Implementing Reliability, Availability, Maintainability and Supportability RAMS for the MRO...

7/28/2019 Implementing Reliability, Availability, Maintainability and Supportability RAMS for the MRO Industry- A Case Study at European Airlines

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ESReDA Seminar on Supporting Technologies for Advanced Maintenance Information Management.

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Implementing Reliability, Availability, Maintainability and Supportability

RAMS for the MRO Industry: a Case Study at European Airlines

Dr Adel A. Ghobbar†

Delft University of Technology, Faculty of Aerospace Engineering, Aerospace Management and Operations

Department, Kluyverweg 1, 2629 HS Delft, The Netherlands

Abstract

Maintenance of aircraft has always taken a very special place. In the first place because the

cost of ownership is so high, that a high availability is requested from the operators. In thesecond place is safety a hot issue in the aerospace industry. It was found that indeed much

emphasis is put on the monitoring of the reliability and component failures. However these

measurements give no insight in the efficiency of the maintenance process. From a field

research all the measures used at three different companies (VLM Airlines, SN Airlines

and KLM Cityhopper) were collected and investigated. It was found that indeed much

emphasis was laid on the reliability of the fleet, but that no measurements were done inside

the maintenance process. The research was performed for VLM Airlines and they agree

that measuring performance indicators is necessary to improve the maintenance process.

Model was created to measure the reliability, availability, maintainability and

supportability RAMS. The model covers the whole maintenance process and the

environment surrounding the process and concluded that was a good approach of the

reality for aircraft maintenance.

Keywords: reliability, availability, maintainability and supportability, aircraft maintenance.

†Email: [email protected]



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1. Introduction

Performance measurement in the MRO industry is highly regulated. Reliability reports

have to be sent to the Civil Aviation Authorities on a monthly and six monthly bases.

These regulations are necessary as aviation is a safety risk business. So the measurement of

the effectiveness of the maintenance is highly regulated. However there is no performance

measurement system in place yet that measures the efficiency of the maintenance process.

It is the goal of this research to develop a model that will measure the effectiveness of the

maintenance process as well as the efficiency.

This research is two folded. In the first place it is a case study of the performance

measurement at three different companies: VLM Airlines, SN Airlines and KLM

Cityhopper. From these airlines documentation about the performance measurement is

collected. The main contribution of this research comes from VLM Airlines, from which

not only the formulas, but also the data was fully available. All the equations were

categorized under the different RAMS factors.

The second part of the research will develop a formula for the availability based on the

different RAMS factors. RAMS analysis defines certain characteristics of a component or

system. Reliability focuses on the failure rate of the component, maintainability on the

repair or maintain rate of the component, availability on the total uptime of the component

and supportability focuses on the demand of the component for support. The goal of the

research is to link and define the factors influencing the availability. The different factors

were set as direct measures that could be implemented in the maintenance process, so that

in a later stage this model can be used in the MRO industry.

2. Literature Review

Maintenance is a very broad concept. It can be carried out on regular basis, or only once in

the lifecycle of a product, it can take very long, or just a couple of hours. In the aviation

industry maintenance takes a special place. The performance of maintenance restores

safety and reliability levels when deterioration or damage has occurred [1]. Maintenance

also has an economical drive as it influences the availability of the fleet. An aircraft that is

not available for service cost the airline operator money while it is not creating any



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income, increasing the total cost of ownership. Maintenance is the action necessary to

sustain or restore the integrity and performance of the airplane. It includes inspection,

overhaul, repair, preservation and replacement of parts [2]. However the tasks of the

maintenance department go further than the basic definition above. The maintenance

department is responsible for the ability of the aircraft to fly the published revenue

generating schedule [1]. In normal condition a service level agreement is raised between

the two parties, the airline on the one side the MRO company on the other side, with clear

maintenance times and limits stated.

In this study two types of maintenance will be investigated preventive and corrective.

Corrective maintenance groups all actions performed as a result of failure to restore an

item to a satisfactory condition by providing correction of a known or suspect malfunction

and/or defect. Corrective maintenance in general consists of fault verification, fault

isolation, disassembly, replacement, reassembly, alignment/adjustment and test. This type

of maintenance is known as unscheduled maintenance, and benefit from the use of

diagnostics to ease the burden on the maintenance resource. Preventive maintenance

groups all actions performed at defined intervals to retain an item in a serviceable

condition by systematic inspection, detection, replacement of wear out items, adjustment,

calibration, cleaning etc. It is carried out at prescribed points in an aircraft and equipment’s

life. It is also termed scheduled maintenance [3].

