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AIR COMMAND AND STAFF COLLEGE

AIR UNIVERSITY

Reducing the Intercontinental 

Ballistic Missile Alert

Rate and the Impact on

Maintenance Utilization 

Stephen M. Kravitsky

Major, USAF

Air Command and Staff College

Wright Flyer Paper No. 26

Air University Press

Maxwell Air Force Base, Alabama

June 2007
ii

Disclaimer

Opinions, conclusions, and recommendations expressed or implied within are solely 

those of the author and do not necessarily represent the views of Air University, the 

United States Air Force, the Department of Defense, or any other US government 

agency. Cleared for public release: distribution unlimited.

This Wright Flyer Paper and others in the series are 

available electronically at the Air University Research 

Web site http://research.maxwell.af.mil and the AU 

Press Web site http://aupress.maxwell.af.mil.
iii

Foreword

It is my great pleasure to present another of the Wright 

Flyer Papers series. In this series, Air Command and Staff 

College (ACSC) recognizes and publishes our best student 

research projects from the prior academic year. The ACSC 

research program encourages our students to move beyond 

the school’s core curriculum in their own professional development and in “advancing air and space power.” The 

series title reflects our desire to perpetuate the pioneering 

spirit embodied in earlier generations of Airmen. Projects 

selected for publication combine solid research, innovative 

thought, and lucid presentation in exploring war at the operational level. With this broad perspective, the Wright Flyer 

Papers engage an eclectic range of doctrinal, technological, 

organizational, and operational questions. Some of these 

studies provide new solutions to familiar problems. Others 

encourage us to leave the familiar behind in pursuing new 

possibilities. By making these research studies available in 

the Wright Flyer Papers, ACSC hopes to encourage critical 

examination of the findings and to stimulate further research in these areas.

JAY H. LINDELL

Brigadier General, USAF

Commandant



Abstract

We have been at war for four and one-half years. The financial burden of executing Operations Iraqi Freedom and 

Enduring Freedom caused military services to undergo extensive cost-cutting efforts. The intercontinental ballistic 

missile (ICBM) community is not exempt. Recently, the Air 

Force Nuclear General Officer Steering Group (AFNGOSG) 

requested an additional study of lower missile readiness 

rates, presumably to identify any potential cost savings 

from reduced maintenance and security footprints. This 

research offers an initial study by analyzing the impact of 

lowered ICBM alert rates caused by not repairing off-alert 

missiles until a lowered alert-rate threshold is reached and 

any correlation to a potential decrease in daily ICBM maintenance team utilization. 

The intent of this research is to provide an analysis of the 

ICBM maintenance team utilization at the current ICBM 

alert rate and at lowered alert rates. Quantitative research 

methodologies are used to model historical ICBM maintenance data from the 341st Maintenance Group (MG) and 

simulate future maintenance team utilization at both the 

current and decreased ICBM alert rates. 

The results of this simulation and modeling show negligible savings in overall ICBM maintenance team utilization. 

One maintenance section under study showed a statistically significant but slight increase in team utilization as 

the alert rate decreased. Another section under study exhibited a slight decrease in team utilization deemed statistically significant, however, extremely hard to quantify as 

the increase in team utilization was only .62 percent. The 

remaining four maintenance sections under study had statistically the same team utilization at all alert-rate levels.

vii

Acknowledgements

Many individuals were vital to the accomplishment of this 

research paper. I would like to thank CMSgt Leslie S. Ellis, 

Technical Advisor in the 341st Maintenance Group (MXG/

TA), Malmstrom AFB, Montana, and the dedicated personnel in the Plans and Programs branch at the 341st MXG, 

MSgt Norman T. Vercolen and SSgt Sunshine M. Johnson, 

who provided all the data for the model along with Capt 

Tamiko L. Ritschel and SMSgt Michael T. Dawson, Air Force 

Logistic Management Agency, Maxwell-Gunter Annex AFB, 

Alabama, who designed and programmed the model for this 

study. Additionally, I would like to extend a special thanks 

to Col Randy B. Tymofichuk, Air War College, Class 2006, 

Maxwell AFB, Alabama, who provided both exceptional 

knowledge and mentoring throughout the entire process of 

my research.



Introduction

The intercontinental ballistic missile (ICBM) system has 

been a key deterrent force of the United States for over 40 

years. The maintenance teams within the ICBM system 

maintains alert rates (total number of missiles alert ready 

divided by the total number of missiles in the force) at 

approximately 98 percent annually. The current version of 

the weapon system, the Minuteman III, is deployed at three 

wings: Malmstrom AFB, Montana (200 ICBMs); Minot AFB, 

North Dakota (150 ICBMs); and F. E. Warren AFB, Wyoming 

(150 ICBMs). Each unit maintains high alert rates, directly 

attributed to the maintenance effort providing a deterrent 

posture and combat capability. The ICBM system is the foundation of a rapid global response and maintains the highest alert readiness of America’s Cold War–era nuclear triad 

(ICBMs, submarine-launched ballistic missiles, and bomber 

forces). The weapon system continues to be alert ready 24/7 

fulfilling national security requirements.

Supporting the global war on terrorism (GWOT) and 

ongoing operations around the world created a budget 

shortfall, forcing the Air Force to consider measures to save 

resources. The Air Force expects to be $733 million short in 

personnel funding, and based on current burn rates, there 

was a projected $3 billion shortfall in operations and maintenance (O&M) funds by the end of the fiscal year 2005 (FY 

05).1 With fewer funds available, increased security requirements including physical security upgrades, and new 

methods for countering threats against the ICBM force in 

the post-9/11 world, coupled with the most intensive ICBM 

weapon system modernization program in history, ICBM 

programs, requirements, and policies are under review.2

Recently, the Air Force Nuclear General Officer Steering 

Group (AFNGOSG) requested an “additional study of lower 

requirements for missile readiness (alert rate).”3 There are 

many perceived benefits of lowering the ICBM alert rate. 

One of these benefits that is by lowering the alert rate the 

ICBM maintenance team utilization will also decrease, thus 

generating cost and resource savings.

The simulation used is a joint effort using Arena Rockwell 

Automation software in cooperation with the Air Force Logistics Management Agency (AFLMA) to model maintenance-
team utilization at various alert rates (see appendix A for 

Arena synopsis). Data input for the model consisted of historical data during a six-month period (1 May 2005–31 October 

2005) from the 341st Maintenance Group (MG), Malmstrom 

AFB. Using data inputs and an alert rate of 98.5 percent as 

the standard, an initial forecast was developed as a baseline. 

In subsequent simulation runs, the alert rate was reduced to 

90 percent and 80 percent, respectively. The output of each 

modified alert rate was then compared to the current alert 

rate’s output followed by comparisons in maintenance-team 

utilization. 

The expectation is that lowering the ICBM alert rate will 

not generate any statistically significant savings in the maintenance-team utilization, and any decrease in maintenanceteam utilization will only occur in two of the maintenance 

sections under study. Ongoing maintenance of the launch 

facilities will negate any savings realized. 

Background

A detailed review of maintenance execution and prioritization, ICBM maintenance teams, impact of modifications programs, security resources and environment, and ICBM alert 

rates was conducted. These areas were critical to formulate 

data to model and build a simulation. To comprehend ICBM 

maintenance, a general understanding of the aforementioned 

items is essential. This section provides a brief explanation 

of the current ICBM maintenance-operating environment. 

Maintenance Execution and Prioritization

Executing ICBM maintenance is unlike any other type of 

Air Force maintenance. Maj Gen Tom Neary, USAF, retired, 

former 20th Air Force commander, stated it best, “While ope­rations at most Air Force installations occur on a collocated 

flight line, ICBM maintenance technicians . . . can travel up 

to [and in some cases more than]150 miles one way to reach 

their ICBM launch and alert facilities.”4 In order to perform 

maintenance, ICBM maintenance technicians dispatch to 

missile fields ranging in size from 8,600 square miles (Minot 

AFB) to 23,000 square miles (Malmstrom AFB).
When a maintenance team dispatches to the missile 

field, they perform maintenance at a launch facility (LF) 

or a missile alert facility (MAF) (see appendix B for MAF 

and LF diagrams). A LF is a hardened silo containing one 

missile. It has operational ground equipment that makes it 

independent of external support. The facility is hardened to 

withstand conventional and nuclear attacks. Additionally, 

an LF has a launcher support building (LSB), a groundlevel-buried building adjacent to the launcher, housing a 

diesel generator, environmental control systems, and other 

real property installed equipment (RPIE). These sites are 

unmanned; however, a security system designed to detect 

unauthorized activity at the LF maintains security.5 A MAF 

contains an above ground support facility and a below 

ground launch control center (LCC). The LCC houses a twoperson launch crew who “pull alert duty” and operational 

ground equipment (OGE) necessary to exercise command 

and monitor status over the LFs and to communicate with 

higher command authorities.6 Like an LF, it too contains a 

support building with various RPIE items.

Additionally, a structured maintenance priority system 

serves as a guide in the conduct of both day-to-day maintenance and weapon system modernization. Air Force Space 

Command Instruction (AFSPCI) 21-114, Intercontinental 

Ballistic Missile (ICBM) Maintenance Management, 1 May 

2003, table A2.1, delineates maintenance prioritization on a 

scale of 1 to 9 (see appendix C for priority designators). Priority 1 (P1) maintenance is the highest priority and requires 

the repair of critical equipment needed for safe weapon system operations and maintenance actions needed to prevent 

damage to the weapon system, to avoid personnel injury, 

and to render the weapon system safe.7 Priority 2 (P2) maintenance, for the purpose of this study, deals with bringing off-alert missiles (failed missile or ground equipment 

rendering the missile not launch capable) to alert status, 

and maintenance required to reposture LFs returned from 

modification or test programs. These actions make up the 

majority of the major maintenance activities.

Major maintenance involves maintenance requiring an 

open launcher closure (LC) door.8 The LC covers the silo 

portion of the LF and is only opened during activities that 

require aerospace vehicle equipment (AVE) replacement 
consisting of reentry system (RS), missile guidance set 

(MGS), propulsion system rocket engine (PSRE), and missile booster replacement, or other seldom-performed tasks 

such as LC repair. Other priorities having varying degrees 

of importance require scheduling based on the priority system described above. 

Maintaining the Minuteman III weapon system can be 

a daunting task for maintenance supervisors and technicians. For safety reasons, the maximum duty period for dispatching personnel is limited to16 hours in any combination of on- or off-base duty. In addition, technicians earn 

an uninterrupted 12-hour-rest period upon completion of 

an off-base dispatch or eight hours rest if they remained 

overnight at a MAF.9 With preparatory dispatch maintenance activities taking anywhere from one and one-half to 

four hours, long-drive times from the missile-support base 

to the LFs, security requirements to “penetrate” (gain access 

to the below ground silo) the site, severe weather conditions, 

and postmaintenance activities, actual maintenance time at 

a LF may be limited. These factors, coupled with restrictions placed on field maintenance activities due to increased 

security requirements and an increased maintenance tempo 

accomplishing weapon-system modification programs, make 

the scheduling and execution of maintenance difficult.

Maintenance Teams

Several maintenance teams are utilized on a day-to-day 

basis to maintain the LFs and to keep missiles alert ready. 

Although not every team included in this study has a direct 

impact on the alert rate, the effect of lowering the alert rate 

may or may not affect their utilization. The maintenance 

tempo is dependent on the number and priority of tasks 

requiring attention on any given day. The teams listed below 

perform maintenance on an LF, and at times, compete for 

critical resources (e.g., security teams to penetrate a site). 

Consequently, one team’s requirements may offset another 

team’s ability to perform maintenance.

1. Electromechanical Team (EMT) technicians perform 

electronic, electromechanical, security, electrical system 

repair, troubleshooting, and coding of the ICBM weapon 

system. An EMT normally consists of two Air Force Specialty 
Code (AFSC) 2M0X1 technicians.10 EMT technicians mainly 

troubleshoot and repair the OGE on LFs and MAFs.

2. Missile Maintenance Team (MMT) personnel remove 

and replace (R&R) and transport Minuteman III aerospace 

vehicle equipment. They also perform maintenance on Minu­teman III umbilical cables, the suspension system, and the 

LC system. The MMTs assist missile-handling technicians 

(MHT) in the R&R of Minuteman III missiles. An MMT consists of five AFSC 2M0X2 technicians.11 The MHTs were not 

included in this study because a MMT will always assist LF 

task performance and normally have control of the LF during maintenance.

3. Facilities Maintenance Team (FMT) personnel perform 

on-site repair of LF and MAF power and environmental systems. A FMT normally consists of two AFSC 2M0X3 technicians.12 FMT technicians predominantly troubleshoot and 

repair the RPIE on LFs and MAFs.

