cm-06x01.pdf

This course is prepared under the Erasmus+ KA-210-YOU Project titled
«Skilling Youth for the Next Generation Air Transport Management»
Contemporary Managerial
Tools in Aviation
Decision-Making Tools in Aviation Management
Assoc. Prof. Dr. Savaş. S. Ateş
Assist. Prof. Dr. Mahmut Bakır

Decision-making refers to the process of selecting the best course of action from
various available alternatives to achieve specific operational goals and objectives within
an organization. Operations management involves overseeing the processes and
activities that produce goods and services, and effective decision-making is crucial for
optimizing these processes and resources.
Contemporary Managerial Tools in Aviation 2
Definition of decision-making

Effective decision-making in operations management involves collecting and analyzing
data, considering various factors and constraints, evaluating potential outcomes, and
selecting the best alternatives that align with the organization's strategic objectives. It
requires a combination of analytical skills, knowledge of operations processes, and a
keen understanding of the organization's goals and customer expectations.
Definition of decision-making

• Resource Allocation: Deciding how to efficiently allocate labor, materials, equipment, and
time to meet production or service demands.
• Process Improvement: Making choices to enhance operational processes, such as
streamlining workflows, adopting new technologies, or reengineering existing processes
for efficiency and cost reduction.
• Capacity Planning: Determining the appropriate production capacity level to meet
customer demand without overcapacity or undercapacity issues.
• Inventory Management: Managing inventory levels, reorder points, and safety stock to
ensure product availability while avoiding excessive capital tied up in inventory.
• Quality Control: Setting quality standards, inspecting processes, and making adjustments
to maintain or enhance product or service quality.
• Supplier Selection: Choosing suppliers, negotiating contracts, and managing
relationships to maintain a dependable supply chain.
Key aspects of decision-making

• Scheduling: Creating the optimal production or service schedule, including shifts, work
hours, and order processing, to maximize efficiency and customer satisfaction.
• Cost Management: Implementing strategies for cost control, such as cost reduction
initiatives, budgeting, and variance analysis.
• Technology Adoption: Evaluating and selecting technologies like automation, robotics, or
data analytics to improve operations.
• Risk Management: Identifying and managing operational risks, such as supply chain
disruptions, equipment breakdowns, or market fluctuations.
• Continuous Improvement: Deciding to implement methodologies like Six Sigma or Lean
to enhance operational effectiveness over time.
• Environmental and Social Responsibility: Making decisions aligned with sustainability
and social responsibility goals, including waste reduction, energy conservation, and ethical
sourcing.
Key aspects of decision-making

Decision-making tools are techniques, methods, or frameworks that help
individuals and organizations make informed and rational choices among
various alternatives. There are numerous decision-making tools that can be
used in management issues.
Decision-making tools

Decision Matrix: A decision matrix, also known as a decision-making grid, is a
structured approach to evaluate and compare different alternatives based on a set of
criteria. It involves creating a table where each option is assessed against each criterion,
assigning weights to criteria based on their importance, and calculating a total score for
each option. The option with the highest score is typically the preferred choice.
Example: When selecting a supplier, a company might use a decision matrix to assess
potential suppliers based on criteria like cost, quality, reliability, and lead time.

Alternative Cost Quality Delivery Performance Total Score
Supplier A 3 3 2 4 12
Supplier B 2 3 5 4 14
Supplier C 4 5 2 4 15
An Example of Decision Matrix for Supplier Selection

SWOT Analysis: SWOT stands for Strengths, Weaknesses, Opportunities, and Threats.
It is a strategic planning tool used to identify and evaluate the internal strengths and
weaknesses of an organization and the external opportunities and threats it faces. By
analyzing these factors, decision-makers can formulate strategies that leverage
strengths, mitigate weaknesses, capitalize on opportunities, and address threats.
Example: A business might conduct a SWOT analysis to assess its market position,
internal capabilities, and external market conditions to make strategic decisions.

