Improving Maintenance Decision Making in the Finnish Air Force Through Simulation
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Transcript of Improving Maintenance Decision Making in the Finnish Air Force Through Simulation
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8/18/2019 Improving Maintenance Decision Making in the Finnish Air Force Through Simulation
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Vol. 38, No. 3, May–June 2008, pp. 187–201issn 0092-2102 eissn 1526-551X 08 3803 0187
informs ®
doi10.1287/inte.1080.0349© 2008 INFORMS
Improving Maintenance Decision Making inthe Finnish Air Force Through Simulation
Ville Mattila, Kai VirtanenSystems Analysis Laboratory, Helsinki University of Technology, FIN-02015 HUT, Finland
{[email protected], [email protected]}
Tuomas RaivioGaia Consulting Oy, Systems Analysis Laboratory, Helsinki University of Technology,
FIN-02015 HUT, Finland, [email protected]
We used discrete-event simulation to model the maintenance of fighter aircraft and improve maintenance-relateddecision making within the Finnish Air Force. We implemented the simulation model as a stand-alone tool that
maintenance designers could use independently. The model has helped the designers to study the impact of maintenance resources, policies, and operating conditions on aircraft availability. It has also enabled the FinnishAir Force to advance the operational capability of its aircraft fleet. We designed the model to simulate bothnormal and conflict operating conditions. The main challenge of the project was the scarcity and confidentialityof data about the fighter aircraft, their maintenance, and various operational scenarios, especially during conflictsituations.
Key words : simulation: applications; military: defense systems; reliability: availability, maintenance/repairs. History : This paper was refereed. Published online in Articles in Advance June 4, 2008.
Afighter aircraft typically requires several hoursof maintenance per hour of flight activity. Thismaintenance involves a diversity of operating poli-
cies, processes, people, and materials. In a fleet of
fighter aircraft, these elements form a complex main-
tenance system. The system performance directly
affects aircraft availability, i.e., the number of aircraft
that can be used in flight missions. Ability to assess
how maintenance-related decisions or operating con-
ditions affect the system is critical in maintaining the
fleet’s operational capability.
We used discrete-event simulation, which has been
widely applied in studying manufacturing systems
(Law and Kelton 2000), to model the maintenance
of the fighter aircraft fleet in the Finnish Air Force(FiAF). It lends itself to analyzing a maintenance sys-
tem because manufacturing and maintenance share
common features, such as workforce considerations,
tasks times, material-handling delays, and equipment
reliability. We also found simulation to be a suitable
method for modeling the FiAF maintenance system
because it enabled us to study the system from many
aspects that the FiAF maintenance designers were
likely to consider. The model describes the essen-
tial features of flight operations and maintenance
including planned and unplanned maintenance, air bases, aircraft repair shops, and maintenance person-
nel. Moreover, it describes both normal and conflict
operating conditions.
Some earlier studies on military operations also
applied simulation to consider the effects of reliability
and maintainability on aircraft operational capability.
For example, Balaban et al. (2000) and Ciarallo et al.
(2005) developed simulation models for availability
of cargo and mobility aircraft, respectively. Upadhya
and Srinivasan (2004) built a simulation model for
availability of generic aircraft and helicopters in com-
bat operations. Rodrigues et al. (2000) used a simula-tion model to assess the spare-parts management for
A-4 aircraft. Kang et al. (1998) considered two simu-
lation models for managing spare-parts and compo-
nent repairs. In a recent paper, Kladitis et al. (2007)
used simulation to analyze the impact of a new
avionics system on the availability of B-52H bombers.
However, these models either considered different
187
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types of flight operations than our model did or con-
sidered a more narrowly defined problem; they did
not take a system-wide view of maintenance. Pohl
(1991) used operational test data to devise a simula-tion model for operations of the F-15E aircraft. The
model described maintenance in much the same way
as ours did but limited the discussion to consideration
of a fixed-size squadron in a single air base. To the
best of the authors’ knowledge, no previous simula-
tion models in the open literature have considered the
maintenance of a fleet of fighter aircraft at the depth
of the model that we present in this paper.
Our primary challenges in constructing the model
were scarcity and confidentiality of data. In particular,
no data were available for modeling certain elements
of conflict conditions such as battle-damage proba- bilities or repair-time distributions. Some confidential
data, which FiAF could not share with the authors,
included parts of the contingency plans on conflict-
time maintenance policies. We found two approaches
useful in overcoming these challenges. First, in situa-
tions where data were unavailable, we asked subject
matter experts from different organizational levels
to provide their opinions. Second, we designed the
model such that the confidential information was
included in the input data; the maintenance designers
who performed the corresponding simulation anal-
ysis could thus handle the confidential data inde-
pendently. Implementing the model as a stand-alone
tool with a graphical user interface (GUI) facilitated
our second approach because it made the model
approachable to the designers. The scarcity of data
also affected the validation of the model. We were
able to perform limited comparisons between the sim-
ulation output and actual performance data from the
maintenance system. Therefore, we used subject mat-
ter experts on multiple occasions to assess the under-
lying assumptions as well as the model output.
We introduced the model in the FiAF units that per-
form aircraft maintenance; it has enabled these units
to address many maintenance-related issues. Exam-
ples include the forecasting of aircraft availability, the
analysis of the resource requirements for international
operations, and the feasibility study of a readjusted
periodic maintenance program. The project has also
provided FiAF with new knowledge about possible
applications of simulation techniques. For example,
the Finnish Army subsequently devised a simulation
model for the maintenance system of newly acquired
transport helicopters with collaboration from FiAF.
