sia report

Offering strategic advice to Singapore Airlines

Customer satisfaction and operations efficiency

Special Report 2011

Executive Summary

The Strategy Team at Singapore Airlines (SIA) conducted a statistical investigation to provide the Board of Directors with recommendations as to how to strengthen the company’s competitive advantage. The two core competencies analyzed were customer satisfaction and operations at SIA. Singaporean travellers are less satisfied on average with SIA’s services than travellers from the US and the UK. Economy-‐class travellers at SIA are more satisfied with value-‐for-‐money as their ratings are on average 25% higher than those of Business-‐class travellers. The Boeing 777 is found most comfortable amongst Economy travellers, whereas the Airbus A380 wins in terms of Business class comfort. Asiana Airlines rates higher than SIA in terms of seat comfort in both Economy and Business-‐class. Concerning operations, SIA should maximize efforts to increase passenger load factor, as a 1% increase results in 220,174,000 SGD annual net income. Also, SIA should reduce the advertising budget; for every 1 SGD invested, net income is reduced by 22 SGD. In terms of the fleet age, SIA has one of the lowest of the industry and it should strive to maintain this position; for every year the average fleet age increases, SIA suffers an annual net income loss of 97,376,000 SGD. In total, 8 recommendations are given in the report.

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Table of contents

Introduction 3

PART I – Customer satisfaction 4 Model

Data Collection

Statistical analysis

PART II – Operations efficiency 12 Model

Data Collection

Statistical analysis

Recommendations 16 Contact 19

Appendix 20

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Introduction

A few days ago, on Nov. 3rd of 2011, Singapore Airlines (SIA) published a 49% drop in second

quarter net profit. Rising external pressures such as wildly fluctuating fuel prices, countries being

more protective over domestic carries, and security concerns, are threatening SIA’s leading

position. In addition, competitors are hot on SIA’s heels striving at closing the gap in both service

excellence and efficiency. The Board of Directors at SIA is unsure of what strategy to pursue in

order to regain its sustained competitive edge. As part of SIA’s Strategy Team, we have therefore

been asked by the Board to look into possible areas of improvement, at any level of the firm.

SIA’s core objective is to provide excellent service to its customers. Moreover, change is not just

seen as inevitable, but as a way of maintaining competitive advantage over our industry rivals.

SIA’s corporate culture fosters a strong sense of continuous innovation, unique customer service

and profit-‐consciousness in all of its employees. The company is both a cost-‐leader and a

differentiator in its industry, which defies Michael Porter’s view of both being mutually exclusive.

SIA is the exception to Porter’s strategy rule and this has attracted a lot of attention from its

competitors. Now that these are closing in, SIA must continue to gain insight as to how to

continue to outperform its rivals through further innovation. SIA recognizes that to sustain its

differentiation, it must maintain continuous improvement. As Chew ChooSeng, former SIA CEO

and current Chairman of both Singapore Exchange and Singapore Tourism Board, once said:

“The day we (SIA) stop having visions or objectives to work to, then that is the day we atrophy. I

can assure you we have no intention of doing that (…) Our passengers are our raison d’être. If SIA

is successful, it is largely because we have never allowed ourselves to forget that important fact.”

Our approach to the Board’s pressing request is to statistically analyze two of SIA’s core

competencies: customer satisfaction and operations efficiency. The former deals with information

gathered from customer reviews based on aircraft type, travel class, seat dimensions etc. whereas

the latter focuses on issues such as maintenance costs, load factor, fuel cost and other

operational factors of the business. The report will be subdivided into two parts which will then

be integrated to provide holistic recommendations to the Board.

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PART I: Customer satisfaction at Singapore Airlines

It is irrefutable that SIA has a reputation for delivering premium services to its customers. The

company is characterized by top-‐management commitment to excellence, customer-‐focused staff

and systems, and a customer-‐oriented culture. Our Strategy Team (ST) at SIA is therefore focusing

its efforts on better understanding customer preferences to better satisfy their needs; all

feedback is taken very seriously at SIA since it is an influential source of innovation. In order to

make suitable recommendations, we will use relevant statistical techniques to answer the

following main questions:

• Does customer nationality affect the perceived level of service quality at SIA?

