PARSim, a Simulation Model of the Royal Canadian Air Force

(RCAF) Pilot Occupation

An Assessment of the Pilot Occupation Sustainability under High Student

Production and Reduced Flying Rates

René Séguin

Defense Research Development Canada Centre for Operational Research and Analysis,

101 Colonel By Drive, Ottawa, ON, Canada

Keywords: Simulation, System Dynamics, Pilot Production, Pilot Absorption, Military Occupation, Personnel

Management, Force Generation, Powersim.

Abstract: Training personnel to operate extremely complex and expensive equipment requires a large monetary

investment and takes lengthy periods of time. It goes without saying that careful planning is of the outmost

importance. Such is the case for military pilots. The Pilot Production, Absorption, Retention Simulation

(PARSim) model that was developed by the Centre for Operational Research and Analysis (CORA)

simulates the flow of pilots from recruitment, through training, onto the operational training units and into

the various operational fleets, accounting for attrition, instructor pilots and staff requirements. A key feature

of the model is that it simulates the acquisition of experience, dynamically adjusting the experience

acquisition rate in response to the existing experience level on the squadrons and the availability of

resources. The model is a tool that can be used to perform what-if analysis quickly and easily. This paper

describes the simulation model and reports on a study where the impact of high production combined with

reduced budgets is analysed.

1 INTRODUCTION

Training pilots in the Royal Canadian Air Force

(RCAF) as in many other air forces in the world

requires a large investment in time and resources. As

such the number of each type of pilots trained needs

to be carefully planned and managed. Pilots in the

RCAF go through several phases of training to

acquire their wings. They are then assigned to an

operational training unit (OTU) to learn to fly a

specific type of military aircraft and then to an

operational (ops) squadron to acquire superior skills.

This is accomplished by flying with or under the

supervision of an experienced pilot or mentor. Once

the required skills are obtained the need for

supervision is lifted, the pilot upgrades to a superior

level and can start mentoring new recruits himself.

The mentoring period for each pilot usually lasts

several months and for some aircraft types it may

take up to two years. The rate at which pilots

upgrade depends on the number of mentors and the

amount of flying hours available. The process of

training and mentoring pilots is part of what is

commonly known in military jargon as Force

Generation (FG) whereas the term Force

Employment (FE) is used for all operations,

missions and tasks that military personnel

accomplish.

The process of mentoring is often referred to as

the absorption of new recruits. The various schools

are responsible for the production of new pilots and

squadrons are responsible for their absorption for

final training. As squadrons have fixed size (known

as Preferred Manning Level (PML)), usually

established by the RCAF and government policies,

each new recruit posted to a squadron pushes

another pilot out of the squadron; usually an

experienced pilot which will be moving to another

posting requiring experience (instructor or staff).

The production and the absorption rates are

obviously closely linked but another crucial factor

also needs to be considered: the attrition of pilots.

Attrition mainly occurs for experienced pilots as

younger pilots are under contract and restricted from

being released. For steady state to be achieved,

Séguin, R.

PARSim, a Simulation Model of the Royal Canadian Air Force (RCAF) Pilot Occupation.

DOI: 10.5220/0005234700510062

In Proceedings of the International Conference on Operations Research and Enterprise Systems (ICORES 2015), pages 51-62

ISBN: 978-989-758-075-8; ISSN: 2184-4372

 2023 by His Majesty the King in Right of Canada as represented by the Minister of National Defence and SCITEPRESS – Science and Technology Publications, Lda. Under

CC license (CC BY-NC-ND 4.0)

production needs to match attrition and squadrons

need to be able to absorb all pilots produced;

otherwise, the pilot occupation becomes under or

over staffed. If production is not absorbed by

squadrons, pilot candidates accumulate in waiting

“queues” and gradually lose their skills. If

production is too high and pushed onto squadrons

anyway, the mentoring process is slowed down as

fewer and fewer mentors are available for a

constantly increasing number of mentees (as the size

of squadrons is fixed). Unless quickly corrected, this

process goes into an unstoppable downward spiral.

Similarly, but at a slower pace, if production is too

low to compensate for attrition, experienced pilots

are lost which slows down the mentoring process

and thus the rate at which younger pilots upgrade

and become mentors themselves. In summary, the

pilot occupation can be viewed as a system in a

delicate equilibrium and with a large inertia due to

the lengthy training and upgrade process.