THY is the largest airline in Turkey
Istanbul hub location attracts global transfer traffic
THY offers high levels of connectivity
THY has a young fleet
THY has an efficient level of unit cost
THY's Asia Pacific network is small versus those of
Gulf airlines
THY's financial performance has been inconsistent
in recent years
THY's brand could regain former heights
Istanbul Airport will provide much more growth
potential
THY could improve its aircraft utilization
THY's new widebody orders should enhance its
network
Airlines are vulnerable to external events
THY's costs will be burdened by rising fuel prices
Competition from Pegasus/LCCs is growing
Competition from the Gulf remains strong
Turkish Airlines SWOT Analysis
CAPA (2018). Turkish Airlines SWOT: More growth for the Istanbul superconnector. Retrieved July 19, 2023, from
https://centreforaviation.com/analysis/airline-leader/turkish-airlines-swot-more-growth-for-the-istanbul-superconnector-449802

Cost-Benefit Analysis (CBA): CBA is a quantitative tool that compares the costs of an
action or project with the expected benefits. It involves identifying and quantifying both
the costs and benefits, usually in monetary terms, and then calculating the net benefit
(benefits minus costs). A positive net benefit indicates that the action or project is
economically justified.
Example: Before investing in a new technology, a company might perform a cost-benefit
analysis to determine if the expected benefits (e.g., increased productivity) outweigh the
costs (e.g., purchase and implementation costs).

An Example of Cost-Benefit Analysis
Technology A Technology B Technology C
Total Costs $15,000 $19,000 $12,000
Total Benefits $21,000 $26,000 $18,000
Cost-Benefit ratio 1.40 1.37 1.50

Pareto Analysis: Pareto Analysis, also known as the 80/20 rule, is a tool used to
prioritize tasks or issues based on the principle that a small percentage of causes
(usually 20%) is responsible for a large percentage (usually 80%) of the effects. By
identifying and addressing the most significant factors, decision-makers can focus
resources where they will have the greatest impact.
Example: An organization might use Pareto Analysis to identify the 20% of product
defects that are causing 80% of customer complaints, allowing them to prioritize quality
improvement efforts.

Decision Trees: Decision trees are visual representations of decision-making processes
that help analyze complex decisions with multiple branches and outcomes. They involve
creating a tree-like structure with decision nodes (choices), chance nodes (uncertain
outcomes), and terminal nodes (final outcomes). Probabilities and values are assigned
to each branch to calculate expected values, aiding in the selection of the best decision
path.
Example: Decision trees can be used in financial decision-making, such as whether to
invest in a new project, considering various scenarios and their associated probabilities.

Force Field Analysis: Force Field Analysis is a tool for identifying and evaluating the
driving forces (factors supporting change) and restraining forces (factors opposing
change) related to a specific decision or goal. By visually mapping these forces,
decision-makers can assess the balance and develop strategies to strengthen driving
forces or weaken restraining forces to facilitate decision implementation.
Example: A team might use Force Field Analysis to assess the factors influencing the
adoption of a new company-wide software system, helping them identify barriers to
change and develop strategies to overcome them.

These are just a few of the many decision-making tools available. The choice
of tool depends on the nature of the decision, the complexity of the problem,
and the available data and resources. Each tool provides a structured and
systematic approach to making decisions, enhancing the quality of decision-
making processes.

Decision-making tools play a significant role in aviation management due to the complex
and safety-critical nature of the aviation industry.
Safety: Decision tools like risk assessment models and safety management systems
help identify hazards, assess risks, and mitigate them, contributing to aviation's
outstanding safety record.
Efficiency and Cost Management: Tools like operations research and optimization
models aid in efficient scheduling, resource allocation, and cost control, optimizing
resources, reducing costs, and enhancing profitability.
Regulatory Compliance: Decision-making tools assist in interpreting and complying
with aviation regulations, ensuring legal adherence and avoiding penalties.
Fleet Management: Tools like fleet planning models and maintenance scheduling
software optimize fleet operations, reduce downtime, and extend aircraft lifespan.
Importance of decision-making tools in
aviation

Emergency Response: Decision tools assist in crisis management, resource allocation,
and swift decision-making during emergencies.
Environmental Impact: Tools focusing on fuel efficiency and emissions reduction help
make environmentally responsible decisions, aligning with sustainability goals.
Route Optimization: Decision tools for route planning reduce fuel consumption, flight
times, and emissions, saving costs and promoting environmental sustainability.
Customer Satisfaction: Decision tools like revenue management systems optimize
pricing and seat allocation, maximizing revenue while ensuring passenger satisfaction.
Supply Chain Management: Tools streamline aviation supply chains, reducing costs
and ensuring critical resource availability, from parts sourcing to in-flight catering.
Importance of decision-making tools in
aviation