• Does customer satisfaction vary by travel class at SIA?

• Does customer satisfaction at SIA vary by aircraft model?

• Does customer satisfaction at SIA differ from that of other 5-‐star1 airlines?

• How are seat characteristics (e.g. length, width, privacy, comfort) reviewed by customers?

Across aircraft models?

Model

Customers flying Economy and Business on SKYTRAX’s 5 star airlines were chosen as population.

Analysis of First-‐class travellers was amended as not enough data sets from First-‐class travellers

were available. We identified the following parameters and variables: passenger nationality,

travel class (economy, business), seat reviews economy (legroom, seat recline, seat width, TV

screen, access to seat), seat reviews business (sleep comfort, sitting comfort, seat length, seat

width, seat privacy), flight user review and airplane model.

Data collection

Secondary data was used to conduct the analyses of SIA’s customer satisfaction. The largest

airline and airport review and ranking site SKYTRAX was chosen for secondary data for SIA’s

customer satisfaction. Annually, SKYTRAX carries out international-‐traveller surveys to find the

best cabin staff, airport, airline, airline lounge, in-‐flight entertainment system, on-‐board catering

1 SKYTRAX Airline Ranking – http://www.airlinequality.com/StarRanking/5star.htm

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and several other elements of air travel. SKYTRAX is well known for their annual World Airline

Awards as well as the World Airport Awards. Apart from these rankings SKYTRAX offers customers

the chance to engage in an airline forum where they can publish seat reviews and flight

experiences, and evaluate these with certain criteria.

Concerning the Economy seat evaluation, customers can select which aircraft type they have

flown with and add several other criteria like passenger volume (called pax size), seat layout or if

it was a window, middle or aisle seat. Customers rank the overall flight experiences on a scale

from 1 to 10 with 10 being the highest. For the seat characteristics -‐ legroom space, seat recline,

seat width, viewing TV screen, access in/out of seat -‐ customers can rank it with 1 to 5 stars where

the latter is the highest ranking. Moreover, they can add a comment for the overall experience.

Figure 1 – Singapore Airlines Economy Class seat review example

In order to evaluate the Economy seat satisfaction and to find some similarities, the seat

characteristics, the overall passenger rating and the nationality were used to analyse. The five star

rating was coded to one star as 1 and five stars as 5. Premium customer can select the aircraft

type they have flown with and specify if they flew in the First or Business class. For the seat

characteristics – sleep comfort, sitting comfort, seat length, seat width, seat privacy -‐ customers

can give 1 to 5 stars for every characteristic where five stars is the highest ranking. Moreover they

can add a comment for the overall experience.

Figure 2 – Singapore Airlines Business Class seat review example

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For the project, only Economy and Business class comfort reports were analyzed. Similar to the

Economy class seat, the five star rating was coded to one star as 1 and five stars as 5. Random

sampling was used for economy and business class reviews as sampling technique.

Statistical Analysis2

Passenger nationality

A one-‐way ANOVA test was conducted in order to determine whether airline ratings vary by

passenger nationality. Taking a random sample of 10 SIA reviews per nationality (Australia,

Singapore, UK, USA), it was possible to compare whether the mean evaluation differed or not.

ANOVA’s output showed a significant p-‐value of 0.0108, proving that there was in fact evidence

for a difference in review rating across nationalities. The Tukey-‐Kramer procedure was used to

determine which nationalities differed in mean rating. As it turned out, the mean rating of

Singaporeans was significantly lower than that of the British and the Americans. Singaporeans

may therefore seem less satisfied on average than travellers from the US and UK. It may either be

because the SIA staff make in general greater efforts to satisfy Westerners, or because

Singaporeans are on average more demanding about service quality. Recommendations for these

results are given at a later stage of the report.