Figure 1 shows a high level diagram of the flow

of pilots through the system. The top half

corresponds to the undergraduate portion of pilot

training done at the various schools. The bottom half

represents all graduate pilots in all positions:

operational squadrons (Sqns), instructor pilots (IP) at

OTUs and in training (Trg) schools, and staff

positions. The diagram only shows a few of the

RCAF fleets: fighter (FTR), rotary-wing (RW)

tactical helo, maritime helo, search-and-rescue helo,

multi-engine (ME) tactical transport and maritime

patrol.

The terms in green at the top are the various

entry training programs (TPs) into the pilot streams:

Regular Officer TP (ROTP), Direct Entry Officer

(DEO) TP, Continuing Education Officer TP

(CEOTP), and Community College Entry Program

(CCEP). ROTP is the usual university education and

pilot training program; students usually attend

university for the first three years, then continue to

Phase I followed by their last year of university

before continuing with pilot training. DEO is for

officers that change occupation and already have a

degree. CEOTP is similar to ROTP with the

exception that members obtain their degree from a

community college rather than from a Military

College; as for ROTP, candidates of CEOTP go to

college between Phase I and II but also after Phase

III. Finally, CCEP candidates, as DEO candidates,

have already completed a college degree but they

also already have a pilot license which allows them

to bypass Phase I.

The red arrows indicate where attrition from the

occupation is possible. The yellow boxes contain the

inexperienced pilots (mentees) that need mentoring.

The light green boxes contain experienced pilots and

the olive green boxes represent operational

squadrons. More details will be provided below

when the model is discussed.

Numerous “external” factors affect the system

Figure 1: High-level diagram of the pilot occupation.

ICORES 2015 - International Conference on Operations Research and Enterprise Systems

and can result in significant issues if not carefully

managed. For example, attrition may increase

significantly if a large group of pilots leave the Air

Force to join civilian airlines once their contract is

completed. A second example would be if budgets

are decreased and pilots see a reduction of their

yearly flying rate (YFR). Another example is when

the Air Force fleet is modified due to the

introduction of a new type of airplane or the

transition from a legacy platform to a newer aircraft.

All these issues push the system away from steady

state and carefully crafted plans with timely actions

need to be devised to avoid serious consequences.

Pilot production and absorption issues have been

studied for decades (Mooz, 1969) but it seems that

increased efforts have started in the early 80’s

(Moscrip, 1980) with lots of studies in the last 20

years (Taylor, 1992a, 1992b; Graff et al., 1994; Thie

et al., 1995). Simulation work on pilot training has

started in Canada with the work of Boulet (1993,

1994a, 1994b). Following work done under the

RAND Corporation’s Project Air Force (Taylor,

Moore and Roll, 2000), CORA developed a dynamic

simulation model of the mentoring process

(Latchman, Corbett and Hunter, 2001) followed by a

more complete model of the pilot occupation

(Latchman and Hunter, 2002). The RAND

Corporation has continued to study pilot absorption

issues concentrating on the fighter community

(Taylor at el., 2002; Bigelow et al., 2003a; Bigelow

et al., 2003b; Marken et al., 2007; Taylor, Bigelow

and Ausink, 2009). At the same time, CORA

continued to evolve its model to simulate the whole

RCAF pilot occupation from recruitment to

retirement and to include all fleets employed. A

study on the effects of increasing UAV pilot

requirements on the fighter community can be found

in Garner and Villem (2005).

For several years the RCAF has experienced a

period of shortage of seasoned pilots for several staff

positions. In the last few years the RCAF has strived

to achieve a higher production of pilots at the

training schools to bring up the staffing level closer

to what it should be. Due to the inertia of the system

this is a lengthy process. Unfortunately, this year the

RCAF budget has been reduced which may impact

the amount of flying hours each fleet will be

allocated. Absorbing higher levels of newly winged

graduates is already difficult for squadrons, but

combined with reduced resources it is a very

difficult challenge. After describing the simulation

model, this paper reports on a study where the

impact of high production combined with reduced

budgets is analysed.

