Profit maximization

Profit Maximization
Of Multi–Stops Operating Model

FIRST: SECOND:
Right Aircraft Most Profitable
to Assign Route to Operate.

Profit Maximization of Multi–Stops Operating Model
Laila Adam1 and M. Salem2
Felix Airways - Sana’a Yemen

The competitive environment of aviation industry is driving most airlines to re-
evaluate their strategies to survive, and many strategies are setup, either to reduce the
cost or to improve the profits as e-ticketing, simplify the Business, e-freight, to meet the
challenge of Low Cost Carriers in their markets. This paper is addressing a new
approach for maximizing profits in Multi-Stops Operating Model in long haul operation
of legacy airlines, based on demand passengers of multi destinations, corresponding
market fares and cost of available ton kilometers of the assigned aircraft, a linear
program is developed and many constrains are set up. The profit is maximizing by
solving the right number of frequencies, load factor per operating sector and cabin load
factor for each respective destination. Based on these results many scenarios are
implemented to define the right aircraft to operate and the most profitable routes in the
network. The program can be extended further, to study the effects of a new destination
on the existing routes / network. While market opportunity is another important factor
for developing a decision matrix with a profit and defining the complete picture of the
airline network.

Nomenclature
A320 = Aircraft Family of Airbus – A320
ASK = Available Seat Kilometers
ASM = Available Seat Miles
ATK = Available Tonne Kilometers
B737 = Aircraft Family of B737.
BCG = Boston Consulting Group
CAN = Guangzhou – China
CASK = Cost per Available Seat Kilometer
DOC = Direct Operating Cost
DXB = Dubai – Emirates
JED = Jeddah – Al –Saudia
JKT = Jakarta – Indonesia
LCC = Low Cost Carriers
LP = Linear Program
LOS = Lagos – Nigeria
Nm = Nautical Mile
RASK = Revenue per Available Seat Kilometer
RPK = Revenue Passenger Kilometer
RTK = Revenue Tone Kilometer
SAH = Sana'a – Yemen

1
Pricing &Yield Manager, Commercial Department, Sana’a-Yemen/laila.adam@felixairways.com
2
Senior Consultant, CEO - Office, Sana’a-Yemen/consultant@felixairways.com
1
Anna Valicek - paper

TOC = Total Operating Cost

Pij = Number of Passengers from city i to city j
Fij = Fare the sector - city i to city j ( one way )
L ij = Load Factor of i-j sector
D ij = Distance in km of i-j sector
S = Typical Seat Configuration of the assigned Aircraft.
C = Operating Cost per Available Tonne Kilometer
f = Number of Frequencies

I. Introduction

M OST of decisions making relay on the issue of how to make the most of the company's resources of raw
material, capital, manpower, time and facilities. Linear Programming (LP) is a technique that aims at
optimizing performance in terms of combinations of facilities and resources. Linear Programming can be
viewed as an analytical process and a holistic decision support tool that offers many advantages to managers.
Problems that involve allocation of resources, such as blending of raw material, production planning, raw
material allocation and manpower management planning can solved used the LP technique (Calvert, et al, 1989;
Grass, 1990; Shenoy, 1989).
In addition to its ability to calculate an optimal solution, LP also offers managers the capability of building
scenarios through its extensive "what if"? Analysis facility. While most practical LP problems would require a
very long time to be solved on hand, computers can be utilized to arrive at a solution in a very short time
(minutes or even seconds). In this respect, LP type problems can be solved in a number of ways. Several
dedicated software packages are available, but are often expensive, difficult to use and take a long time to learn.
As a result, the power of LP is utilized only by larger organizations. Spreadsheet technology offers a cheaper,
simpler and more flexible alternative to solving LP problems.

Spreadsheet package make it very simple to construct 'What if?' models; where a range of possible decisions can
be input and the resulting effects immediately calculated. These models can be very powerful and simple to
build but they rely upon the decision-maker to arrive at the correct answer. The model itself is not prescriptive
in providing the best answer, but does enable the decision-maker to determine which strategies are feasible. A
third method uses graphs, but this method can only be used for solving simple problems with only two variables
and limited number of constrains. This method is used extensively in teaching the concepts, but offers little to
the practicing decision-maker involved with real problem situations.