ANOVA Sample Stats Australia Singapore UK USA

Sample Size 10 10 10 10 Sample Mean 7.500 6.500 9.5000 9.2000 Sample Std Dev 2.877 3.028 0.7071 0.9189

OneWay ANOVA Table SS df MS F-‐Ratio p-‐Value

Between Variation 60.6750 3 20.2250 4.3057 0.0108 Within Variation 169.1000 36 4.6972 Total Variation 229.7750 39

Confidence Interval Tests Tukey Lower Tukey Upper

aus-‐sing -‐1.6114 3.6114 aus-‐UK -‐4.6114 0.6114 aus-‐USA -‐4.3114 0.9114 sing-‐UK -‐5.6114 -‐0.3886 sing-‐USA -‐5.3114 -‐0.0886 UK-‐USA -‐2.3114 2.9114

2 Refer to Appendix B for background information on statistical theory used

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Travel class

One would expect customer satisfaction to increase accordingly with SIA’s travel class: lowest for

Economy, and highest for those in First class. However, SIA attracts customers with increasingly

higher demands. The expectations of those in Economy might not be as high as those in Business

or First. Traveller’s in first class, for the incredible premium they pay, they probably expect the

world from SIA’s staff and are most likely to be sensitive to any irregularities or inefficiencies in

the services provided. A one-‐way ANOVA was conducted in order to investigate this in depth.

ANOVA Sample Stats Economy Business First

Sample Size 10 10 10 Sample Mean 9.5000 7.100 8.800 Sample Std Dev 0.7071 2.601 1.229


Between Variation 30.4667 2 15.2333 5.2063 0.0122 Within Variation 79.0000 27 2.9259 Total Variation 109.4667 29

Confidence Interval Tests Tukey Lower Tukey Upper

Economy-‐Business 0.50259 4.29741 Economy-‐First -‐1.19741 2.59741 Business-‐First -‐3.59741 0.19741

From results obtained in ANOVA, there is evidence to show that the mean level of customer

satisfaction does in fact vary across travel classes. The Tukey-‐Kramer procedure shows there is a

difference between average satisfaction in Business and in Economy class; surprisingly it is higher

in the latter. The Tukey-‐Kramer procedure also reveals that, although the difference between

Business and First is not significant, it is in fact quite close as the Upper Critical Range between

the two is of only 0.1947. These results reveal how on average, Business class customers are not

as satisfied as Economy class users. It seems that value-‐for-‐money is not as high for Business class

as it is for Economy as the average ratings for the latter are 25% higher.

Economy seats across SIA fleet

SIA customers rated on SKYTRAX how comfortable the seat was in terms of certain seat

characteristics (legroom, seat recline, seat width, entertainment centre, and access to the seat)

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for a specific aircraft model (Boeing 747, Boeing 777-‐200, Airbus A380 and Airbus A330). Using a

two-‐way ANOVA it is possible to study two factors: aircraft model and seat characteristic.

ANOVA Sample Means

A330

A380

B747

B777

Totals

Access seat 2.500 3.000 2.500 3.750 2.938

Legroom 1.750 3.500 3.500 4.250 3.250

Seat recline 2.750 3.250 3.000 3.500 3.125

Seat width 2.750 2.750 2.750 4.000 3.063

TV screen 3.250 3.500 3.000 3.750 3.375

Totals 2.600 3.200 2.950 3.850

TwoWay ANOVA Table SS df MS F-‐Ratio p-‐Value

Seat Characteristic 1.825 4 0.456 0.512 0.7273 Model 16.700 3 5.567 6.243 0.0009 Interaction 8.175 12 0.681 0.764 0.6839 Error 53.500 60 0.892

Total 80.200 79

The results from the two-‐way ANOVA show the aircraft model is significant on rating (p-‐value =

0.0009), seat characteristic is not significant (p-‐value = 0.7273) and the interaction between the

two factors is not significant (p-‐value = 0.6839).

Again, the Tukey Kramer procedure was used to determine which aircraft models differ in

passenger rating. StatTools only gives the option of using Tukey Kramer for a one-‐way ANOVA, so

in this case, it is done manually (see “Economy model seat TWO ANOVA” worksheet). The results

obtain are as follows:

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Comparisons Mean Differences Absolute Within Critical Range?