2 METHODOLOGY AND MODEL

Even though small individual pieces of the pilot

production and absorption system could potentially

be analysed analytically, the sheer complexity of the

problem with its feedback loops, conflicting

objectives, dynamic and stochastic nature, and

numerous concurrent events makes simulation the

only viable option to study the RCAF pilot

occupation in its entirety. At CORA, a System

Dynamics (Sternman, 2001) approach has been used

since the early stages of development of the pilot

system model. Initially, only the undergraduate

portion of pilot training was modelled, then a

separate OTU model was designed, followed by a

fully connected model of the training system and the

main fleets of the RCAF (Latchman and Hunter,

2002). Since then, the model has been known as the

Pilot Production, Attrition, Retention Simulation

(PARSim). Corbett (2013) documented the

mathematical foundations of the initial model. In

2006, with the imminent introduction of the new

strategic airlift fleet and the transition from the old

fleet of tactical airlift aircraft to a new platform, the

model was expanded to be able to study

modifications to the RCAF fleet. Further

refinements were implemented over the following

years, the latest being the capability to take into

account YFR constraints. Since its inception, 12

years ago, the model has been used numerous times

to study various issues such as how to overcome

high attrition (Latchman and Hunter, 2002), how to

get rid of large queues for courses, what are the

optimal fleet intake/absorption levels, how to

optimally plan the transition of platforms or the

introduction of a new capability.

The model has been implemented using the

Powersim Software simulation environment

(Powersim, 2014). It is comprised of four major

types of modules: 1) training phases modules where

pilots gradually learn to fly more complex missions

and more specific aircraft, 2) operational fleets

modules where pilots acquire experience through the

dynamic mentoring process, 3) cross-flow (CF)

modules to manage the transfer of experienced pilots

from one type of aircraft to another, and 4)

intermediary modules to calculate various quantities

used to obtain the correct behaviour from the other

modules.

The model is complex as it simulates the whole

pilot occupation from recruitment to retirement. It is

a high-level integrated model that does not track

individual pilots but rather groups of similar pilots

(for example, ME tactical transport pilots, fighter

PARSim, a Simulation Model of the Royal Canadian Air Force (RCAF) Pilot Occupation

demonstration team pilots, etc.). Therefore, it does

not track data such as years of service and time in

rank. It is designed to assess the health of the

occupation as a whole and ensure that every fleet is

staffed properly and at the right level of experience.

The model is used as an options test bed to perform

what-if analyses. Depending on the type of analysis

required the amount of input required can be

substantial. For long-term high-level issues,

approximate starting values can be used but for very

precise and shorter horizon studies, great care must

be taken to seed the model correctly. Since the

model is never used from scratch, it has to be seeded

with current values. For example, the number of

pilots of each type, the number of students in each

course, the number of people waiting in queues, etc.

In several studies, transitioning issues had to be

analysed. For this, the ability to vary the values of

“elements” over time is essential. In Powersim, this

is done by cloning constants at the appropriate time.

For example, when standing up a new fleet, the

number of pilots and thus the number of students

required for the OTU will be slowly increased from

year to year and the model will slowly start sending

newly winged graduates (NWGs) to the new fleet.

Obviously, a few more elements need to be cloned

to complete the task of standing up a new fleet and

they need to be carefully timed but the principle is

the same.

2.1 Operational Fleet and Cross-Flow

Modules

As an illustration, Figure 2 shows a module for one

of the operational fleets; the whole model consists of

33 such modules. Specific details of the figure are

not important here, the goal is to provide an

appreciation for the model. In this particular module,

the main portion is at the top right and flows of

pilots come in from and go out to other modules of

the model through the cyan coloured flows. The

main entry is through the leftmost cyan “cloud”

flow, where pilots arrive from the school and are put

in a queue (first yellow block) for the next OTU

course (second yellow block). Once this course is

completed, pilots are qualified on a specific aircraft

but need to acquire experience through the

mentoring process. While acquiring this experience

they are dubbed inexperienced and “stored” in the

third yellow box. After upgrading to the experienced

status, they move to the fourth yellow box. The flow

of pilots between these two stages is a crucial piece

of the model and is controlled via the mentoring sub-

model at the bottom right where every week the

resources (level of YFR and number of mentors

(experienced operational pilots)) are assessed to find

out how much flying hours can be allocated to each

mentee for the upgrade process.

Figure 2: A fleet sub-model.

ICORES 2015 - International Conference on Operations Research and Enterprise Systems

There is a second cyan inflow of pilots in the

diagram which is connected directly to the OTU

box; this second flow corresponds to experienced CF

pilots arriving from another fleet (transfers). This is

a minor input channel for the large majority of fleets

but can be used extensively if a fleet has a need for

very experienced pilots. This flow is connected

directly to the OTU as it is not modelled as a push

from the school but rather as a pull from the fleet

and as such these pilots have priority over NWGs.