RAMS analysis is born from the statistical prediction of the performance of a component,

and is often expressed in terms of probability. So, reliability is the probability that the

component or system will be able to perform the mission and availability the probability

that the component or system will be able to perform its function at the time required.

RAMS analysis is in most cases applied at a component level, where these inherent

characteristics are measured with tests. In most cases RAMS factors are set during the

design of the component; these are called the inherent factors. Once the component is

produced, these factors will be evaluated during the functional life. After these tests the

production process or the operational process will be reengineered, in order to increase the

achieved reliability, for example preventive maintenance is introduced in the operational



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life. These are the achieved RAMS factors. The achieved RAMS factors are focused on the

design and the maintenance of the component, but operational elements such as support

delays or administrative delays are excluded. When these are included, the parameters are

called the operational RAMS parameters as they include all the elements of the process,

and do not only focus on the components characteristics. When the operational availability

is much lower then expected, the maintenance process can be reengineered, in order to

achieve lower supportive delay times [4].

Availability is the ratio of the actual operating time to the scheduled operating time. It is

the probability to be in service when required [5, 6]. Availability is the most important

terminology used for evaluation of the effectiveness of any process or activity, if the

machines are repairable systems. Steady-state availability of a fleet, for a given period, is

defined as the percentage of time during which the fleet is producing its designed output

adequately [7]. Supportability is the inherent characteristics of an item related to its ability

to be supported by the required resources for the execution of the specified maintenance

task [8].

The framework of the RAMS analysis is used in order to see how the efficiency and

effectiveness of the maintenance process can be measured. As RAMS factors are most

commonly applied at component level or system level a transposition towards a process

level is required. As RAMS is not applied yet at a process level inside the maintenance,

repair an overhaul, MRO companies, a new measurement system has to be developed in

order to use the new model. This led to the following research questions:

1. What are the current measures in use in different airlines and MRO industry.

2. How can RAMS be applied at a process level?

a) Modification of the RAMS formulae

b) Define the gaps between the current measures taken and the measures

required for the RAMS model.



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3. Experimental Design

Due to a missing data to calculate the availability a predictive model has been developed in

this research. For every parameter of the RAMS factor assumptions have been made in

order to approximate the reality. The simulation tried to model the availability in terms of

reliability, maintainability and supportability. This approach leads to a simulation in which

the effect of lack of support can be investigated.

The availability equation (Eq. 1) can be used for a certain period of time in which a certain

number of flight hours are flown and a certain number of scheduled flights are performed.

The number of corrective maintenance is dependable on the failure rate and the number of

scheduled flights, the number of preventive maintenance actions is dependable on the

maintenance rate and the number of flight hours flown.

FH ADT LDT AMT LDT AMT

ADT LDT AMT LDT AMT

Am

j

j j

n

i

ii

m

j

j j

n

i

ii

O

+

++++

++++

−=

∑∑

∑∑

==

==

11

11

)()(

)()(

1 (1)

For

i = 0,1,…,n

j = 0,1,…,m

and,

n = λ × Scheduled Flights

m = µ × Flight Hours

Reliability is covered in the equation by the failure rate and the defect rate. The failure rate

indicates the number of cancellations and delays that occur in the considered span of time.

C D λ λ λ += (2)

Where λ D is the delay rate and λ C is the cancellation rate.

The probability that rectification is needed after an inspection is adapted from the

reliability theory. It is assumed that the chance that no fault found during an inspection

decreases exponentially. The probability of an exponentional distribution is given by:

P( X = t ) = f (t ) = e−λ t This is the same reasoning that the reliability theory uses: the

chances that no defect occurs over time will decrease exponentially. This results in the



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following formula:

j e X P ω −== )0( (3)

Where ω represents the defect rate, and j equals the number of inspections after a

maintenance task is performed, and is equal to:

s Inspectionof Number

Found Defectsof Number=ω (4)

In maintainability a distinction between preventive and corrective maintenance is made. It

is assumed that preventive maintenance is scheduled and that corrective maintenance

occurs only when a failure interrupts the flight schedule.

Preventive maintenance or the time to maintain consists of the Active Maintenance Time

(ATM) and the time lost due to unavailability of resources (LDT), as summarized in

equation (4). Equation (4) reads: the Time to Maintain for task j equals the Active

Maintenance Time spent on performing task j plus the Logistic Delay Time that was

encountered while performing task j . So it equals the time actually spent on maintaining

the aircraft added with the idle time due to logistic delays.