A330 -‐ A380 -‐0.600 0.6 Yes A330 -‐ B747 -‐0.350 0.35 Yes A330 -‐ B777 -‐1.250 1.25 No A380 -‐ B747 0.250 0.25 Yes A380 -‐ B777 -‐0.650 0.65 Yes B747 -‐ B777 -‐0.900 0.9 No

The Boeing 777 is better rated (3.85 out of 5) than the Airbus A330 (2.6) and the Boeing 747

(2.95) in terms of seat comfort. It is hard to compare the Boeing 777 with the 747 as they both

serve different purposes. However, the Boeing 777 competes directly with the Airbus A330 in

terms of range, passenger capacity etc. These results can give management insight as to whether

they should reduce the number of A330 and replace for B777. Recommendations will be given at

a later stage of the report.

Business seats across SIA fleet

This section is similar to the previous. A two-‐way ANOVA was conducted to study the effect of

two factors: passenger reviews of Business-‐class seats (as measured by seat length, seat privacy,

seat width, sitting comfort and sleeping comfort), and aircraft models (Airbus A380, Boeing 747,

Boeing 777-‐200 and Boeing 777-‐300).

ANOVA Sample Means

A380

B747

B777-‐2

B777-‐3

Totals

Seat length 4.167 3.333 3.333 4.167 3.750

Seat privacy 4.500 2.833 2.500 4.500 3.583

Seat width 4.500 3.500 3.667 4.833 4.125

Sitting comfort 4.000 4.000 3.333 2.333 3.417

Sleep comfort 3.667 2.833 3.000 3.833 3.333

Totals 4.167 3.300 3.167 3.933

TwoWay ANOVA Table SS df MS F-‐Ratio p-‐Value

Seat Characteristic 9.467 4 2.367 1.871 0.1214

Model 21.092 3 7.031 5.558 0.0014

Interaction 26.533 12 2.211 1.748 0.0677

Error 126.500 100 1.265

Total 183.592 119

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From the previous Tables, it is observed how Factor A (Seat characteristic) is not significant as the

p-‐value is greater than the critical 0.05. Factor B however, the aircraft model, is in fact very

significant with a p-‐value of 0.0014. Moreover, it can be concluded that there is no interaction

between the two factors (p-‐value = 0.0677), although this is borderline. From these results, it is

possible to proceed onto determining which aircraft models differ. The Tukey-‐Kramer procedure

was used to achieve this, by finding the critical range for Factor B (see Excel sheet for

calculations).

Comparisons Mean Differences Absolute Within Critical Range?

A380 -‐ B747 0.867 0.866667 No A380 -‐ B777-‐2 1.000 1 No A380 -‐ B777-‐3 0.233 0.233333 Yes B747 -‐ B777-‐2 0.133 0.133333 Yes B747 -‐ B777-‐3 -‐0.633 0.633333 Yes B777-‐2 -‐ B777-‐3 -‐0.767 0.766667 Yes

Whereas for Economy seats the Boeing 777-‐200 had better comfort ratings that the Boeing 747

and the Airbus A330, for Business seats, the Airbus A380 is the clear winner. The Tukey-‐Kramer

procedure reveals that the A380 is considered to be more comfortable than both the Boeing 747

and 777-‐200, amongst Business class passengers. No conclusion can be reached regarding the

A380 and the B777-‐300 as there seems to be no difference from the results above.

Economy seats across 5-‐star airlines

So far, the analysis has been internal to SIA. Now, an external view of the firm is taken, comparing

SIA to its direct competitors. A one-‐way ANOVA was conducted to investigate whether the mean

passenger rating varies between Economy seats at SIA, Qatar Airways, Asiana Airlines, Cathay

Pacific, and Kingfisher Airlines.

ANOVA Sample Stats SIA(E) Qatar(E) Asiana(E) Cathay (E) Kingfisher (E)

Sample Size 50 50 50 50 50 Sample Mean 8.220 8.780 9.3200 5.660 8.160 Sample Std Dev 2.234 1.166 0.9570 2.918 1.346


Between Variation 394.8240 4 98.7060 28.0551 < 0.0001 Within Variation 861.9800 245 3.5183 Total Variation 1256.8040 249

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Confidence Interval Tests Difference of Means Tukey Lower Tukey Upper