The model always verifies the health of the provider

fleets before allowing the transfers out to ensure that

no fleet is disadvantaged in the process. The health

is assessed using the ratio of mentors to mentees (or

experience ratio) and the manning ratio of the

trained effective strength (TES) (number of ops

pilots (mentors plus mentees)) to the PML of the

fleet. The experience ratio should always be above

50% and the manning ratio should be close to 100%.

There are two cyan flows out of the Experienced

block: one that sends pilots out to the various

schools to become IPs to compensate for IP attrition

and one for CFs out to satisfy the pull for CFs from

other fleets. Note that since CFs are defined as

pulled quantities and the health of the various fleets

may not allow for all requests to be satisfied, the

model has to decide how many and which requests

can be satisfied, and from which fleets.

Every time there are requests for CFs from

OTUs, the model first evaluates how many pilots

can be provided for CF purpose. This is based on a

proportion of the number of experienced ops pilots

that each healthy fleet can provide. This establishes

the proportion of the total CF requests that can be

satisfied by these fleets. In a second step the model

calculates what proportion of these achievable

requests can be filled by each healthy fleet. Then a

random process based on these proportions is used

to determine which fleet will actually be providing

the CFs. In a very healthy situation, the probability

of a large fleet to be a provider is greater than that of

a small fleets and the use of randomness ensures that

small fleets provide some CFs some of the time.

It is worth noting that the model also deals with

the following two elements of the CF process: 1) a

fleet may simultaneously be a provider of CFs as

well as a receiver and, 2) CF requests for fleets are

defined annually but the pilots pulled in as CFs must

be spread over OTU courses offered throughout the

year. Finally, a constraint (that is rarely restrictive)

has been used to simplify implementation of the CF

process: a provider fleet is allowed to give a

maximum of one pilot per course per requesting

fleet.

Red flows are for attrition; they are controlled by

the top left sub-model and can happen any time over

the year. In the main flow there is a loop for

experienced pilots to move between the sets of

operational and staff positions. Movement of pilots

between these two groups is managed with the sub-

model on the bottom left. It ensures first that a

mandated minimum number of staff positions are

always filled. Then, it verifies if there are enough

pilots in the ops squadron or too many. If any of the

manning levels are incorrect, the model transfers

pilots from one group to the other. This adjustment

is done once a year to mimic the re-assignment

posting cycle of the RCAF.

In the key mentoring sub-model at the bottom

right, resources are evaluated each week to find out

how many flying hours can be allocated to each

individual pilot for the upgrade process. It is also in

this sub-model that the verification of the hours

acquired is done to grant upgrade to mentees. Even

though in practice mentees may upgrade with

varying levels of flying hours due to their individual

skills, an average value is used in the simulation for

all mentees. Note that only flying hours are

considered in the model to grant the upgrade. In

practice, simulation hours may also be used and

required. As flying hours is a much scarcer resource,

it is assumed that the simulation hours required can

be acquired during the several months it takes to

acquire the flying hours.

The number of weekly hours available for the

upgrade process depends on the number of

experienced pilots (mentors) e available (which

varies due to attrition, posting and upgrade of

pilots), on the number of inexperienced pilots i to

upgrade, on the annual flying budget allocated to the

specific fleet y and on flying limits ̅ and ̅ imposed

on pilots (both mentors and mentees). For the budget

component, only hours that can be used for FG are

included which means that pure FE hours (pure FE

is taken to mean here that none of these FE hours

can be used for training purposes (for example,

expeditionary operations)), OTU hours and any

other reserved hours (for example, Standardization

and Evaluation Team) have to be removed. Some

fleets may also allow a mentor to train two mentees

at a time on some flights; p is the percentage of

flights where this is allowed. Even though pilots

may fly at different rates in practice, it is assumed

that all mentors fly at the same rate as well as all

mentees (however, mentors and mentees may fly at a

different rate). The equation for the weekly number

of hours for each mentee is:

PARSim, a Simulation Model of the Royal Canadian Air Force (RCAF) Pilot Occupation

min



1



∗

∗







1



∗







(1)

Some fleets also have augmentees which are

experienced pilots that can help in the upgrade

process but at a reduced flying rate compared to

normal mentors. For those fleets, equation (1) would

be slightly modified. Furthermore, experienced

pilots that come in from other fleets also need to

upgrade on the new aircraft type but they require

less time than NWGs and thus have to be tracked

separately. This means a portion of the mentoring

sub-model is repeated. Finally, as the model is

seeded with the current number of pilots, some of

them will be at various stages of the upgrade

process. The bottom section of the mentoring sub-

model tracks those groups of pilots that have been

seeded with a varying number of flying hours

already acquired at the start of the simulation.