TPM j = AMT j + LDT j (5)

With the active maintenance time:

j R j j I j T X T AMT ,, += (6)

Where

=

=

found defect if X

found defect noif X

j

j

1

0

T I,j represents the time needed to inspect a component and T R,j equals the time for repair, if

a defect was found. It is assumed that on average the duration of an inspection T i,j will be

approximately the same, where it can take an apprentice a little longer and a more

experienced mechanic a little shorter. This is best represented by a normal distribution,

where the mean will be the average duration of the maintenance task and the variance will

give an indication in how far the duration of a job is under control. If the variance is high

this means that the distribution is less concentrated around the mean, and that some

mechanics can inspect a component in a record time and it takes some mechanics really



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long to fill-full the job. This could be solved, providing training, resulting in an increased

skill level of the mechanics, could solve this. T R,j represents the time needed to repair a

defect if required, for the same reasons as described above, it is assumed that the time to

repair will have a normal distribution.

The corrective maintenance is considered to be all the maintenance due to a delay or a

cancellation. No differentiation is made between inspection and repair time. If there are

many delays of short duration and only some with a considerable length, this is best

modeled using an exponential distribution. So for the delay time of the aircraft also an

exponential distribution is used. For the duration of a cancellation it is assumed that 60

minutes is lost, which is the average duration of a flight.

Supportability which is measured by the LDT is added to the AMT by adding the delay

caused by lack of resources to both the inspection time and the repair time respectively.

From equation 36 and 37, this results in:

)( ,,,,,, j RSD j R j j I SD j I j T T X T T TPM +++= (7)

Where T SD,i,j is the time lost due to logistic delay (LDT).

F SDGE SD M SDPSDSD T T T T T ,,,, +++= (8)

and n = number of inspection task performed. This last formula induces that the effect of

waiting time due to unavailability of personnel or due to unavailability of spare parts for

example is the same. So,

( )∑=

++×++=

n

i

i RSDi Rii I SDi I ADT T T X T T TTM 1

,,,,,, )( (9)

4. Experimental Factors

Two different variables are used in the ANOVA-GLM analysis: covariates and categorical

variables. Covariates are variables of a continuous function and can have any value

between the lower and upper limit. Categorical variables are variables that can only take

the value specified by the levels. Availability is used as the response and is calculated by

the following equation (10).



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FH ADT TPM TCM

ADT TPM TCM AO

+++

++

−= 1 (10)

As one can see is that time lost due to supportability issues is included in the formula.

However no time related data is available from VLM Airlines about supportability. For

this reason supportability will be used as a categorical factor where the value 0 means that

there is no prove of any support issues and the value 1 indicates that there is prove of a

support issues. The factors are summarized in Table 1. Flight hours are left out of the

analysis as it cannot be controlled by the maintenance department.

Factor Levels Values Units

Time for corrective maintenance (TCM) - 6.0 – 710.0 Hours

Time for preventive maintenance (TPM) - 2.0 – 280.0 Hours

Supportability (S) 2 0,1 No, Yes

Table 1 - Independent factors for the availability, (-) indicates covariates.

In the case of reliability, the occurrence of corrective maintenance is the same as the

failure rate of the system. As was already discussed the system is failing when it is not able

to perform the scheduled flights, so when the flight is disturbed by a delay or by a

cancellation. The number of failures is calculated in relation with the number of scheduled

flights. So one can see which percentage of the flights is disturbed. The failure rate is given

by equation (11):

FlightsScheduled of No

onsCancellatiTechof No DelaysTechof No Ri

.

.... +

== λ (11)

No categorical factors are used here. From the data the maximum number of delays

occurred in a week is 6 and the maximum number of cancellations that occurred in a week

is 4. These are summarized in Table 2.


Cancellation (C) - 0.0 – 4.0 -

Delays (D) - 0.0 – 6.0 -

Scheduled Flights (SF) - 147.0 – 524.0 -

Table 2 - Independent factors for the failure rate (reliability)



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For maintainability the corrective maintenance the mean maintenance time is calculated.

This is done by adding the time lost due to the delays and the time lost due to

cancellations, and dividing it by the total number of incidents.

onsCancellatiof No Delaysof No

onsCancellatitoduelost Time Delaystoduelost Time

MTCM .. +

+

= (12)

It is assumed that the time lost due to one cancellation is constant and equals 60 minutes.

