FINDINGS AND RECOMMENDATIONS FROM THE PHASE III STUDY

The most important products of this study have, of course, been thedeveloped maintenance metrics models, along with their underlyingcausal analyses. These metrics provide the necessary computationalequations and supporting informOtion to enable the user community toapply them immediately to aircraft equipment maintenance demandestimation problems. At this point, no further development isrequired on these metrics as applied to peacetime Air Forceoperations.

During the course of development of the metrics, certain importantoverall findings were revealed. As discussed in Section 4.3, theconventional usage factors (sortie rate, landings, and flying hours)proved to be of minor importance as measures of the maintenancedemand of individual aircraft subsystems. In fact, an importantlesson learned Irom the developed models was ti . re is usuallynot any one strong parameter that drives maintenance action demand.Instead, combinations of parameters from all three categories(equipment, operation, and environment) must be considered in orderto develop the best maintenance metrics model for each individualaircraft subsystem. Certain classes of subsystems will be biasedtoward certain causal parameters, however. For instance, landinggear and flight surfaces seem to be influenced by usage. Enginesare greatly influenced by operational considerations, such asthrottle excursions (mission profiles) and environmental factors;e.g., corrosion problems. Modern avionics maintenance demand isgenerally more dependent on design complexity than on operational orenvironmental considerations. Also, it is important to note thatthere are important differences in the causal parameters comprisingthe maintenance metrics of cargo/transports, bombers, and fighters.

59

I wI oISn.,_ _.. ... .......

These differences are due largely to the differingdesign/performance, operational, and environmental regimes whichapply to each category of aircraft.

The analysis of the causal factors underlying the developed metricsalso turned up some important general findings. The base surveysdiscovered large amounts of unrecorded (in the standard Air Forcedata ' btems) ground operating time. This ground operating timewould have biased the results of the maintenance metrics developmenteffort if it had not been captured and included in the models.Spares shortages and long lead times are a chronic problem in thefield and definitely affect maintenance efficiency andcannibalization. Base maintenance manning is adequate as tonumbers, but there is a definite shortage in the skilled middlegrades (specialist 5-level). There are adequate numbers oflow-skilled 3-levels and management-type 7- and 9-levels. This voiddefinitely affects maintenance efficiency.

Climate and weather were found to have a definite impact on aircraftsubsystems maintenance demands. Although these impact factors werenot generally as strong in statistical significance as some designand operational factors, they definitely were present in the data.The mean temperature of the study bases was generally positivelyrelated to maintenance demand under peacetime operating conditions.Interestingly, under peacetime operations, adverse weather had anegative effect on maintenance demand. That is, the worse theweather, the less the equipment required maintenance. It is positedthat this phenomenon may be due to less flying, hence less failureduring adverse weather. It is also possible Lhat bad weatherhampers maintenance to the point that the maintenance action rate isslowed. In other words less maintenance gets done and recorded perday.

Another climatic impact that showed strongly in the data wascorrosion problems. This statistic appeared as expected with highand/or dry bases experiencing few problems and warm/wet/sea airbases having serious problems.

The climate and weather impacts of the typical environmentalparameters, (total snowfall, mean minimum temperature, and aircraftcorrosion problems) are illustrated in Figures 14, 15, and 16.

60

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In conclusion, the most important end products of this study are newmaintenance metrics that can increase the accuracy with which themaintenance demand rates of the various aircraft subsystems arepredicted. These improved measures can now be used by the Air Forceuser community in their prediction and estimation efforts for.

1. Manpower determination studies.

2. Cost-of-ownership studies.

3. New basing and deployment planning.

4. Design trade studies for future aircraft.

64

REFERNCES

The following documents were used in the development of themaintenance metrics models.

1. AFHRL-TR- 77-45k iIfe Ccle Cost of C-130E Weapon System,Air Force Human Resources Laboratory (ASD) Wright-Patterson AFB, Ohio, July 1977.

2. AFFDL-TR-73-152 ananbl~/elaiil~atoSjstrn~ppor CotsAir Force Flight Dynamics

Laboratory, Wright-Patterson AFH, Ohio, December 1973.

3. Logistics Composite Model Users ReferenceGuide, AirForce Logistics Command Report 70-1, AD703- 328,Headquarters Air Force Logistics Command, January 1970.

4,. The__Lo istics Comp~osite Model Users Reference Guide,AFLC Report 74-1, Research Division (MCCTR), HQ AirForce Logistics Command, Wright-Patterson AFB, Ohio,January 1970.

