A comparative analysis of the operational performance of Taiwan's major airports

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Journal of Air Transport Management 10 (2004) 353–360

A comparative analysis of the operational performance of

Taiwan’s major airports

Rong-Tsu Wang

a

, Chien-Ta Ho

b,

*, Cheng-Min Feng

c

, Yung-Kai Yang

d

a_{Institute of Business and Management and Department of International Trade, College of Management, Vanung University, Taiwan} b_{Department of International Trade, Lan Yang Institute of Technology, Taiwan}

c_{Institute of Traffic and Transportation, College of Management, National Chiao Tung University, Taiwan} d_{Taiwan High Speed Rail Corporation, Taiwan}

Abstract

The paper evaluates and compares the operational performance of ten major airports in Taiwan. The measure of operational performance is based on the relationship between four factors: airport, passengers, airline companies, and ﬁre services. To overcome the restrictions of the small sample size, grey relation analysis is used to group the initial evaluation indicators and to select the representative indicators.

Keywords: Airport; Performance evaluation; Representative indicators; Grey relation analysis

1. Introduction

The Taiwan Government has recently become inter-ested in evaluating the performance of its airports. The role of air transport in Taiwan is under review because the high-speed railway is about to open direct passenger transportation links between Taiwan and Mainland China which are imminent, and Taiwan is an emerging logistics centre for the Asia Paciﬁc Region. In the past, studies of the operational performance of air transpor-tation have primarily concentrated on evaluating airline performance and the economic efﬁciency of routes. Much less has been done on airports with most of the focus on productivity, competitive power, service standards, and service quality. Only a few shed light on the operational performance of airports.1

Furthermore, previous work outside the US has tended to concentrate on comparative studies of different international airports, and thus excluding

airports used for domestic or regional services. Whether the analysis of large airports is applicable to smaller airports, and the nature of heterogeneity and homo-geneity between the operational performances of air-ports of different classes, has been explored a little. Here ten major domestic airports in Taiwan are studied for their operational performance. They are; class A airports (Chiang Kai-Shek, Kaohsiung and Taipei), class B airports (Tainan, Hualien, Taitung and Ma Kung), and class C airports (Taichung, Chiayi and Kinmen).

2. Conceptual framework

An airport is a place where the suppliers of air transportation services (airline companies) and the users of such services meet and conduct their business. The operations and management of airports are handled by their administrations that view both the airline compa-nies and passengers as consumers. Running an airport is thus the same as running any other enterprise from the perspective of corporate ethos and operational efﬁ-ciency. In doing so, airports must be cognizance of more general concerns suchas safety and security whichare less important in some other types of business.

*Corresponding author.

E-mail address:brucedaa@ms26.hinet.net (C.-T. Ho).

1

Yu (2000),Hooper and Hensher (1997),Gillen and Lall (1997), and Martin and Roman (2001) on productivity, Wang (1999) and the

Institute of transportation, Ministry of Transportation and Commu-nications (1999) on competitive issues, and Yen (1995) and Ching (1998)on service quality.

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Practically, the efficiency of an airport’s operations can be seen to rest on four features: the airport, airline companies, passengers, and aviation control and fire services (Fig. 1). Here the airport is evaluated in terms of its; labour force (number of employees), terminal facilities (floor area of the terminal building, number of boarding gates, and number of check-in counters), aviation facilities (size of the apron, the number of car parking places, and accommodation of traffic volume), and revenue (total revenue and non-aviation income). The airline companies cover the output of transporta-tion (take-offs and landings, cargo tonnage, number of take-offs and landings during peak hours, and the number of routes) while passenger considerations include the total number of people served (passengers) and the number of people during peak hours. The aviation control and fire service covers police and firefighters (number of firefighters stationed on-site) and aviation control (number of aviation controllers). The evaluation of the overall operational performance is conducted by examining the productivity of employees, airline service levels, passenger service levels, and aviation and fire service levels.

In the context of this paper, an attempt has been made to ensure that indicators for evaluation purposes are meaningful, e.g., measures suchas the ratio of floor area per aviation controller are excluded. Second, if data for any specific element are unavailable, items carrying similar meaning for evaluation are used as substitutes, e.g., the number of aviation controllers is replaced with the number of air traffic controllers. This leaves 28 indicators for the evaluation exercise, 6 falls under the category of employee productivity, 6 under airline service level, 7 under passenger service level, and 9 under aviation and fire service level (seeTable 1).