We will now discuss the section of the main flow

where NWGs are moving from the queue (the term

PAT, for Personnel Awaiting Training, is commonly

used in military settings) to the OTU course. This

section is structurally the same for all courses in all

phases of training and is illustrated in Figure 3.

As can be seen in the figure, rules are used to

govern the flow between the queue and the course.

These include such elements as the minimum 

and

the maximum 

number of students on each course,

the frequency of the course and the number of CFs c

the OTU was actually successful in obtaining from

other fleets for this specific offering of the course. If

q is the number of students in the queue, the

dynamic equation used for calculating the student

loading is the following:

  

0

 min, 



(2)

Student course failures need to be implemented

carefully as the majority of the classes are small and

using simply a proportion of students would be

inaccurate. A binomial distribution has been

implemented using a vector of Bernoulli random

variables (Corbett, 2013; Law and Kelton, 2000). It

is also worth mentioning that failures at more

advanced stages of jet training result in students

being moved to another stream rather than being

removed from the pilot occupation.

Finally, it is important to mention that in

thistraining section (as in numerous other training

sections of the model) the flow of students that

successfully complete the course and move on to the

next block is linked to the load into the course and

“controlled” by a delay defined by the length of the

course duration. This delayed link (illustrated by the

dash lines in Figure 3) plays a similar role as

delayed signals in discrete event simulations.

Figure 3: OTU portion of a fleet sub-model.

2.2 Operational Fleet – Special Case

Modules

Some fleets have extra elements in their module. For

example, the current ME tactical airlift fleet is

connected directly to the new tactical airlift fleet to

be able to directly and gradually transfer pilots.

Usually an initial cadre of experienced pilots and

some staff positions are first transferred to establish

the new training capability (Standardization and

Evaluation Team and IPs) for the fleet. Then, more

experienced pilots are transferred to be trained and

establish an operational squadron. The initial

training for the new aircraft may be accomplished

through a shorter conversion course if the new

aircraft is not too drastically different. Some NWGs

are slowly being sent to the new OTU. Gradually all

remaining experienced pilots (ops and staff) are

moved to the new fleet. The legacy OTU is closed at

some stage and NWGs are then only sent to the new

fleet. When the legacy fleet is completely retired,

some mentees may not have completed their upgrade

process. Unless they are very close to completion,

they may have to start over on the new aircraft. This

is a waste of time and resource and shows how

important it is to precisely plan the transition from

the legacy fleet to the new one.

Another important special case is the feeder

fleet. Two different variations are possible: pilots are

either fed just after the OTU or only after they have

acquire experience. The first case is for fleets that

use the same aircraft but in different roles. In this

situation, once basic training at the OTU is

completed, pilots are streamed to squadrons for the

upgrade process. In the model, this is reflected by

having one fleet with an OTU that feeds other fleets

directly at the mentee stage; the modules of these

receiving fleets do not have an OTU portion. The

ICORES 2015 - International Conference on Operations Research and Enterprise Systems

second case is for a fleet sending experienced pilots

to another fleet in a very similar fashion as was done

in the case of the transition from a legacy aircraft to

a new model. However, in this case, the feeder fleet

is not retired and continues operation. This model

can be used to start a new capability that is similar to

an existing one or for a specialized capability that

requires previous experience in another capability.

In the first case, the link between the feeder fleet and

the receiving fleet may be severed after some time.

In all cases, the receiving fleet has an OTU as it uses

a different aircraft. Depending on the situation, the

OTU may or may not receive NWGs, but

experienced pilots fed in usually have to go through

a full OTU course.

