measuring the complexity in an airplane engine maintenance...

International Journal of Engineering & Technology IJET-IJENS Vol:16 No:02 8

160802-4747-IJET-IJENS © April 2016 IJENS I J E N S

Abstract— In this research, the manufacturing complexity of

the Saudi Airlines Engineering Industries (SAEI) is measured to

determine the complexity of their maintenance plants. We

analyze the results based on the measurement of the complexity

model to determine the most influential operations affecting the

maintenance process and causing delays using the Pareto analysis

(ABC analysis). When the complexity is considered for the

planned and actual durations, there is no part mix ratio available

for the data, and the planned and actual durations result in

minor complexity measurements. The levels of complexity for the

planned and actual durations are 1.375 and 2.1123, respectively.

An ABC analysis is also conducted, and the results indicate that

certain processes affect both plans.

Index Term— Complexity; Pareto Analysis; ABC analysis;

I. INTRODUCTION

TODAY’S dynamic environment makes it difficult for

factories and manufacturing plants to achieve their objectives.

To achieve these targets, factories and manufacturing plants

are often required to perform at their best capabilities. To

survive with the changing environment, companies objectives

to be more flexible in their processes and systems to fulfill

customers’ demands. This flexibility may yield benefits, such

as increased production and product customization. However,

if not controlledorganized, such flexibility might lead to

higher costs, longer lead times, , larger inventories, and

customer dissatisfaction.

This study has two aims. The first objective is to measure

the complexity of maintenance lines. Saudi Airlines

Engineering Industries (SAEI) has provided us with the data

for one maintenance line. The line has a different number of

processes. The second objective is to determine the factors

that can help reduce the complexity of these lines. Such a

reduction can be realized in many ways, such as JIT, product

& process standardization and other techniques used for

complexity management. However, these methods do not

answer the following question: How complex is the system

and to what degree can the complexity be reduced? Changing

the part mix ratio can help to reduce the complexity level.

The main objectives of the study are to

1. Measure the complexity of maintenance lines.

R. Alamoudi is with the Department of Industrial Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.

M. Balubaid are with the Department of Industrial Engineering, King

Abdulaziz University, Jeddah, Saudi Arabia (e-mail: [email protected]).

2. Determine the factors that can help reduce the

complexity of these lines.

II. LITERATURE REVIEW

In this section, we discuss the various definitions of

complexity and attempt to identify the characteristics of

complexity and complex systems. We provide a brief

overview of the literature on complexity to lead into the

proposed model of measuring the complexity of

manufacturing systems.

According to Park and Kermer [1] there is no widely

accepted common definition of complexity due to the

vagueness that the term itself has.. Weaver [2] explained that a

complex system is a large number of parts that interact in a

non-simple manner. Yates [3] states five characteristics of

complex systems. He proposed that complexity rises whenever

one or more of the following five characteristics are present:

(a) significant interactions; (b) high number of parts, degrees

of freedom, or interactions; (c) non-linearity; (d) broken

symmetry; and (e) non-holonomic constraints. Allen and

Torrens [4] define a complex system as one that can respond

in more than one way to its setting.

Acorrding to Sedra [5], the main characteristics of complex

systems are collected under structural (static) and behavioral

(dynamic) features in the literature. The structural feature of

complexity illistrate the number of parts, variety of parts,

strength of interactions, connective structure, and hierarchical

structure (see Table I). The behavioral aspect involves the

characteristics of dynamism, nonlinearity, being far from

equilibrium, historicity, adaptively, self-organization,

emergent structures, and evolution (see Table II).

TABLE I

STATIC CHARACTERISTICS OF EVALUATING COMPLEX SYSTEMS

Measuring the Complexity in an Airplane

Engine Maintenance Plant

Rami H Alamoudi, and Mohammed A Balubaid*



TABLE II

DYNAMIC CHARACTERISTICS OF EVALUATING COMPLEX SYSTEMS

Park and kremer [1] discussed that complexity for

manufacturing systems can be categorized into two broad

main areas equivalent to application or focus areas: design

complexity and manufacturing complexity. In addition Park

and Kremer [1] discussed how these types of complexity have

been defined beyond ambiguous and quantified through

different approaches.

Research have showed that complexity can affect a

manufacturing company’s performances, and those of its

supply chain [6]. Where Perona and Miagliotta [6] model

suggests that the ability to control complexity within

manufacturing and logistic systems can improve efficiency

and effectiveness at a supply chain wide scale.

