From Descriptive to Prescriptive Analytics: Turkish Airlines Case Study

Year 2023, Volume: 13 Issue: 1, 99 - 125, 30.06.2023

Musab Talha Akpınar , Kadir Hızıroğlu , Keziban Seçkin Codal

https://doi.org/10.31679/adamakademi.1232332

Abstract

Recent years, evolving technologies have increased importance of data analytics and have extended the potential of using data-driven for decision-making process in different sectors as it has also been shown in civil aviation. The aviation industry supports $2.7 trillion (3.5%) of the world’s GDP thus, it has always been seen to have an inherently strategic role. Propose of this study is an integrated model that combines descriptive analytics (multidimensional analytics) predictive analytics (data mining and more) and prescriptive analytics (MCDM and DEMATEL) in order to extract the critical factors for the improvement of airline baggage optimizations. The data has taken from Turkish Airlines which is one of the biggest 10 airlines in terms of the passenger number. Descriptive analytics results have set a precedent implication of multidimensional reports for service sector. In addition, rules that arise as outcomes of predictive analytics have really significant knowledge for marketing and planning department in civil aviation. Furthermore, they will help to solve some optimization problem in air transportation sector. Owing to prescriptive analytics, displayed results supported by the MCDM and DEMATEL methods. Therefore, all stages of the analytics have been shown step by step on the real-world data implementation.

Keywords

Decision Support Systems, Air Transportation, Civil Aviation, Analytics, Descriptive, Predictive, Prescriptive

Tanımlayıcı Analizden Öngörüsel Analize: THY Vaka Çalışması

Abstract

Son yıllarda gelişen teknolojiler, veri analitiğinin önemini artırmış ve sivil havacılıkta da görüldüğü gibi farklı sektörlerde karar verme süreçlerinde veri odaklı kullanım potansiyelini genişletmiştir. Havacılık endüstrisi, dünya GSYİH'sının 2,7 trilyon dolarını (%3,5) desteklemektedir, dolayısıyla her zaman doğası gereği stratejik bir role sahip olduğu görülmüştür. Bu çalışmanın önerisi, havayolu bagaj optimizasyonlarının iyileştirilmesi için kritik faktörleri çıkarmak amacıyla tanımlayıcı analitiği (çok boyutlu analitik), tahmine dayalı analitiği (veri madenciliği ve daha fazlası) ve normatif analitiği (MCDM ve DEMATEL) birleştiren entegre bir modeldir. Veriler, yolcu sayısı bakımından en büyük 10 havayolundan biri olan Türk Hava Yolları'ndan alınmıştır. Tanımlayıcı analitik sonuçları, hizmet sektörü için çok boyutlu raporların emsal teşkil etmesini sağlamıştır. Ayrıca öngörü analitiği sonucunda ortaya çıkan kurallar, sivil havacılıkta pazarlama ve planlama departmanı için gerçekten önemli bir bilgi birikimine sahiptir. Ayrıca, hava taşımacılığı sektöründeki bazı optimizasyon problemlerinin çözülmesine yardımcı olacaklardır. Kuralcı analitik sayesinde, MCDM ve DEMATEL yöntemleri tarafından desteklenen sonuçlar görüntülenir. Bu nedenle, analitiğin tüm aşamaları gerçek dünya veri uygulaması üzerinde adım adım gösterilmiştir.

Keywords

Analytics, Decision Support Systems, Air Transportation, Civil Aviation, Descriptive, Predictive, Prescriptive