The utilization of spreadsheet technology to support managerial decision-making has been greatly advanced
through the ever –increasing processing power of the personal computer (PC). Spreadsheet software such as
Excel from Microsoft offers advanced capabilities for solving linear and non-linear problems. It is argued here
that the availability of advanced spreadsheet functions such as Excel's 'Solver' provides managers with an
excellent decision support tool that exploits the power of linear programming. Such a tool should constitute an
integral part of any manager's decision-making tools. Enhancing decision-making capabilities of managers must
incorporate improving both the efficiency and, to a greater extent, the effectiveness of decision-making.
Presenting decision-makings with easy to understand information and quick solutions improves decision-making
efficiency, while the availability of 'What if?' scenario analysis allows for enhanced decision-making
effectiveness (Caine and Robson)

A. Statement of problem
In multi stops operating airline business model, most of the airline operates tentatively on the combined and
complex routes, some of them applying the strategy of lowering the fares while this may tend to huge loses in
spite of filling the aircraft, the question how to avoid such a situation and plan properly to implement the right
policy, how to addressing high yield routes, what are the right ratio of sector load factor that maximize the
profit.

2

B. Literature
Linear Program in Aviation:
The airline industry uses linear programming to optimize profits and minimize expenses in their business.
Initially, airlines charged the same price for any seat on the aircraft. In order to make money, they decided to
charge different fares for different seats and promoted different prices depending on how early you bought your
ticket. This required some linear programming. Airlines needed to consider how many people would be willing
to pay a higher price for a ticket if they were able to book their flight at the last minute and have substantial
flexibility in their schedule and flight times. The airline also needed to know how many people would only
purchase a low price ticket, without an in-flight meal. Through linear programming, airlines were able to find
the optimal breakdown of how many tickets to sell at which price, including various prices in between.

Airlines also need to consider plane routes, pilot schedules, direct and in-direct flights, and layovers. There are
certain standards that require pilots to sleep for so many hours and to have so many days rest before flying.
Airlines want to maximize the amount of time that their pilots are in the air, as well. Pilots have certain
specializations, as not all pilots are able to fly the same planes, so this also becomes a factor. The most
controllable factor an airline has is its pilot’s salary, so it is important that airlines use their optimization teams
to keep this expense as low as possible. Because all of these constraints must be considered when making
economic decisions about the airline, linear programming becomes a crucial job.

Decision Matrix:

Decision matrix is an important tool to reflect multi decisions possibilities, is created by two main factors.
A well known matrixes are Cost per trip vs. Cost per seat, really it depends about what analysis want and how to
build it decision
In our case many decisions can be developed by two scenarios.
The first one is targeting aircraft, while the second is targeting route. The matrix is addressing four outcomes
possibilities for the related objective as shown in Fig. (1) Based on, how we design the matrix, the first quarter
is for the favor of the setting objective, and the third one is for the favor of competitors, while the second and
the fourth quarter are compromise decision. And one should balance between benefits of all options.
The matrix can be represented a direct comparable values with same scale or a relative value to the targeting
objectives, both ways are applicable in practice,
It is an important tool management in most of the fields as Marketing, Operation, Education, Social and General
Science

Decision Matrix Targeting Aircrafts
In this case, many aircrafts are examined having
similar capabilities in range (Distance) and capacities
(Seats) to operate for certain route, two factors are
evaluated and measured, and the first one is the profits
and the second one is marketing opportunity

Profits: the main factor to consider, in the
study that driving the results.
Market Opportunity: this reflects the
Market size, that not utilize and included in
study, since program try to fit the size market
to maximize the profit, so the program
addressing the right traffic size in terms of
the right seat allocation for each sector which
reflect in terms of passenger load factor, Fig. ( 1 ). Decision Matrix
Decision Matrix Targeting Routes
Whence we define the right aircraft to operate, we
have to reset the inputs to address many routes, to define the most profitable routes, to assign the first priorities
for operation, of course market opportunity will be consider also in this matrix.