3. Grey relation analysis and TOPSIS

Initially there is a need to reduce the number of indicators by selecting the most representative one. In general, these can be selected by grouping in a way that minimizes the differences within a group and maximizes the differences between those groups. If the samples are large and normally distributed, methods such as factor analysis, cluster analysis, and discriminate analysis can be used. However, if the sample size is small and the distribution of samples is unknown, grey relation analysis offers a tractable alternative. In addition, the TOPSIS method is employed in conjunction with grey relation analysis to calculate performance scores and rankings (Feng and Wang, 2000, 2001).

Grey system theory was developed by Deng (1982). The fundamental deﬁnition of ‘greyness’ is the informa-tion that is incomplete or unknown; thus an element from an incomplete message is considered to be a grey element. ‘Grey relations’ refer to the measurements of changing relations between two systems or between two elements that occur in a system over time. This method of analysis that is based on the degree of similarity or difference of development trends among elements used to measure the relation among elements is called ‘grey relation analysis’. During system develop-ment, should the trend of change between two elements be consistent, it is seen to enjoy a higher level of synchronized change and can be considered as having a stronger relationship. Otherwise, the grade of relation is smaller.

Let X be a factor set of grey relation, x0AX represent

the referential sequence, and xiAX represent the

com-parative sequence. x0ðkÞ and xiðkÞ represent the

respec-tive numerals at point k for x0 and xi: If the average

relation value gðx0ðkÞ; xiðkÞÞ is a real number, then it can

be deﬁned as (Deng, 1989): gðX0; XiÞ ¼ 1 n Xn k¼1 gðX0ðkÞ; XiðkÞÞ:

The average value of gðx0ðkÞ; xiðkÞÞ must satisfy the

following four axioms: normal interval, duality sym-metric, wholeness and approachability.

Axiom 1. Norm Interval 0ogðX0ðkÞ; XiðkÞÞp1; 8ðkÞ;

gðX0ðkÞ; XiðkÞÞ ¼ 1 iff X0ðkÞ ¼ XiðkÞ;

gðX0ðkÞ; XiðkÞÞ ¼ 0 iff X0ðkÞ; XiðkÞA+;

where+ is an empty set. Axiom 2. Duality symmetric x; yAX;

gðx; yÞ ¼ gðy; xÞ iff X ¼ fx; yg: Airport

Terminal facilities Aviation facilities

Aviation and Fire Services

Police and firefighters Aviation control Airline Companies Transportation output Number of participants Passengers Total number Total number during peak hours

Operation performance

Employee productivity Airline service level Passenger service level Aviation and fire service level

Airline service level Passenger service level

Aviation and fire service level

Aviation and fire service level Employee productivity

Fig. 1. Conceptual framework for evaluating airport operational performance.

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Axiom 3. Wholeness

Xi; XjAX ¼ fX_sjs ¼ 0; 1; 2; y; ng; n > 2; gðXi; XjÞagðXj; XiÞ:

Axiom 4. Approachability gðX0ðkÞ; XiðkÞÞ

decrease along with Xj 0ðkÞ XiðkÞj increasing.

If the four axioms are satisﬁed, gðx0; xiÞ is then

designated as the grade of grey relation in xi

correspon-dence to x0:gðx0ðkÞ; xiðkÞÞ is said to be the grey relational

coefﬁcient of the same at point k. Deng has proposed a mathematical equation that will satisfy these four axioms of grey relation, which is

gðX0ðkÞ; XiðkÞÞ ¼

miniAIminkjX0ðkÞ XiðkÞj þ z maxiAImaxkjX0ðkÞ XiðkÞj

X0ðkÞ XiðkÞ

j j þ z maxiAImaxkjX0ðkÞ XiðkÞj

;

where z is the distinguished coefﬁcient (zA[0,1]), the function of which is to reduce its numerical value by maxiAImaxkjX0ðkÞ XiðkÞj getting large, so as to effect

its loss-authenticity and to heighten the remarkable difference among relation coefﬁcients.