5. LCOM 11 Simulation Software Us ers Re ference Gui de,AFMSMET 78-1, AFMSMET Wright-Patterson APB, Ohio,May 1, 1978.

6. SimulatinR Maintenance Manning for New Weapo ystem;_Maintenance Manpower Manalement Du r iq& Weapon System_

DeveopmntAFHRL,-TR- 74-97(l), Wright- Patterson AFB,Ohio; Advanced Systems Division, Air Force HumanResources Laboratory, ADA 011986, December 1974.

7. SultnManeacMan foNew Weap 1 ystems:uMi ldin&_ktd Opekrating a Simul ation Model (Volume TI),

AFIIRL-TR- 74- 97(11), Air Force Systems Command, BrooksAF8, Texas, December 1974.

8. SimulatinK Maintenance Manni ng foqr New We~apon~astems:Maintenance Data Anali sis Proqram,_AFHRL-TR-74--97(ITI),

Wright-Patterson AFB, Ohio, Advanced Syustems Division,Air Force Human Resources Laboratory, ADA 025342, 1975.

65

9. SimulatjflR Maintenance Manning for New Weapon qy-!tenms-Data Base Manaxement Prograis , AFHRL-T(-714-97(lV),Wright- Patterson AFB,-Ohio, Advanced Systems Division,Air Force Human Resources Laboratory, ADA 011989,December 1974.

10. Simulating Maintenance Manning for New_ Weapon SyjstemsL:Manpwer ro~am, AFHRL-TR- 74-97(V), Wright- Patterson

APB, Ohio, Advanced Systems Division, Air Force HumanResources Laboratory, ADA 011990, 1915.

11. SimulatingMaintenance Mann in& for New Weapon ystem!s:Voum VMai ntenanc eManpower Matrix Prog 'ram.

AFHRI1-TR-74-97(VI), Air Force Systems Command, BrooksAFB, Texas, May 1976.

12. Common Data Etato rgas(DP tnadSseVersion 1.0. User Documentation, Air Force Maintenanceand Supply Management Engineering Team, Wright-Patterson AFB, Ohio, March 8, 1978.

13. BCS.-GI109 "CDEP ProductionPs~emqq," Boeing ComputerServices, Seattle, Washington, February 1979.

14. BCS-1Ol0l-019-Rl,Mainstream~ - TS InteractiveStatistI Package (STATPK), Boeing ComputerServices, Seattle, Washington, March 1978.

15. SPSS Statistical Pac;kg e for the Social Sciences,Norman Nie, Dale H. Bent, C. Hadlai Hall; McGraw-Hill Book Company, Inc,, 1970.

66

GLOSSARY OF ABBREVIATIONS AND ACRONYMS

AB Air BaseACFT AircraftAFB Air Force BaseAFE Ar Force EuropeAFHRL Air Force Human Resources LaboratoryAFMEA Air Force Management Engineering AgencyAFTO Air Force Technical OrderAGE Aerospace Ground EquipmentAMS Avionics Maintenance SquadronAVG AverageBIT Built In TestBMW Bomb WingCU CubicEAC Experience Analysis CenterEDAD Estimate of Maintenance Action DemandENVIRON EnvironmentEQUIP EquipmentF-CLOCK Failure ClockFOD Foreign Objects Damage

FT FootFTW Fighter Training WingHF High Frequency

HR HourIFF Identification Fried or FoeI/N Input/OutputLBS PoundsLCOM Logistics Composite ModelMAC Military Airlift CommandMAD Maintenance Action DemandMAINT MaintenanceRAW Military Airlift WingNDC Maintenance Data CollectionMH HanhourtIER Maintenance Impact Estimating RelationshipMIN MinuteMHK Maintenance ManhourIIM Maintenance Manpower Model

NO HonthMRD Maintenance Resource Demand

NO NumberNORM Not Operationally Ready MaintenanceNORS Not Operationally Ready SupplyNRTS Not Repairable This Station

67

OCALC Oklahoma City Air Logistics CenterOPNL OperationalOR Operationally ReadyORG OrganizationObS Operations and SupportSAALC San Antonio Air Logistics CenterSAC Strategic Air CommandSPSS Statistical Package for the Social SciencesSRU Shop Replaceable UnitTAC Tactical Air Command

TACAN Tactical Air NavigationTFW Tactical Fighter WingTO Technical OrderTR Technical Report

T W Tactical TraLning Wing

UHF Ultra High FrequencyUSAFE United States Air Forces in EuropeWUC Work Unit CodeWT Weight

68

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