Airline Revenue Management: Current Practices and Future Directions
Autor Curt Cramer, Andreas Thamsen Limba Engleză Hardback – 11 noi 2021
This textbook has been recommended and developed for university courses in Germany, Austriaand Switzerland.
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Specificații
ISBN-13: 9783658337209
ISBN-10: 3658337206
Pagini: 113
Ilustrații: XV, 113 p. 46 illus., 26 illus. in color.
Dimensiuni: 168 x 240 x 14 mm
Greutate: 0.41 kg
Ediția:1st ed. 2021
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Gabler
Locul publicării:Wiesbaden, Germany
ISBN-10: 3658337206
Pagini: 113
Ilustrații: XV, 113 p. 46 illus., 26 illus. in color.
Dimensiuni: 168 x 240 x 14 mm
Greutate: 0.41 kg
Ediția:1st ed. 2021
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Gabler
Locul publicării:Wiesbaden, Germany
Cuprins
Fundamentals of airline revenue management.- Traditional forecasting & optimization in revenue management.- Modern analytical methods.- Overbooking.- O&D revenue management.- Ancillary revenues.- Data-enabled airline business models and future directions in revenue management.
Notă biografică
Curt Cramer is an Advanced Analytics and Data Science executive in the retail industry. He has gathered extensive applied analytics experience in 15 years of consulting and line management responsibilities across industries, including leisure travel. He holds a Ph. D. in Engineering/Computer Science from the University of Karlsruhe/KIT.
Andreas Thams is an Honorary Professor for Airline Management at University of Applied Sciences Worms. He held various commercial management positions in the airline, travel, and logistics industry, particularly in the field of revenue management. He has a Ph. D. in Econometrics from Freie Universität Berlin.
Andreas Thams is an Honorary Professor for Airline Management at University of Applied Sciences Worms. He held various commercial management positions in the airline, travel, and logistics industry, particularly in the field of revenue management. He has a Ph. D. in Econometrics from Freie Universität Berlin.
Textul de pe ultima copertă
This book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications help to familiarize the reader with the relevance of the corresponding ideas and concepts for a commercial airline organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches and to gain an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen their understanding of the contents covered.
Contents
•Fundamentals of airline revenue management
•Computerized revenue management
•Estimation and forecasting
•Optimization, types of control and overbooking
•Network revenue management
•Ancillary revenues
•Data science and revenue management
About the authors
Andreas Thams is an Honorary Professor for Airline Management at University of Applied Sciences Worms. He held various commercial management positions in the airline, travel and logistics industry, particularly in the field of revenue management. He has a Ph.D. in Econometrics from Freie Universität Berlin.
Curt Cramer is an Advanced Analytics and Data Science executive in the retail industry. He has gathered extensive applied analytics experience in 15 years of consulting and line management responsibilities across industries, including leisure travel. He holds a Ph.D. in Engineering/Computer Science from the University of Karlsruhe/KIT.
Caracteristici
Aa comprehensive overview of current practices and future directions in airline revenue management Ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts Suitable to get an introductory understanding of data science and machine learning methods