The TOPSIS method (Hwang and Yoon, 1981) h as

the advantage of being simple and yields an indisputable preference order. But it does assume that each indicator takes monotonic (increasing or decreasing) utility. TOPSIS is based on the concept that the chosen indicator should have the shortest distance from the ideal solution and the farthest from the worst solution. The ideal solution is the one that enjoys the largest beneﬁt indicator value and the smallest cost factor.

The steps involved in carrying this out are: Step 1: Normalization of indicator values

Normalization aims at obtaining comparable scales. There are different ways of normalizing the indicator

Table 1

The initial indicators

Code Name of indicator Equation for evaluation

Employee productivity

OP1 Number of take-offs and landings to number of employees Number of take-offs and landings/number of employees

OP2 Cargo tonnage to number of employees Cargo tonnage/number of employees

OP3 Floor area of terminal building to number of employees Floor area of terminal building/number of employees

OP4 Revenue to number of employees Total revenue/number of employees

OP5 Non-aviation income to number of employees Non-aviation income/number of employees

OP6 Number of passengers to number of employees Number of passengers/number of employees

Airline service level

OA1 Floor area of terminal to number of airlines Floor area of terminal building/number of airlines

OA2 Size of apron to number of airlines Size of apron/number of airlines

OA3 Volume to number of airlines Trafﬁc volume /number of airlines

OA4 Volume to number of take-offs and landings Trafﬁc volume/number of take-offs and landings

OA5 Volume to the number of routes Trafﬁc volume/number of routes

OA6 Service standards of runway Trafﬁc volume/take-offs and landings during peak hours

Passenger service level

OC1 Take-offs and landings to number of passengers Take offs and landings/total number of passengers

OC2 Number of airlines to number of passengers Number of airlines/number of passengers

OC3 Number of routes to number of passengers Number of routes/number of passengers

OC4 Number of car parks to the number of passengers during peak

hours

Number of car parks/number of passengers during peak hours OC5 Degree of congestion Floor area of terminal/number of passengers during peak hours

OC6 Number of boarding gates to number of passengers Number of boarding gates/number of passengers

OC7 Number of check-in counters to number of passengers Number of check-in counters/number of passengers

Aviation and fire service level

OS1 Number of police and ﬁreﬁghters to number of take-offs and

landing

Number of police and ﬁreﬁghters/number of take-offs and landings

OS2 Number of police and firefighters to the number of airlines Number of police and firefighters/number of airline companies

OS3 Number of police and firefighters to number of passengers Total number of police and firefighters/number of passengers

OS4 Number of police and firefighters to floor area of terminal Number of police and firefighters/floor area of terminal

OS5 Number of police and firefighters to number of car parks Number of police and firefighters/number of car parks

OS6 Number of police and firefighters to the size of the apron Number of police and firefighters/size of the apron

OS7 Number of police and firefighters to the number of flight routes Number of police and firefighters/number of flight routes

OS8 Number of aviation controllers to the number of take-offs and

landings

Number of aviation controllers/number of take-offs and landings

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values. Here vector normalization is used. This utilizes the ratio of the original value ðxijÞ and the square root of

the sum of the original indicator values. The advantage of this approach is that all indicators are measured in dimensionless units, thus facilitating inter-indicator comparisons. This procedure is usually utilized in TOPSIS using rij¼ Xij ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pm i¼1X2ij q ;

where i is the ithairport, j is the jthevaluation indicator, rij is the indicator value after vector normalization for

the ithairport and jthevaluation indicator, xij is the

original value of indicators for the ithairport and jth evaluation indicator, and m is the number of airports.

Step 2: To determine ideal ðAþÞ and worst ðA_Þ

solution Aþ¼ fðmax i rij jAJÞ; ðmini rij jAJ 0 Þ i ¼ 1; 2; :::; mj g ¼ fAþ₁; Aþ₂; y; Aþ_j ; y; Aþ_kg; A¼ fðmin i rij jAJÞ; ðmaxi rij jAJ 0 Þ i ¼ 1; 2; :::; mj g ¼ fA₁; A₂; y; A_j ; y; A_kg;

where J={j=1,2,y, k|k} positively relates to the beneﬁt criteria, J0={j=1,2,yk|k} positively relates to the cost criteria.