SIA(E)-‐Qatar(E) -‐0.5600 -‐1.5833 0.4633 SIA(E)-‐Asiana(E) -‐1.1000 -‐2.1233 -‐0.0767 SIA(E)-‐Cathay (E) 2.5600 1.5367 3.5833 SIA(E)-‐Kingfisher (E) 0.0600 -‐0.9633 1.0833 Qatar(E)-‐Asiana(E) -‐0.5400 -‐1.5633 0.4833 Qatar(E)-‐Cathay (E) 3.1200 2.0967 4.1433 Qatar(E)-‐Kingfisher (E) 0.6200 -‐0.4033 1.6433 Asiana(E)-‐Cathay (E) 3.6600 2.6367 4.6833 Asiana(E)-‐Kingfisher (E) 1.1600 0.1367 2.1833 Cathay (E)-‐Kingfisher (E) -‐2.5000 -‐3.5233 -‐1.4767

Only those directly relevant to SIA are highlighted above. It seems that for Economy-‐class seats,

SIA is rated significantly higher than Cathay Pacific, although lower that Asiana Airlines. In fact,

Cathay Pacific is the lowest rated out of all the 5-‐star airlines studied, whereas Asiana is the leader

in this area.

Business seats across 5-‐star airlines

A similar test was conducted for Business class seats. There wasn’t as much data available for this

class as for Economy; only 4 airlines were compared, and with a smaller sample size of 20.

ANOVA Sample Stats SIA(B) Qatar(B) Asiana(B) Cathay(B)

Sample Size 20 20 20 20 Sample Mean 7.000 8.550 9.3000 7.050 Sample Std Dev 2.317 1.432 0.7327 3.456


Between Variation 77.8500 3 25.9500 5.2161 0.0025

Within Variation 378.1000 76 4.9750

Total Variation 455.9500 79

Confidence Interval Tests Difference of Means Tukey Lower Tukey Upper

SIA(B)-‐Qatar(B) -‐1.5500 -‐3.4033 0.3033 SIA(B)-‐Asiana(B) -‐2.3000 -‐4.1533 -‐0.4467 SIA(B)-‐Cathay(B) -‐0.0500 -‐1.9033 1.8033 Qatar(B)-‐Asiana(B) -‐0.7500 -‐2.6033 1.1033 Qatar(B)-‐Cathay(B) 1.5000 -‐0.3533 3.3533 Asiana(B)-‐Cathay(B) 2.2500 0.3967 4.1033

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The one-‐way ANOVA conducted is considered to be significant (p-‐value = 0.0025). In terms of

Business class ratings, Asiana still outperforms SIA. The average rating for SIA in Business class is 7

(out of 10) whereas for Asiana it’s 9.3. This difference is confirmed when conducting the Tukey

Kramer procedure, as highlighted in the previous Tables. When it comes to Economy seats, SIA

should learn from Asiana Airlines since it outperforms it in seat comfort for both Economy and

Business class. This could involve having SIA spies on Asiana flights to better understand the root

of their success.

PART II: Operations efficiency at Singapore Airlines

The operations behind SIA are equally important as customer satisfaction. Whereas in the

previous part the attention was focused to external services (i.e. customer-‐focused), in this

section we look at the internal services at SIA. We especially focus on factors influencing the

financial performance of SIA as these figures are essential for the future success of SIA’s

operations.

Model

The population consists of available data for SIA over the last eleven years beginning in the year

2000. Parameters and variables defined for this study were revenue, net income, advertising &

sales costs , aircraft maintenance and overhaul costs, fuel costs, costs of in-‐flight meals, rental on

lease of aircraft (all in thousand SGD), load factor passenger (in %), distance flown (in million km),

number of employees (person), number of aircraft (in unit), age of aircraft (in month), amount of

destination cities (in unit), distance flown (in million km), time flown (in hrs).

Data collection

Secondary data was used to conduct the analyses of SIA’s operational efficiency. The database

CEIC Data3 was chosen as the source for the data set. CEIC Data offers datasets for economic

research on emerging and developed markets around the world. CEIC Data provides detailed

information about SIA operational performance on the parameters named above. Random

sampling was used as sampling technique.