Deshmukh et al. [7] have formulated an entropic measure

for static manufacturing complexity. However, it is designed

to be applied to only flexible manufacturing systems (FMSs).

Cho et al [8] proposed a novel model that can capture both

direct and indirect interactions among resources, which are not

limited to machining or forming operations.

We will apply this model in the current study. An

explanation of the model is provided in the next part.

A. Measurement Using a Complexity

1. Let us consider a manufacturing system that produces

n different types of parts and consists of m machines.

2. The interaction index matrix, which accounts for the

existence of interactions among processes in terms of

the processing time and waiting time (l), will be:

(1)

3. Matrix (Pl) is called the processing time matrix,

where diagonal elements represent processing times

and off-diagonal elements are zeros and is given as

(2)

4. The interaction matrix ( l), which represents

processing time-based interaction and waiting time-

based interaction, is given as

(3)

5. The total interaction matrix is

(4)

When we consider part mix ratios if they exist in the

model, then the overall interaction matrix will be

(5)

where

6. The normalized direct interaction matrix is

(6)

where

7. The normalized general interaction matrix is

(7)

which reflects all higher-order (indirect) interactions

over k connections (arcs), i.e., k=1 corresponds to

direct interactions, k=2 corresponds to second-order

interactions over 2 connections, and so on.

8. The overall influence of the i-th machine in the

system is (8)

ljlijlil=

0

1

ìíî

nni

ll

n

ll PΛPΛPΛΠΠ

1111

yl =1l=1

n

å

π̂i= p̂

ijj =1

må

Indices to show self-

interaction in terms

of process time.

Indices for influence of

machine of machine

in terms of waiting time. ljli+1jlil

=0

1

ìíî



9. The normalized influence of the i-th machine in the

system is (9)

10. Finally, the static complexity can be calculated using

Shannon’s entropy theorem:

(10)

III. COMPLEXITY MODEL

In this section, we measure the manufacturing complexity

of the SAEI Airplane Engine Repair Facility using the model

presented above. We gathered the data from SAEI. The data

are shown below, where the expected starting and ending

dates of each process for each module are listed. We were

provided with three schedules of three different engines that

were processed in one production line.

A. First Engine

The schedule of the first engine detailed with planned and

actual dates is shown in Table III. TABLE III

FIRST ENGINE PLAN AND ACTUAL DATES OF OPERATION

ST

EP

S

OPERA

TION

PLANNED ACTUAL DURA

TION

DIFFE

RENCE ST

AR

T

EN

D

DURA

TION

ST

AR

T

EN

D

DURA

TION

1

MOD.

REMOV

AL

1-AP

R-

14

3-AP

R-

14

3

1-AP

R-

14

20-AP

R-

14

20 -17

2

MOD.

DISASS

EMBLY

4-AP

R-

14

8-AP

R-

14

5

21-AP

R-

14

28-M

AY

-14

38 -33

3 CLEANI

NG

9-AP

R-

14

10-AP

R-

14

2

29-MA

Y-

14

30-M

AY

-14

2 0

4 N.D.T

11-AP

R-

14

11-AP

R-

14

1

31-MA

Y-

14

1-JU

N-

14

1 0

5 BENCH

INSP.

12-AP

R-

14

14-AP

R-

14

3

2-JU

N-

14

4-JU

N-

14

3 0

6

PARTS

REPAIR

(MRP)

15-AP

R-

14

19-M

AY

-14

35

6-JU

N-

14

10-JU

N-

14

5 30

7

QEC

PARTS

REP. (R.SHO

P)

15-

AP

R-

14

19-

MAY

-14

35

6-

JU

N-

14

10-

JU

N-

14

5 30

8

PARTS

PURCHA

SING

(SC.)

15-

AP

R-

14

19-

MAY

-14

35

6-

JU

N-

14

10-

JU

N-

14

5 30

9

PARTS

REPAIR

(R.SHO

P)

15-

AP

R-

14

19-

MAY

-14

35

6-

JU

N-

14

10-

JU

N-

14

5 30

10 KIT

20-

MA

Y-

14

20-

MAY

-14

1

11-

JU

N-

14

12-

JU

N-

14

2 -1

11

MOD.

ASSEMB

LY

21-

MA

Y-

14

30-

MAY

-14

10

13-

JU

N-

14

23-

JU

N-

14

11 -1

12

MOD.