Step 3: To calculate the separation measure

The separation of each airport from the ideal airport ðSþ_i Þ and the worst airport ðS_i Þ uses

Sþ_i ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xk j¼1 ðrij Aþj Þ 2 v u u t ; S_i ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xk j¼1 ðrij Aj Þ 2 v u u t i ¼ 1; 2; :::; m:

Step 4: To calculate the relative closeness to the ideal solution ðC_iÞ This is deﬁned as C_i ¼ S i Sþ_i þ S i 0oC_i o1:

Step 5: To rank the preference order according to the descending order of C_i :

4. Application

As indicated earlier, 10 class A, B, and C airports are used in the analysis. Class A airports are key domestic airports and also provide backup for the international airports. Class B airports are domestic airports with heavy domestic air trafﬁc. Class C airports are similar to class B airports, but have lighter trafﬁc. CKS is the only international class airport in Taiwan but for purposes of the analysis is included as a Class A airport.

The data used for 2001 are taken from a variety of studies. The sources are data from various airports and the accounting ofﬁces of the air transport branches of the Civil Aviation Administration. Based on the grey relation analysis, indicators are established for the four groupings; employee productivity, airline service level, passenger service level, and aviation and ﬁre service

level, in accordance withthe coefﬁcient of each

indicator. The grouped indicators and representative indicators for class A, B, and C airports are presented in

Table 2.

The values of the indicators are converted into performance scores through TOPSIS. The operational performances of class A, B, and C airports are rated according to employee productivity, airline service level, passenger service level, aviation and ﬁre service level, and total performance (Table 3).

The analysis allows comparisons between the opera-tional efﬁciency of the various airports. In terms of operations performance, class A airports are ranked, CKS, Taipei and Kaohsiung. Each has some particular operational challenges to meet. For example, the aviation and ﬁre service level at CKS is the poorest in the class, whilst Taipei International Airport comes out the poorest in terms of passenger service. As for Kaohsiung International Airport, the employee produc-tivity is poor.

Turning to class B airports, the airline service level in Ma Kung Airport is the poorest of the four airports in this class. Taitung Airport, on the other hand, performed fairly well when compared with the other airports. Tainan Airport has the poorest passenger service level while Hualien Airport has the poorest ratings for bothemployee productivity and aviation and ﬁre service levels in this class. Taichung Airport has the poorest aviation and ﬁre service levels of the three class C airports. The airline service level in Kinmen Airport is the worst in the group and Chiayi Airport has the worst employee productivity and passenger service level.

This analysis suggests that the various airports may ﬁnd it useful to pursue a variety of measures to enhance

their performance. Table 4 provides some general

indications of how this may be done.

Table 5 that focuses on labour considerations show that the airports studied have high employee productiv-ity in terms of cargo handling. Both class A and B airports are also efficient in terms of the number of take-offs and landings handled by their workers and regarding the passengers they handle. Class A and C airports obtain significant non-aviation income for the number of employees they have while as a group, class B airports tend to make good use of their floor space.

Turning to service levels (Table 6), all airport classes show operational efficiency in the way they use floor area relative to the airlines using their facilities and in the way they handle traffic volume relative to the

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numbers of airlines and routes that they deal with. Class A and B airports have ratios of trafﬁc volume of their runways to the number of take-offs and landings, while class B and C airports value the service standards of the

runways. No airports do well regarding the size of their runways relative to the number of airlines they serve.

Table 7 shows that all classes of airports pay close attention to the degree of congestion, which may be seen

Table 2

The grouped indicators and representative indicators

Group Representative indicator of eachgroup Indicators within eachgroup

Class A Airports

Employee productivity OP-I OP1: Number of take-offs and landings to number of employees OP1

OP-II OP2: Cargo tonnage to number of employees OP2

OP-III OP5: Non-aviation income to number of employees OP3, OP4, OP5

OP-IV OP6: Number of passengers to number of employees OP6

Airline service level OA-I OA1: Floor area of terminal to the number of airlines OA1

OA-II OA3: Trafﬁc volume to the number of airlines OA2, OA3

OA-III OA4: Trafﬁc volume to number of take-offs and landings OA4, OA6

OA-IV OA5: Trafﬁc volume to number of routes OA5

Passenger service level OC-I OC1: Number of take-offs and landings to number of passengers OC1