2.3 Training and Intermediary

Modules

The top portion of Figure 1 provides a high-level

view of the training system which will now be

discussed in more details. As already mentioned, all

courses are modelled on the pattern shown in

Figure 3. The various courses are successively

linked as presented in Figure 1. There is a split after

Phase IIa where students are assigned to one of the

three generic types of aircraft: RW, ME or FTR. The

proportion sent to each type is based on the relative

size of each community and is usually entered as a

constant, but can be changed over time in the rare

cases where the proportion is expected to change

due to a significant modification of the RCAF fleet

composition.

There is another split at the junction of the

training and fleet sections. This is where pilots are

assigned to specific military aircraft types. For

example, in Figure 1, students who finish Phase III

RW are assigned to one of the three RW fleets.

These splits are calculated dynamically by the model

in intermediary modules and based on the size of the

fleets involved. The dynamic nature of these splits is

really useful for fleets transitioning from a legacy

aircraft to a new model as the flow going to the old

model is gradually stopped while the flow going to

the new model is increased (aircraft models are

usually retired in a tiered fashion). Various

constraints and rules may be added and used in these

dynamic calculations to prevent, for example, the

assignment of NWGs to fleets that only use

experienced pilots.

2.4 Running the Simulation

Inputs and outputs are accomplished through

Microsoft Excel workbooks, and Powersim interacts

directly with those. Usually around 20 workbooks

are used: one for each operational fleet, one for the

undergraduate portion of pilot training and a few for

specialized output analysis. Further input values are

defined as clones and several control variables are

defined directly in the model itself.

The simulation step is one week. The model is

run for 30 years to ensure that no unwanted

behaviour is slowly building. Each run takes only a

few seconds on a standard laptop computer. Usually,

about ten runs are done but as the pilot occupation is

a system in delicate equilibrium, the results of each

run are nearly identical: the system collapses, the

system is stable or a trend is observable. On rare

occasions, one of the runs may display a different

behaviour but it is rare and it is always a collapse

caused by extreme factors combining with an

already undesirable trend visible on all other runs.

Table 1 summarizes the input data required for

each fleet and training phase, as well as some

miscellaneous inputs.

Table 1: Input table.

FLEET DATA

Initial number of ops pilots

Initial number of staff positions filled

Minimum number of staff positions that have to be filled

Initial number of mentees

Initial size of the OTU PAT queue

Established number of ops positions

Attrition rate for experienced ops pilots

Attrition rate for staff

Maximum number of flying hours allowed for mentors

Maximum number of flying hours allowed for mentees

FG YFR for the fleet

Number of cross flow pilots requested per year

Number of hours required of a NWG to upgrade

Number of hours required of a CF pilot to upgrade

Percentage of times a mentor can train two mentees

OTU course duration

Number of courses per year

Maximum loading

Minimum loading

Minimum experience ratio to be consider healthy

TRAINING PHASE DATA

Course duration

Number of courses per year

Maximum loading

Minimum loading

Failure rate

EXTRA DATA

Yearly recruitment values for each entry program

IP requirement for each school

IP attrition rate

Source of IP as proportion

Breakout percentage after Phase IIa

A large amount of output is produced by the

model and several graphs spanning the 30 year

PARSim, a Simulation Model of the Royal Canadian Air Force (RCAF) Pilot Occupation

horizon are automatically produced by Excel based

on the data generated by the simulation. A series of

graphs is produced for each fleet and each training

phase. The majority of these charts are also provided

in an aggregated form on a single graph to help an

experienced analyst quickly assess the health of the

occupation for the issue under study. Another series

of graphs show results where values have been

summed for all fleets. Finally, one graph showing

the staffing transition can be produced for fleets

being converted from a legacy model to a new

model of aircraft.

Table 2 summarizes the various graphs available

and Figures 4 to 7 illustrate a few of these graphs.

Table 2: Output graphs.

TRAINING GRAPHS

Throughput

PAT pool size

PAT pool size for all training phases on a single graph

Average wait

FLEET GRAPHS

OTU intake

PML vs. TES (PML tracking)

Experience ratio

Number of staff position filled

Flying hours used and remaining

Upgrade time

Attrition

CF provided and received

PAT pool size

Waiting time

AGGREGATED GRAPHS

Number of NWGs

PML vs. TES

Number of staff position filled

Attrition

OTU PAT pool size

TRANSITION FLEETS (sum of the two fleets)

Mentors, Mentees, Staff positon filled, PML vs. TES, OTU

throughput

In Figure 4, student wait time to get onto an

OTU is shown. The curve illustrates the case where

a large pool of students initially had to wait for more

than a year. Once an issue was corrected, the

situation vastly improved over the next two years.