3 CEIC Data Company Ltd. -‐http://ceicdata.securities.com.libproxy1.nus.edu.sg/login.html

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Statistical Analysis4

Time flown

A simple regression reveals that SIA should increase their time flown by 14,306 hours for the next

year in order to follow the trend it achieved over the last years. It can be stated, that this

regression model with R² of 0,9054 and p-‐value smaller than 0.0001, accounts for 90.54% of the

variability and is in fact significant to SIA operations.

Summary Multiple R R-‐Square Adj. R-‐Square

0.9515 0.9054 0.8991

Confidence Interval 95%

Regression Table Coefficient

Standard Error

t-‐value p-‐value Lower Upper

Constant -‐28274558.65 2392019.497 -‐11.824 <

0.0001 -‐

33373027.52 -‐23176089.78

Year 14305.58 1194.2148 11.979 <

0.0001 11760.17 16850.99

Regression equation: Time flown (hrs.) = -‐28,274,558.65 + 14305.58 * (YEAR)

Distance flown

Similar results can be drawn from the regression made on the distance flown per year. With

R²=0,8971 and a p-‐value less than 0.0001, this regression accounts for 89.71% of the variance and

is significant to SIA operations. With every year, SIA should increase their total km flown by about

11 million km to maintain their growth rate.


Regression Table

Coefficient Std. Error

t-‐Value

p-‐Value Lower Upper

Constant -‐21804.98 1932.6 -‐11.2827 < 0.0001 -‐25924.22 -‐17685.74 Year 11.033 0.96485 11.4348 < 0.0001 8.98 13.09

Regression equation: Distance flown (M. km.) = -‐21,804.98 + 11.033 * (YEAR)

4 Refer to Appendix for background information on statistical theory used

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Destination cities

Destination cities also explains a lot of the variance and has a quite significance for SIA

operations; R²=0.7298 and the p-‐value is 0.0069, which is below the critical 0.05 value. Every year

SIA adds 1.36 cities to their network. Equivalently, SIA should continue to introduce roughly four

cities to their network every three years.


Regression Table

Coefficient Std. Error

t-‐Value

p-‐Value Lower Upper

Constant -‐2661.464 676.805 -‐3.9324 0.0077 -‐4317.547 -‐1005.381 Year 1.3571 0.3371 4.0255 0.0069 0.5321 2.1820

Regression equation: Number of destination cities = -‐2661.46 + 1.3571 * (YEAR)

Age of aircrafts and fuel costs

The correlation between the fuel costs, the age of SIA’s aircrafts and the aircraft maintenance

costs is significant. With a correlation of 0,765 we can state that as the age of the aircraft

increases, the associated expenditure on fuel also increases. In addition to that, we can see with a

negative correlation of -‐0.506 that the more SIA invests in aircraft maintenance, the lower fuel it

will require, most likely due to higher propulsive and aerodynamic efficiencies.

Correlation Table Fuel Cost Age A/C Aircraft Maintenance &

Overhaul costs

Fuel Cost 1.000 Age A/C 0.765 1.000 Aircraft Maintenance & Overhaul costs -‐0.506 -‐0.484 1.000

Influences on Net Income

We conducted a multiple regression in order to evaluate the factors which have a significant

effect on SIA’s net income. This Backward regression model explains 96.6% of the influencing

factors of SIA’s Net Income.

Regression Table Coefficient Std. Error t-‐Value p-‐Value

Constant -‐7548040.167 2066950.362 -‐3.6518 0.0147 Advertising & Sales Cost -‐22.5434 5.27029 -‐4.2774 0.0079 Rental on Lease of Aircraft -‐12.0216 1,5429 -‐7.7913 0.0006 Load factor passenger 220174.074 33664.23 6.5403 0.0013 Distance flown -‐232093.181 41415.49 -‐5.6040 0.0025 Age A/C -‐97376.665 19404,97192 -‐5,0181 0,0040 Time flown 200.12 34,91969066 5,7308 0,0023

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Step Information Multiple R R-‐Square Adj. R-‐Square Exit Number

All Variables 0,9934 0,9869 0,8558 Destination cities 0,9927 0,9855 0,9204 1 In-‐flight meals 0,9923 0,9847 0,9438 2 Number of employees 0,9911 0,9823 0,9513 3 Number A/C 0,9830 0,9663 0,9259 4