The Analytical Hierarchy Process (AHP) is a structured decision-making technique
developed by Thomas Saaty in the 1970s. It is widely used in various fields, including
aviation management, to assist in complex decision-making processes that involve
multiple criteria and alternatives. AHP helps decision-makers prioritize and select the
best course of action by breaking down a decision problem into a hierarchy of criteria
and sub-criteria, facilitating a systematic and quantitative evaluation of alternatives.
For more detailed information, please refer to the following source:
Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical
modelling, 9(3-5), 161-176.
Analytical Hierarchy Process (AHP)

1. Problem Formulation:
Clearly articulate the decision that needs to be made in the context of aviation management. This could
range from aircraft selection to airport expansion planning.
2. Hierarchy Construction:
Break down the decision problem into a hierarchical structure consisting of the main goal, criteria, sub-
criteria, and alternatives. In aviation, the goal might be to select a new aircraft, with criteria like fuel
efficiency, passenger capacity, and cost, and various aircraft models as alternatives.
3. Pairwise Comparisons:
For each level of the hierarchy, conduct pairwise comparisons to determine the relative importance of
elements within that level. Decision-makers use a scale (typically 1 to 9) to express the importance of
one element over another. These comparisons generate preference matrices.
4. Consistency Check:
AHP ensures that decision-makers' judgments are consistent by computing a consistency ratio for each
comparison matrix. If the ratio exceeds a predefined threshold (usually 0.1), the decision-maker should
revisit and adjust their judgments.

5. Weight Calculation:
Using mathematical algorithms, AHP calculates the relative weights of criteria and sub-criteria based on
the pairwise comparison matrices. These weights represent the importance or significance of each
element in the hierarchy.
6. Score Calculation:
For each alternative, score them against each criterion or sub-criterion based on their performance.
This could involve quantitative data, expert opinions, or a combination of both.
7. Aggregation:
Multiply the scores of each alternative by the corresponding weights of criteria and sub-criteria and sum
them to obtain an overall score for each alternative.
8. Decision and Sensitivity Analysis:
Rank alternatives based on their overall scores. The alternative with the highest score is often the
preferred choice. Additionally, assess how changes in criteria weights or scores of alternatives affect the
final ranking, allowing decision-makers to explore the robustness of their decision.

Structure of the AHP model
Badri, M. A. (2001). A combined AHP–GP model for quality control systems. International
Journal of Production Economics, 72(1), 27-40.

An airline operator looks for a supplier for ground handling services, and the following
criteria stand out in the supplier selection.
Example
Ground Handling
Supplier
Selection
Compatibility Quality Flexibility Cost
Step 1. Establish a hierarchical structure with the top-level goal (ground handling selection) and four
second-level criteria.

Step 2. Determine the relative importance of these criteria with respect to the goal. Prepare a
pairwise comparison matrix for this. Pairwise comparison matrix is based on the relative
importance scale (1-9).
Example
Relative Importance Scale
Scale Definition
1 Equal importance
3 Moderate importance
5 Strong importance
7 Very strong importance
9 Extreme importance
2,4,6,8 Intermediate values
Saaty, R. W. (1987). The analytic hierarchy process—what it is
and how it is used. Mathematical modelling, 9(3-5), 161-176.
Pairwise comparison matrix
Compatibility 1
Quality 3 1
Flexibility 2 0.5 1
Cost 5 7 3 1
How much more important is the criterion in the row
compared to the criterion in the column?

Example
Compatibility 1
Quality 3 1
Flexibility 2 0.33 1
Cost 5 7 3 1
How much more important is the criterion in the row
compared to the criterion in the column?
Quality is of a moderate importance than compatibility.
Cost is of a strong importance than compatibility.
Quality is of a strong importance than flexility (1/3=0.33).
Note that the values in the diagonal take the value of 1.
Compatibility 1 1/3 1/2 1/5
Quality 3 1 1/0.33 1/7
Flexibility 2 0.33 1 1/3
Cost 5 7 3 1
For each pairwise comparison, adopt a value of 1/x,
and complete the upper triangle of the matrix.