Again no data about the supportability is known, so supportability is introduced as a

categorical factor as shown in Table 3.


No. of cancellations (NC) - 0.0 – 3.0 -

No. of delays (ND) - 0.0 – 3.0 -

Total delay time (DT) - 0-533 Minutes

Support (S) 2 0,1 No, Yes

Table 3 - Independent factors for the TCM, maintainability (corrective).

Preventive maintenance is maintenance when the aircraft is not scheduled for a flight. The

data is extracted from the Oracle database where three different categories were found:

Check , Scheduled Maintenance and Unscheduled Maintenance. The last term is added to

preventive maintenance as it didn’t disturb the schedule, albeit unscheduled maintenance is

per definition not real preventive maintenance. Factors are shown in Table 4.

sinspectionof No

enance Mad Unschedulespent Timeenance MaScheduled spent TimeCheck spent Time MTPM

.

intint ++

=


Time in check (TC) - 6.0 – 710.0 Hours

Time in Scheduled Maintenance (TSM) - 2.0 – 280.0 Hours

Time in Unscheduled Maintenance (TUSM) - 1.5 - 510.0 Hours

Table 4 - Independent factors for the TPM, maintainability (preventive).



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5. Results Analysis

The simulation is used to find the influence of certain parameters on the total time for

preventive maintenance. The simulation is tested over 1000 samples to calculate the total

time. In simulation 1 & 2 the effect of the defect rate is investigated. In figure 1 the downtime is simulated with a low defect rate (0.11). Figure 2 the results of a simulation with a

high defect rate (0.50) are shown. The figures reveal that a higher defect rate will result in

a higher probability that the down time due to the task will be longer. For example for

Figure 1, it becomes clear that the P(DMT = 15’) = 0,09% and P(DMT = 250’) = 0,7%.

From Figure 1 it can be observed that P(DMT = 15’) = 0,057 and P(DMT = 250’) = 2%.

This means that the probability for a long down time increases with the defect rate.

Active Maintenance Time

0 , 0 0 %

1 , 0 0 %

2 , 0 0 %

3 , 0 0 %

4 , 0 0 %

5 , 0 0 %

6 , 0 0 %

7 , 0 0 %

8 , 0 0 %

9 , 0 0 %

1 0 , 0 0 %

0 50 100 150 200 250 300 350 400 450 500 550

Time (minutes)

Occurence (%)

Figure 1 - Distribution of the down time (Simulation 1)


0 , 0 0 %

1 , 0 0 %

2 , 0 0 %

3

, 0 0 %

4 , 0 0 %

5 , 0 0 %

6 , 0 0 %

0 50 100 150 200 250 300 350 400 450 500 550

Time (minutes)

Occurence (%)




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The effect of supportability during inspections is shown in Simulation 3 & 4 focuses on the

effect of a delay caused by a lack of resources. In simulation 3 the case when a mechanic is

10 minutes late is simulated. Comparing Figure 3 & 4 shows that in the case of a small

delay occurring during inspection, the two normal distributions coming from the inspection

time and repair time solve into one tailed normal distribution. Simulation 4 simulates the

case that a supporting tool, for example a Borescope is not available during 300 minutes.

The figure show a very rough distribution, meaning the predictability of the expected value

of the down time is very inaccurate


0 , 0 0 %

1 , 0 0 %

2 , 0 0 %

3 , 0 0 %

4 , 0 0 %

5

, 0 0 %

6 , 0 0 %

0 50 100 150 200 250 300 350 400 450 500 550

Time (minutes)

Occurence (%)



0 , 0 0 %

0 , 1 0 %

0 , 2 0 %

0 , 3 0 %

0 , 4 0 %

0 , 5 0 %

0 , 6 0 %

0 , 7 0 %

0 , 8 0 %

0 , 9 0 %

0 50 100 150 200 250 300 350 400 450 500 550

Time (minutes)

Occurence (%)




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The effect of supportability during the repair time. Simulation 5 & 6 focuses on the effect

of a delay caused by lack of resources in the repair time. Figure 5 shows that a short delay

during repair changes the shape of the normal distribution provoked by the repair time. The

effect is however not as large as the effect provoked by a large delay of 300 minutes

caused by a lack of spare parts. As can be seen in Figure 6 the normal distribution caused

by the repair time has vanished and is distributed over a large time span. The induction

from this is that it becomes more difficult to predict the length if the average waiting time.