It can be obtained from the table above that the most influencing factors for SIA’s net income are

advertising and sales cost, rental on lease of aircraft, the load factor for passengers, the total

distance flown in km, the age of the aircrafts and the total time flown per year. Factors like the

amount of destination cities, costs of in-‐flight meals, number of employees or number of aircrafts

have no significant impact on the net income. For every SGD invested in Advertising and Sales, SIA

generates losses of 22.5 SGD. The same account for the distance flown of SIA aircrafts. Every

additional km flown lowers SIA’s net income by 232.10 SGD5. As the average fleet age increases

by one year, the annual net income will be decreased by 97,376,000 SGD. In addition to that, for

every SGD spent on leasing aircrafts, SIA loses 12 SGD in profit. On the other side, if SIA is able to

increase the load factor by one unit (i.e. 1 %) it would generate 220,174,000 SGD in income.

Additionally, an extra hour of flying per year increases SIA’s net income by about 200,000 SGD.

Regression equation: Net Income (1000 SGD) = -‐7548040.17 – 22.5 * (Advertising & Salest Cost

in 1000 SGD) – 12.02 * (Rental on Lease in 1000 SGD) +220,174 * (Load Factor) – 232093.18 *

(Distance flown in Million Kilometers) – 97376.665 * (Average age of aircraft fleet) + 200.12 *

(Time flown)

5 See units in Excel sheet. Net Income in thousands, distance traveled in millions.

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Recommendations

From the statistical analysis conducted hitherto, the Strategic Team identified main issues of

concern for the Board, and thus proposes the following recommendations:

Issue # Issue Recommendation

1. Singaporean travellers are

significantly less satisfied with

the service at SIA (6.5)6 than

travellers from UK (9.5) and

USA (9.2).

SIA should ensure staff places equal importance

on local and foreign passengers, if not doing so

already. Otherwise, Singaporeans may be

naturally more demanding and sensitive to staff

mistakes. SIA may need to offer higher

compensations to these customers if problems

arise. A qualitative analysis should be further

conducted on passenger reviews on SKYTRAX.

2. Economy-‐class passengers are

on average more satisfied

(9.5) than those in Business-‐

class (7.1). Value-‐for-‐money in

the former class may

therefore be perceived as

higher than that of the latter.

SIA should ensure that the premium paid for

Business is aligned with the increased service

provided. SIA should further investigate into

specific reasons for the lower relative satisfaction

in Business class (e.g. quality in-‐flight meals,

variety of drinks, seat comfort etc.). A qualitative

analysis should be further conducted on

passenger reviews on SKYTRAX.

3. On average, Economy-‐class

passengers rate the Boeing

777 more comfortable (3.85)

than Airbus A330 (2.6) and

Boeing 747 (2.95).

Conduct a qualitative analysis on the passengers’

reviews on SKYTRAX. Assuming all other factors

equal (e.g. fuel consumption, maintenance costs

etc.), In the future, SIA should reconsider

renewing the lease for A330, and consider

replacing these for the much higher rated B777.

6 ( ) Average rating

17

4. On average, Business-‐class

passengers rate the Airbus

A380 more comfortable

(4.167) than the Boeing 747

(3.3) and 777-‐200 (3.167).

SIA should further investigate reviews for First-‐

class customers. If positive as the ones obtained

in this case, SIA should continue to place orders

for the A380, which could replace the older and

less comfortable Boeing 747s (see Appendix A)

5. Both Economy and Business-‐

class passengers rate on

average SIA lower than Asiana

Airlines (12-‐25% lower).

SIA should investigate the cause of this.

Comparing websites, services provided, user-‐

friendliness, iPad apps, on-‐board services etc.

Conducting on-‐board spying to better understand

Asiana’s success. A qualitative analysis should be

further conducted on passenger reviews on

SKYTRAX.

6. The regression analysis on

distance flown, time flown

and cities served stated that

SIA should increase their km

flown per year by 11mn, hours

by 14,306 and add 1.35 cities

per year.

SIA should further analyze which of its routes is

reaching capacity limits and therefore increase

the capacity by introducing new airplanes.

Moreover it should constantly revise which

possible new destinations it could add to its

network. South America and Africa remain

largely unexploited.