4. Periodic Maintenance Team (PMT) personnel perform 

preventative maintenance in accordance with (IAW) a scheduled periodic maintenance cycle. A PMT normally consists of 

at least five AFSC 2M0X3 technicians.13

5. Corrosion Control Team (CCT) personnel perform corrosion maintenance at LFs, MAFs and on-base locations. 

Like a PMT, a CCT has a periodic preventative maintenance 

schedule to extend the life of the weapon system. A CCT 

normally consists of four civil service technicians.

6. Minuteman Integrated Life Extension (Rivet MILE) 

Teams are a maintenance support program that provides 

programmed depot maintenance at the space wings. Rivet 

MILE Team actions include the refurbishment of LFs and 

MAFs during recurring cycles and are critical to sustain the 

weapon system beyond 2020.14

Although this study does not look at maintenance manning directly, appropriate maintenance manning levels are 

crucial to accomplishing the maintenance workload. Current 

manning of AFSCs 2M0X1, 2M0X2, and 2M0X3 personnel 

at ICBM units seems to be healthy. In fact, at the 341st 

MG, AFSCs 2M0X1, 2M0X2, and 2M0X3 manning rates 

(number of personnel assigned as opposed to the number 

authorized) are 119 percent, 106 percent, and 138 percent, 

respectively.15 However, worldwide AFSCs 2M0X1, 2M0X2, 

and 2M0X3 manning rates are expected to drop to 93 per-
cent, 91 percent, and 88 percent, respectively, between now 

and FY 09.16 Reductions may be attributed to estimated 

reenlistment and retirement rates coupled with accession 

numbers falling below the norm required to sustain the 

2M AFSCs during FY 05–FY 08. The potential ramifications 

are a reduced number of three- and five-skill-level workers needed to accomplish maintenance in an environment 

of increasing workloads due to current and future modifications. This may require personnel who are more senior and 

currently serving at the supervisory level to perform maintenance instead of their current supervisory duties. 

Modification Programs and Increased Workloads

In 1999 the ICBM community embarked on a massive 

$8 billion service-life extension and force modernization 

program expected to continue through 2012, thus increasing the Minuteman III weapon system’s viability until 2020. 

Today, the propulsion replacement program (PRP), guidance 

replacement program (GRP), PSRE life extension program, 

single reentry vehicle program (SRV), safety enhanced reentry vehicle program (SERV), and environmental control system (ECS) replacement program are a few of the 64 modification programs either funded, or proposed, dealing with 

AVE and OGE.17

These programs place huge demands on maintenance 

technicians and security forces. Aside from the normal 

accomplishment of scheduled, unscheduled, and periodic 

maintenance, team utilization increased due to the workload associated with the modification program without an 

increase to the 2M0X1, 2M0X2, or 2M0X3 AFSC’s manning 

authorizations. At the onset of the modification programs, 

estimation showed that each ICBM unit would have an 

increase of 66 maintenance dispatches, six open LC doors, 

and four RS convoys per month.18

Security

Protection of the Minuteman III weapon system is critical to national security. The primary goal of security is the 

protection of the nuclear weapon, the RS. When a maintenance team dispatches to penetrate a LF, they have a 

specified number of security escorts, called a security 
escort team (SET) allotted. SETs are required only when 

a maintenance team is to penetrate the silo portion of the 

LF. Additional requirements ensure a much larger security force is available to respond if a hostile event occurs. 

A larger security force is required when performing major 

maintenance activities.19 The post-9/11 environment and 

revisions of security directives, such as the Department 

of Defense (DOD) C5210.41-M, Nuclear Weapon Security 

Manual (U), March 1983, increased security forces (SF) 

personnel requirements for day-to-day maintenance activities and physical-security modifications on LFs. Maintenance production is often directly tied to the number of SF 

personnel available to provide site security. With increased 

demands on SF personnel and no subsequent increase 

to manning, lower-priority maintenance is sacrificed to 

accomplish higher-priority maintenance to maximize alert 

readiness. Maintenance production supervisors must 

deconflict all requirements to ensure maintenance teams, 

security escorts, and fielded security forces are available to 

meet weapon system maintenance demand. 

Alert Rates

When we had 1,052 [ICBMs], we had room to 

spare. Now with 500 [ICBMs], I can guarantee you, 

we’ve got 500 targets for those 500 missiles, so 

the value of each one of these ICBMs is extremely 

important, and we can see that in the words of our 

President and the words from all our leaders.

Brig Gen Thomas F. Deppe, USAF

Maintaining high alert rates is a team effort of ICBM 

operators, security forces, civilians, civil engineers, and 

several others.20 However, ICBM maintenance-technicians 

carry the lion’s share of the work on their backs keeping 

the weapon system operating 24/7 in very demanding environments. Whether mandated by Strategic Air Command 

Regulation (SACR) 66-12, Maintenance Management, vol. 1, 

Intercontinental Ballistic Missile Maintenance Management 

Policy and Supervisory Responsibilities, 30 October 1989, 

in the Cold War era, or currently by AFSPCI 21-114, ICBM 
maintenance directives have changed little when dealing 

with alert rates as compared below:

All maintenance actions and all management efforts must support 

the Single Integrated Operational Plan (SIOP). A high alert rate is 

required; however it must be the product of effective management 

without compromising safety, security, or maintenance discipline. 

It addresses the concept that wings (ICBM) establish a level of alert 

sorties below which it responds . . . at an intensity to preclude an 

unacceptable alert rate.21

All maintenance actions and management efforts must be directed 

towards maximum availability of ICBMs in support of the United 

States Strategic Command requirements and directives. All maintenance organizations are mandated to use all resources in the most 

effective and efficient way.22

The ICBM maintenance philosophy has always been one 

of aggressively repairing off-alert missiles and providing the 

war fighter with the maximum number of alert-ready assets. 

Any change to this philosophy will require a major paradigm 

shift. The primary causes of off-alert conditions are scheduled and unscheduled maintenance due to failures with 

AVE or OGE. Scheduled off-alert conditions include any of 

the many weapon system modification programs such as 

the GRP, PRP, SERV, PSRE, limited life component (LLC) 

recycles of the RS, and Rivet MILE. These programs produce 

the bulk of the maintenance requirements and are time sensitive in nature. A single unit’s inability to execute these 

programs can affect the entire modernization program’s 

delivery schedules and potentially increase program costs. 

Additionally, weapon-system failures also cause off-alert 

conditions. OGE failures make up a small part of unscheduled off-alert conditions. The majority of weapon-system 

failures driving off-alert conditions are MGS failures. In 

fact, from 1 April 2004 through 31 March 2005, Minuteman 

III rejections data showed that 49.6 percent of the fleet’s 

MGSs failed. The 341st MXG was slightly below the average 

at 49.5 percent.23

All units calculate off-alert conditions daily and forward 

that information to higher headquarters in order to calculate the total ICBM force daily alert rate. The following tells 

how to calculate a unit’s alert rate: if the 341st Space Wing 

has four scheduled off-alert missiles and three unscheduled 

off-alert missiles, the alert rate is 96.5 percent (193 on-alert 
missiles/200 total missiles [based on number of hours off 

alert]). 

The alert rate is a critical aspect of this study. This 

study’s purpose is to highlight maintenance teams not performing maintenance on off-alert missiles until the alert 

rate reaches a lower threshold. Using alert-rate thresholds 

of 90 percent and 80 percent, respectively, this study compared maintenance team utilization at the current rate of 

98.5 percent against the aforementioned rates of 90 percent 

and 80 percent to determine if maintenance team utilization decreased.

Research Methodology

This research used statistical modeling and simulation 

to evaluate the potential for decreased maintenance utilization at various alert rates. In quantitative research, the 

researcher classifies features, counts them, and constructs 

statistical models in an attempt to explain what is observed, 

and it tends to allow the researcher to remain objectively 

separated from the subject matter.24 Models were created 

using Rockwell Automation Arena software and successive 

computer simulations were run. In their book Simulation 

with Arena, the authors explain the value of such simulations this way: 

Computer simulation refers to methods for studying a wide variety of models or real-world systems by numerical evaluation using 

software designed to imitate the system’s operations and characteristics, often over time. From a practical standpoint, simulation 

is the process of designing and creating a computerized model of 

a real or proposed system for the purpose of conducting numerical 

experiments to give us a better understanding of the behavior of that 

system for a given set of conditions.25

This study incorporated data obtained from an existing organization’s operation over a six-month period. This 

allowed us to construct a model that replicates the system’s 

behavior and extend it over a three-year period. The simulations injected stimuli of real-world occurrences; certain 

entities (maintenance tasks performed) were changed to 

lower the missile alert rate and to gain an understanding of 

maintenance team utilization under these new conditions.
10

System modeling used a discrete-event simulation.26

Discrete-event simulations concern the modeling of a system as it evolves over time by a representation in which the 

state variables change instantaneously at separate points 

in time. These points in time are the ones at which an event 

occurs, where an event is defined as an instantaneous 

occurrence which may change the state of the system.27

Each model was run 30 times and comparisons of model 

runs were made to ensure validity and reliability between 

models at a 95 percent confidence level. The results of the 

baseline simulation closely corresponded to actual system 

performance of the period used for the study, thus validating the correctness of the models logic.

Team Utilization Model

A model developed in coordination with AFLMA analysts 

demonstrates the impacts of lowering the missile alert rate 

on daily maintenance team utilization. Using Rockwell 

Automation Arena software, a model was constructed using 

six months of historical maintenance production data from 

the 341st MG. To make the data collection and analysis 

manageable, data utilized was from only one organization. 

The unit selected is the most challenging unit from a maintenance point of view. The 341st has the most expansive 

missile field and 50 more missiles than the other two ICBM 

units. Even though the unit has additional personnel to 

compensate for the increased number of missiles, the sheer 

size of the missile complex presents a bigger challenge. 

Although similar maintenance philosophies and practices 

exist between all three units, any attempt to generalize the 

model between units must take into account differences in 

personnel and geography. 

To construct a realistic model, several factors were considered beginning with input data. The model used realworld maintenance data for the MMT, EMT, FMT, PMT, Rivet 

MILE, and CCT over a 180-day period, weekends and holidays excluded, as maintenance performance is drastically 

reduced on the weekends and security escort availability is 

skewed. On weekends, security forces normally allocate the 

standard number of available assets, while maintenance 
11

teams are at a minimum. This had the potential to show 

an increase to the overall average of SFs available, allowing 

the model to increase maintenance production during the 

workweek. The MAF and all back-shop maintenance were 

also omitted from the study, as their impact on the alert 

rate is more indirect. Additionally, special maintenance 

activities such as the annual code change (surge maintenance where 50 LFs are penetrated in a three- to four-day 

period) were also omitted, as it would skew the data. These 

tasks are higher headquarters directed and time sensitive in nature. A unit normally suspends all maintenance 

of lower and equal priority and schedules every available 

asset (maintenance and security) during this period. Again, 

including this period would show higher available assets 

and not a true representation of day-to-day operations. 

However, with the exception of code change and weekends, 

all teams put in the schedule, whether for LF or MAF maintenance, counted in the daily team availability. Once these 

items were excluded from the 180-day period, there were 

114 days of data for model use.28

Another consideration was the current maintenance 

philosophy and how production-scheduling decisions are 

made using the priority system in AFSPCI 21-114. The priority system allows managers to allocate resources to each 

job whether they are maintenance teams or SF personnel. 

With that, final considerations were how many maintenance 

teams and SF personnel were available over the six-month 

period, how were they apportioned to perform maintenance, 

and what maintenance tasks came up over the six months 

and their frequency. These considerations aided in the identification of entities and resources for the simulation.

Entities

Most simulations involve entities that move around, 

change status, affect and are affected by other entities in the 

system, and affect the output performance measures.29 The 

entities for this simulation are the jobs to be processed. After 

analyzing the empirical data and applying the researcher’s 

knowledge of ICBM maintenance management and current 

guidelines in AFSPCI 21-114, the following entities were 

developed as the primary drivers for the alert rate and daily 
12

maintenance team utilization. These drivers in priority 

order are: P1, maintenance is the highest priority because 

clearing these types of discrepancies is critical to weapon 

system safety. Given the national priority of these assets, 

it will always trump all other maintenance for resources. 

P2, other off-alert maintenance. This is an off-alert condition caused by ground equipment, not a MGS failure. An 

EMT or FMT can clear the condition without performing 

major maintenance activities. P3, major maintenance (Mx). 