INSTAL

LATION

31-

MA

Y-

14

4-

JU

N-

14

5

24-

JU

N-

14

2-

JU

L-

14

9 -4

13 TEST

5-

JU

N-

14

9-

JU

N-

14

5

3-

JUL

-14

4-

SE

P-

14

64 -59

B. Second Engine

The schedule of the second engine detailed with planned

and actual dates is shown in Table IV. TABLE IV

SECOND ENGINE PLAN AND ACTUAL DATES OF OPERATION

ST

EPS

OPERAT

ION

PLANNED ACTUAL DURAT

ION

DIFFER

ENCE STA

RT

EN

D

DURA

TION

STA

RT

EN

D

DURA

TION

1

MOD.

REMOV

AL

24-

FEB

-14

26-

FE

B-

14

3

24-

FEB

-14

17-

MAR-

14

22 -19

2

MOD.

DISASSE

MBLY

27-

FEB

-14

3-

MAR-

14

5

18-

MA

R-

14

15-

AP

R-

14

29 -24

3 CLEANI

NG

4-

MA

R-

14

5-

MAR-

14

2

16-

AP

R-

14

17-

AP

R-

14

2 0

4 N.D.T

6-MA

R-

14

6-M

AR-

14

1

18-AP

R-

14

19-AP

R-

14

2 -1

5 BENCH

INSP.

7-MA

R-

14

9-M

AR-

14

3

20-AP

R-

14

2-M

AY

-14

13 -10

6

PARTS

REPAIR

(MRP)

10-MA

R-

14

13-AP

R-

14

35

3-MA

Y-

14

14-JU

N-

14

43 -8

7

QEC

PARTS

REP.

(R.SHOP

)

10-

MA

R-

14

13-

AP

R-

14

35

3-

MA

Y-

14

14-

JU

N-

14

43 -8

8

PARTS

PURCHA

SING

(SC.)

10-

MA

R-14

13-

AP

R-14

35

3-

MA

Y-14

14-

JU

N-14

43 -8

9

PARTS

REPAIR

(R.SHOP

)

10-

MA

R-14

13-

AP

R-14

35

3-

MA

Y-14

14-

JU

N-14

43 -8

10 KIT 14- 14- 1 15- 16- 1 0

⌣pi

=p̂i

p̂i

i=1

m

å



AP

R-14

AP

R-14

JUN

-14

JU

N-14

11 MOD.

ASSEMB

LY

15-

AP

R-14

24-

AP

R-14

10 15-JUN

-14

1-

JU

L-14

17 -7

12 MOD.

INSTALL

ATION

25-

AP

R-14

29-

AP

R-14

5 2-

JUL

-14

9-

JU

L-14

8 -3

13 TEST

30-

AP

R-14

4-

M

AY

-14

5 10-JUL

-14

14-

JU

L-14

5 0

C. Third Engine

The schedule of the third engine detailed with planned and

actual dates is shown in Table V. TABLE V

THIRD ENGINE PLAN AND ACTUAL DATES OF OPERATION

ST

EPS

OPERAT

ION

PLANNED ACTUAL DURAT

ION

DIFFER

ENCE STA

RT

EN

D

DURA

TION

STA

RT

EN

D

DURA

TION

1 MOD.

REMOV

AL

20-JAN

-14

22-

JA

N-14

3 20-JAN

-14

27-

JA

N-14

8 -5

2 MOD.

DISASSE

MBLY

23-JAN

-14

27-

JA

N-14

5 28-JAN

-14

23-

FE

B-14

27 -22

3 CLEANI

NG

28-

JAN

-14

29-JA

N-

14

2

24-

FEB

-14

25-FE

B-

14

2 0

4 N.D.T

30-

JAN

-14

30-JA

N-

14

1

26-

FEB

-14

13-M

AR-

14

16 -15

5 BENCH

INSP.

31-JAN

-14

2-FE

B-14

3

14-MA

R-14

19-M

AR-14

6 -3

6 PARTS

REPAIR

(MRP)

3-FEB

-14

9-

M

AR

-14

35

20-

MA

R-

14

23-

AP

R-

14

35 0

7

QEC

PARTS

REP. (R.SHOP

)

3-

FEB

-14

9-M

AR

-14

35

20-MA

R-

14

23-AP

R-

14

35 0

8

PARTS

PURCHA

SING

(SC.)

3-

FEB

-14

9-

MAR

-14

35

20-

MA

R-

14

23-

AP

R-

14

35 0

9

PARTS

REPAIR

(R.SHOP

)

3-

FEB

-14

9-

MAR

-14

35

20-

MA

R-

14

23-

AP

R-

14

35 0

10 KIT 10-

MA

10-

M1

24-

AP

10-

M17 -16

R-

14

AR

-14

R-

14

AY

-14

11

MOD.