FiAF Aircraft Maintenance
The FiAF aircraft fleet consists of Boeing F-18 Hor-
net fighters, BAe Hawk Mk51 jet trainers, and other
types of aircraft used in transportation, air surveil-
lance, flight training, and liaison duty. We consid-
ered the flight operations and maintenance of the F-18
Hornet aircraft during normal and conflict conditions
(“conflict” refers to a situation in which the aircraft
fleet is involved in an actual engagement with an
enemy). However, because detailed Hornet informa-
tion is classified, we discuss Hawk maintenance inthis paper. At the modeling level, we found that the
maintenance principles and the appearance mecha-
nisms of unexpected failures are very similar; in gen-
eral, they differ only in model parameters. Hence, the
principles we report here apply to the Hornet as well.
The FiAF aircraft fleet has three primary operational
units that are called air commands (Figure 1). Within
each air command, a fighter squadron is responsible
for aircraft flight operations and specific maintenance
activities. Each air command also has a separate repair
FiAF
Headquarters
Air commands Air command 1
Air command 2
Air command 3
Headquarters
Fighter squadron
Air command’s
repair shop
Depot-level
repair shops
Other units
Other units
Figure 1: The primary operational units for flight operations and aircraft
maintenance of FiAF include three air commands that are further divided
into fighter squadrons and repair shops. Separate, depot-level repair
shops perform the most demanding maintenance.
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shop for more complex maintenance tasks. Depot-
level maintenance units of the national aerospace
defense industry perform the most demanding main-
tenance. The organization that Figure 1 shows remainsessentially the same during both normal and con-
flict conditions, although the decentralization of the
units during a conflict may change their geographic
locations.
Normal Conditions
During peacetime, the activities of an air command
are centralized at a single air base. The general goals
of aircraft maintenance are to assure that sufficient
numbers of aircraft are available for training and pos-
sible reconnaissance flight operations at all times, and
to preserve the long-term operating condition of theentire fleet. An air command should also be able to
raise the level of preparedness when necessary.
Daily aircraft maintenance consists of flight-
mission-related inspections. The aircraft that perform
flight missions undergo a preflight inspection before
the first mission, whereas a turnaround inspection is
performed after each mission. In these inspections,
the aircraft are checked according to given specifica-
tions and the necessary replenishments are made.
The aircraft periodically undergo more elaborate
maintenance. The frequency of periodic maintenance
is based on accumulated flight hours. The main-tenance intervals as well as the number and con-
tents of periodic maintenance types depend on the
type of aircraft. The Hawk undergoes six different
types of periodic maintenance that are referred to
Maintenance activity Timing Maintenance unit Maintenance level
Preflight inspection Before first flight of the day Fighter squadron OR (Organizat ional-level)
Turnaround inspection After each flight Fighter squadron OR
Periodic maintenance
Type I Every 50 flight hours Fighter squadron OR
Type II Every 125 flight hours Air command’s repair shop IN (Intermediate level)Type III Every 250 flight hours Air command’s repair shop IN
Type IV Every 500 flight hours Depot-level repair shops DE (Depot-level)
Type V Every 1,000 flight hours Depot-level repair shops DE
Type VI Every 2,000 flight hours Depot-level repair shops DE
Failure repairs Unplanned, as required Fighter squadron/Air command’s OR/IN
repair shop
Table 1: The maintenance of aircraft is categorized into different maintenance levels. Each maintenance unit
performs the maintenance of a specific level.
as type I,II, ,VI maintenance. Unplanned mainte-
nance is performed in case of a failure. Some failures
are noncritical—the aircraft are repaired only during
the next periodic maintenance; however, some failuresmust be addressed immediately. A repair typically
involves diagnosing the defect cause and repairing or
replacing the failed component.
The above activities are categorized into different
maintenance levels and the aircraft maintenance units
are categorized according to their capability to per-
form the activities (Table 1).
The organizational-level (OR-level) maintenance
mainly includes turnaround and preflight inspections,
minor periodic maintenance such as type I main-
tenance, and minor failure repairs such as simple
component changes. The fighter squadron operatesthe OR-level maintenance unit, which is located in
the main air base of the air command during normal
conditions. Intermediate level (IN-level) maintenance
includes more complicated periodic maintenance and
failure repairs. The air command’s repair shop, which
is also located in the main air base, performs IN-level
maintenance. Depot-level (DE-level) repair shops,
which are not located within the main air base, handle
major periodic maintenance.
Conflict Situations
In a conflict situation, the aircraft are exposed to battledamage or may be destroyed during flight missions.
Any of the maintenance units may handle battle-
damage repairs during a conflict depending on their
capability and the type of repair. These repairs require
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personnel with specific skills or materials that are
rarely needed during normal conditions.
The overall goals of aircraft maintenance change
as maintenance needs increase. The emphasis is onassuring the availability of aircraft in high-intensity
operations and the restoration of failed or damaged
aircraft to a mission-capable condition in the shortest
possible time. If necessary, periodic maintenance can
be temporarily suspended. However, aircraft perfor-
mance or reliable operation must never be reduced
severely. Changes in flight intensity and in the main-
tenance workload are difficult to anticipate because
they depend on the evolution of the conflict.
In a conflict situation, the FiAF air commands move
their units from the main air base to one or more
decentralized air bases to protect the air bases fromthe enemy. The organization of the air force and the air
commands remains largely the same. The decentral-
ized air bases are located in diverse areas that are typ-
ically sparsely inhabited; they utilize public roads as
a runway. They can typically support the flight oper-
ations and certain maintenance activities of a given
number of aircraft. The maintenance activities that are
allocated to an air base generally depend on the level
of infrastructure that is readily available at the loca-
tion. For example, a given air base may support all
activities that occur in the main air base of an air com-
mand during peacetime. This type of air base wouldhave facilities for all of the OR- and IN-level mainte-
nance. In turn, an air base may support the OR-level
only or merely the daily maintenance of the aircraft.
Decentralization changes the operating environ-
ment of the maintenance units if some of the infras-
tructure is inferior to that found in the main air
base. For example, the hangars in a decentralized
Air command Class 1 air base
Class 2 air base I
Class 2 air base II
Class 2 air base III
IN-level facility
Fighter squadronFacility for daily
maintenance
OR-level facility
Figure 2: In the simulation model, we divide an air command into an IN-level maintenance facility and a fighter
squadron that consists of OR-level maintenance facilities and facilities for daily maintenance.
base may provide less space for larger equipment.