These activities require an open LC door and other major 

maintenance such as the modification programs, AVE component removal, and LLC requirements are included here. 

For purposes of this study, MGS R&R due to failure do 

not count in this entity. It may seem counterintuitive to 

perform major maintenance prior to working an unscheduled off-alert MGS failure. However, a joint letter currently 

in coordination from AFSPC, director of Operations and 

LC states, “We have directed our wings to maintain their 

maintenance schedule instead of making changes that free 

resources to immediately work off-alerts [missiles].”30 P4, 

MGS R&R. A failed MGS has a direct impact on the alert 

rate, and the current maintenance philosophy dictates the 

immediate replacement of this failed component. However, 

for this study, MGS R&R is also a variable and will become 

a higher priority at the 80 percent and 90 percent alert rate. 

P5, other MMT, FMT, and EMT maintenance activities that 

are generally lower-priority work orders. P6, Rivet MILE 

maintenance. This is a depot detachment assigned directly 

to the wing performing depot level tasks. P7, PMT and CCT 

periodic maintenance tasks. Table 1 displays the discrete 

distributions for daily job-type arrivals (i.e., the percentage 

of time that a specified number of tasks are likely to occur 

on a given day) based upon the data analyzed.

Number of Jobs

Task Type 0 1 2 3 4

P1 73% 21% 5% 1% N/A

Other off-alert 85% 14.9% 0.1% N/A N/A

Major Mx 22% 68% 10% N/A N/A

Table 1. Distribution for entity type and daily occurrence rate
13

Resources

Entities often compete with each other for service from 

resources that represent things like personnel and equipment.31 The entities listed above are allocated resources 

based upon the entities priority and resource availability. 

The following are resources identified for use in the model: 

EMT, FMT, MMT, PMT, CCT, Rivet MILE, and CCT as well as 

security escort teams. Table 2 displays the discrete distributions assigned to EMT, FMT, and MMT based on historical data.

For RM and CCT, the assumption was that two teams will 

be available on a daily basis and PMT will have three teams 

available (discussed in the limitations section). Additionally, 

concerning SF availability, the daily guard count fluctuates 

due to present-for-duty rates, maintenance requirements, 

and additional guarding requirements due to LF security 

system malfunctions. Considering the constant fluctuation 

in guard counts, AFLMA analysts felt a triangular distribution would be more appropriate and a distribution with a 

Number of Jobs

MGS R&R 75% 24.5% 0.5% N/A N/A

Other MMT 69% 28% 3% N/A N/A

Other FMT 3.5% 22.5% 68% 6% N/A

Other EMT 8% 23% 32% 32% 5%

Rivet MILE 29% 31% 40% N/A N/A

PMT 37% 39% 21% 3% N/A

Corrosion 37% 52% 11% N/A N/A

Table 1. (continued)

Table 2. Distribution for daily team availability

Number of teams available and corresponding percentage

Available Team 1 2 3 4 5 6

EMT 18% 28% 46% 7.10% 0.90% N/A

FMT 10% 12% 27% 33% 11% 7%

MMT 25% 53.50% 21.50% N/A N/A N/A
14

minimum of 9.5 percent, a mode of 22.2 percent, and maximum of 30.5 percent daily available security escorts was 

established based on the historical data.

Model Operation

Analysts ran the simulation 30 times for 945 days for 

each run, representing three work years (weekdays only) 

which included a 180-day “warm-up” period, allowing the 

system to stabilize before data analyses. It is important to 

note that the simulation did not take 945 days to get the 

required output; however, in many simulations, as the time 

frame becomes longer (like months instead of a day), most 

results being averaged over the run become less variable.32

The base model (98.5 percent alert rate) verification 

ensured it was representative of current operations. Two 

additional models were created that allowed the alert rate 

to drop to 90 percent and 80 percent, respectively, before 

any action (working MGS R&Rs or other off-alerts) to sustain those rates was permitted. All three models ran with 

the same parameters for comparison of data outputs. The 

base model works all tasks possible within the constraints 

of maintenance team and SET availability. The 90 percent and 80 percent models perform within the same constraints; however, MGS R&Rs and other off-alert tasks are 

not worked unless the alert rate drops below the alert-rate 

threshold. In the base model, the SETs are allocated in the 

following priority order: P1; other off-alert maintenance; 

major maintenance; MGS R&R; other MMT; other FMT; and 

other EMT, RM, PMT, and CCT. The only changes to SET 

allocation at the 90 percent and 80 percent models were 

MGS R&R and major maintenance swap priorities. This 

only affects the draw of resources when the alert rate drops 

below the established threshold. Additionally, the SETs are 

normally allocated for quick reaction maintenance (QRM) 

teams (QRM teams are either the FMT or the EMT or a combination of both scheduled for 24-hour coverage to respond 

to P1 maintenance) and were deleted from the daily guard 

count because they were considered a sunk cost.

On a daily basis, the model also generates three scheduled off-alert (F-CAT) missiles to approximate what would 

normally be encountered at a missile unit. These missiles 
15

include the training LF and two LFs undergoing Rivet MILE 

maintenance and impact the daily alert rate. During the 

simulation’s operation, the following tasks affect the alert 

rate: approximately 10 percent of all P1 tasks (data for this 

study showed 10 percent of P1 maintenance results in an 

off-alert condition), the three scheduled F-CAT missiles per 

day, MGS R&Rs, other off-alert tasks, and major maintenance tasks. Each occurrence of these tasks counts as 24 

hours of off-alert time (this is representative of a realistic 

off-alert time as some tasks may take more time and some 

less to restore alert status). If a missile remains off alert 

due to the aforementioned constraints, it will continue to 

accumulate 24 hours of off-alert time until repaired. The 

daily alert rate is based on 200 available missiles, resulting in 4,800 possible alert hours per day (24 x 200). The 

alert rate is calculated at the end of each day, taking into 

account the above tasks that were worked that day and 

any work-in-progress remaining. This forms the basis for 

decision making for the next day’s maintenance in the 90 

percent and 80 percent models. For example, if the end-ofday alert rate in the 80 percent model is calculated to be 80 

percent, no MGS R&R or other off-alert tasks will be worked 

that day; when the alert rate drops below 80 percent, these 

are worked and only then. The model also calculates team 

utilization.

Additionally, team utilization is calculated at the end of 

each day by dividing the number of a particular team type 

pulled as a resource by the number of the particular team 

available on that day. For example, if the simulation generates three EMTs available as a resource and there are only 

enough SETs available to support two teams, the EMT’s 

utilization rate would be 66.66 percent (three teams available but two teams supportable) for that particular day. The 

simulation, based on the discrete distributions described 

earlier, generates the total number of teams available on 

any given day (see appendices D–H for a graphical representation of submodel processes). 

Assumptions

Several assumptions made prior to building the model 

and simulation to best capture actual execution of mainte-
16

nance that could not be readily extrapolated from the historical data are listed below:

1. All tasks that occur take one full day to complete, 

and the maintenance teams assigned to complete a task 

are dedicated to that task only. The one exception to this 

rule is that the simulation allows an EMT to perform more 

than one tape load (a required task after a MGS R&R and 

some major maintenance tasks) per day.33 The simulation 

assigns a value of .5 EMTs for a MGS R&R and the major 

maintenance tasks that require a tape load. Due to the 

short duration of the task, the EMTs can perform multiple 

tape loads in a single shift.

2. Priority 1 conditions result in an off-alert condition 

approximately 10 percent of the time. Allocation of these 

jobs is approximately 78 percent to the FMT and the remainder to the EMT. Other off-alert conditions were distributed 

equally between the FMT and the EMT.

3. Periodic cancellations occur within the simulation to 

capture weather or other events that are normally encountered. Based on the time frame in which the historical data 

was captured, the mild weather caused no cancellations. 

Additionally, mass maintenance cancellations were not 

captured in the historical data. Accordingly, an assumption 

was made to plan for these events in the simulation to capture real-world cancellations and was programmed to occur 

initially at the 10-day point and every 40 days thereafter.

4. The model also assumed that vehicles and equipment 

will always be available and does not account for potential 

fleet-grounding issues for vehicles or equipment. 

Limitations

1. Certain teams such as the RM and the CCT are comprised of civilian employees who normally work a four-day 

workweek. Furthermore, the PMTs normally work on a 

three-day schedule when performing periodic inspections 

on a LF. The model does not account for these as it runs on 

a five-day workweek schedule, causing the model to reflect 

lower utilization rates of those teams. However, these sections are flexible and often will work on a Friday if the mission requires them to do so.
17

2. Additionally, the PMT and the CCT do not always penetrate the LF when performing maintenance. For example, 

when the PMT does a periodic inspection on a LF, they only 

penetrate the LF on one of the three days of maintenance. 

The other two days are spent performing maintenance in the 

LSB. The model only accounts for those tasks that required 

LF penetration. Therefore, utilization rates for those team 

types may appear substantially lower than other team types 

due to the exclusion of this other productive maintenance.

3. Team utilization is based solely on tasks requiring 

the LF penetration and does not account for MAF and LSB 

penetration or maintenance at the support base. Again, 

these tasks do not require security escorts and may have 

an indirect impact to the alert rate, and the model does not 

account for these activities. 

4. The biggest limitation of the simulation is not being able 

to account for the human factor. A model of the maintenanceoperating environment can never capture risk management 

and smart maintenance practice decisions made by those 

with the authority and the experience. Senior maintenance 

managers have the authority to choose which maintenance 

task of equal priority to repair when resources are limited or 

even to work a lower priority job, if the situation dictates. It 

is that kind of decision making that allows for risk mitigation and alert-rate optimization, and to keep modification 

programs on schedule. Unfortunately, a computer program 

cannot simulate making these decisions. 

Results and Analyses

After running the three models 30 times, the average team 

utilization rates listed in table 3 were output from the simulation at the 80 percent, 90 percent, and 98.5 percent alertrate levels from the 180- day to 945-day period (stabilized 

model period). For a detailed statistical review of each team’s 

simulation run and the results after 30 iterations at each 

alert-rate level (see appendices I statistical formulas and J–O 

for data extracted from the model). For the MMT, FMT, RM, 

and PMT, the difference in team utilization between the 98.5 

percent, 90 percent, and 80 percent models proved statistically insignificant. However for the EMT, the differences 
18

between both the 80 percent and 90 percent simulations as 

compared to the 98.5 percent simulation showed a 2.8 percent and 2.7 percent increase in team utilization, respectively (which is statistically significant) at the lower alert-rate 

levels. A potential reason for the increase may be attributed to the fact that the bulk of the EMT’s jobs are “other 

maintenance” tasks. With fewer missiles on alert, additional 

resources may have become available to allow the EMT to 

work lower priority jobs, which were previously diverted for 

higher-priority tasks such as tape loads after the MGS R&Rs 

and other off-alert maintenance required to maintain a 98.5 

percent alert rate. Additionally, the CCT showed a decrease 

of .62 percent between the 98.5 percent and 80 percent simulations, which was statistically significant in the confidence 

interval between models. Such a slight decrease in team utilization is extremely hard to quantify. As far as all teams are 

concerned, any difference in team utilization between simulations was minimal. The following may shed some light as 

to why.

Whether a unit has 10 percent or 20 percent of their 

missiles in off-alert status, the LFs containing the assembled weapon system and critical components must remain 

operational. The operational ground equipment and real 

property installed equipment at each LF needs to remain 

in an operational state to monitor status and provide critical functions (e.g., environmental controls to support the 

missile). Regardless of alert status, the EMT, FMT, PMT, 

RM, and CCT (and in some instances MMT) will continue 

Table 3. Average team utilization rates (180–945 days)

Team 80% 90% 98.5%

MMT 0.779979 0.788052 0.785959

EMT 0.91369 0.917988 0.890874

FMT 0.605873 0.605264 0.604766

RM 0.558268 0.556527 0.558747

PMT 0.299477 0.301713 0.301144

CCT 0.369517 0.3715405 0.375718

Note: 30 simulation runs at various alert rates
19

to perform maintenance at all unit LFs. Periodic inspections by the PMT or the CCT do not affect a missile’s alert 

status, so these teams will strive to meet critical inspection 

interval requirements. The EMT will continue to troubleshoot ground and security systems faults ensuring weapon 

system safety, security, and command and status monitoring are maintained at all times. The FMT will continue to 

troubleshoot and repair all power and environmental problems at the 200 LFs. Additionally, the RM will continue to 

take missiles off alert for programmed maintenance to sustain the weapon system. The bottom line is 200 LFs (150 at 

the other units) must be maintained regardless of the designated alert rate. Additionally, the aforementioned teams 

will always be utilized to execute maintenance. Another 

potential contributing factor to team utilization rates may 

be the actual value of each missile at the various alert-rate 

levels.