Example
Compatibility 1 0.33 0.5 0.2
Quality 3 1 3 0.14
Flexibility 2 0.33 1 0.33
Cost 5 7 3 1
Sum 11 8.66 7.5 1.67
The column sum of the pairwise comparison matrix is
obtained.
Then, each matrix element is divided by the column sum
to obtain the normalized pairwise matrix.
Normalized Pairwise comparison matrix
Compatibility 1/11 0.33/8.66 0.5/7.5 0.2/1.67
Quality 3/11 1/8.66 3/7.5 0.14/1.67
Flexibility 2/11 0.33/8.66 1/7.5 0.33/1.67
Cost 5/11 7/8.66 3/7.5 1/1.67
Sum 11 8.66 7.5 1.67
Compatibility 0.09 0.04 0.07 0.12
Quality 0.27 0.12 0.040 0.08
Flexibility 0.18 0.04 0.13 0.20
Cost 0.45 0.81 0.40 0.60

Example
In the final step, the criterion weights are calculated
by taking the average of all the elements in the row.
Note that the total of criterion weights sums up to 1.
Compatibility Quality Flexibility Cost Criteria
weight
Compatibility 0.09 0.04 0.07 0.12 0.08
Quality 0.27 0.12 0.040 0.08 0.22
Flexibility 0.18 0.04 0.13 0.20 0.14
Cost 0.45 0.81 0.40 0.60 0.57
According to the table on the side, the most
important criterion in airline selection is cost
(0.57), followed by quality (0.22), flexibility (0.14),
and compatibility (0.08).
The issue of consistency in the AHP method is complex and falls outside the scope of this
introductory lesson. For more information, you can enjoy the tutorial provided in the link
below:
https://www.youtube.com/watch?v=VTZft7SpV0g

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a
decision-making tool used to select the best alternative from a set of options based on
multiple criteria. It was developed by Hwang and Yoon in the 1980s and is widely
employed in various fields, including business, engineering, and project management.
TOPSIS is particularly useful when decision-makers must consider both the positive and
negative aspects of alternatives and aim to find the option that is closest to the ideal
solution.
For more detailed information, please refer to the following source:
Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2,
100021.
Technique for Order of Preference by
Similarity to Ideal Solution (TOPSIS) Method

• Ideal Solution: It defines the best value for each criterion. For benefit criteria (favoring
higher values), it's the maximum observed value; for cost or loss criteria (favoring
lower values), it's the minimum observed value.
• Normalized Decision Matrix: This matrix normalizes performance data for each
alternative and criterion to a common scale (usually 0 to 1) for fair comparison.
• Weighted Decision Matrix: Decision-makers assign weights to criteria based on their
importance, reflecting preferences. Weighting ensures each criterion's significance in
the decision process is considered.
Key Concepts of TOPSIS

1. Define the Decision Problem: Clearly define the decision problem, including the set of alternatives and the criteria
for evaluation.
2. Normalize the Decision Matrix: Transform the raw data in the decision matrix into a normalized form, where all
values are on the same scale (usually between 0 and 1).
3. Assign Weights to Criteria: Decision-makers assign weights to each criterion based on their relative importance.
4. Calculate the Weighted Normalized Decision Matrix: Multiply each normalized value in the decision matrix by its
corresponding criterion weight. This results in a weighted normalized decision matrix.
5. Determine the Ideal and Anti-Ideal Solutions: For each criterion, identify the ideal solution (maximum or
minimum value) and the anti-ideal solution (opposite of the ideal solution). These values serve as benchmarks for
comparison.
6. Calculate the Distance from Ideal and Anti-Ideal Solutions: Calculate the Euclidean distance between each
alternative and the ideal and anti-ideal solutions for each criterion. The distance from the ideal solution measures how
well an alternative performs, while the distance from the anti-ideal solution represents its worst performance.
7. Calculate the Similarity Scores: Determine the similarity score for each alternative by dividing the distance from
the anti-ideal solution by the sum of the distances from the ideal and anti-ideal solutions.
8. Rank the Alternatives: Rank the alternatives based on their similarity scores. The alternative with the highest
similarity score is considered the best option.
TOPSIS Process