Because of the changes, the durations of different
maintenance tasks can be increased. Decentralization
can also increase the logistic delays involved in trans-ferring materials, tools, and equipment between ware-
houses and air bases.
The Simulation Model
We constructed a simulation model that describes the
flight operations and maintenance of fighter aircraft
during normal and conflict conditions. The model
has three air commands, each with a specific num-
ber of aircraft. The aircraft carry out flight missions,
which bring about different maintenance needs. In
the simulation, maintenance is carried out in facilities
within the air commands and in one DE-level facility
that represents the DE-level repair shops of the actual
maintenance organization. The model input data dic-
tate the exact configuration of the flight operations
and maintenance and also govern whether normal or
conflict conditions are simulated. The model output
consists of aircraft availability and other performance
measures such as queuing times, resource utilization,
and attained flight intensity.
The Structure of the Air Commands
In the simulation model, the functional entities of
an air command include the fighter squadron andan IN-level maintenance facility that represents the
air command’s repair shop. We model two types
of maintenance facility in the fighter squadron, one
for flight-mission-related inspections and one for
other OR-level maintenance (Figure 2). The DE-level
maintenance facility in the simulation model operates
separately from the air commands.
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Each air command can operate in up to four air
bases. The class 1 air base corresponds to the main
peacetime air base of an air command. It includes
a facility for daily maintenance as well as OR- andIN-level facilities. The other three, which are class 2
air bases, represent alternate bases that include facili-
ties for daily and OR-level maintenance. In a simula-
tion of normal conditions, an air command uses the
class 1 air base. However, in a conflict simulation, the
air command typically operates in both class 1 and
class 2 air bases. It has up to four facilities for daily
maintenance, four OR-level facilities, and an IN-level
facility.
The Simulation Logic of Flight Operations and
Maintenance NeedsFrom an individual aircraft perspective, the simula-
tion consists of daily flight operations, daily mainte-
nance, periodic maintenance, and failure and damage
repairs (Figure 3).
First, an aircraft waits in its home air base until
it is assigned to a flight mission. We determine the
number of aircraft required for a mission and the
duration of a mission randomly from suitable prob-
ability distributions (as we discuss later), and select
the required numbers of aircraft from the air bases
of the air command. As an additional criterion, we
select those aircraft that have waited the longest. If amission is generated when no aircraft are available in
the air command, the model records the mission as
noncompleted in the output.
Wait for
flight
mission
Failed,
damaged, or in
need of periodic
maintenance?
Yes
Facility for
daily
maintenance
OR-level
facility
IN-level
facility
DE-level
facility
NoCarry out
flight
mission
Figure 3: In the simulation, an aircraft waits in its home air base until it
is assigned to a flight mission. After completing the mission, it undergoes
any necessary maintenance activities and then returns to wait for the next
mission.
The model assesses the need for maintenance after
a flight mission. It does not include aborting a mis-
sion because of failure or battle damage because
the missions are described as time delays with nospecified objectives. We model periodic maintenance,
failure repairs, and damage repairs using different
maintenance activities that we characterize depend-
ing on the type of activity. Periodic maintenance is
performed on the basis of cumulative flight hours and
a predetermined maintenance interval. Time between
failures is measured in flight hours. To model battle
damage, pass-fail probabilities are used to determine
the type of damage sustained during the mission.
Failures are mutually exclusive, i.e., only one type of
failure can occur at a time. This also applies to differ-
ent types of battle damage. All types of maintenanceneeds can, however, be realized during a mission. For
example, an aircraft may sustain both battle damage
and failure.
Each type of maintenance activity is assigned to
a unique facility where the activity is always car-
ried out. Typically, lower-level activities, such as those
that correspond to type I periodic maintenance, are
assigned to OR-level facilities, and higher-level activ-
ities to IN- and DE-level facilities. If an aircraft
has multiple maintenance needs, maintenance is per-
formed in the highest-level facility required by the
activities. Aircraft are not transferred between facili-
ties. An aircraft that requires maintenance is imme-
diately transferred to the selected facility and will
remain unavailable for flight duty until the mainte-
nance has been completed.
Aircraft daily maintenance involves turnaround
inspections. All aircraft that return from a flight mis-
sion and do not require maintenance, or that return
from maintenance in one of the OR-, IN-, or DE-level
facilities, undergo a turnaround inspection. After an
inspection, an aircraft returns to wait for the next
flight mission. We did not model the preflight inspec-tions because test simulations indicated that their
effect on the performance measures of interest is
negligible.
The Simulation Logic of Aircraft Maintenance
The aircraft downtime consists of the maintenance
in OR-, IN-, and DE-level facilities. The simulation
model considers aircraft that are in a turnaround
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Transfer to the
maintenance
facility
Wait for
material
delivery
Maintenance
Wait for
available
mechanics
Transfer to
home base
Figure 4: The total maintenance delay is the sum of the transfer delay to
the maintenance facility, possible waiting time for materials and person-
nel, duration of maintenance, and the transfer delay back to the home
base.
inspection to be available for flight duty. Thus, the
inspection time does not affect aircraft availability.
The time in maintenance in OR-, IN-, and DE-level
facilities involves the duration of the actual mainte-
nance and logistic delays as Figure 4 illustrates.
The transfer delays to and from a maintenance facil-
ity are specific to the facility. For example, the transfertime to a DE-level facility, which is not located within
the air command, is typically longer than the transfer
time to OR- or IN-level facilities.
A set of material requirements characterizes each
type of maintenance. The materials are modeled as
generic items, which can represent, for example, spare
parts, equipment, or tools. A maintenance activity
cannot begin until the necessary materials are avail-
able in the maintenance facility. The need for materi-
als is assessed when an aircraft arrives in a facility.