A single off-alert missile influences the overall alert rate 

differently between models. In the 98.5 percent model, 

there is a baseline of 200 missiles with which to work. The 

impact of one off-alert missile in this model is a decrease 

of .5 percent to the alert rate (199 divided by 200 equals 

99.5 percent). In the 90 percent and 80 percent models, 

the baseline changed to 180 and 160 missiles, respectively. A single off-alert missile now has an impact of .56 

percent in the 90 percent model and .625 percent in the 

80 percent model. Maintenance teams, consequently, will 

have to work harder to maintain the status quo at the new 

thresholds.

When running the 80 percent and 90 percent simulation models, it took approximately 75 days for the alert rate 

to drop from 98.5 percent to 90 percent and 179 days for 

the alert rate to drop from 98.5 percent to 80 percent (see 

fig. 1 for graphical representation). The simulation expectation was that team utilization would decrease for the MMT 

while potentially increasing for all others during the periods 

allowing missiles to fall off alert without repairing them to 

reach new target alert-rate thresholds. Tables 4 and 5 show 

the utilization-rate averages over the first 75 days for the 90 

percent and 98.5 percent models and averages over the first 

179 days for the 80 percent and 98.5 percent models.
20

Team utilization for the MMT did decrease significantly 

during the periods of decreasing alert rates. Their utilization rates decreased by approximately 11.5 percent during the 75-day period (98.5 percent alert rate to 90 percent 

alert rate) and approximately 9.4 percent during the 179-

day period (98.5 percent alert rate to 80 percent alert rate) it 

took to achieve the new thresholds. However, once the new 

target alert rates (80 percent and 90 percent) were reached, 

the MMT utilization immediately increased back to previous utilization rates to maintain the new target thresholds 

of an 80 percent or 90 percent alert rate. As far as the EMT, 

FMT, PMT, RM, and CCT are concerned, during the 75- and 

179-day period it took for the alert rates to reach the new 

lowered alert-rate thresholds, team utilization rates varied 

between models and slightly increased or decreased during those periods. However, any increase or decrease during those periods was statistically the same (see appendices 

P–U for data extracted from the model).

Table 4. Average team utilizations rates (1–75 days) 

Team 90% 98.5%

MMT 0.675 0.79

EMT 0.857 0.861

FMT 0.596 0.605

RM 0.559 0.566

PMT 0.304 0.293

CCT 0.362 0.359

Note: 30 simulation runs at various alert rates 

Table 5. Average team utilizations rates (1–179 days)

Team 80% 98.5%

MMT 0.685 0.779

EMT 0.878 0.873

FMT 0.603 0.602

RM 0.56 0.558

PMT 0.301 0.295

CCT 0.36 0.362

Note: 30 simulation runs at various alert rates
21

Recommendations

The intent of this research was to determine if ICBM 

maintenance team utilization would decrease if higher 

headquarters permitted ICBM units to drop to alert rates of 

90 percent or 80 percent. In short, units would not repair 

off-alert missiles until they reached the new target-alert 

rate threshold, and then only perform off-alert maintenance 

essential to maintain that threshold. The thought process 

behind the study was that if team utilization decreased at 

these new levels, Air Staff and AFSPC leadership would 

have the ability to make manpower decisions to generate 

cost savings by reducing the ICBM maintenance-manpower 

footprint. However, as this paper shows, there is no corresponding decrease in ICBM maintenance team utilization 

with a decrease in the alert rate. With that, the following 

recommendations may inform any final decisions made by 

senior leaders concerning lowering the alert rate or reducing ICBM maintenance manning.

First continue to repair off-alert missiles and allow ICBM 

units the flexibility to continue to repair unscheduled offalert missiles in a timely manner while maintaining their 

maintenance schedule. This will provide United States Strategic Command (USSTRATCOM) personnel with sufficient 

assets for their planning purposes. In most cases, it will 

not result in a missile’s off-alert time exceeding 96 hours.34

One of the biggest problems of allowing the off-alert missiles to stack up, to as many as 20 to 40 missiles in the 

Figure 1. Alert-rate decrease over time for various models
22

unit under study or up to 100 missiles fleetwide, is the time 

it would take to generate the missiles to alert status during higher states of readiness. Generation under current 

operating parameters occurs in days but may take months 

if the alert rate were allowed to fall to decreased levels. The 

immediate replacement of up to 100 MGSs in order to generate poses several problems. In a related study conducted 

by Col Randy B. Tymofichuk, the simulation was exercised 

in reverse to demonstrate the timing required to generate 

from an 80 percent alert rate back to 98.5 percent using the 

same parameters (team distributions, weekday maintenance 

only, etc.) as models used in this paper. A lack of personnel 

manpower and security extended recovery time. This makes 

such practices unacceptable from a war-fighting readiness 

perspective. His assessment showed a full recovery time of 

62 workdays (based on the unit under study) and did not 

account for the delivery of assets from the depot required to 

generate three ICBM wings, thus making even this timing 

extremely optimistic.35

Receiving assets from the depot is another key reason 

to continue to repair off-alert missiles in a timely manner. 

There is not an infinite supply of missile guidance sets. Every 

time a missile guidance set fails, it is shipped to the Boeing 

Guidance Repair Center (BGRC) for repair or modification 

IAW the GRP and immediately returned to the serviceable 

inventory. Leaving failed MGSs in the field until a lowered 

alert rate is achieved jeopardizes both the GRP modification 

schedule and PRP. A PRP booster requires a GRP-modified 

MGS installed on it. At some point, leaving so many failed 

MGSs in the field will affect both programs, which can come 

at a cost. The Air Staff estimates any delays to the PRP 

modification scheduling that breach contract requirements 

could cost up to $100 million annually.36

Second then focus on scheduled maintenance. The ICBM 

prime contractor, Northrop-Grumman in coordination with 

the ICBM Systems Program Office, has been working to 

develop an integration plan that coordinates the execution of all modification programs and LLC exchanges (LLC 

schedules are very rigid, as time-change compliance is mandated by the Department of Energy). The goal is to provide 

ICBM units with a roadmap for use in building unit-level 

monthly and long-range maintenance schedules as well as 
23

to help deconflict resource utilization and prioritization to 

keep delivery schedules intact. Any reduction to the alert 

rate caused by not repairing off-alert missiles in a timely 

manner will throw the plan into disarray and jeopardize the 

modification programs. 

ICBM maintenance managers at the unit level have effectively balanced resources, modification schedules, and 

surge maintenance while providing the war fighter high alert 

rates. This in large part may explain why the simulation 

showed no real savings in maintenance team utilization. 

Unit-level leadership needs to continue to make decisions 

on when to pursue the cost benefit of performing scheduled 

maintenance in lieu of unscheduled off-alert maintenance. 

Although this simulation showed negligible changes in team 

utilization at lower alert rates for teams that perform periodic maintenance activities such as the PMT or the CCT, 

the loss of only one or two days of periodic maintenance can 

take months to recover.37 Unit-level decision makers are in 

the best position to maximize scheduling and maintenance 

effectiveness.

Third, ICBM maintenance manning requires a formal 

study. Air Force leaders are trying to find ways to save money 

by cutting resources to meet the costs of Operation Iraqi 

Freedom, Operation Enduring Freedom, and the GWOT. 

Their desire to reduce manning while maintaining acceptable 

operational rates relates directly to this paper as an example 

of possible savings that does not appear plausible. The lack 

of a current manning standard for the 2M0X1, 2M0X2, or 

2M0X3 career fields place any study at risk. The Strategic 

Air Command accomplished the last formal manpower study 

of the ICBM community. According to Headquarters AFSPC 

Manpower and Personnel and Manpower Requirements, “In 

the past 20 plus years, the AFSCs have drastically changed 

which makes it difficult to determine exactly which standard 

would be [used] for the 2M0s.”38 Additionally, CMSgt Joesph 

Lafferty, Headquarters Air Force Information and Logistics, 

AFSC 2M0 functional manager, confirmed, “No manning 

standard exists for the 2M0 community.”39

Before resorting to extreme measures (e.g., lowered alert 

rates), a manning study of all 2M0 AFSCs at ICBM units 

is recommended to establish levels based on the actual 

workload requirements of the current environment. Addi-
24

tionally, at the completion of all ICBM modernization programs (tentatively scheduled to take place sometime in 

2012), 2M0 AFSC requirements need restudied to meet new 

maintenance demands. A modernized weapon system may 

result in increased time between component failures and 

decreased maintenance requirements.

Finally, previous concepts could be revived and modified 

to decrease ICBM maintenance manning. One issue brought 

up during the 2003, 2M0/2W2 Worldwide Conference was a 

merger of the 2M0X1 (EMT) and 2M0X3 (FMT) AFSCs. However, “due to current issues going on in these AFSCs, this is 

not something Air Force Information and Logistics (ILMW) 

will [wants to] pursue at this time.”40 ILMW, in coordination 

with Headquarters AFSPC/LCM and Air Training Command 

(AETC), needs to look at the possibility of combining these 

two AFSCs again. Additionally, within the 2M0X2 AFSC, 

the same organizations may want to look at combining the 

Missile Maintenance and Missile Handling Team sections. 

All 2M0X2 technicians attend the same technical school 

training and within a MMT or MHT technician’s career, the 

chances are good that they will serve in both sections. The 

commonality between the sections supports a merger. Combining both the EMT with the FMT and the MMT with the 

MHT could yield manpower savings. This alone warrants a 

serious study. Merging sections is not new to ICBM maintenance. The current the EMT section is the result of merging the EMT, security system-maintenance teams, and the 

combat targeting teams, which took place in the late 1970s 

and early 1980s after weapon-system modifications.

A final recommendation would be for Headquarters 

AFSPC/LCM to submit a formal request to AFLMA to conduct a more detailed study of optimizing ICBM maintenance 

team and security force utilization using data from all three 

ICBM units. This study may yield potential manpower savings by finding a better way to conduct field operations 

between maintenance and security forces.

Conclusion

The argument that lowering ICBM alert rates would 

cause a reduction in ICBM maintenance team utilization, 
25

and potentially allow for savings in manpower resources, 

lacks empirical basis. As the research, modeling, and analyses cited in this paper show, allowing the missile alert rate 

to drop to 90 percent or 80 percent did little to change team 

utilization. In fact, four of the teams under study (MMT, 

FMT, PMT, and Rivet MILE) had utilization rates that were 

the same from a statistical point of view whether the alert 

rate was 98.5 percent, 90 percent, or 80 percent. For the 

EMT section, the utilization rate actually increased at lower 

alert rates. Again, the bulk of EMT tasks are “other maintenance” tasks. With fewer missiles on alert, additional 

resources may have become available to allow the EMT to 

work lower priority tasks. Finally, the CCT did show one 

statistically significant decrease of .62 percent in team utilization at the 80 percent alert rate level. Thus, no evidence 

suggests that reduced alert rates will generate manpower 

savings. However, deleterious effects are likely.

Senior leaders in the ICBM and war-fighting community 

must take into account and discuss several items prior to 

reducing the alert rate. While operating under extremely 

tight budget constraints, senior leaders must consider the 

potential costs associated with lowering the alert rate. The 

cost of potential modification program contract breaches 

in actual dollars and the cost of not sending the MGSs to 

BGRC to keep inventories at required levels may impact the 

number of required war-fighting assets needed to generate 

during real-world contingencies. Additionally, the time and 

resources necessary to generate all missiles to alert status 

during potential contingencies should be of great concern. 

Finally, ICBM maintenance manning levels need attention.

As previously indicated, the 2M0 AFSCs will suffer a manning decrease between now and the FY 07–FY 08 time frame 

bringing all three 2M0 AFSCs below authorized manning 

levels. However, the modernization program will continue 

through FY 12 along with required maintenance. Additionally, whether the alert rate is lowered or not, maintenance 

technicians must still perform maintenance on either 200 

or 150 LFs, depending on the unit. Team utilization rates 

stayed consistent regardless of alert rate levels because 

of this. With that, before any further reduction in ICBM 

maintenance manning is discussed to save future costs, 

a manpower study must be accomplished to determine a 
26

manpower standard. A valid standard could help affect 

manpower adjustments that yield savings. 

For more than 40 years maintenance professionals have 

kept ICBMs on alert at the levels mandated by the war 

planners; they will continue to do so. As lowering the alert 

rate causes no reduction in maintenance team utilization 

or manpower savings, we must continue to provide high 

alert rates while balancing current and future maintenance 

requirements. Maintaining current alert rates maintains 

weapon system health, continues to feed the modernization program, and provides maximum flexibility to the war 

fighter with no significant increase in costs.

