1.
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.

2.
2
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

3.
3
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.

4.
4
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

5.
5
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

6.
6
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

7.
7
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
OneWay
ANOVA
Table
SS
df
MS
F-­‐Ratio
p-­‐Value
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)

8.
8
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:

9.
9
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

10.
10
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
OneWay
ANOVA
Table
SS
df
MS
F-­‐Ratio
p-­‐Value
Between
Variation
394.8240
4
98.7060
28.0551
<
0.0001
Within
Variation
861.9800
245
3.5183
Total
Variation
1256.8040
249

11.
11
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
OneWay
ANOVA
Table
SS
df
MS
F-­‐Ratio
p-­‐Value
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

12.
12
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

13.
13
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.
Confidence
Interval
95%
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

14.
14
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.
Confidence
Interval
95%
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

15.
15
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.

16.
16
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.
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.

18.
18
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.
19
Contact
To
have
a
deeper
understanding
of
this
subject,
please
contact
Strategy
Team
9:
Jose
Arizaga
a0090258@nus.edu.sg
Teo
Kim
Chwee
g0705678@nus.edu.sg
Motoka
Mouri
a0092027@nus.edu.sg
Marc
Trevisany
a0090321@nus.edu.sg

20.
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.

21.
21
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.