The maximum maintenance crew size and the proba-
bility distribution of the duration expressed in main-tenance man-hours, both of which are defined sepa-
rately for each activity, characterize the maintenance
delay. After a possible wait for materials, a mechan-
ics crew gathers to carry out the maintenance. If all
mechanics in the maintenance facility are busy, the
aircraft waits in a first-in-first-out queue until one
becomes available. The number of mechanics allo-
cated to the crew is, by default, the maximum crew
size. If the number of available personnel is less than
the maximum crew size, all available mechanics are
allocated. Finally, the net duration of the maintenance
is its duration in maintenance man-hours divided by
the allocated number of mechanics.
The logic for turnaround inspections differs slightly
from the maintenance in other facilities. The wait
for available materials and personnel and the
actual duration of maintenance determine the total
maintenance delay. Because the transfer delay is neg-
ligible, the model does not include it.
GUI
(VBA)
Simulation
parameters file
(Excel)
Simulation
results file
(Excel)
Input Input
Output
Initial state
file (Excel)
The simulation
model
(Arena)
Figure 5: We implemented the simulation model such that simulation
parameters are either fed through the GUI or through the simulation set-
tings file. The initial state of simulation is defined in the initial state file.The simulation output is written in a results file.
ImplementationWe implemented the simulation model using the
Arena software (Kelton et al. 1998); Arena is based
on the SIMAN language (Pegden et al. 1995) and
is intended for construction and analysis of dis-
crete-event simulation models. Figure 5 depicts the
implementation.
The simulation uses a GUI that we implemented
using Visual Basic for Applications (VBA) (Seppanen2000).
The model input data consist of the simulation
parameters and the initial system state. The simula-
tion parameters define characteristics of the air com-
mands, maintenance needs, and flight operations. The
initial state defines all the data needed to initialize
the system, e.g., the accumulated flight hours and the
location of each aircraft. The output includes aircraft
availability and various flight and maintenance statis-
tics. All external files of the model are Excel spread-
sheets; this makes it easy to manage several sets of
input data and to postprocess the model output.
Distribution Selection and Estimationof Simulation ParametersAn inherent part of the modeling is defining the
model input data as a function of the operating
conditions and air base structure. As we discussed
above, we used Hawk Mk51 unclassified data for the
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simulation parameters of operations during normal
conditions. We also used these parameters as a start-
ing point for determining the parameters in conflict
operations.
Needs and Sources of Data
A FiAF reference data set, which contained either
complete statistics or averaged values of the quan-
tities in question, was available for definition of
the simulation parameters. It included data from
actual flight operations and aircraft maintenance dur-
ing time periods of one to six years. Throughout
the model construction, FiAF project-team members
cooperated with the authors on the model develop-
ment; discussions included the general principles of
flight operations, aircraft maintenance, and differentmodeling solutions. In addition, we convened two
expert panels. The first included FiAF maintenance
personnel. The second included two senior mainte-
nance professionals from a DE-level repair shop. In
open discussions, the experts provided their views on
specific input data and modeling assumptions.
We determined some of the model parameters (e.g.,
the parameters for the duration of daily flight and
maintenance activities, the number of maintenance
personnel in the maintenance facilities, periodic-
maintenance intervals, and transfer delays of aircraft)
easily from the reference data set. Because the actualdata on spare parts and material inventories are clas-
sified, we did not consider material handling. There-
fore, subsequent simulations do not consider material
handling delays.
We needed to estimate the parameters for other
items of input data from statistical data or extract
them based on the opinions of subject matter experts.
These items included:
—Probability density function (p.d.f.) for the times
between failures;
—Probabilities of sustaining each type of damage
during a single flight mission;
—P.d.f. for the duration of each type of periodic
maintenance, failure repair, and damage repair;
—Maximum size of the crew participating in each
type of periodic maintenance, failure repair, and dam-
age repair;
—P.d.f. for the times between flight missions;
—P.d.f. for the duration of a mission.
Distribution Selection and Parameters for
Periodic Maintenance
The reference data included values for the mean and
standard deviation of the duration of type I periodicmaintenance. Because type I maintenance consisted
of relatively straightforward tasks, we could not con-
sider durations longer than the mean duration to be
more likely than short ones. Therefore, we chose a
symmetric triangular distribution as the model.
We collected the maintenance statistics of the dura-
tions of maintenance types II–VI from the IN-level
repair shop of the Air Force Academy, which is the
FiAF primary training unit. This repair shop han-
dles both IN- and DE-level periodic maintenance,
unlike the repair shops in the air commands. Based
on statistical tests and on the histograms of the datasamples, we determined that the maintenance dura-
tions should be modeled with a distribution that has
a longer tail on the right side. The subject matter
experts in both panels agreed with this conclusion.
For type II and IV maintenance durations, three fam-
ilies of distributions, Weibull, Beta, and Gamma, pro-
vided the best fit according to the Chi-square and
Kolmogorov-Smirnov tests (Law and Kelton 2000).
We ultimately chose the Gamma distribution, which
also seemed suitable for maintenance types III, V, and
VI, because the different types of periodic mainte-
nance have many similar tasks.
In choosing the parameter values for the dis-
tributions (Table 2), we first considered type II
maintenance.
We calculated the initial values for the parameters
as maximum likelihood estimates based on the sta-
tistical data, and presented the resulting distribution
to the subject matter experts on the expert panels
and within the project team. They assessed how well
this distribution represented maintenance in the over-
all maintenance organization. Based on their feed-
back, we adjusted the value of the scale parameter
and the constant in the distribution expression repre-
senting the minimum maintenance duration upwards
in the final choice of parameters; we left the shape
parameter unchanged. We also generalized the shape
parameter in type II maintenance to all other main-
tenance types from III to VI. Finally, we selected the
scale parameters and minimum maintenance dura-
tions such that the ratios of the standard deviation
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Maintenance
type Facility Crew size Duration (maintenance man-hours)
I OR-level 4 Tria8 38 68 (Triangularly distributed
with a minimum value of 8 hours,
mode of 38 hours, and maximum of
68 hours)
II IN-level 4 200+Gamma2 50 (Gamma
distributed with shape parameter
equal to 2 and scale parameter
equal to 50)
III IN-level 4 500+Gamma2 125
IV DE-level 5 1,300+Gamma2 300
V DE-level 5 1,500+Gamma2 300
VI DE-level 6 2,000+Gamma2 500
Table 2: We determined the crew sizes and the p.d.f.s of the maintenance
durations for periodic maintenance using statistical data and expert opin-
ion. The assignment of maintenance types to maintenance facilities was
readily available in the reference data.
and distribution mean remained approximately the
same as for type II maintenance.