7. With a correlation of 0.765 the

age of the aircraft and the

associated fuel costs have a

correlation of 0.765 In

addition to that, a negative

correlation of -‐0.506 exists

between the aircraft

maintenance and the fuel

costs.

SIA should try to continue their efforts in having

one of the youngest fleets in the industry. It was

statistically proven that the maintenance costs

can be reduced with a young fleet. Moreover this

young fleet consumes less fuel than an older one.

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8. The most influencing factors

for SIA’s net income are

advertising and sales cost,

rental on lease of aircraft, the

load factor for passengers, the

total distance flown in km, the

age of the aircrafts and the

total time flown per year

For the detailed significance and influences of the

parameters please refer to Part II. As the

passenger load factor has a positive influence on

SIA’s net income, it is advisable that SIA tries to

increase their load factor by a good revenue

management which optimizes the capacity for

every route offered. Moreover, we can obtain

that the age of aircraft has a significant negative

influence on SIA’s net income. As stated earlier,

SIA should try to keep its fleet as young as

possible. Although leasing has a negative

influence on the net income of SIA, it helps SIA to

staff airplanes more flexible according to

demand. In addition to that leasing costs can be

deducted from the tax payables. Therefore no

change in SIA’s leasing strategy is recommended.

The advertising budget should be reviewed, and

possibly reduced, as it is not proving to be

effective for increasing net income.

The statistical analysis has served a strong purpose of determining areas of improvement. A

limitation however remains in the fact that no specifics can be given in terms of what exactly

needs to be improved. A powerful tool arises when combining a quantitative analysis with a

qualitative one. For this reason, SIA should conduct in-‐depth qualitative analysis from customer

reviews, from both SKYTRAX and obtained internally through SIA.

19

Contact

To have a deeper understanding of this subject, please contact Strategy Team 9:

Jose Arizaga

[email protected]

Teo Kim Chwee

[email protected]

Motoka Mouri

[email protected]

Marc Trevisany

[email protected]

20

Appendix

Appendix A: SIA Fleet in units

This appendix should be used when considering whether the Boeing 777 should replace the less comfortable A330 (terminate some leases), and whether the Boeing 747 fleet should be replaced by the more comfortable and fuel-‐efficient A380. Singapore should however investigate into the newer 747-‐8 version.

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Appendix B: Background Theory One Way-‐ANOVA The analysis of variance (ANOVA) is used to evaluate differences among more than two groups. ANOVA analyzes the variation among and within groups in order to compare the means of the groups. Accordingly, the total variation (SST) is divided into two variations: Among-‐Group variation (SSA) and Within-‐Group variation (SSW). In ANOVA, it is assumed that populations are normally distributed, selected randomly and independently, and have equal variance. The null hypothesis is that there are no differences in the population means. On the other hand, the alternative is that not all the c population means are equal. H0: μ1 = μ2 = … = μc (c:groups) H1: Not all μj are equal (j = 1, 2, …, c)

The Fstat test statistic is examined after variances are computed as followsi:

Source of Variation

Degree of Freedom

Sum of Squares Mean Squares (Variance)

F

Among Groups

c -‐ 1 SSA

MSA (SSA / c-‐1)

Within Groups

n -‐ c SSW

MSW (SSW / n-‐c)

Total n -‐ 1 SST

MST (SST / n-‐1)

Fstat

=MSA/MSW

Two Way-‐ANOVA When there are two factors of interest, the analysis is extended to Two-‐way ANOVA. In this analysis, we can see whether there is interaction effect in addition to each factor effect. If the interaction effect is significant, each factor cannot be examined in this analysis. The Simple Linear Regression The simple linear regression is used to examine whether there is a linear relationship between two variables with t-‐stat test statistic, when the four assumptions are accepted: linearity, independence of errors, normality of errors, and equal variance. The model and hypotheses are the followings: Yi = β0 + β1Xi + εi (Yi: independent variable, Xi: dependent variable, εi: random error term)

H0: β1 = 0 (no linear relationship) H1: β1 ≠ 0 (linear relationship exists)

i David M. Levin et al., Statistics for Managers using Microsoft Excel (Pearson, sixth edition), 413.

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