In Figure 5, the level of staffing for a single fleet

is shown for the four categories of pilots tracked by

the model. The case illustrated here is for a scenario

that is relatively stable. The constant variations

noted on every curve are mainly due to pilots

moving from one category to another but also due to

attrition.

In Figure 6, the attrition for experienced staff

and ops pilots is displayed. This scenario is for a

case where attrition is constant and where attrition of

staff officers is slightly higher than for ops pilots.

Figure 4: Wait time at a fleet OTU.

Figure 5: Number of pilots in each type of position for a

single fleet.

Figure 6: Attrition for experienced pilots of a single fleet.

In Figure 7, flying hours usage is displayed.

Hours used at the OTU as well as on squadron for

the mentoring process are shown. Using the fleet’s

YFR allocation, the remaining hours that can be

used for pure FE are also displayed. This graph

illustrates a situation where around 75% of the YFR

allocation is required for FG.

ICORES 2015 - International Conference on Operations Research and Enterprise Systems

Figure 7: Flying hours usage for various tasks.

3 RESULTS

As stated earlier, the aim of the study was to assess

the impact of planned YFR reductions while the

RCAF had started the process of increasing the size

of the occupation by producing more NWGs at the

training schools. The targeted increase in NWG

production was planned to gradually reach around

35% in the next five years and the imminent planned

reduction in YFR was on average 16% with some

fleets seeing as little as 5% reduction but others as

much as 39%. As it is anticipated that these two

factors would make it very difficult for squadrons to

absorb NWGs, the model was allowed to use all

YFR for FG and measure how much would be left, if

any, for pure FE.

In the first scenario, the production increase and

the YFR reductions were permanent. The simulation

stopped after five years (See Figure 8) due to the

complete collapse (experience ratio down to 0%) of

one of the fleets (red curve on the graph). As was

explained in the introduction, this is due to too many

NWGs being pushed into the squadron in turn

pushing too many mentors out and continuously

slowing down the upgrade process until there are no

mentors left. Two other fleets displayed extremely

low experience ratios for extended periods of time

which entails significant risks (pale yellow and teal

curves).

In view of these results, a second scenario was

assessed where the YFR reduction would be in

effect for only three years rather than permanently.

This easing was not sufficient to help the troubled

fleet recover. As the RCAF was already committed

to four years of higher production reaching an

increase of around 25% but future years were still

uncommitted, the next scenario was set with those

four years at the higher levels of production

followed by a return to a normal level for the rest of

the simulation. The YFR reduction was applied for

only three years as in Scenario 2 to assess first if this

relaxed YFR reduction scenario would be feasible. If

so, a permanent YFR reduction scenario could be

assessed. To prevent the rapid erosion of experience

observed in the previous scenarios for some fleets,

OTU intake was greatly reduced during the three

years of YFR reduction. For subsequent years,

intake of NWGs at the squadrons was set to allow

fleets to “survive” with absolute minimal

capabilities.

Figure 8: Experience ratios for Scenario 1.

Contrary to the first two scenarios, experience

levels were relatively satisfactory with only two

fleets experiencing levels below 50% for some

periods. However, as shown in Figure 9, two fleets

had difficulties maintaining sufficient staffing levels

(green and blue curves) and portrayed trends dipping

well below 10% under PML. The red curve is also

showing low levels in the first few years. This curve

is linked to a fleet that is being stood up and is

having difficulties meeting its scheduled growth

planned before YFR cuts were announced. These

difficulties are due to lower NWG intake required to

survive with a low YFR. However, the fleet

eventually meets its target PML after a few years.

Another issue, that is evident in Figure 10, is the

fact that four fleets need to use virtually all their

flying hours to be able to absorb NWGs. This is

shown by the blue and orange curves never straying

far from the 0% mark and the green and yellow

curves being generally above the -10% mark. It is

also clear that the three years of YFR reduction are

strongly affecting several fleets as all curves show

an upward trend in the first few years indicating that

all the fleets graphed needed virtually all of their

YFR allocation just for the upgrade process.

PARSim, a Simulation Model of the Royal Canadian Air Force (RCAF) Pilot Occupation

Figure 9: PML tracking for Scenario 3.