No data were available on the sizes of maintenance
crews that actually perform the maintenance. There-
fore, we determined the crew sizes with the help of
the subject matter experts.
Distributions and Parameters for Failure Repairs
We used two failure types for modeling all failures
(Table 3).The first type represents the failures that are com-
monly repaired at the OR-level, and the second the
failures that are repaired at the IN-level. Because
detailed knowledge on failure statistics was not
available, we assumed times between failures to be
exponentially distributed. The mean times between
failures were directly available from the reference
data set. For durations of both types of failure repairs,
the reference data included the mean and standard
Duration
Failure Time between Crew (maintenancetype Facility failures (flight hours) size man-hours)
1 OR-level E xp(18.6) (Exponentially 3 4+ gamma2 1
distributed with a
mean of 18.6 hours)
2 IN-level Exp(43.3) 4 78+ gamma2 11
Table 3: The parameters for failure repairs included the assignment to
maintenance facility, time between failures, crew size, and duration.
deviation. We chose the Gamma distribution to rep-
resent the repair durations. We further set the shape
parameters of the distribution equal to those of the
periodic maintenance. We selected scale parametersand minimum maintenance durations so that the
ratios of the mean and standard deviation remained
the same as in the distributions for periodic main-
tenance because both types of maintenance involve
similar tasks and are performed by the same repair
shops. Again, we selected crew sizes based on expert
opinion.
Flight Mission Characteristics
Finally, we derived the parameters for the flight oper-
ations from the statistics of all the Air Force Academy
flight missions during one year. Based on the refer-ence data, we could model times between flight mis-
sions using an exponential distribution with a mean
of 30 minutes. Flight duration, on the other hand, fol-
lows a normal distribution with a mean of 45 minutes
and standard deviation of 12. We assumed that a sin-
gle aircraft is required in each mission.
Model ValidationWe validated the simulation model by comparing its
output with actual performance data. We also con-
ducted a sensitivity analysis of the impact of inputdata to key performance measures of the model and
let subject matter experts assess the underlying mod-
eling assumptions and simulation results.
Comparison to Actual Performance Data
We chose to compare the actual and simulated air-
craft availabilities because availability is the key
performance measure in actual maintenance-related
decision making. The three-month moving average of
availability during a period of four years was avail-
able for the validation. The simulation model con-
tained 51 aircraft divided among three air commands
operating in one class 1 air base. We initialized the
accumulated usage hours of the aircraft with a set of
values that was available but that did not relate to
the situation in the data. We used a warm-up period
of six months to erase the results from the initial
transient phase and to reach the steady state of the
simulation. We compared the simulated availabilities
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1.0
0.9
0.8
0.7
0.6
0.50 1 2 3 4
Time (years)
A i r c r a f t a v a i l a b i l i t y
Simulated
Actual
Figure 6: The simulated availabilities of 10 independent replications are
very close to the actual Hawk Mk51 availability during a four-year time
period. The figure is based on the three-month moving averages of both
actual and simulated availabilities.
from 10 independent simulation replications with the
actual availability data (Figure 6).
The average availability that the model predicted
was approximately 0.72; however, the data showed
an average availability of approximately 0.70. Some
of the difference is because the input data did not
consider material handling as we discussed above. In
addition, the simulation did not seem to reproducea drop in the actual availability just after the second
year because of additional modification work that the
aircraft underwent during the time period. We did
not consider the modification work in the input data
because our purpose was to describe average flight
operations and maintenance. Otherwise, the simula-
tion seemed to reproduce the actual availability well.
Sensitivity Analysis
We can use sensitivity analysis to assess how changes
in input data affect simulation output. The analysis
implies that the model is valid if the simulation out-
put is affected in the same way as the actual system
would be under corresponding changes. Because sets
of reference data from a wide range of operating con-
ditions are not generally available for such analysis,
the sensitivity results are frequently assessed subjec-
tively by both model constructors and subject matter
experts.
Therefore, we conducted the sensitivity analysis by
examining which of a set of 12 input data items
affected the average aircraft availability significantly
in the previously described simulation of the four-year time period. We used design of experiments
(Montgomery 2001) for the analysis and devised a
212−4 fractional factorial design involving 256 simu-
lation runs to estimate the effects of the items. In
the design, we set the simulation parameters corre-
sponding to the items to either −1 or +1 level as
Table 4 describes, but left other simulation parameters
unchanged.
We should also note that in Table 4 the num-
ber of mechanics in the maintenance facilities repre-
sents the effective amount of personnel resources, i.e.,
all mechanics are capable of performing all requiredmaintenance work within the facility. We also con-
sidered flight intensity in terms of the time between
flight missions, but did not include mission duration
in the design because the effects of both variables
are very similar and the exclusion of either variable
helped to limit the required number of simulation
runs. We combined failure types 1 and 2 into the over-
all mean time between failures for the same reason.