3

Theoretical - Mathematical Model:
The problem can be setup by:

Maximize
Profit = Revenue - Cost

-- 1

Subjected to :

II. Case Study:
The decision to continue the existing
operation of CGK to JED, or to study the
other options as operating to CAN, LOS,

Data Collection:
The existing data
of the routes
SAH-DXB, SAH-JED
SAH-JKT, DXB-JKT
JED-JKT

From the Rapid System LOS
Number of passengers, average yield, DXB CAN
SAH
distance between routes,
While for the new routes as

LOS-SAH, LOS-DXB, LOS-CAN
CAN-DXB, CAN-SAH,
CAN-LOS, DXB-LOS, DXB-CAN

We are addressing number of operators
between these sectors, average load factor,
number of frequencies and type of aircraft
used; from that value we consider 5-10 Map. 1. Route CAN-DXB-SAH-LOS
market percentages.
Also estimation for new routes can be evaluated by considering airport movement, for specified sector, and
select 5-10 market share.
Both ways should support the final result.
The following routes are evaluated

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CAN-DXB-SAH-LOS, CAN-DXB-SAH-JED, CAN-DXB-SAH-KRT, CAN-DXB-SAH-ADD, CAN-DXB-
SAH-CAI, JED-SAH-DXB-CGK

While we define the aircraft to operate, as the following

Aircraft Capacity Cost per ATK
Type (Seat) (USD)
A330-200 277 0.35
A310-300 200 0.36
B737-800 154 0.41

Program:

By using the solver package in Microsoft Excel, equation (1) is used with its constrains as in table (1)

Table. 1. Input of Route: CAN-DXB-SAH-LOS

Results:

1- Aircraft Evaluation :
Sector: JED-SAH-DXB-CGK

By using the previous program, for sector JED-SAH-DXB-CGK, and relates Aircrafts inputs. As shown in
table (2) ,

Table. 2. Results by Aircrafts for JED-SAH-DXB-CGK route
5

Analysis of the Results:
Based on the evaluation of
the operating sector and the
actual demand of Route JED-
SAH-DXB-CGK, the result
is shown in the above table in
terms of the profits (USD)
and market opportunity, so by
developing the decision
matrix, considering A320-
200, as the origin aircraft of
the analysis,
In the 1st quarter:
Only A310-300 means that
A330-200 in a better position
in terms of profits gains and
has a greater market
Figure 2. Decision Matrix – Selecting Right Aircraft
opportunity than A310-300.
th
In the 4 quarter:
Only B737-800 means that A330-200 is in a better position in terms of profit gains and but the market
opportunity is still not utilize completely as this aircraft has a small capacity compare to A330-200.

2- Sector Evaluation
Based on A330-200 operation:

Table. 3. Results by Routes for A330-200 operation

Analysis of the Result:
The previous analysis proves
that the right asset / aircraft to
operate is A330-200 for the
existing sector - JED-SAH-
DXB-CGK, the next step are
what the right routes to
operate by this type of
Aircrafts. So many routes
addressed in this part of the
study which shows obviously
represented by the decision
matrix:
In the 3rd Quarter:
All routes in this quarter
mean that Route JED-CGK
has a market opportunity over
the others while the most
important issue is the profit
Figure 3. Decision Matrix – Defining the Right Market
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gains from the addressed routes, which can be ranked as the following.
CAN-JED, CAN-ADD , CAN-CAI, CAN-KRT, CAN-LOS, and finally JED-CGK

Implementation in Practice:
The study can be used in many situations as
1– Aircraft Evaluation:
The purpose is to select the right aircraft in the fleet that developed maximum profit based on the historical
data. So that we are assigning the right aircraft for the right routes.
2- Route - Profit Maximization:
Based on forecasting passengers of the sectors, airlines can developed the planning scheme that maximize
the profits by implementing and allocating the seats by the right ratio of load factors developed by program.
3- Extended Operation to new destination:
By this program it can be easily study and evaluate the effects of introducing a new destination to the
existing combined routes, to see is it worth to operate or not?.