OC-II OC2: Number of airlines to number of passengers OC2

OC-III OC3: Number of routes to number of passengers OC3, OC7

OC-IV OC5: Degree of congestion OC4, OC5

OC-V OC6: Number of boarding gates to number of passengers OC6

Aviation and fire service level OS-I OS8Number of police and firefighters to number of take-offs and landing OS1, OS8

OS-II OS2: Number of police and ﬁreﬁghters to number of airlines OS2

OS-III OS3: Number of police and ﬁreﬁghters to number of passengers OS3

OS-IV OS7: Number of police and ﬁreﬁghters to number of routes OS4, OS7

OS-V OS6: Number of police and ﬁreﬁghters to size of the apron OS5, OS6, OS9

Class B Airports

Employee productivity OP-I OP1: Number of take-offs and landings to number of employees OP1

OP-III OP3: Floor area of terminal building to number of employees OP3

OP-IV OP6: Number of passengers to number of employees OP4, OP5, OP6

Airline service level OA-I OA1: Floor area of terminal to number of airlines OA1

OA-II OA5: Trafﬁc volume to number of routes OA2, OA5

OA-III OA3: Trafﬁc volume to number of airlines OA3

OA-IV OA4: Trafﬁc volume to number of take-offs and landings OA4

OA-V OA6: Service standards of runway OA6

Passenger service level OC-I OC1: Number of take-offs and landings to number of passengers OC1

OC-II OC6: Number of boarding gates to number of passengers OC2, OC6

OC-III OC7: Number of check-in counters to number of passengers OC3, OC7

OC-IV OC4: Number of car parks to number of passengers during peak hours OC4

OC-V OC5: Degree of congestion OC5

Aviation and fire service level OS-I OS1: Number of police and firefighters to number of take-offs and landings OS1, OS3, OS8, OS9

OS-II OS2: Number of police and ﬁreﬁghters to number of airlines OS2, OS5, OS7

OS-III OS4: Number of police and firefighters to floor area of terminal OS4

OS-IV OS6: Number of police and ﬁreﬁghters to the size of the apron OS6

Class C Airports

Employee productivity OP-I OP4: Revenue to number of employees OP1, OP4, OP6

OP-III OP5: Non-aviation income to number of employees OP3, OP5

Airline service level OA-I OA1: Floor area of terminal to the number of airlines OA1, OA2

OA-II OA6: Service standards of runway OA4, OA6

OA-III OA3: Trafﬁc volume to the number of airlines OA3

OA-IV OA5: Trafﬁc volume to the number of routes OA5

Passenger service level OC-I OC7: Number of check-in counters to number of passengers OC1, OC3, OC7

OC-II OC2: Number of airlines to number of passengers OC2, OC6

OC-III OC4: Number of car parks to number of passengers during peak hours OC4

OC-IV OC5: Degree of congestion OC5

Aviation and fire service level OS-I OS1: Number of police and firefighters to number of take-offs and landings OS1, OS7

OS-II OS2: Number of police and ﬁreﬁghters to number of airlines OS2, OS5

OS-III OS9: Number of aviation controllers to number of routes OS4, OS6, OS8, OS9

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as an indicator of passenger service standards. Class A and C airports both place an emphasis on the ratio of the number of take-offs and landings and the total number of passengers they process. They also have a high ratio of the number of airlines to the number of passengers using their facilities. Both Class B and C airports have a high ratio of car parking spaces to the number of passengers during peak hours, and the ratio of the number of check-in counters to the number of passengers. In addition, Class A airports tend to offer a large the number of routes given its passenger ﬂow.

Table 8 deals withaviation and ﬁre services. It shows that all classes of airports have high ratios of

police and firefighters to the number of airlines they serve class A and B airports exhibit high ratio numbers of police and firefighters to the sizes of their aprons. Class B and C airports have a high ratio of the number of police and firefighters to the number of takes-off and landings while class A and C airports have a high ratio of the number of police and firefighters to the total number of passengers. In addition, Class A airports put an emphasis on the number of police and firefighters to the number of routes served, and the ratio of the number of aviation controllers to the number of take-offs and landings.