Table 4 lists the 95 percent confidence intervals for the
changes in average availability due to the changes in
the input data items. The effect of each item is statis-
tically significant. We selected the −1 and +1 levels
in the design so that the −1 level would presumably
result in lower availability and the +1 level in higher
availability. Because the effects for all items are posi-
tive, the results are consistent with our initial expecta-
tions. The time between flight missions has the largest
effect because flight intensity governs the amount of
all maintenance needs. The model is also sensitive to
the number of DE-level mechanics and the durations
of type IV, V, and VI periodic maintenance that the
DE-level facility performs. The subject matter experts
expected this because many aircraft underwent com-
plex periodic maintenance during the observed time
period. The number of OR-level mechanics and the
duration of type I periodic maintenance performed in
the OR-level facilities have the smallest effect because
the number of mechanics was high relative to the
maintenance needs. Decreasing the number of person-
nel from the +1 to −1 level did not congest the facil-
ities and showed little effect on aircraft availability.
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95 percent confidence
interval of the change in
Input −1 level +1 level average availability
Number of mechanics in maintenance facilities
Daily maintenance 13 17 00065±00011
OR-level 5 7 00016±00010
IN-level 13 17 00081±00009
DE-level 22 28 01578±00039
Time between flight missions Exp(26) Exp(34) 02182±00038
Duration of periodic maintenance
I Tria103 418 733 Tria57 342 627 00018±00007
II 220+gamma22 50 180+gamma18 50 00071±00011
III 550+ gamma22 125 450+gamma18 125 00075±00014
IV 1,430+ gamma22 300 1,170+ gamma18 300 00518±00019
V 1,650+ gamma22 300 1,350+ gamma18 300 00189±00019
VI 2,200+ gamma22 500 1,800+ gamma18 500 00583±00014
Overall mean time between failures 11.7 14.3 00138±00018
Table 4: We conducted a sensitivity analysis by examining the effects of 12 items of input data on simulated
aircraft availability. The effect of each item was statistically significant. Because the changes in availability were
positive for all items, the directions of the effects were also consistent with our initial expectations.
Some interaction effects of two input data items
were also significant. The change in aircraft availabil-
ity resulting from a change in one of the correspond-
ing items depends on the level of the other item.
Therefore, we cannot interpret the effects of single
variables literally. They indicate the relative impor-
tance of the items, however. For brevity, we chose not
to present the interaction effects in this paper.
Expert Validation
In addition to assessing the results of the sensitiv-
ity analysis, subject matter experts were also involved
in other aspects of model validation. The two expert
panels discussed the underlying modeling assump-
tions and output of preliminary versions of the
model, and the FiAF project-team members repeat-
edly addressed both modeling solutions and simula-
tion results.
In the final phase of model construction, we
arranged two user training sessions to introduce both
the model and basic principles of the simulation
approach to FiAF maintenance designers. The train-
ing was necessary because the designers would use
the model independently at a later time. We also
saw the training as an opportunity to further validate
the model. We asked the designers to give feedback
on any of its features. Thus, they contributed to the
validation both as end users and as subject matter
experts.
Because the underlying system of flight activities
and aircraft maintenance is large and multifaceted,
we discussed many issues of wide-ranging scope with
the experts during model validation, e.g., the forma-
tion of maintenance teams and the sequence of activ-
ities during individual maintenance tasks with the
second expert team. We addressed higher-level issues
such as the nature of conflict-time operating condi-
tions or appropriate performance measures of main-
tenance primarily with the project team. Overall, the
need to involve experts with different backgrounds in
model construction and validation was apparent.
In meeting with the experts who were not mem-
bers of the project team, the team members took
part in introducing the background and objectives
of the project. Our impression was that this greatly
helped to make the experts receptive to the simu-
lation approach and committed them to improving
the model. In the meetings, we used the guidelines
of a structured walk-through that Law and Kelton
(2000) describe, and allowed as much time as neces-
sary to discuss the modeling assumptions and simula-
tion results. We also took great care to devote enough
time to introducing the basics of simulation model-
ing to the experts. In the user training sessions, the
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designers were able to explore what the model does;
thus, they gained a far clearer view of its functionality
than they would in a standard classroom presenta-
tion. These actions allowed us to be confident that theassessments the experts ultimately gave were based
on a sufficiently detailed understanding of the model
and the simulation approach.
To summarize, the experts contributed to the val-
idation in two ways. First, they helped us to adjust
the simulation model and its input data during model
construction. Second, they helped to confirm that the
final model described the flight operations and air-
craft maintenance with enough accuracy to make it
sufficient for practical use.
Practical Use of the Model
The simulation model provides FiAF with a quan-
titative analysis tool for the flight operations and
maintenance of its aircraft fleet. In normal operating
conditions, the model can help maintenance design-
ers to allocate appropriate personnel and material
resources for an exercise with high flight intensity.
While this is important to the designers, their ultimate
concern is to learn how to maximize the conflict-time
operational capability of the fleet. As an example of a
conflict-related application of the model, we cooper-
ated with the FiAF project team to simulate a scenarioin which we examined the aircraft periodic mainte-
nance policy. The simulation provided information on
the number of aircraft that can be expected to be avail-
able and the maximum number of flight missions that
can be performed during the conflict.
The Conflict Scenario
The conflict scenario we considered involved four dif-
ferent phases. In the first phase, the level of readi-
ness is increased resulting in higher flight intensity.
In phase two, the flight intensity is further increased;
each air command moves to operate from the nor-
mal main air base into four decentralized air bases
and carries out flight operations and maintenance
24 hours a day. In the third phase, there is actual
conflict in the form of aerial battles and the aircraft
begin to sustain battle damage. In the fourth and final
phase, the flight intensity is decreased as the conflict
approaches an end.
In the scenario, we examined the aircraft periodic
maintenance policy. The maintenance facilities can
become congested at some point during the conflict
because of the increased flight intensity and the needfor battle-damage repairs. The periodic maintenance
can then be temporarily suspended to guarantee that
a sufficient number of aircraft are available for flight
missions. We assume that the decision of whether to
suspend periodic maintenance is made at the begin-
ning of each phase of a scenario. If the maintenance
is suspended, it will not be continued in any of
the remaining phases. However, any ongoing mainte-
nance will be completed. We simulated four alterna-
tive policies:
(1) All periodic maintenance is suspended at the
beginning of the first phase.(2) All periodic maintenance is suspended at the
beginning of the second phase.