Notes

(All notes appear in shortened form. For full details, see the appropriate 

entry in the bibliography.)

1. Byron, “Air Force Officials Project Budget Shortfall.” 

2. Dawson, “Maximizing Security Forces Response Time.”

3. Minutes, Air Force Nuclear General Steering Group.

4. Neary, “Twentieth Air Force From B-29s to ICBBs,” 4.

5. Gilbert, “ICBM Handbook (Draft).” 

6. Ibid.

7. Air Force Space Command Instruction (AFSPCI) 21-114, 6–9.

8. Ibid., 20.

9. Ibid., 70.

10. Ibid., 53.

11. Ibid.

12. Ibid., 52.

13. Ibid., 53.

14. Gilbert, “ICBM Handbook (Draft).” 

15. Hughes, “341st Maintenance Group Summary Supplemental.”

16. Punnett, briefing.

17. Briefing, ICBM Long-range Requirements Planning, 5.

18. Briefing, Directorate of Logistics and Communications, Maintenance.

19. Dawson, “Maximizing Security Forces Response Time,” 3.

20. Ogden, “Missiles on Target.” 

21. Strategic Air Command Regulation 66-12, vol. 1, 1-1.

22. AFSPCI 21-114, 6.

23. Briefing, ICBM Program Team, W. R. Faris, subject: Minuteman 

Field Performance, Minuteman Guidance Technical Review No.18, author 

has copy, 20 April 2005, 9.

24. Neill, “Qualitative versus Quantitative Research.”

25. Kelton et al., Simulation with Arena, 7.

26. Discrete-event simulation is used to simulate components that normally operate at a higher level of abstraction than components simulated 
27

by continuous simulators. Within the context of discrete-event simulation, 

an event is defined as an incident, which causes the system to change its 

state in some way. For example, a new event is created whenever a simulation component generates output. Successions of these events provide 

an effective dynamic model of the system being simulated. What separates 

discrete-events simulation from continuous simulation is the fact that the 

events in a discrete-event simulator can only occur during a distinct unit 

of time during the simulation events are not permitted to occur in between 

time units. (Source: Craig, “Extensible Hierarchical Object-Oriented Logic 

Simulation.”) 

27. Law and Kelton, Simulation Modeling and Analysis, 6.

28. By removing weekend and specialty maintenance activities, only 114 

available data days were available for the model’s use. The author, along 

with AFLMA analysts, felt that the 114 days provided ample data for a realistic representation of the actual maintenance environment, a goal while 

constructing the model. Adding the specialty and weekend maintenance to 

the models would have skewed the distributions for maintenance team and 

security forces personnel availability. It would provide the simulation with 

more teams and security forces personnel than available in the normal 

maintenance environment, potentially invalidating the entire model.

29. Law and Kelton, Simulation Modeling and Analysis, 24.

30. Fraser, draft memorandum.

31. Kelton et al., Simulation with Arena, 26.

32. Ibid., 8.

33. A tape load is a task performed any time a replacement MGS is installed on a missile or the existing data in the MGS is overwritten. An EMT 

loads critical data into the MGS’s computer allowing it to process, issue, 

and execute certain commands. This task is very short.

34. Fraser, draft memorandum.

35. Tymofichuk, “The Importance of Maintaining Maximum Combat 

Effectiveness,” 30.

36. Directorate of Nuclear Operations, Deputy Chief of Staff, Air and 

Space Operations, Maintenance, Headquarters USAF/Air and Space Operations-Nuclear Operations (XOS-NO) Point Paper, Subject: “Propulsion 

Replacement Program Contract,” 10 November 2005.

37. Dawson, “Maximizing Security Forces Response Time,” 16.

38. Deborah Grace, Headquarters Air Force Space Command/Manpower and Personnel and Manpower Requirements, interview by author, 

17 November 2005.

39. CMSgt Joseph Lafferty, Headquarters United States Air Force/Installations and Logistics Maintenance, interview by author, 4 January 

2006.

40. Lafferty, briefing.

29

Appendix A

Arena Simulation Synopsis

Arena version 10.0 is a product of Rockwell Automation, Inc. Arena is a Microsoft based computer simulation 

program. Arena allows users to perform discrete event and 

batch and continuous simulation. Several users including 

manufacturing firms, supply chain managers, the Department of Defense, health care professionals, contact centers, 

and business processors have used Arena to analyze and 

predict system performance. Additionally, 350,000 users 

worldwide have used Arena to improve business processes, 

provide better service, lower costs, and avoid unnecessary 

costs. 

Arena allows users to build flexible, animated models 

or processes, operations or systems, and then analyze the 

performance of those models. 

Sources: 

1. “Arena® Forward Visibility for Your Business,TM” available at http://www.avidcom.com/portfolio/arena_DOD/

Arena _Demo_on_Demand.html. 

2. “Your Path to Success with Simulation, Systems Modeling,” available at http://www.sm.com.

31

Appendix B

Launch Control Support and Control Equipment Building
32

Appendix B-1

Minuteman III launch facility
33

Table A2.1. Priority Designators. 

Appendix C

AFSPCI 21-114 (1 May 2003)

Maintenance Priority Designators

Missile Maintenance Priority Designators

Maintenance 

Priority

Application

1 Repair of critical equipment needed for safe operation of the weapon 

system

Maintenance actions needed to prevent damage or further damage 

to the weapon system, avoid injury to personnel or render the

weapon system safe

2 Priority 2 maintenance is listed by order of relative priority

Return of an LCC to operational status when three or more are nonoperational in the same squadron

Maintenance required to retain/return “A Category (CAT)” sorties to EWO 

alert status

Actual EWO generation of “F CAT” and “L CAT” sorties

Time change requirements for re-entry systems when the due date is 

within 30 days

Maintenance required to reposture LFs and LCCs being returned from 

modification/test programs

When a known environmental compliance discrepancy exists which could 

result in a violation of federal, state or local regulations or Air Force/base 

instructions

Repair of severed, damaged or seriously degraded Hardened Intersite 

Cable System (HICS)

Multiple outages of command and control systems (Strategic 

Automated Command and Control System (SACCS), Milstar, Air Force 

Satellite Communications (AFSATCOM), Survivable Low Frequency 

Communications System (SLFCS), ICBM SHF Satellite Terminal (ISST) and 

UHF Radio System) which will seriously jeopardize alert notification to two 

or more LCCs in a squadron

Restoration of squadron IPD collection capability to the Missile Support 

Base

Maintenance required to deposture LFs and LCCs committed to 

modification/command approved or directed test programs
34

Table A2.1. (Continued)

Maintenance 

Priority

Application

3 Discrepancies expected to affect alert posture or degrade impact 

accuracy

Discrepancies which are time sensitive as directed by technical data 

or which, because of the nature of the discrepancy, require periodic 

monitoring

Maintenance required to return an LCC to operational status when two 

are non-operational in the same squadron

Return of a single command and control communications system at an 

LCC involving SACCS, Milstar, AFSATCOM, SLFCS, EWO-2, Hardened Voice 

Channel (HVC), ISST or UHF Radio System

All PMC conditions not specifically identified as Priority 4

A hardness/survivability PMC discrepancy within the launch tube or 

which affects the missile

Maintenance to clear discrepancies which require camper alert teams

Support of Dash 6 periodic maintenance schedules even though the 

package may be composed of discrepancies of lower priority

Support equipment requiring emergency repair or calibration, the lack of 

which will delay or prevent mission accomplishment

Critical end items and repairable spares designated “Priority Repair”

Actions to accomplish immediate MCLs

Maintenance required to bring serviceable quantities to established 

critical levels

Time change requirements for RS when due date is within six months

Discrepancies expected to affect systems or subsystems which will not 

directly impact alert posture but may result in a guarded site or a PMC 

condition or a safety deficiency if not corrected in optimum time

4 Hardness/survivability discrepancies in the LERs, but not in the launch 

tube

Outages on non-command and control communications systems

Impairments to any command and control communications systems

Scheduled training dispatches/tasks

Training devices requiring repair which prevent or delay training

Return of an LCC to operational status when four are operational in the 

same squadron

5 Hardness/survivability discrepancies in the LCC 

TCTOs and MCLs, which if not promptly completed, could exceed 

recession date; also MCLs designated as “Urgent”

Overdue periodic inspections and overdue time change items

Site or support equipment discrepancies not expected to result in a PMC 

condition, but if corrected will enhance safety, weapon system operation 

or reliability

Impairments to non-command and control communication systems
35

Maintenance 

Priority

Application

6 Periodic Inspections, TCTOs, MCLs and time change items

Communications preventative maintenance inspections (PMI)

Routine maintenance of training devices

Scheduled calibration of support equipment not listed under a higher 

priority

7 Minor repair of missiles and support equipment not listed under a higher 

priority

Fabrication and repair of weapon system items not carrying a higher 

priority of non-weapon system items

Communication discrepancies which don’t affect equipment status

8 Informational entries

9 Deferred discrepancies

Note:

1.  The Maintenance Group Commander has the authority to work lower priorities over higher

priorities to meet mission requirements consistent with safety and security.

2.  Shop maintenance required to repair items needed to clear site discrepancies will carry the

priority of the site discrepancy if repair is not adequately covered elsewhere in this attachment.

3.  For minor hardware hardness discrepancies not expected to be a degraded condition, classify as a Priority 5. Additional hardness/survivability discrepancy guidance is available on a

case-by-case basis through 20 AF/LG/LGMO.

4.  Be consistent when prioritizing like discrepancies.

5.  The following are guidelines to assist MMOC and Materiel Control determine UJCs:

5.1.  For initial requests of items from base supply, use UJC “AA.”

5.2.  For backordered items, use the following UJCs:

5.3.  Supplies required to correct a red X condition, use UJC “1A.” The lack of the item(s)

prevents mission accomplishment because the end item is not operationally ready, out of commission or inoperative. These are MICAP conditions.

5.4.  Supplies required to correct red W conditions, use UJC “JA.” Lack of the requested item(s)

impair primary mission accomplishment because the end item is not fully equipped or is operating in a limited or restricted capacity. These are MICAP conditions.

5.5.  Supplies required to correct red diagonal conditions, use UJC “BQ.” The item required is

one that impairs assigned combat/support mission or tasks of the force/activity involved. Also

use when the training for such missions and tasks can be accomplished but with decreased

effectiveness and efficiency. These are delayed discrepancies.

6.  The maintenance priority designator for an outage affecting the command and control communications at multiple sites should be elevated to reflect the higher maintenance priority of

the communications outage. Contact Communications Job Control for determination.

Reprinted from AFSPCI 21-114, Maintenance Priority Designators, 1 May 2003.

Table A2.1. (Continued)

37

Appendix D

Alert Rate and Miscellaneous

Model Flow Diagrams

FCATs

Daily

Alert Rate

Process

Dispose Create

Missile

Mass

Cancellations

(CANX)

Add

Alert

Process

Assign

Dispose

Dispose

Daily

Alert

Rate

Calculate

Alert

Rate

Assign

New

Alert

Rate

Assign New

Alert Rate

Assign

Dispose

39

Appendix E

Priority 1 and Other Off-Alert

Model Flow Diagrams

SF

Avail 50%

True

True

True

False

False False

False False False

Dispose O Alert

MNX 

Priority1

Dispose

P1s

IN

No

CANX

True

True

False False

False

True

P1

Accumulator

10% o

Alert?

78%

To

FMT

Other

OA

Out

Other Off Alet

MNX

Other

Off

Alet

No

CANX

No CANX

/ AR

Under

Off Alert Accumulator

1 EMT Avail

Off Alet

EMT

Off Alet

FMT

Add Alert

EMT

OA

Balks

1 FMT Avail

Other OA

Balks SF

FMT

OA

Balks

Add

Alert

P1

FMT

P1

EMT

P1s

Out

41

Appendix F

MGS R&R and Major Maintenance

Model Flow Diagrams

MGS

Change

In

Avail Less

EMT

Than .5

MGS

Change

MGS

Change

Balk EMT

MGS

Change

Balk MMT

MGS

Change Balk SF

MGS

Change

Accumulator

Add Alert

MGS

Change

Out

MGS

Change

No CANX/ Under AR SF

Avail?