ASSEMB

LY

11-

MA

R-

14

20-

M

AR

-14

10

11-

MA

Y-

14

19-

M

AY

-14

9 1

12

MOD.

INSTALL

ATION

21-MA

R-

14

25-M

AR

-14

5

20-MA

Y-

14

24-M

AY

-14

5 0

13 TEST

26-MA

R-

14

30-M

AR

-14

5

25-MA

Y-

14

28-JU

N-

14

33 -28

D. List of Processes

The list of processes is shown in Table VI. TABLE VI

LIST OF PROCESSES

# PROCESSES

1 MOD. REMOVAL

2 MOD. DISASSEMBLY

3 CLEANING

4 N.D.T

5 BENCH INSP.

6

PARTS REPAIR (MRP)

QEC PARTS REP.

PARTS PURCHASING

PARTS REPAIR

7 KIT

8 MOD. ASSEMBLY

9 MOD. INSTALLATION

10 TEST

E. Explanation of Each Process in Table VI

1. Module Removal: Disassembling the engine into

different modules.

2. Module Disassembly: Disassembling each module

to several kits, i.e., the kits from which the module is

assembled.

3. Cleaning: Cleaning the kits for further processes.

4. N.D.T: During aircraft maintenance, nondestructive

testing (NDT) is the most economical way of

performing inspection, and this is the only way to

discover defects. To maintain a defect-free aircraft

and ensure a high degree of quality and reliability.

5. Bench Inspection

6. Process number 5 is a decision and must be chosen

among 4 processes:

a) Parts Repair (MRP): Material requirements

planning is a control system used

to manage manufacturing processes. Most MRP

systems are software based, although MRP can

also be performed by hand.



b) Quick Engine Change (QEC): Engine

manufacturers define this process. For example,

GE defines full QEC engine as an engine ready

for installation, including basic engine hardware,

all buyer-furnished equipment, quick engine

change hardware and, depending on the engine

model, exhaust nozzle and inlet cowl. Rolls

Royce defines QEC as a basic engine plus

electrical system, fuel, oil and air systems.

c) Parts Purchasing and Replacing: If the part is

damaged and cannot be repaired, it must be

purchased from abroad.

d) Parts Repair: Repairing the parts damaged if

capable.

7. Kit: Kitting is reassembling the kits comprising each

module.

8. Module Assembly: Assembling each module from

the kits.

9. Module Installation: Assembling the engine from

the modules created in the previous process.

10. Testing: Testing the engine.

F. Complexity Calculation

In this section, we calculate the complexity for the planned

duration and compare it against the actual duration. The

calculation of the planned operation model is provided, and

the actual operation model is included in Appendix A.

1) Complexity calculation for planned operation

We determine the complexity of the planned duration by

first calculating the interaction index matrix (see Table VII).

TABLE VII

INTERACTION INDEX MATRIX FOR EACH ENGINE

Second, the process time matrix is calculated as shown

below in Table VIII

TABLE VIII

PROCESS TIME MATRIX FOR EACH ENGINE

Third, Table IX include the interaction matrix for each

engine.

(TABLE IX) INTERACTION MATRIX FOR EACH ENGINE



Fourth, the total interaction matrix is calculated by adding

the summation of each cell (see Table X). (TABLE X)

TOTAL INTERACTION MATRIX

The summation of the total interaction matrix is 411.

Fifth, the normalized direct interaction matrix is calculated

by dividing the total of each cell by the summation of the total

interaction matrix (see Table XI):

TABLE XI

NORMALIZED DIRECT INTERACTION MATRIX

Before the final steps, the normalized interaction matrix is

calculated for each engine ( see Table XII).

TABLE XII

NORMALIZED INTERACTION MATRIX

In Table XIII, the normalized general interaction matrix is

included. TABLE XIII

NORMALIZED GENERAL INTERACTION MATRIX

Finally, table showing the summation of each row, the

influence of each operation in the system, and the natural

logarithm (Ln) of the influence and the complexity level is

provided below in Table XIV

.



TABLE XIV

NATURAL LOGARITGM OF TE INFLUENCE

The level of complexity for the planned durations is

approximately 1.375, meaning that the planned operation

schedule is in a state of minor complexity level. The next step

is to calculate the complexity for the actual operation schedule

to compare between both plans and determine the next course

of action.