(3) All periodic maintenance is suspended at the
beginning of the third phase.
(4) The periodic maintenance is not suspended
during the scenario.
Scenario Input Data
The scenario input data are based on the previously
described set of simulation parameters. We modeled
the different phases of the conflict by changing the
simulation parameters as Table 5 describes. All otherparameters remain unchanged.
The description of battle damage in the third and
fourth phases is an essential part of the simula-
tion. Because no data were available for estimat-
ing the battle-damage parameters, we determined the
parameters with the help of the FiAF project team.
We assumed three types of damage in the scenario
(Table 6).
Number of Daily duration of
operative flight operations Time between
Duration air bases per and maintenance flight missions BattlePhase (days) air command (hours) (min.) damage
1 30 1 8 Exp(24) no
2 30 4 24 Exp(20) no
3 10 4 24 Exp(20) yes
4 30 4 24 Exp(40) yes
Table 5: We modeled the conflict scenario by changing a set of simulation
parameters according to the different phases of the conflict.
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Probability Duration
Damage during a single Crew (maintenance
type Facility flight mission size man-hours)
1 OR-level 0025 4 2.6+ gamma2 07
2 IN-level 0015 4 6.5+ gamma2 18
3 DE-level 001 4 130+ gamma2 357
Table 6: We determined the battle-damage parameters for phases 3 and 4
with the help of FiAF project-team members.
We obtained the initial state of simulation for the
scenario as the final state of a long simulation from a
suitable but artificial initial state.
Simulation Results
We conducted 40 independent simulation replicationsfor each alternative policy. Figure 7 illustrates the
development of aircraft availability averaged across
the replications.
The most critical phase of the conflict is the actual
combat phase, during which the availability decreases
rapidly. If policy 4 is employed, the periodic main-
tenance will use up the maintenance resources and
delay the battle-damage repairs. The availability con-
sequently drops to as low as 0.4; this means that it is
very unlikely that the air commands could meet all
their operational goals. Policies 1 and 2 produce the
highest availability.
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
00 10 20 30 40 50 60 70 80 90 100
Time (days)
A i r c r a f t a v a i l a b i l i t y
Phase 1 Phase 2 Phase 3 Phase 4
Policy 4
Policy 3
Policy 2
Policy 1
Figure 7: Policies 1 and 2 maintain a clearly higher aircraft availability
than policies 3 and 4; this indicates that some periodic maintenance must
be suspended during the conflict.
We concluded that some of the periodic mainte-
nance must be suspended to maintain operational
capability, if maintenance resources, battle damage,
and flight intensity are as the scenario assumed. Itseems that the maintenance policy should be changed
before the actual combat phase. Although some types
of periodic maintenance can be performed in the early
phases, postponing the change of policy can prove
problematic in practice because the phase durations
are not known with certainty. We should also note
that suspending periodic maintenance affects the fail-
ure rate of the aircraft. The impact of periodic main-
tenance on the failure rates of aircraft is a challenging
topic that requires further research. Because no sta-
tistical data on this dependence were available and
the nature of the maintenance policy very preemp-tive, we kept the failure rate unchanged in the simu-
lations. The simulation results therefore represent the
best-case benefit of suspending the maintenance.
We also considered the daily number of completed
flight missions. If mission requests arrive with high
intensity, the air commands may not be able to
respond to all of them because of aircraft unavailabil-
ity (Figure 8).
We averaged the results across 40 independent
replications. Based on the results, it would again be
beneficial to suspend periodic maintenance at some
1.0
0.9
0.8
0.7
0.6
0.5
0.4
D a i l y p r o
p o r t i o n o f c o m p l e t e d f l i g h t m i s s i o n s
0 10 20 30 40 50 60 70 80 90 100
Time (days)
Phase 1 Phase 2 Phase 3 Phase 4
Policy 4
Policy 3
Policy 2
Policy 1
Figure 8: The daily proportion of completed flight missions during the con-
flict scenario indicate, as the availability results did, that operational
capability is best maintained with policies 1 and 2.
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point during the conflict. At the same time, the effect
of each policy on the flight operations became clearer.
The proportion of mission requests to which there
was no response during the busiest phase of theconflict was approximately 10 percent with policies
1 and 2. The ratios were 25 percent and 40 percent,
respectively, for policies 3 and 4.
The above results show that the consideration
of maintenance policies is essential to maintain-
ing the operational capability in a conflict situation.
A maintenance organization that is sized for nor-
mal conditions will have difficulty handling increased
maintenance needs even if the battle damage prob-
abilities are reasonably small. Additional simula-
tions could assess the amount of additional resources
required during a conflict, which maintenance activ-ities should be suspended, and when they should
be suspended. Overall, the model provides valuable
information to support the decision making that is
involved in devising contingency plans for aircraft
maintenance.
Model Construction ChallengesWe faced several challenges in constructing the
model. The primary one was scarcity of data. No sta-
tistical data were available for modeling elements of
the flight operations and aircraft maintenance suchas battle-damage probabilities and repair-time distri-
butions. We found that subject matter experts from
different units and organizational levels needed to
be involved in determining the corresponding model
components. The experts provided their views on
the issues at hand, and the authors explained the
benefits and drawbacks of incorporating a given mod-
eling solution to them. In addition to being essen-
tial in determining given modeling assumptions, the
communication with the experts helped us to refine
our overall view of the flight operations and aircraft
maintenance.
Because we designed a number of components in
the model with the help of subject matter experts,
we needed to carefully assess whether the overall
model validly described the usage and maintenance
of the aircraft. We did extensive sensitivity analyses
to quantify how the output of the model would be
affected by departures from modeling assumptions
or input data. The analyses included the examination
of the structural assumptions associated with several
key components of the model, e.g., the logic of air-
craft maintenance. We also tested the effect of chang-ing the distributions of maintenance durations. As
Table 4 summarizes, we used an experimental design
to examine the effects of input data. We presented
results of our analyses to the experts and allowed
them to use the model. Therefore, we are confident
that we captured the views of the experts correctly in
the final model.