Table 3

The ranking of airports Class A airports

Aspects Rank1 Rank2 Rank3

Employee productivity CKS Taipei Kaohsiung

(0.643) (0.357) (0.179)

Airline service level Taipei CKS Kaohsiung

(0.639) (0.428) (0.139)

Passengers service level CKS Kaohsiung Taipei

(0.643) (0.632) (0.266)

Aviation and ﬁre services level Taipei Kaohsiung CKS

(0.613) (0.439) (0.356)

Total performance CKS Taipei Kaohsiung

(0.517) (0.475) (0.391)

Class B airports

Aspects Rank1 Rank2 Rank3 Rank4

Employee productivity Magong Taitung Tainan Hualien

(0.860) (0.430) (0.189) (0.164)

Aviation and ﬁre service level Tainan Magong Taitung Hualien

(0.547) (0.471) (0.223) (0.153)

Passengers service level Taitung Magong Hualien Tainan

(0.930) (0.606) (0.551) (0.126)

Airline service level Hualien Tainan Taitung Magong

(0.764) (0.595) (0.499) (0.406)

Total performance Magong Taitung Tainan Hualien

(0.583) (0.463) (0.399) (0.382)

Class C airports

Aspects Rank1 Rank2 Rank3

Employee productivity Taichung Chi Mei Chiayi

(0.683) (0.473) (0.061)

Aviation and ﬁre service level Chiayi Chi Mei Taichung

(0.749) (0.342) (0.236)

Passengers service level Chi Mei Taichung Chiayi

(0.508) (0.458) (0.443)

Airline service level Taichung Chiayi Chi Mei

(0.570) (0.466) (0.398)

Total performance Taichung Chi Mei Chiayi

(0.537) (0.448) (0.398)

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5. Conclusions

The paper has applied the grey relation analysis in the clustering of indicators for an evaluation of the

performance of a group of Taiwan’s airports. The technique allows a streamlining of the number of indicators used I evaluation and helps to overcome limitations when using a small sample. The relative total

Table 4

The priority strategy for improving airport operating efﬁciency Class of

airport

Airport Priority recommendation strategy Review the stafﬁng

conditions

Review the dispatch of police and ﬁreﬁghters Increase the proportion of non-aviation income Enhance passenger satisfaction

Employee productivity indicators for different airports

Code Name of indicator Class A Class B Class C

OP1 Number of take-offs and landings to number of employees | |

OP2 Cargo tonnage to number of employees | | |

OP3 Floor area of terminal building to number of employees |

OP4 Revenue to number of employees |

OP5 Non-aviation income to number of employees | |

OP6 Number of passenger to number of employees | |

Table 6

Airline service level indicators for different airports

OA1 Floor area of terminal to the number of airlines | | |

OA2 Size of apron to the number of airlines

OA3 Trafﬁc volume to number of airlines | | |

OA4 Trafﬁc volume to number of take-offs and landings | |

OA5 Trafﬁc volume to the number of routes | | |

OA6 Runway service standards | |

Table 7

Passenger service level indicators of different airports

OC1 Number of take-offs and landings and number of passengers | |

OC2 Number of airlines to number of passengers | |

OC3 Number of ﬂight to number of passengers |

OC4 Number of car parks to the number of passengers during peak hours | |

OC5 Degree of congestion | | |

OC6 Number of boarding gates to number of passengers | |

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scores method is applied to select the indicators for each group of airports in Taiwan. The results indicate that the total performance and the rating of the airports of all classes differ when examined in the context of several efﬁciency criteria.

Acknowledgements

The authors are very grateful for the ﬁnancial support of the National Science Council in Taiwan (Research Project Number: NSC92-2416-H-238-002).

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Table 8

Comparing aviation and ﬁre service level indicators of different airports

OS1 Number of police and ﬁreﬁghters to number of take-offs and landings | |

OS2 Number of police and ﬁreﬁghters to number of airlines | | |

OS3 Number of police and ﬁreﬁghters to number of passengers | |

OS4 Number of police and firefighters to floor area of terminal building |

OS5 Number of police and ﬁreﬁghters to the number of car parks

OS6 Number of police and ﬁreﬁghters to the size of the apron | |

OS7 Number of police and ﬁreﬁghters to number of routes |

OS8 Number of aviation controllers to number of take-offs and landings |