A marketing analytics company has adopted the SERVQUAL model to measure the
service quality performance of four regional airlines. Remember that SERVQUAL
includes 5 service dimensions. The performance of the four regional airlines on a scale
of 1-5 according to the SERVQUAL dimensions is shown in the decision matrix below.
Note that all criteria are of the benefit type, meaning that higher values are desirable.
Example
Initial Decision Matrix
Tangibles Reliability Assurance Empathy Responsiveness
Eagle Air 4 3 5 2 4
Falcon Air 3 4 3 4 5
Hawk Air 5 2 4 3 3
Vulture Air 2 5 2 5 2

Step 1: Normalize the Decision Matrix
To make the criteria comparable, you need to normalize the decision matrix. This
involves transforming the data in each column (dimension) so that it ranges from 0 to 1.
To normalize the data, you can use the following formula:
Example
Normalized Decision Matrix
Eagle Air 0.54 0.41 0.68 0.27 0.54
Falcon Air 0.41 0.54 0.41 0.54 0.68
Hawk Air 0.68 0.27 0.54 0.41 0.41
Vulture Air 0.27 0.68 0.27 0.68 0.27

Step 2: Calculate the Weighted Normalized Decision Matrix
Multiply each value in the normalized decision matrix by the corresponding for that
criterion. In this example, let's assume that each criterion has equal weight, i.e., 0.20
(1/5).
You can use the following formula:
Example
Weighted Normalized Decision Matrix
Eagle Air 0.11 0.08 0.14 0.05 0.11
Falcon Air 0.08 0.11 0.08 0.11 0.14
Hawk Air 0.14 0.05 0.11 0.08 0.08
Vulture Air 0.05 0.14 0.05 0.14 0.05

Step 3: Determine the Ideal and Anti-Ideal Solutions
Ideal Solution (Positive Ideal Solution): For each criterion, identify the maximum value in
the weighted normalized decision matrix.
Example
Ideal Solutions 0.14 0.14 0.14 0.14 0.14
Anti-Ideal Solution (Negative Ideal Solution): For each criterion, identify the minimum
value in the weighted normalized decision matrix.
Negative Ideal
Solution
0.05 0.05 0.05 0.05 0.05

Step 4: Calculate the Euclidean Distance
Calculate the Euclidean distance between each alternative and the Ideal and Anti-Ideal
Solutions. The Euclidean distance is computed as follows:
Example
For Eagle Air:
2 2 2 2 2
* (0.11-0.14) (0.08-0.14) (0.14-0.14) (0.05-0.14) (0.10-0.12) 0.105
S
i
 
     
 
 
2 2 2 2 2
(0.11-0.05) (0.08-0.05) (0.14-0.05) (0.05-0.05) (0.10-0.07) 0.105
S
i
 
      
 
 

Step 4: Calculate the Euclidean Distance
Calculate the Euclidean distance between each alternative and the Ideal (Si+) and Anti-
Ideal (Si-) Solutions.
Example
Si+ Si-
Eagle Air 0.105 0.115
Falcon Air 0.086 0.119
Hawk Air 0.115 0.105
Vulture Air 0.141 0.115

Step 5: Calculate the Similarity Scores (Closeness Coefficients)
Calculate the similarity scores (Si) for each alternative using the formula:
Example
Si+ Si- Vi
Eagle Air 0.105 0.115 0.523
Falcon Air 0.086 0.119 0.580
Hawk Air 0.115 0.105 0.477
Vulture Air 0.141 0.115 0.449

Step 6: Rank the Alternatives
Rank the alternatives in descending order based on their similarity scores. The airline
with the highest similarity score provides the highest service quality.
Example
Vi Rank
Eagle Air 0.523 2
Falcon Air 0.580 1
Hawk Air 0.477 3
Vulture Air 0.449 4
To better comprehend the example, you can access step-by-step TOPSIS calculation table
with formulas from the following link:
https://osf.io/n74bh

cm-06x01.pdf

Recommended

Recommended

More Related Content

Similar to cm-06x01.pdf

Similar to cm-06x01.pdf (20)

More from NextGenATM Erasmus+ Project

More from NextGenATM Erasmus+ Project (20)

Recently uploaded

Recently uploaded (20)

cm-06x01.pdf