Another challenge we faced was confidentiality
of data. FiAF representatives could not provide the
authors with access to highly classified information.
However, some of this information was necessary to
model scenarios that FiAF ultimately wished to con-sider. For example, it included the contingency plans
on maintenance policies and anticipated flight intensi-
ties, battle-damage rates in various conflict scenarios,
and the statistics from the normal time maintenance
of F-18s in the air commands.
To overcome this difficulty, we implemented the
model such that the choice of input data fully governs
its operations logic. For example, we did not hard-
code the conflict-time maintenance policies. Instead,
we modeled these policies by selecting suitable values
for a set of input parameters. We could isolate con-
fidential information for separate handling by FiAF.
Naturally, determining the structure of the model
did require some discussions on confidential issues
between the authors and the FiAF project team. The
team members described in general terms what the
model should be able to do. Based on their descrip-
tion, we implemented the corresponding model com-
ponents and revised them repeatedly until the model
was satisfactory. Thus, we managed to guarantee that
the model had the right functionality for considera-
tion of any relevant normal or conflict-time scenarios
although we could not use classified informationdirectly.
ConclusionsThe practical use of the simulation model implies
that it offered FiAF a valuable aid in improving
maintenance-related decision making. We first intro-
duced the model and initiated the project in FiAF
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headquarters. In the early phases, the FiAF project-team members were the primary users. They applied
the model to produce short-term forecasts of aircraft
availability. They also used the model to analyze theaccumulation of maintenance needs in some of thelarger exercises and to assess the resource require-ments of a smaller group of aircraft that participated
in a combined exercise with Air Forces from othercountries.
We delivered the model to the air commands aswell as to other units of FiAF. The units applied themodel to analyze the effects of a readjusted periodic
maintenance policy for the F-18s.The project also served as a pilot study to advance
the application of simulation techniques in aircraft
maintenance, air base logistics, and other areas atFiAF. For example, shortly after the completion of
the simulation model that we discussed in this paper,FiAF cooperated with the Finnish Army on a simula-
tion project to analyze the maintenance system of theArmy’s new transport helicopters.
The model is suitable for training purposes.Because it is GUI-based and does not require detailedknowledge of the underlying simulation software, it
is useful in classroom demonstrations or individually by trainees. However, users still need some time to
acquaint themselves with the model. Therefore, train-
ing has thus far been limited to situations where theschedule allows a thorough introduction to the model
functionality. Some of the graduating students of theAir Force Academy have applied the model for sim-
ulation analyses in their theses.The process of constructing the simulation model
has also brought indirect benefits. The subject matter
experts involved in the construction were required todescribe the organization and interaction of given ele-
ments of the maintenance system. The FiAF project-team members and some of the other experts said thatthis involvement helped them to obtain new insights
into the system. They regarded this as an additionalproject benefit.
In the future, FiAF will use simulation to design andcontrol flight operations and aircraft maintenance. Its
research directions include the simulation of smallerelements of the maintenance system, e.g., a singledecentralized air base. We have also begun to pur-
sue the scheduling of aircraft periodic maintenance byusing simulation-based optimization techniques.
AcknowledgmentsWe gratefully acknowledge the help of the people at FiAFwho were involved in this project. In particular, we thankMajor Riku Lahtinen for his invaluable support to the entireeffort.
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Major Riku Lahtinen, Armaments Division, FiAF
Headquaters, PO Box 30, 41161 Tikkakoski, Finland,
writes: “I have acted as the head of the project
team of the Finnish Air Force (FiAF) and as the
primary contact between FiAF and the authors in
the project that is described in the paper ‘Improv-
ing Maintenance Decision Making in the Finnish Air
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Force Through Simulation.’ With this letter, I’d like
to verify that the paper gives an accurate account of
the details of the project and the benefits that it has
provided.“The Armaments Division, together with the Mate-
rial Command, carry out the development of the air-
craft of FiAF to meet operational and airworthiness
requirements. The division also plans and coordinates
the research efforts that are undertaken to support
the development. Our task is to guarantee that the
aircraft are safe, powerful, and properly equipped
at all times: in training, in increasing number of
international operations, as well as in all degrees of
readiness. This requires us to continuously improve
the quality of the maintenance processes. Since the
resources are not unconstrained, the quality must bedeveloped by considering the efficiency of the pro-
cesses as well.
“We had ongoing collaboration with the Systems
Analysis Laboratory, Helsinki University of Technol-
ogy, and asked them to propose how we could study
the effect of maintenance on aircraft availability. The
research team of the Systems Analysis Laboratory
first conducted a pilot simulation study where the
operations of a single airbase were considered. We
regarded this pilot study as a success and decided
on requesting a model of the maintenance of the
entire fighter aircraft fleet. The specifics of the result-
ing model are described in the paper.
“The benefit of the simulation model has been
unquestionable. It has given us a sophisticatedapproach to analyze things with a less labored way
than earlier. Besides the Armaments Division, other
units have benefited from the model in assessing
proposed improvements to maintenance practices.
Although the details of these analyses are mostly con-
fidential and can not be elaborated here, I can state
that they are significant parts in the development of
aircraft maintenance in FiAF.
“Another result of the project has been the emer-
gence of fresh conversation and exchange of ideas
between different branches of aircraft maintenance.
The people with different backgrounds were exposed,in a positive sense, to each others’ viewpoints dur-
ing the discussions that went on in the project. I can
say with confidence, that my understanding of the
different branches has improved and I truly believe
that this is the case for a number of other people.
Since these people are our most important assets, the
impact of the project has been an important one.
“Our experiences from the project have been posi-
tive and we see simulation applications as an integral
part in maintenance-related decision making in the
future.”
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