2) Complexity calculation for actual operation

The level of complexity for the actual operation is 2.1123,

representing a slight increase over the calculation of the

planned operation. Here, a minor complexity level exists as

well. Unfortunately, unlike the planned operation, there is a

significant difference when comparing the influences of each

step in both plans.

IV. ABC ANALYSIS

The complexity calculations for both the planned and actual

operation models indicate that the 5th

and 6th

steps have the

greatest influence on the system, i.e., nearly 36% each. We

conducted an ABC analysis (Pareto Diagram) to prioritize and

identify the most important sequences affecting the

complexity calculation by comparing the influence levels.

This will help management in tracking the most influential

sequences in the operation plan.

A. Determining the Most Influential Sequence in the

Operation Model

In this section, we present the Pareto diagrams starting with

the planned operation schedule, followed by the actual

operation schedule. TABLE XV

CUMULATIVE INFLUENCE OF PLANNED OPERATION

The Table XV above illustrates that operations 6, 5, 8, 7,

and 4 are the most influential sequences affecting the

complexity

, and their cumulative influences amount to nearly 80%,

meaning that if we can focus on these processes first and

investigate the causes behind the delays occurring in these

processes, we can eliminate 80% of the problem.

Unfortunately, analysis of the actual operational schedule

indicated that there is a variance in the process.

TABLE XVI

CUMULATIVE INFLUENCE OF ACTUAL OPERATION

Table XVI illustrates that processes 1, 9, 5, 10, 6, 2, and 8

are the most influential sequences affecting the complexity.

This means that a total of 7 processes affect the complexity in

a severe way, in contrast to the planned operation, in which 5

processes are of particular interest.

V. CONCLUSIONS

When implementing the complexity for the planned and

actual durations, no part mix ratio was available for the data,

and the planned and actual durations resulted in minor

complexity measurements. The levels of complexity for the

planned and actual durations were 1.375 and 2.1123,

respectively.

We also conducted an ABC analysis and found that some

processes affected both plans:

Parts Repair (MRP)

QEC Parts Rep

Parts Purchasing

Parts Repair

Mod. Assembly

According to the analysis, we recommend the following

actions to reduce the complexity level and maintain the

stability of process times:



1. Complexity model:

The development of a static complexity measure can

support managerial decisions on improving system

operations. Hence, the proposed complexity measure

is one in which both direct and indirect interactions

are characterized by the form of influences.

2. Part mix ratio:

A part mix ratio should be determined and

implemented to reduce the complexity of the planned

durations. Unfortunately, as noted above, when we

spoke with SAEI engineers, they informed us that

they had not established a part mix ratio because

engine maintenance was not periodically

implemented. Thus, SAEI engineers should establish

a part mix ratio for the engines and should implement

it in the complexity measurement calculations to

reduce the complexity level for both the actual and

planned durations in a manner that will increase

machine and man power utilization and thus reduce

the delay time and cycle time.

3. A system should be implemented to follow up and

inspect the workers to prevent delays. This system

should be based on the planned durations after

implementing the part mix ratio and reaching a

suitable degree of complexity.

The productivity report should be resumed in the future

because a measurable method to track progress in a

quantitative manner is now available.

ACKNOWLEDGMENT

We would like to thank SAEI for providing us with the

data. In addition, we like to thanks Eng. Mohammed hussain

and Eng., Alhusan Naita for their data collections to carry out

this work.

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complexity in design and manufacturing of a product family and its

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pp. 536-544

[3] Yates (1978) , “Complexity and the limits to knowledge”, American Journal of Physiology - Regulatory, Integrative and Comparative

Physiology, Vol.235, No.5, pp. R201-R204

[4] Allen and Torrens (2005) “Knowledge and complexity”, Futures, Vol.37, No.7, pp. 581-584.

[5] Serdar Asan, Seyda. (2009) "A methodology based on theory of

constraints’ thinking processes for managing complexity in the supply chain."

[6] Perona, Marco, and Giovanni Miragliotta. (2004) "Complexity management and supply chain performance assessment. A field study

and a conceptual framework." International journal of production

economics 90 (1): 103-115. [7] Deshmukh, A.V., Talavage, J.J., Barash, M.M., 1998, “Complexity in

manufacturing systems- Part 1: Analysis of static complexity”, IIE

Transactions, 30, (7) pp 645-655. [8] Cho, S., R. Alamoudi, and S. Asfour.(2009) "Interaction-based

complexity measure of manufacturing systems using information

entropy." International Journal of Computer Integrated Manufacturing 22 (10): 909-922.

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