0 , 0 0 %

2 , 0 0 %

4 , 0 0 %

6 , 0 0 %

8 , 0 0 %

1 0 , 0 0 %

1 2 , 0 0 %

0 50 100 150 200 250 300 350 400 450 500 550

Time ( minutes)

Occurence (%)



0 , 0 0

%

1 , 0 0 %

2 , 0 0 %

3 , 0 0 %

4 , 0 0 %

5 , 0 0 %

6 , 0 0 %

7 , 0 0

%

8 , 0 0 %

9 , 0 0 %

1 0 , 0 0 %

0 50 100 150 200 250 300 350 400 450 500 550

Time (minutes)

Occurence (%)




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Simulation 3 & 4 investigated the effect of support delay during inspection times. Small

delays during inspection times do not cause a tremendous change of the shape of the

distribution. However it can be seen that the mean has shifted from 23.62 minutes to 33.12

minutes, which is an increase of approximately 10 minutes, which equals the enforced

delay time. From Figure 4 it becomes clear that no prediction of the expected value of the

down time can be made. When the waiting time for a spare part is on average 300 minutes,

the predictability of the down time is low. The same holds for the transportation of

personnel, as the effect of a delay caused by any lack of resources is the same. From this

simulation it can be concluded that when a defect occurs in an outstation where no

resources are available it has tremendous consequences.

The analysis of variance (ANOVA) is used to investigate the significance of factors and

their interactions. It explains the variation attributable to the various experimental factors

and their interactions. Only factors and interactions which are significant at a 0.01 level or

0.05 level are considered. Table 5, illustrate a summary of the results of the general linear

model (GLM) for the reliability (failure rate) as the dependent variable, whereas the

number of cancellations, C , the number of delays, D and the scheduled flights, SF are used

as independent variables. All dependent factors are covariate factors. Table 5 shows that all

factors except for the scheduled flights are significant.

Table 6 shows the coefficients from the GLM model. The coefficients of the significant

factors or interactions are in bold. The coefficient of both the delay and cancellation are

positive. This means that an increase in the number of delays and the number of

cancellations will result in a higher failure rate. This is of course logical and concords with

reality. However these two factors are offset by a negative coefficient in combination with

the log SF . This means that when the number of scheduled flights increases the effect of

the delays and cancellation will become smaller. The same result holds for the combined

effect of delay and cancellation which also shows a negative coefficient. This means that if

there are more delays, the effect of the cancellation will decrease. This is logical as the

number of failures is the sum of the number of delays and the number of cancellations. The



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last significant combination is the interaction between the delays, cancellations and the log

SF . As can be seen the coefficient is also negative.

Table 5 - A summary of unbalanced ANOVA (GLM) results for failure rate ( p-values)

Factor Type Levels Values

Analysis of Variance for FR (λ), using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F P

LOG SF 1 0,0008162 0,0000011 0,0000011 2,40 0,123

D 1 0,0008647 0,0000072 0,0000072 15,48 0,0001

C 1 0,0007429 0,0001622 0,0001622 348,73 0,0001

LOG SF*D 1 0,0000078 0,0000051 0,0000051 11,00 0,0011

LOG SF*C 1 0,0001234 0,0001505 0,0001505 323,59 0,0001

D*C 1 0,0000042 0,0000287 0,0000287 61,69 0,0001

LOG SF*D*C 1 0,0000283 0,0000283 0,0000283 60,85 0,0001

Error 170 0,0000790 0,0000790 0,0000005

Total 177 0,0026667

Superscript

1

indicates significance at the 0.01 levelSuperscript 5 indicates significance at the 0.05 level

No superscript denotes a lack of significance at both levels (at 0.01 and 0.05 level)

Table 6 - Coefficients of fitted model: Sources for the failure rate (main effect).

Response D C LOG SF LOG SF * D LOG SF * C D * C LOG SF * D * C

FR 0,015 0,064 -0,002 -0,005 -0,023 -0,025 0,009

Significant factors and interactions are shown in bold

In terms of maintainability the effect of corrective maintenance TCM , Table 6, illustrate

the result of the general linear model. The categorical factor of supportability is left out of

the model, as it was shown during the analysis that it was no significant, neither as a single

factor nor in interaction with another factor. The response is the time for corrective

maintenance and the independent factors are the delay time, DT , number of delays ND and

the number of cancellations NC . The time lost due to a cancellation is assumed to be

constant and equals 60 minutes.