Finally, an important outcome of this scenario

which is resulting from the intake reduction strategy

is that the queues of pilots waiting to start their OTU

course are growing as intake is now insufficient to

absorb production. In this scenario, the total intake is

annually about 10% too low. This implies that

queues would quickly build up to levels that are

unacceptable as students would have to wait longer

and longer before starting they course. If the wait is

too long, pilots lose their skills and have to be at

least partially retrained at extra cost and further

burden to the training resources.

Figure 10: YFR usage in Scenario 3.

In view of the poor performance observed for

Scenario 3, a permanent YFR reduction was not

examined as alluded to in the description of that

scenario. Therefore, for the last scenario that was

run, the goal was to generate a plan where: 1) NWGs

would all be absorbed, 2) all fleets would have some

hours available for pure FE, 3) upgrade would be

completed in 30 months or less, 4) all fleets would

be staffed at 95% or more and, 5) experience ratios

would be at least 50%.

Production of NWGs was similar as in

Scenario 3: four years at a higher level and back to

normal levels after that. Intake of NWGs was

reduced for the three years of YFR reduction but

was subsequently increase to at least match

production to prevent the growth of large queues at

OTUs. However, to achieve the plan some extra

measures had to be adopted for certain fleets. The

growth of a fleet that was in the process of being

stood up was slowed down. For five of the fleets, the

YFR reduction had to be applied only for a single

year rather than three. Furthermore, for three of

these five fleets, the YFR had to be increased to

levels higher than before the reduction. Although the

percentage of increase was significant for these three

fleets with an average of 29%, the total number of

flying hours added was modest since these fleets are

not the most intense flyers.

On the positive side and despite the reduced

production (compared to what was planned), the

occupation was still able to grow by about 15% in

seven years. However, even though the scenario’s

objectives were met, the situation was not perfect.

As can be seen in Figure 11, some fleets still do not

have many hours available for pure FE and several

have virtually none during the first few years. As for

OTU queues, even though no build-up was observed

in the long run, the total number of pilots waiting did

increase during the first years due to the YFR

reduction and associated lowered OTU intake. The

increase was equivalent to about 70% of the total

annual intake and it took a very long time to clear

up. This implies that some pilots would experience

long wait times; a further indication that the scenario

is far from completely satisfactory.

All these results are signs that the original YFR

allocation was too low to allow the absorption of a

high pilot production by the schools and that actions

of a more strategic nature are necessary to obtain a

sustainable plan for the pilot occupation.

Figure 11: YFR usage in Scenario 4.

ICORES 2015 - International Conference on Operations Research and Enterprise Systems

4 CONCLUSIONS

Training air force pilots is costly and lengthy. The

relationships between school production, OTU

absorption, flying rates, experience levels, cross-

flows and attrition are complex and volatile. The

pilot occupation is a system in a delicate equilibrium

and with a large inertia. A single action or decision

may have drastic long-term effects. Complex,

concerted and multi-faceted efforts are often

required to solve problems encountered. In view of

all this, simulation is a necessity.

The PARSim simulation model has been

presented. It is a realistic high-level representation

of the pilot occupation. It is an efficient, powerful

and versatile what-if analysis tool. It can help assess

what combination of actions may provide the

maximum benefit, how quickly can changes be

implemented and, what side-effects decisions taken

for a portion of the occupation may have on the rest

of the system.

The tool has been used for several projects over

the years and results were provided here on one of

the studies: assessment of the impact of reduced

budget and thus flying rates combined with the

simultaneous absorption of a high production of

students at the training school. A feasible but not

completely satisfactory plan was devised. It showed

that actions of a more strategic nature are necessary

to obtain a sustainable plan for the pilot occupation.

The tool will undoubtedly continue to be

improved. Currently the model does not directly take

into account hours acquired in simulators and it

could be beneficial to include this element in the

model to assess directly the impact of their use. It

could also be useful to implement the cross-flow

feature between the rotary wing fleets and

potentially between all fleets.

ACKNOWLEDGEMENTS

I would like to thank my colleagues Charles Hunter,

Sonia Latchman, Norman Corbett and Pieter De

Jong for their work and help on previous versions of

the tool. Acknowledgements are also due to the

numerous officers of the RCAF who have over the

years provided invaluable information to allow the

tool to be developed and improved.

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ICORES 2015 - International Conference on Operations Research and Enterprise Systems