True

True

True True True

False

False False False False

No

CANX MMT Less

Than 1

Dispose Process

Major

MNX

For

EMT

Major

MNX

In

Major MNX

MMT and

EMT

EMT

Avail

MMT

Avail

2 SF

Avail

No

CANX

Major MNX

MMT

Only

Major MNX

Accumulator

Major MNX

Balk EMT

Major MNX

Balk MMT

Add

Alert

Major MNX

Balk

SF

Major

MNX

Out

Dispose

True True True True True

False False

False False False

43

Appendix G

Other MMT, EMT, and FMT Maintenance 

Flow Diagrams

Other

MMT

MNX

Other

MMT

MMT

Avail SFAvail NoCANX

Other

MMT

MNX

Other MMT

Out

Other MMT Balk

SF

Other MMT Team Balk Other

MMT Accumulator

Dispose

True True True

False False False

Other

EMT

MNX

Other EMT

EMT

Avail

SF

Avail

No

CANX

Other

EMT

MNX

Other

EMT

Out

Other EMT

Balk

SF

Other

EMT Team

Balk Other

EMT

Accumulator

Dispose

True True True

False False False

Other

FMT

MNX

Other

FMT IN

FMT

Avail

SF

Avail

No

CANX

Other

FMT

MNX

Other

FMT

Out

Other FMT

Balk

SF

Other

FMT Team

Balk Other

FMT

Accumulator

Dispose

True True True

False False False

45

Appendix H

PMT, RM, and CCT Maintenance

Model Flow Diagrams

PMT PMT

PMT

Avail

SF

Avail

No

CANX

PMT

MNX

PMT

Out

PMT

Balk

SF

PMT Team

Balk

PMT

Accumulator

RM

Balk

SF

RM Team

Balk

RM

Accumulator

Dispose

True True True

False False False

RM RM

RM

Avail

SF

Avail

No

CANX

RM

MNX

RM

Out Dispose

True True True

False False False

CCT CCT CCT

Avail

SF

Avail

No

CANX

CCT

MNX

CCT

Out

Other CCT

Balk

SF

CCT Team

Balk CCT

Accumulator

Dispose

True True True

False False False

47

Appendix I

Statistical Formulas for Model

Confidence Intervals

1. Average team utilization rate from 30 simulation iterations at each alert rate (Avg)

= average per iteration

j = number of the specific iteration (could be 1–30)

n = total number of iterations

2. Variance calculations (Var)

S2 = variance 

n = number of iterations

Xi = utilization rate average of specific iteration (1–30)

X = overall utilization rate average 

3. Degrees of freedom (D)

�

f = degrees of freedom

S1

2 = variance of 80% or 90% simulation run
S2

2 = variance of 98.5% simulation run

n = total number of simulation runs (30) per model

4. T distribution (T-Stat)

The T-Stat is a standard numerical value assigned based on the 

confidence level and degrees of freedom. No calculations were performed to gain the value of the T-Stat.

5. Confidence interval (CI)

X 1 = overall average of team utilization rates at 80% or 90% models (whichever is being compared)

X 2 = overall average of team utilization at 98.5% model

S 1 = variance calculated at 80% or 90% model (whichever is being 

compared)

S 2 =variance calculated at 98.5% model

n1 or n2 = number of iterations per model (30 in both cases)