III. Conclusion
The study explores the use of decision matrix, in multi stops operating model. By using linear program, many
out comes can be addressed as profits and market opportunity, the problem is solved in two steps,
First –Aircraft Evaluation, i.e. to define right aircraft for operation.
Second- we evaluate the routes to reflect the higher route that delivers maximum profit.
The study shows that A330-200 is the right aircraft to operate, while route CAN-JED is the highest route that
delivers maximum profit.
The Decision Matrix of Profit-Market Opportunity has greater benefits over the classical Decision Matrix of
Cost per trip-Cost per Seat. As it is addressed the profit, which includes revenue and cost, and additional factor,
i.e. Market Opportunity, while the classical Decision Matrix only addressing cost, which gives only comparison
for costs of seats and trips.

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Appendix

1- Definitions:

Sector Load Factor:
This load factor measure the load carried from point A to point B that issued by flight coupon, passengers are
point to point traveler, so they are partially filled the aircraft. The program only considers the optimum load
factor in the calculation that maximum the profit.

On Board Load Factor:
The most important factor in the analysis, it measured and counted the entire passengers on board of the aircraft
between point to point (sectors) including those who traveling long haul destinations.

Market Opportunity:
It is the difference between the numbers of passengers, of what is available in the market and number of
passengers that used in the program. It a percentage that measure the possibility of further utilizing the market.
That mean, the program selects only the right of passengers that maximize the profit of the route, while the
remaining passengers consider as a Market Opportunity that not utilize. If we force the program to utilize the
total market, definitely the profit will drive down, due to increase of frequencies to meet the total market and
there is a possibility of lose occurrence.

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2- Passengers and Fares Estimation for the new destinations:
SECTORS PRICE FREQUENCIES PASSENGERS FARES
CAN - LAGOS (LOG) USD days/week USD
LH 1160 5
KQ 1514 3
EK 1702 5
234 580
CAN - ADDIS ABABA (ADD) USD days/week USD
LH 1160 3
KQ 1230 1
EK 1262 5
162 580
CAN - KHARTOUM (KRT) USD days/week USD
LH 1160 2
KQ 1230 3
EK 1717 5
180 580
CAN - CAIRO (CAI) USD days/week USD
LH 1160 5
KE 1200 3
SQ 1420 2
EK 1508 2
KQ 1514 5
QR 1661 7
450 580
CARRIERS

CAN - DARALSALAM (DAR) USD days/week USD
KQ 1230 3
EK 1262 4
QR 1717 5
216 615
CAN - NAIROBI (NBO) USD days/week USD
KQ 1230 6
EK 1262 7
QR 1717 5
324 615
CAN - DUBAI (DXB) USD days/week USD
CA 659 4
TG 711 7
KQ 762 5
SQ 972 4
CZ 987 7
KE 1170 7
EK 1350 7
QR 1511 5
828 330
CAN - BAHRIAN (BAH) USD days/week USD
LH 1160 2
QR 1540 5
EK 1561 2
162 580
CAN - JEDDAH (DXB) USD days/week USD
LH 1160 4
SQ 1421 3
CA 1702 5
EK 1572 6
QR 1579 5
414 580

Table. 4. Estimation 9
Number of Passengers

Acknowledgments
Preparation of this paper would not have been possible without the help of many people and we would like to
take this opportunity to express our sincere gratitude to them.

Many thanks to the top management level of Yemen – Yemen Airways and Felix Airways. Those whom sprit
our aviation knowledge. Especially Eng. Mohammed Abdulla Alarrasha – CEO of Felix Airways.

References
Airbus (2008), Global Market Forecasts 2007-2026.
Boeing (2008), Current Market Outlook 2008-2028, Seattle, Washington, 2008.
Elwood S. Buffa (1988), " Modern Production/Operations Management " pp.559-569.
Robert J. T., Robert C. K. (1975), “Decision Making Through Operations Research " pp.157-159
Wikipedia ( 2008 ), Competition between Airbus and Boeing, 2009.
Yemenia Yemen Airways (2008), “Monthly Traffic Reports" Sana,a, 2008.

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