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M.Sc. In Air Transport Management Program  › Courses  › Advanced Information Systems for Air Transportation (2015-2016)
Advanced Information Systems for Air Transportation (2015-2016)
Course Instructors

The course instructors for the Advanced Information Systems for Air Transportation will be Benny Mantin, Sertac Karaman, and Kemal Ure. Their bio can be seen in the links below:

Assoc.Prof. Benny Mantin
Asst.Prof. Sertac Karaman
Asst.Prof. Nazim Kemal Ure

Course Information

Information about the course can be reached from this link.

Course Description

An airline’s ultimate success in creating value depends on how efficiently and effectively and efficiently it manages its processes and its data. This course provides the background on both current and future information systems and business analytics methods from airline management perspective. Specifically, by the end of the course students are expected to:
  • Understand the current strategic IT structure/mechanisms in Turkish Airlines and in modern airlines
  • Understand the future trends in the IT side of the airline businesses
  • Understand potential breakthrough technologies (both hardware and software side) that might be a game changer in the airline business
  • Develop a keen understanding of the business analytics and what it can do for them now and in the future (in light of advancing hardware and software technologies)
  • Demonstrate learnt understanding through projects/applied demos
Course Materials

Cases will also be an important component of the course.
SkyJet: this is a comprehensive case that reinforces several important skills in Excel while utilizing revenue management concepts:
  • Forecasting: Utilizing a year worth of demand data, students are required to build a forecasting model that captures different levels of seasonality while capturing other unique events in order to decide on protection levels.
  • Simulations: Constructing simulation models in Excel is an important skill. This case covers several aspects that are commonly observed in the airline industry such as buy-up and buy-down thereby encompassing formation of normal and binomial distributions.
  • Optimization: Developing optimizations model in airlines hub and spoke network, this case guides the students through the importance of Solver in Excel.
There is no main textbook. However some of the analytics lectures will use materials from the following textbooks:
  • Saxena and Srinivasan, Business Analytics: A Practitioner’s Guide, 2013, Springer
  • Kantdarzic, Data Mining: Concepts, Models, Methods and Algorithms, 2011, Wiley
Academic Assessment

There will be a 3 hour exam within one month of completing the course. Final exam will form the balance of the student’s final grade of 50 percent.
Case Study will account for 10 percent.
There will be a significant project that will also count towards the students’ final grade 30 percent.
Class participation will form 10 percent of the final grade.

The participation grade is based on the instructors’ evaluation of the quality of each student's progress and contribution during the course.  Please carefully read all assigned materials, make a serious attempt to complete exercises and answer assigned questions, and be ready and willing to actively engage in the classroom learning experience.  Students may be asked to explain concepts in class.  The implicit assumption is that we all have something to contribute to the collective learning experience each day, and we all want to benefit from it.

Tentative Course Schedule

There are two pillars in this course: IT in the airline industry and data analytics. These will be supported by a third pillar: application through a team project.
Airline IT will introduce students to the broad framework of information systems and how they are adopted by the airline industry with a focus on some key application areas. Trends in the industry will be reviewed along with guidelines for development of IS.

Analytics will cover the following elements:
  • Descriptive analytics: data analysis and visualization; clustering
  • Predictive analytics: forecasting and statistical techniques, simulations; machine learning, collaborative filtering (beyond Excel)
  • Prescriptive analytics: analytical models for decision making, optimization
  • Network Flow and Optimization
  • Case studies for data analysis in air transportation
  • Exposure to advanced data analysis tools
Within groups of 3-4 students, teams are required to develop an IT/Analytics solution. Examples include the following possibilities:
  • Using existing data, carry out a complete analysis that can be automated to derive insights for the airline.
    • Transacted data can informs decision makers on trends
    • Price volatility can reveal information about your competitors, customers
    • Transform textual feedback to valuable insights (for example, using http://www.airlinequality.com/Forum/turkish.htm or http://www.airlineratings.com/passenger-reviews/34/turkish-airlines)
  • Developing the framework for a new solution:
    • An app that will inform passengers of their baggage (in line with IATA Resolution 753)
Lecture Notes

Main Lecture Notes  
Monday AM Introduction to the Course
Introduction to IS; Types of IS
Monday PM IT/IS Strategy and Outsourcing
IS Trends in the Industry
Introduction to Data Analytics
Tuesday AM Developing IS
Analytics with Excel
Tuesday PM Analytics with Excel
Analytics: Machine Learning
Wednesday AM Optimization: Linear Programming
Optimization Interactive Session
Wednesday PM Optimization Interactive Session
Thursday AM IT: Social Media, Text Analytics
Data Visualization Tools
Thursday PM Data Visualization Tools
IT: Big Data
Turkish Airlines IS Perspective
Friday AM Focus on Irregular Operations
Maintenance&Engineering (M&E); Maintenance, Repair and Overhaul (MRO)
Focus on Airline Reservation System, GDS, RM
Analytics with Excel
Friday PM Analytics with Excel
Turkish Airlines IS Perspective
Saturday AM Network Flow and Optimization
Exposure to Network Modelling
Queuing and Optimization Tools
Saturday PM Project Presentations