48
49

Appendix J

MMT Team Utilization Rates*

Model Run MMT 90% MMT 98.5% MMT 80% MMT 98.5%

1 0.782419 0.793082 0.785457 0.793082

2 0.770672 0.785685 0.771539 0.785685

3 0.792645 0.78394 0.776107 0.78394

4 0.809178 0.811354 0.77785 0.811354

5 0.76109 0.791343 0.790458 0.791343

6 0.763048 0.759141 0.766097 0.759141

7 0.766967 0.786551 0.747821 0.786551

8 0.802876 0.778723 0.7748 0.778723

9 0.785902 0.781116 0.763924 0.781116

10 0.777416 0.797654 0.758926 0.797654

11 0.838773 0.759576 0.786117 0.759576

12 0.817454 0.791773 0.792214 0.791773

13 0.826799 0.765012 0.786552 0.765012

14 0.78611 0.776326 0.802213 0.776326

15 0.798517 0.784819 0.767401 0.784819

16 0.758692 0.763705 0.797432 0.763705

17 0.802872 0.786119 0.807444 0.786119

18 0.761736 0.792003 0.762614 0.792003

19 0.794162 0.792864 0.791552 0.792864

20 0.781988 0.775022 0.772633 0.775022

21 0.790035 0.767187 0.763048 0.767187

22 0.777413 0.796563 0.799599 0.796563

23 0.784373 0.823542 0.803743 0.823542
24 0.779146 0.781325 0.771535 0.781325

25 0.817012 0.780678 0.793295 0.780678

26 0.798305 0.81114 0.777845 0.81114

27 0.774795 0.786775 0.794807 0.786775

28 0.769137 0.791561 0.768928 0.791561

29 0.779585 0.802869 0.771745 0.802869

30 0.79243 0.781332 0.775674 0.781332

Avg 0.788052 0.785959 0.779979 0.785959

Var 0.0004 0.000225 0.000225 0.000225

df 53.76433 57.99995

T-Stat 1.674116 1.672029

CI -0.00555 0.009736 -0.01246 0.000497

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

0.86%

0.84%

0.82%

0.80%

0.78%

0.76%

0.74%

0.72%

0.70% Team Utilization Rate

Utalization by Model Run

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

MMT 80%

MMT 90%

MMT 98.5%

Model Run MMT 90% MMT 98.5% MMT 80% MMT 98.5%

50
51

Appendix K

EMT Team Utilization Rates*

Model Run EMT 90% EMT 98.5% EMT 80% EMT 98.5%

1 0.920336 0.89127 0.926016 0.89127

2 0.893106 0.877439 0.921787 0.877439

3 0.927242 0.894809 0.912209 0.894809

4 0.91973 0.888188 0.920394 0.888188

5 0.91085 0.890623 0.921615 0.890623

6 0.919892 0.897611 0.906817 0.897611

7 0.917097 0.893889 0.916265 0.893889

8 0.924781 0.899488 0.925269 0.899488

9 0.906809 0.891958 0.88345 0.891958

10 0.922119 0.893013 0.927833 0.893013

11 0.912371 0.883491 0.913578 0.883491

12 0.900033 0.891661 0.89285 0.891661

13 0.92558 0.892281 0.919986 0.892281

14 0.929388 0.886308 0.915778 0.886308

15 0.919954 0.886242 0.916227 0.886242

16 0.93076 0.888922 0.923811 0.888922

17 0.900766 0.894005 0.909336 0.894005

18 0.929112 0.89521 0.925157 0.89521

19 0.918975 0.888178 0.913577 0.888178

20 0.914632 0.89426 0.905375 0.89426

21 0.907106 0.895337 0.915452 0.895337

22 0.919866 0.889877 0.880452 0.889877

23 0.926014 0.885155 0.912544 0.885155

24 0.915867 0.90577 0.895987 0.90577

25 0.92376 0.882745 0.922805 0.882745

26 0.919799 0.885719 0.915834 0.885719

27 0.917996 0.894833 0.926568 0.894833

28 0.91529 0.883004 0.919204 0.883004

29 0.927017 0.895924 0.904641 0.895924

30 0.923388 0.88902 0.919898 0.88902
Avg 0.917988 0.890874 0.91369 0.890874

Var 8.43E-05 3.32E-05 0.000147 3.32E-05

df 48.78819 41.4661

T-Stat 1.677224 1.682878

CI 0.033795 0.030432 0.018691 0.026941

0.94%

0.93%

0.92%

0.91%

0.90%

0.89%

0.88%

0.87%

0.86%

0.85% Team Utilization Rate

Utalization by Model Run

EMT 80%

EMT 90%

EMT 98.5%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

52

Model Run EMT 90% EMT 98.5% EMT 80% EMT 98.5%
53

Appendix L

FMT Team Utilization Rates*

Model Run FMT 90% FMT 98.5% FMT 80% FMT 98.5%

1 0.615901 0.597757 0.597214 0.597757

2 0.594059 0.581253 0.620813 0.581253

3 0.600654 0.602584 0.597868 0.602584

4 0.606084 0.604243 0.618131 0.604243

5 0.599052 0.58447 0.60794 0.58447

6 0.615191 0.596171 0.603265 0.596171

7 0.610664 0.604505 0.58967 0.604505

8 0.610232 0.618819 0.608607 0.618819

9 0.589311 0.596059 0.617159 0.596059

10 0.629158 0.62747 0.61526 0.62747

11 0.609317 0.614568 0.610855 0.614568

12 0.590867 0.591965 0.583836 0.591965

13 0.58194 0.606339 0.60462 0.606339

14 0.618896 0.601068 0.603513 0.601068

15 0.628027 0.593586 0.625383 0.593586

16 0.599281 0.607443 0.602986 0.607443

17 0.58749 0.631089 0.606706 0.631089

18 0.606568 0.601565 0.622279 0.601565

19 0.613321 0.603674 0.613317 0.603674

20 0.615645 0.601999 0.607539 0.601999

21 0.603877 0.595722 0.601443 0.595722

22 0.602879 0.609268 0.60888 0.609268

23 0.592852 0.598888 0.593185 0.598888

24 0.590166 0.612467 0.60214 0.612467

25 0.598538 0.600743 0.603007 0.600743

26 0.60879 0.609104 0.603547 0.609104

27 0.619552 0.605745 0.608287 0.605745

28 0.594317 0.615477 0.590303 0.615477
29 0.609163 0.624855 0.607183 0.624855

30 0.616116 0.604099 0.601247 0.604099

Avg 0.605264 0.604766 0.605873 0.604766

Var 0.000144 0.000129 9.42E-05 0.000129

df 57.83271 56.5992

T-Stat 1.672029 1.672522

CI 0.00455 0.005546 0.00346 0.005673

0.64%

0.63%

0.62%

0.61%

0.60%

0.59%

0.58%

0.57%

0.56%

0.55% Team Utilization Rate

Utalization by Model Run

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

FMT 80%

FMT 90%

FMT 98.5%

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

Model Run FMT 90% FMT 98.5% FMT 80% FMT 98.5%

54
55

Appendix M

PMT Team Utilization Rates*

Model Run PMT 90% PMT 98.5% PMT 80% PMT 98.5%

1 0.305867 0.295431 0.292808 0.295431

2 0.298467 0.302402 0.306748 0.302402

3 0.315 0.315462 0.31109 0.315462

4 0.299785 0.308503 0.311094 0.308503

5 0.310234 0.291935 0.305446 0.291935

6 0.294564 0.295005 0.29108 0.295005

7 0.292389 0.311101 0.300655 0.311101

8 0.305014 0.307167 0.302394 0.307167

9 0.306728 0.284965 0.307608 0.284965

10 0.29979 0.298907 0.266277 0.298907

11 0.279758 0.295852 0.290645 0.295852

12 0.305871 0.287162 0.312821 0.287162

13 0.305862 0.309798 0.318057 0.309798

14 0.301975 0.301517 0.294133 0.301517

15 0.306305 0.302849 0.292356 0.302849

16 0.293258 0.312393 0.302834 0.312393

17 0.294996 0.301953 0.286286 0.301953

18 0.296743 0.308909 0.312399 0.308909

19 0.298924 0.300654 0.28717 0.300654

20 0.308068 0.304569 0.299354 0.304569

21 0.296298 0.294557 0.289342 0.294557

22 0.299787 0.297159 0.281495 0.297159

23 0.308061 0.308047 0.311099 0.308047

24 0.308063 0.306734 0.308928 0.306734

25 0.296726 0.288892 0.294133 0.288892

26 0.290209 0.303704 0.292383 0.303704

27 0.306315 0.305432 0.3002 0.305432
28 0.306309 0.305437 0.306757 0.305437

29 0.302398 0.304578 0.305457 0.304578

30 0.317625 0.283249 0.303272 0.283249

Avg 0.301713 0.301144 0.299477 0.301144

Var 6.04E-05 6.84E-05 0.000127 6.84E-05

df 57.78053 53.25821

T-Stat 1.672029 1.674116

CI -0.0029 0.004033 -0.00593 0.0026

0.33%

0.32%

0.31%

0.30%

0.29%

0.28%

0.27%

0.26%

0.25%

0.24% Team Utilization Rate

Utalization by Model Run

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

EMT 80%

EMT 90%

EMT 98.5%

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

56

Model Run PMT 90% PMT 98.5% PMT 80% PMT 98.5%
57

Appendix N

Rivet MILE Team Utilization Rates*

Model Run RM 90% RM 98.5% RM 80% RM 98.5%

1 0.563969 0.550914 0.550914 0.550914

2 0.561358 0.553525 0.569843 0.553525

3 0.53329 0.556136 0.536554 0.556136

4 0.565927 0.578982 0.55483 0.578982

5 0.54765 0.54047 0.548956 0.54047

6 0.548303 0.562663 0.555483 0.562663

7 0.575718 0.555483 0.574413 0.555483

8 0.563969 0.578982 0.567885 0.578982

9 0.565274 0.55483 0.546345 0.55483

10 0.548956 0.575065 0.545692 0.575065

11 0.561358 0.567885 0.577676 0.567885

12 0.568538 0.578982 0.548303 0.578982

13 0.548303 0.577023 0.577676 0.577023

14 0.570496 0.52154 0.573107 0.52154

15 0.524804 0.537859 0.534595 0.537859

16 0.545039 0.545039 0.550914 0.545039

17 0.580287 0.565274 0.55483 0.565274

18 0.532637 0.55483 0.560705 0.55483

19 0.541123 0.541123 0.549608 0.541123

20 0.550914 0.563316 0.568538 0.563316

21 0.561358 0.52154 0.56201 0.52154

22 0.559399 0.563969 0.565927 0.563969

23 0.575065 0.571802 0.556789 0.571802

24 0.587467 0.593342 0.580287 0.593342

25 0.564621 0.558094 0.557441 0.558094

26 0.555483 0.531332 0.552219 0.531332

27 0.542428 0.543081 0.563969 0.543081
28 0.562663 0.565274 0.551567 0.565274

29 0.544386 0.573107 0.553525 0.573107

30 0.545039 0.58094 0.557441 0.58094

Avg 0.556527 0.558747 0.558268 0.558747

Var 0.000217 0.000321 0.000138 0.000321

df 55.93099 50.06291

T- Stat 1.673034 1.675905

CI -0.0093 0.004865 -0.00703 0.006074

0.60%

0.58%

0.56%

0.54%

0.52%

0.50%

0.48%

0.46%

0.44%

0.42% Team Utilization Rate

Utalization by Model Run

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

PMT 80%

PMT 90%

PMT 98.5%

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

58

Model Run RM 90% RM 98.5% RM 80% RM 98.5%
59

Appendix O

CCT Team Utilization Rates*

Model Run CCT 90% CCT 98.5% CCT 80% CCT 98.5%

1 0.3505222 0.36423 0.3498695 0.3642298

2 0.3498695 0.372063 0.3674935 0.3720627

3 0.3603133 0.370104 0.3603133 0.3701044

4 0.3720627 0.39295 0.3733681 0.3929504

5 0.366188 0.351175 0.3681462 0.3511749

6 0.3622715 0.364883 0.3740209 0.3648825

7 0.3779373 0.387076 0.3733681 0.3870757

8 0.3949086 0.386423 0.3701044 0.386423

9 0.3772846 0.364883 0.3772846 0.3648825

10 0.3779373 0.379243 0.3798956 0.3792428

11 0.3544386 0.387076 0.3622715 0.3870757

12 0.3779373 0.381854 0.3727154 0.3818538

13 0.383812 0.376632 0.3746736 0.3766319

14 0.3877285 0.386423 0.383812 0.386423

15 0.3779373 0.389687 0.3714099 0.3896867

16 0.3772846 0.363577 0.3766319 0.363577

17 0.3570496 0.379243 0.366188 0.3792428

18 0.3681462 0.37859 0.3818538 0.3785901

19 0.3707572 0.369452 0.3622715 0.3694517

20 0.3733681 0.379243 0.3772846 0.3792428

21 0.3609661 0.362272 0.3577023 0.3622715

22 0.386423 0.373368 0.3733681 0.3733681

23 0.366188 0.401436 0.3694517 0.401436

24 0.3674935 0.374674 0.3694517 0.3746736

25 0.3857702 0.355744 0.3505222 0.3557441

26 0.3798956 0.382507 0.3629243 0.3825065

27 0.363577 0.374021 0.3466057 0.3740209
28 0.3505222 0.379243 0.3674935 0.3792428

29 0.3720627 0.362924 0.368799 0.3629243

30 0.3955614 0.380548 0.3962141 0.3805483

AVG 0.3715405 0.375718 0.369517 0.375718

VAR 0.0001583 0.00013 0.0001076 0.0001297

DF 57.430721 57.50217

T-Stat 2.1808829 2.1808829

CI -0.010935 0.00258 -0.012334 -6.81E-05

0.41%

0.40%

0.39%

0.38%

0.37%

0.36%

0.35%

0.34%

0.33%

0.32%

0.31% Team Utilization Rate

Utalization by Model Run

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

CCT 80%

CCT 90%

CCT 98.5%

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

60

Model Run CCT 90% CCT 98.5% CCT 80% CCT 98.5%
61

Appendix P

MMT Team Utilization during

Alert-Rate Decrease Period*

Model Run MMT 80% (179) MMT 98.5% (179) MMT 90% (75) MMT 98.5% (75)

1 0.709497207 0.812849162 0.691093333 0.817773333

2 0.706709497 0.781195531 0.686666667 0.78668

3 0.679692737 0.777469274 0.69556 0.720013333

4 0.729044693 0.691798883 0.695533333 0.68668

5 0.667597765 0.74022905 0.644426667 0.688906667

6 0.654558659 0.732759777 0.64 0.653306667

7 0.671312849 0.811916201 0.677773333 0.842226667

8 0.707620112 0.744877095 0.66 0.768893333

9 0.703910615 0.730916201 0.626666667 0.700013333

10 0.741150838 0.768162011 0.5844 0.726693333

11 0.659201117 0.783072626 0.653306667 0.78224

12 0.741156425 0.813782123 0.76888 0.826666667

13 0.664782123 0.770005587 0.651093333 0.79776

14 0.659212291 0.80447486 0.668866667 0.788893333

15 0.710396648 0.824022346 0.67996 0.848893333

16 0.637810056 0.739296089 0.606653333 0.773333333

17 0.661072626 0.756055866 0.757786667 0.78224

18 0.664793296 0.75326257 0.686653333 0.764453333

19 0.68527933 0.828675978 0.68 0.842213333

20 0.683407821 0.776536313 0.702213333 0.84

21 0.700173184 0.798888268 0.73556 0.946666667

22 0.701106145 0.814715084 0.695546667 0.864453333

23 0.689944134 0.78398324 0.697786667 0.79776

24 0.632217877 0.82122905 0.63332 0.860013333

25 0.742089385 0.796100559 0.719986667 0.848893333
26 0.64803352 0.837055866 0.553306667 0.837773333

27 0.656418994 0.746731844 0.7 0.715546667

28 0.74301676 0.811 0.72 0.748893333

29 0.618251397 0.72998324 0.644453333 0.808906667

30 0.674100559 0.791435754 0.68888 0.83332

Avg 0.684785289 0.779082682 0.674879111 0.790003556

Var 0.001169246 0.001312497 0.00218879 0.004205448

df 57.80739604 52.75274805

T-Stat 1.672028889 1.674689154

CI 0.109505025 0.07908976 0.139573832 0.090675057

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

62

Model Run MMT 80% (179) MMT 98.5% (179) MMT 90% (75) MMT 98.5% (75)
63

Appendix Q

EMT Team Utilization during

Alert-Rate Decrease Period*

Model Run EMT 80% (179) EMT 98.5% (179) EMT 90% (75) EMT 98.5% (75)

1 0.882206704 0.909469274 0.89444 0.906186667

2 0.894441341 0.879396648 0.801106667 0.865546667

3 0.860569832 0.874329609 0.825013333 0.863973333

4 0.890821229 0.847307263 0.872773333 0.860506667

5 0.86852514 0.85098324 0.830546667 0.816626667

6 0.935743017 0.868407821 0.900533333 0.887653333

7 0.857810056 0.843446927 0.915546667 0.800306667

8 0.861815642 0.844608939 0.841666667 0.849973333

9 0.892452514 0.86877095 0.87276 0.854413333

10 0.887312849 0.867664804 0.821653333 0.836093333

11 0.865212291 0.887547486 0.841666667 0.898333333

12 0.87149162 0.863424581 0.81552 0.839746667

13 0.90572067 0.868446927 0.89 0.864413333

14 0.91027933 0.873564246 0.871106667 0.8622

15 0.876150838 0.891402235 0.84 0.861426667

16 0.87327933 0.895452514 0.844466667 0.896106667

17 0.902793296 0.858435754 0.89556 0.828853333

18 0.882860335 0.887212291 0.885773333 0.876413333

19 0.886256983 0.867519553 0.812426667 0.85108

20 0.855888268 0.89101676 0.885546667 0.896613333

21 0.907357542 0.873100559 0.902893333 0.846613333

22 0.89522905 0.88403352 0.920533333 0.893866667

23 0.825553073 0.919888268 0.846906667 0.932746667

24 0.823743017 0.879050279 0.84 0.868626667

25 0.823536313 0.85796648 0.708426667 0.811613333
26 0.865463687 0.892195531 0.883346667 0.885533333

27 0.927597765 0.87450838 0.88244 0.859413333

28 0.860340782 0.855765363 0.852213333 0.813826667

29 0.860787709 0.850050279 0.832213333 0.84828

30 0.880106145 0.871944134 0.87112 0.84276

Avg 0.877711546 0.873230354 0.856606667 0.860658222

Var 0.000738947 0.00034404 0.001832431 0.000937987

df 51.19300812 52.52502133

T-Stat 1.675284951 1.674689154

CI -0.005584409 0.014546792 -0.0201449 0.012041784

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

64

Model Run EMT 80% (179) EMT 98.5% (179) EMT 90% (75) EMT 98.5% (75)
65

Appendix R

FMT Team Utilization during

Alert-Rate Decrease Period*

Model Run FMT 80% (179) FMT 98.5% (179) FMT 90% (75) FMT 98.5% (75)

1 0.565022346 0.596111732 0.57648 0.618013333

2 0.631128492 0.604117318 0.625813333 0.59492

3 0.602899441 0.619502793 0.62312 0.633586667

4 0.593620112 0.618452514 0.589386667 0.635813333

5 0.620418994 0.633357542 0.57516 0.610906667

6 0.583441341 0.592497207 0.598226667 0.615586667

7 0.59227933 0.621452514 0.584226667 0.675146667

8 0.632329609 0.599173184 0.655373333 0.625586667

9 0.617162011 0.569675978 0.636946667 0.60492

10 0.601329609 0.622670391 0.605826667 0.664933333

11 0.587731844 0.616614525 0.61092 0.617626667

12 0.61326257 0.593502793 0.620706667 0.599373333

13 0.548335196 0.606351955 0.504226667 0.574893333

14 0.633441341 0.612318436 0.66604 0.669346667

15 0.591005587 0.618 0.626253333 0.620706667

16 0.61075419 0.589882682 0.636706667 0.577586667

17 0.625251397 0.578240223 0.63692 0.565306667

18 0.587653631 0.615759777 0.6018 0.605586667

19 0.617061453 0.58 0.618693333 0.57292

20 0.605446927 0.598368715 0.59292 0.584933333

21 0.614094972 0.548804469 0.492253333 0.486706667

22 0.558675978 0.603659218 0.546693333 0.631146667

23 0.592743017 0.625463687 0.56556 0.615813333

24 0.573575419 0.603094972 0.588 0.613813333

25 0.626553073 0.592318436 0.582693333 0.531626667
26 0.606083799 0.571134078 0.557573333 0.589813333

27 0.616871508 0.591826816 0.569133333 0.58424

28 0.621173184 0.605055866 0.554706667 0.5956

29 0.630206704 0.630301676 0.61896 0.656053333

30 0.596201117 0.594430168 0.617786667 0.57468

Avg 0.603191806 0.601737989 0.595970222 0.604906222

Var 0.000500299 0.000379763 0.00159168 0.001548121

df 56.93201056 57.98883909

T-Stat 1.672522304 1.672028889

CI -007604923 0.010512558 -0.026041439 0.008169439

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

66

Model Run FMT 80% (179) FMT 98.5% (179) FMT 90% (75) FMT 98.5% (75)
67

Appendix S

PMT Team Utilization during

Alert-Rate Decrease Period*

Model Run PMT 80% (179) PMT 98.5% (179) PMT 90% (75) PMT 98.5% (75)