The coefficients are shown in Table 7 and are really surprising. As can be seen the delay

time has a negative coefficient. This means that a shorter delay time would result in a

higher corrective maintenance time. The same holds for the number of delays were it can

be seen that this factor also has a negative coefficient, meaning that fewer delays, would

result in a higher downtime. This is not compatible with reality where every delay causes

an increase in the downtime due to corrective maintenance. This is the result of the

decreasing number of delays over time and the increase in the average delay time. Due to



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this result the total downtime will stay constant, and the analysis shows that a decrease in

delays does not directly result in a decrease in the time for corrective maintenance, but that

a decrease in the number of delays can even result in higher total corrective maintenance

time. The interaction between DT and ND shows that if the delay time increases, the

number of delays becomes more significant. This is logical as the total downtime due to a

delay is the total sum of all the delay times. So if there are more delays, the average delay

time becomes more significant, or vice versa. DT * NC also has a positive coefficient

which means that if the delay time increases the number of cancellations become more

significant. This is the result of the increasing number of cancellations over time and the

increasing average delay time over length. From the data set of these two factors the

ANOVA analysis gives the indication that the delay time and the number of cancellations

are interacting with each other. However there is no physical proof of this.

Table 6 - A summary of unbalanced ANOVA (GLM) results for TCM ( p-values)

Factor Type Levels Values

Analysis of Variance for Log MT , using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F P

DT 1 21,858 6,929 6,929 11,77 0,0011

ND 1 14,923 11,902 11,902 20,21 0,0001

NC 1 90,277 1,525 1,525 2,59 0,111

DT*ND 1 0,558 7,243 7,243 12,30 0,0011

DT*NC 1 0,104 5,203 5,203 8,84 0,004

5

ND*NC 1 1,177 12,193 12,193 20,70 0,0001

DT*ND*NC 1 11,390 11,390 11,390 19,34 0,0001

Error 93 54,769 54,769 0,589

Total 100 195,055

Superscript 1 indicates significance at the 0.01 level

Superscript 5 indicates significance at the 0.05 level

No superscript denotes a lack of significance at both levels (at 0.01 and 0.05 level)

Table 7 - Coefficients of fitted model: Sources for the TCM (main effect)

Response DT ND NC DT*ND DT*NC ND*NC DT*ND*NC

TCM -0.009 -0.879 0.344 0.004 0.005 0.831 -0.005

Significant factors and interactions are shown in bold

6. Conclusions

It was found that the current focus of the performance measurement system is mostly on

the reliability. This is certainly true for VLM Airlines where no data was available for the



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calculation of the maintenance time (maintainability) and the support process

(supportability). The measurement is mostly based on the output of the maintenance

process, namely the number of delays, cancellations, flight incidents. It can be concluded

from the field research that there is a demand for a development of a total performance

measurement system for the MRO industries, in which the processes are mapped and

performance indicators are set, according to the new model.

A new model for availability and maintainability has been developed. The GLM analysis

shows that the data from the simulation model gives the same significance to the

independent factors then the data provided by VLM Airlines. The preventive maintenance

time is the most significant factor. This is logical, as most of the downtime is due to

preventive maintenance. From the ANOVA analysis it can be concluded that the

simulation model is a good approach for a new performance measurement system.

References

[1] Eckels, D., (2007), KLM Cityhopper F50 Technical Reliability Report. KLM

Cityhopper, Amsterdam.

[2] Hessburg, J., (2000), Air Carrier MRO Handbook. McGraw Hill, USA.

[3] Jones, J. V., (2006), Supportability Engineering Handbook, Implementation,Measurement and Management. McGraw Hill, USA.

[4] Kinnison, H.A., (2004), Aviation Maintenance Management. McGraw Hill, USA.

[5] Locks, M. O., (1995), Reliability, Maintainability and Availability Assessment,

ASQC Quality Press, 2nd, USA.

[6] Murty, A. S. R., and Naikan, V. N. A., (1995), Availability and Maintenance Cost

Optimization of a Production Plant. International Journal of Quality and Reliability

Management , vol. 12, pp. 28-35.

[7] Romeu, J. L., (2004), Availability. Selected Topics in Assurance Related

Technologies, vol. 11, no. 6.

[8] Wu, H., Liu, Y., Ding Y., and Liu, J., (2004), Methods to Reduce the Direct

Maintenance Cost for Commercial Aircraft. Aircraft Engineering and Aerospace

Technology, vol. 76, pp. 15-18.

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