1 0.279284916 0.297888268 0.337773333 0.337746667

2 0.357513966 0.273670391 0.333306667 0.239893333

3 0.305374302 0.305368715 0.284426667 0.311093333

4 0.260648045 0.296039106 0.226613333 0.26216

5 0.281134078 0.292324022 0.27544 0.302186667

6 0.273698324 0.314642458 0.297733333 0.31548

7 0.322111732 0.335173184 0.306626667 0.315546667

8 0.309083799 0.288564246 0.302186667 0.2932

9 0.303463687 0.301648045 0.275453333 0.324453333

10 0.292357542 0.292335196 0.288906667 0.32

11 0.309050279 0.296022346 0.3466 0.279946667

12 0.283027933 0.322145251 0.302186667 0.302186667

13 0.31096648 0.296055866 0.337733333 0.275533333

14 0.303497207 0.318385475 0.328866667 0.35548

15 0.312793296 0.284893855 0.3466 0.262186667

16 0.318413408 0.307201117 0.324386667 0.311066667

17 0.337 0.323994413 0.328826667 0.302146667

18 0.297905028 0.322100559 0.306626667 0.31992

19 0.303497207 0.303519553 0.319946667 0.28

20 0.296011173 0.279273743 0.297693333 0.311053333

21 0.28672067 0.271849162 0.213266667 0.26664

22 0.264363128 0.297927374 0.279946667 0.337746667

23 0.36122905 0.273675978 0.342173333 0.266613333

24 0.335128492 0.312787709 0.395506667 0.253266667

25 0.253212291 0.288575419 0.262146667 0.30212
26 0.314648045 0.283027933 0.306586667 0.279986667

27 0.320256983 0.269955307 0.319946667 0.30216

28 0.290435754 0.256927374 0.275533333 0.23548

29 0.290413408 0.284860335 0.288826667 0.275453333

30 0.268111732 0.260659218 0.275493333 0.253253333

Avg 0.301378399 0.295049721 0.304245333 0.293133333

Var 0.000682664 0.000378015 0.001362314 0.000912569

df 53.57988565 55.81831917

T-Stat 1.674116237 1.673033966

CI -0.003625766 0.016283122 -0.003456795 0.025680795

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

68

Model Run PMT 80% (179) PMT 98.5% (179) PMT 90% (75) PMT 98.5% (75)
69

Appendix T

Rivet MILE Team Utilization during

Alert-Rate Decrease Period*

Model Run RM 80% (179) RM 98.5% (179) RM 90% (75) RM 98.5% (75)

1 0.527932961 0.553072626 0.513333333 0.546666667

2 0.567039106 0.586592179 0.54 0.593333333

3 0.606145251 0.55027933 0.64 0.58

4 0.561452514 0.55027933 0.553333333 0.6

5 0.502793296 0.527932961 0.566666667 0.6

6 0.511173184 0.581005587 0.553333333 0.533333333

7 0.578212291 0.583798883 0.593333333 0.56

8 0.583798883 0.547486034 0.593333333 0.526666667

9 0.575418994 0.516759777 0.513333333 0.446666667

10 0.594972067 0.51396648 0.54 0.513333333

11 0.575418994 0.533519553 0.586666667 0.553333333

12 0.594972067 0.539106145 0.6 0.5

13 0.567039106 0.575418994 0.56 0.593333333

14 0.578212291 0.558659218 0.56 0.626666667

15 0.592178771 0.5 0.54 0.553333333

16 0.600558659 0.589385475 0.58 0.606666667

17 0.497206704 0.569832402 0.52 0.56

18 0.519553073 0.553072626 0.493333333 0.6

19 0.592178771 0.558659218 0.626666667 0.56

20 0.569832402 0.558659218 0.56 0.593333333

21 0.553072626 0.586592179 0.546666667 0.653333333

22 0.575418994 0.555865922 0.573333333 0.593333333

23 0.61452514 0.567039106 0.633333333 0.5

24 0.586592179 0.62849162 0.546666667 0.646666667

25 0.530726257 0.527932961 0.466666667 0.546666667

26 0.592178771 0.592178771 0.62 0.586666667

27 0.527932961 0.581005587 0.513333333 0.586666667
28 0.494413408 0.600558659 0.533333333 0.553333333

29 0.505586592 0.491620112 0.586666667 0.533333333

30 0.530726257 0.555865922 0.52 0.54

Avg 0.560242086 0.557821229 0.559111111 0.566222222

Var 0.001291816 0.000940686 0.001755504 0.002013589

df 56.59986843 57.72932642

T-Stat 1.672522304 1.672028889

CI -0.012007181 0.016848894 -0.025852492 0.01163027

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

70

Model Run RM 80% (179) RM 98.5% (179) RM 90% (75) RM 98.5% (75)
71

Appendix U

CCT Team Utilization during

Alert-Rate Decrease Period*

Model Run CCT 80% (179) CCT 98.5% (179) CCT 90% (75) CCT 98.5% (75)

1 0.351955307 0.337988827 0.337988827 0.346666667

2 0.37150838 0.363128492 0.363128492 0.34

3 0.363128492 0.351955307 0.351955307 0.34

4 0.346368715 0.363128492 0.363128492 0.38

5 0.343575419 0.368715084 0.368715084 0.36

6 0.324022346 0.340782123 0.340782123 0.333333333

7 0.365921788 0.37150838 0.37150838 0.353333333

8 0.365921788 0.396648045 0.396648045 0.466666667

9 0.346368715 0.354748603 0.354748603 0.36

10 0.365921788 0.402234637 0.402234637 0.346666667

11 0.396648045 0.37150838 0.37150838 0.393333333

12 0.351955307 0.343575419 0.343575419 0.38

13 0.382681564 0.326815642 0.326815642 0.306666667

14 0.368715084 0.374301676 0.374301676 0.32

15 0.337988827 0.337988827 0.337988827 0.4

16 0.379888268 0.363128492 0.363128492 0.34

17 0.377094972 0.357541899 0.357541899 0.373333333

18 0.335195531 0.346368715 0.346368715 0.373333333

19 0.368715084 0.354748603 0.354748603 0.346666667

20 0.32122905 0.391061453 0.391061453 0.44

21 0.351955307 0.374301676 0.374301676 0.353333333

22 0.354748603 0.329608939 0.329608939 0.346666667

23 0.374301676 0.377094972 0.377094972 0.38

24 0.377094972 0.374301676 0.374301676 0.32

25 0.377094972 0.357541899 0.357541899 0.36

26 0.363128492 0.346368715 0.346368715 0.366666667

27 0.335195531 0.38547486 0.38547486 0.34
28 0.363128492 0.363128492 0.363128492 0.333333333

29 0.349162011 0.351955307 0.351955307 0.32

30 0.396648045 0.37150838 0.37150838 0.346666667

AVG 0.360242086 0.361638734 0.361638734 0.358888889

VAR 0.000365094 0.000354467 0.000354467 0.001161941

DF 57.98735078 45.18727415

T-Stat 1.672028889 1.679427393

CI -0.009585387 0.006792091 -0.009190274 0.014689964

*Note: Overall confidence level = .95 percent

The overall confidence level of 95 percent indicates that if we ran the simulation 100 

times, 95 of the simulation runs would contain the true mean. In this case, the true 

mean difference between the two models under comparison whether it is the 98.5 

percent model as compared to either the 90 percent or the 80 percent model. If the 

models are statistically the same, the confidence intervals between the two models will contain zero. Therefore, when the confidence interval contains zero when 

comparing the difference in means, we are 95 percent confident that the difference 

between these two models is statistically insignificant. This applies to all charts in 

appendices J–U. Tabulations were done by Capt Tamiko L. Ritschel, Air Force Logistics Management Agency (AFLMA), Maxwell AFB, Gunter Annex, Alabama.

72

Model Run CCT 80% (179) CCT 98.5% (179) CCT 90% (75) CCT 98.5% (75)
73

Abbreviations

AETC	 Air Education and Training Command

AFNGOSG	 Air Force Nuclear General Officer Steering

Group

AR	 alert rate

AVE	 aerospace vehicle equipment

BGRC	 Boeing Guidance Repair Center

CANX	 Cancel

CCT	 Corrosion Control Team

DOD	 Department of Defense

ECS	 environmental control system

EMT	 Electromechanical Team

FCAT	 off-alert for scheduled maintenance

FMT	 Facilities Maintenance Team

GRP	 guidance replacement program

GWOT	 global war on terrorism

ICBM	 intercontinental ballistic missile

In	 in

LC	 launcher closure

LCC	 launch control center

LF	 launch facility

LLC	 limited life component

LSB	 launch support building

MG	 Maintenance Group

MGS	 missile guidance set

MHT	 Missile-Handling Technicians

MMT	 Missile Maintenance Team

MNX	 Maintenance

Mx	 major maintenance

O&M	 operations and maintenance

OA	 off-alert

OGE	 operational ground equipment

P1	 Priority 1

P2	 Priority 2

P3	 Priority 3
P4	 Priority 4

PHT	 Periodic Maintenance Team

PRP	 propulsion replacement program

PSRE	 propulsion system rocket engine life

extension program

QRM	 quick reaction maintenance

R&R	 remove and replace

Rivet MILE	 Minuteman Integrated Life Extension

Teams

RM	 Rivet MILE

RPIE	 real property installed equipment

RS	 reentry system

SERV	 safety enhanced reentry vehicle program

SET	 security escort team

SF	 security forces	

SRV	 single reentry vehicle program

74
75

Bibliography

Air Force Space Command Instruction (AFSPCI) 21-114. 

Intercontinental Ballistic Missile (ICBM) Management, 1 

May 2003. (Restricted) Information extracted is nonrestriced. 15 April 2007.

Briefing. Directorate of Logistics and Communications, 

Maintenance, Subject: ICBM Millennium Roadmap (HQ 

AFSPC/LGM), author has file copy.

———. ICBM Long-range Requirements Planning. Subject: 

Logistics Programs Management Plan, December 2004, 

author has file copy.

Bryon, MSgt David. “Air Force Officials Project Budget 

Shortfalls.” Air Force Print News Today, 11 March 2005, 

n.p. http://www.af.mil/news.

Craig, Donald C. “Extensible Hierarchical Object-Oriented 

Logic Simulation with an Adaptable Graphical User 

Interface.” Master’s thesis, Memorial University, Newfoundland, July 1996. http://www.cs.mun.ca/~donald/

msc/node11.html#SECTION00432000000000000000. 

Dawson, SMSgt Michael C. “Maximizing Security Forces Response Time through the Use of Facility Location Methodologies.” Paper, AFIT/GLM/05-05. Wright-Patterson 

AFB, OH: Air Force Institute of Technology, 2005.

Fraser, Maj Gen Douglas M., director of Operations, Headquarters Air Force Space Command. Draft memorandum. To USSTRATCOM/J3/J5. Subject: Intercontinental Ballistic Missile Alert Rate (Draft), n.d., author has 

file copy.

Gilbert, Ken, Pat Johnson, Bob Leginus, and Steve Thomas. “ICBM Handbook (Draft).” CD-ROM, author has file 

copy. 27 March 2000.

Hughes, Col William D., III. “341st Maintenance Group 

Summary Supplemental Data Nov 2005.” Malmstrom 

AFB, MT: 341st Maintenance Group, Malmstrom AFB, 

November 2005, author has copy.

Kelton, W. David, Randall P. Sadowski, and David T. Sturrock. Simulation with Arena, 3rd ed. New York: McGrawHill, 2004.
76

Lafferty, CMSgt Joseph. “HQ USAF/ILMW 2M0 Overview 

Minot AFB.” Briefing. HQ USAF/ILMW, 16 May 2005.

Law, Averill M., and W. David Kelton. Simulation Modeling 

and Analysis, 3rd ed. (Boston: McGraw-Hill, 2000).

Minutes. Air Force Nuclear General Officer Steering Group, 

20 July 2005. (Secret) Information extracted is unclassified.

Neary, Maj Gen Tom, USAF, retired. “Twentieth Air Force—

From B-29s to ICBMs, A Proud Past . . . A Bright Future.” Air & Space Power Chronicles. 17 February 2000. 

http://www.airpower.maxwell.af.mil/airchronicles/cc/

neary.pdf.

Neill, James. “Qualitative Versus Quantitative Research: Key 

Points in a Classic Debate.” 30 June 2004. http://www.

wilderdom.com/research/QualitativeVersusQuantitativeResearch.htm. 

Ogden, Eloise. “Missiles on Target.” Minot Daily News, 8 

December 2005. 

Punnett, Lt Wayne. “2M Air Force Community, Career Field 

Health (using Manpower FY 11 Authorizations).” Briefing. AF/DPPFE, author has copy, 16 May 2005.

Strategic Air Command Regulation 66-12, Maintenance 

Management Vol. 1. Intercontinental Ballistic Missile 

Maintenance Management Policy and Supervisory Responsibilities, 30 October 1989.

Tymofichuk, Col Randy B. “The Importance of Maintaining Maximum Combat Effectiveness through Effective 

Maintenance Production.” Research Report. Maxwell 

AFB, AL: Air War College, 2006, author has copy.