References

• Abate, M., Christidis, P., & Purwanto, A. J. (2020). Government support to airlines in the aftermath of the COVID-19 pandemic. Journal of air transport management, 89, 101931.
• Abdi, Y., Li, X., & Càmara-Turull, X. (2022). Exploring the impact of sustainability (ESG) disclosure on firm value and financial performance (FP) in airline industry: the moderating role of size and age. Environment, Development and Sustainability, 24(4), 5052-5079.
• Abrar, A., Hazizi, I. F., & Elgharbawy, A. (2021). The impact of COVID-19 on the sustainability of the tourism industry. Journal of Halal Industry & Services, 4(1).
• Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques. Technology, Knowledge and Learning, 24(4), 567-598.
• Ahmad, M. W., Akram, M. U., Ahmad, R., Hameed, K., & Hassan, A. (2022). Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights. ISA transactions.
• Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success–A systematic literature review. Decision Support Systems, 125, 113113.
• Akerkar, R. (2014). Analytics on Big Aviation Data: Turning Data into Insights. IJCSA, 11(3), 116-127.
• Amalina, F., Hashem, I. A. T., Azizul, Z. H., Fong, A. T., Firdaus, A., Imran, M., & Anuar, N. B. (2019). Blending big data analytics: Review on challenges and a recent study. Ieee Access, 8, 3629-3645.
• Amirkolaii, K. N., Baboli, A., Shahzad, M. K., & Tonadre, R. (2017). Demand Forecasting for Irregular
• Arora, M., Tuchen, S., Nazemi, M., & Blessing, L. (2021). Airport pandemic response: an assessment of impacts and strategies after one year with COVID-19. Transportation Research Interdisciplinary Perspectives, 11, 100449.
• Ayhan, S., Pesce, J., Comitz, P., Sweet, D., Bliesner, S., & Gerberick, G. (2013, April). Predictive analytics with aviation big data. In Integrated Communications, Navigation and Surveillance Conference (ICNS), 2013 (pp. 1-13). IEEE.
• Baars, H., & Kemper, H. G. (2010, July). Business intelligence in the cloud?. In PACIS (p. 145).
• Bartle, J. R., Lutte, R. K., & Leuenberger, D. Z. (2021). Sustainability and air freight transportation: Lessons from the global pandemic. Sustainability, 13(7), 3738.
• Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change, 163, 120447.
• Belhadi, A., Zkik, K., Cherrafi, A., & Sha'ri, M. Y. (2019). Understanding big data analytics for manufacturing processes: insights from literature review and multiple case studies. Computers & Industrial Engineering, 137, 106099.
• Bussemaker, J. H., Ciampa, P. D., Singh, J., Fioriti, M., Cabaleiro De La Hoz, C., Wang, Z., ... & Mandorino, M. (2022). Collaborative Design of a Business Jet Family Using the AGILE 4.0 MBSE Environment. In AIAA Aviation 2022 Forum (p. 3934).
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4). Dalkey, Norman and Helmer, Olaf. 1963. An Experimental Application of the Delphi Method to the Use of Experts. Management Science, 9: 458–467.
• Chen, J. C., Shyu, J. Z., and Huang, C. Y. (2017). Confguring the Knowledge Diffusion Policy Portfolio of Higher Education Institutes. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 5685-5734.
• Dax, E. C. (1975). Australia and New Zealand. World history of psychiatry, 704-728.
• De Luca, M., Abbondati, F., Pirozzi, M., & Žilionienė, D. (2016). Preliminary Study on Runway
• Delen, D. (2014). Real-world data mining: applied business analytics and decision making. FT Press.
• Delen, D., & Demirkan, H. (2013). Data, information and analytics as services. Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI). IFACPapersOnLine, 50(1), 15221-15226.
• Denman, S., Kleinschmidt, T., Ryan, D., Barnes, P., Sridharan, S., & Fookes, C. (2015). Automatic surveillance in transportation hubs: No longer just about catching the bad guy. Expert Systems with Applications, 42(24), 9449-9467.
• Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.
• Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: A modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 31(4), 337-373.
• Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
• Enrico, Z. I. O., Mengfei, F. A. N., Zhiguo, Z. E. N. G., & Rui, K. A. N. G. (2019). Application of reliability technologies in civil aviation: Lessons learnt and perspectives. Chinese Journal of Aeronautics, 32(1), 143-158.
• Fontela, E., and Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 Report. Switzerland Geneva: Battelle Geneva Research Center.
• Frazzetto, D., Nielsen, T. D., Pedersen, T. B., & Šikšnys, L. (2019). Prescriptive analytics: a survey of emerging trends and technologies. The VLDB Journal, 28(4), 575-595.
• Gössling, S., Hanna, P., Higham, J., Cohen, S., & Hopkins, D. (2019). Can we fly less? Evaluating the ‘necessity’of air travel. Journal of Air Transport Management, 81, 101722.
• Guerra-Gómez, J. A., Pack, M. L., Plaisant, C., & Shneiderman, B. (2015). Discovering temporal changes in hierarchical transportation data: Visual analytics & text reporting tools. Transportation Research Part C: Emerging Technologies, 51, 167-179.
• Gupta, M. K., & Chandra, P. (2020). A comprehensive survey of data mining. International Journal of Information Technology, 12(4), 1243-1257.
• Hazen, B. T., Weigel, F. K., Ezell, J. D., Boehmke, B. C., & Bradley, R. V. (2017). Toward understanding outcomes associated with data quality improvement. International Journal of Production Economics, 193, 737-747.
• Hoffmann, J., Maestrati, L., Sawada, Y., Tang, J., Sellier, J. M., & Bengio, Y. (2019). Data-driven approach to encoding and decoding 3-d crystal structures. arXiv preprint arXiv:1909.00949.
• Huang, C. J., & Kuo, P. H. (2019). Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE access, 7, 74822-74834.
• Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and knowledge discovery, 2(3), 283-304.
• Hubbard, P., & Williams, P. (2017). Chinese state-owned enterprises: an observer's guide. International Journal of Public Policy, 13(3-5), 153-170. IATA. (2016, June). Annual Review 2016, 72nd Annual General Meeting. http://www.iata.org/about/Documents/iata-annual-review-2015.pdf, (06.06.2016).
• Insaurralde, C. C., Blasch, E. P., Costa, P. C., & Sampigethaya, K. (2022). Uncertainty-Driven Ontology for Decision Support System in Air Transport. Electronics, 11(3), 362.
• Jacquillat, A., & Odoni, A. R. (2017). A roadmap toward airport demand and capacity management. Transportation Research Part A: Policy and Practice. ISO 690
• Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C. W., & Jia, J. (2020). Pointgroup: Dual-set point grouping for 3d instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and Pattern recognition (pp. 4867-4876).
• Kalakou, S., Psaraki-Kalouptsidi, V., & Moura, F. (2015). Future airport terminals: New technologies promise capacity gains. Journal of Air Transport Management, 42, 203-212.
• Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1), 82.
• Káposzta, J., Nagy, A., & Nagy, H. (2016). THE IMPACT OF TOURISM DEVELOPMENT POLICY ON THE REGIONS OF HUNGARY. Региональная экономика. Юг России, (1), 10-17.
• Kasturi, E., Devi, S. P., Kiran, S. V., & Manivannan, S. (2016). Airline Route Profitability Analysis and Optimization Using BIG DATA Analyticson Aviation Data Sets under Heuristic Techniques. Procedia Computer Science, 87, 86-92.
• Kaufman, L., & Rousseeuw, P. J. (1990). Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis, 68-125.
• Krishna, K., & Murty, M. N. (1999). Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(3), 433-439.
• Kumar, B. R. (2022). Case 9: Beijing Daxing International Airport. In Project Finance (pp. 139-144). Springer, Cham.
• Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70.
• Li, L., Hansman, R. J., Palacios, R., & Welsch, R. (2016). Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Transportation Research Part C: Emerging Technologies, 64, 45-57.
• Li, T. (2020). A SWOT analysis of China's air cargo sector in the context of COVID-19 pandemic. Journal of air transport management, 88, 101875.
• MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability(Vol. 1, No. 14, pp. 281-297).
• McKenney, J. L., Copeland, D. C., & Mason, R. O. (1995). Waves of change: Business evolution through information technology. Harvard Business Press.
• Milligan, G. W. (1980). An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45(3), 325-342.
• Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons.
• Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67.
• Mlepo, A. T. (2022). Attacks on road-freight transporters: a threat to trade participation for landlocked countries in Southern Africa. Journal of Transportation Security, 15(1), 23-40.
• Mobarakeh, N. A., Shahzad, M. K., Baboli, A., & Tonadre, R. (2017). Improved Forecasts for uncertain and unpredictable Spare Parts Demand in Business Aircraft’s with Bootstrap Method. IFACPapersOnLine, 50(1), 15241-15246.
• Moss, L. T., & Atre, S. (2003). Business Intelligence roadmap: the complete project lifecycle for decision support applications. Addison-Wesley Professional.
• O'Hare, D. (1992). The'artful'decision maker: A framework model for aeronautical decision making. The international journal of aviation psychology, 2(3), 175-191.
• O’Neill, M. (Ed.). (2017, June). IATA ANNUAL REVIEW 2017. Retrieved December 8, 2017, from http://www.iata.org/publications/Documents/iata-annual-review-2017.pdf
• Parker, A. (2007). $2.7 Trillion Up In The Air: Aircraft manufacturer’s predictions; with an infrastructure reanalysis.
• Patriarca, R., Di Gravio, G., Cioponea, R., & Licu, A. (2022). Democratizing business intelligence and machine learning for air traffic management safety. Safety science, 146, 105530.
• Pavement Friction Decay Using Data Mining. Transportation Research Procedia, 14, 3751-3760. ISO 690
• Phillips-Wren, G., Daly, M., & Burstein, F. (2021). Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146, 113560.
• Piccioni, C., Stolfa, A., & Musso, A. (2022). Exogenous shocks on the air transport business: The effects of a global emergency. In The Air Transportation Industry (pp. 99-124). Elsevier.
• Ramanathan, R., Mathirajan, M., & Ravindran, A. R. (Eds.). (2017). Big Data Analytics Using Multiple Criteria Decision-Making Models. CRC Press.
• Ren, Z., Verma, A. S., Li, Y., Teuwen, J. J., & Jiang, Z. (2021). Offshore wind turbine operations and maintenance: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 144, 110886.
• Roy, C., Weeks, S., Rouault, M., Nelson, G., Barlow, R., & Van der Lingen, C. (2001). Extreme oceanographic events recorded in the Southern Benguela during the 1999-2000 summer season. South African Journal of Science, 97(11-12), 465-471.
• Salah, H., & Srinivas, S. (2022). Predict, then schedule: Prescriptive analytics approach for machine learning-enabled sequential clinical scheduling. Computers & Industrial Engineering, 108270.
• Schultz, M., Rosenow, J., & Olive, X. (2022). Data-driven airport management enabled by operational milestones derived from ADS-B messages. Journal of Air Transport Management, 99, 102164.
• Selvan, C., & Balasundaram, S. R. (2021). Data Analysis in Context-Based Statistical Modeling in Predictive Analytics. In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 96-114). IGI Global.
• Serrano, F., & Kazda, A. (2020). The future of airports post COVID-19. Journal of Air Transport Management, 89, 101900.
• Smith, B.C., Gunther, D.P., Venkateshwara Roa, B., and Ratliff, R.M. (2001) “E-Commerce and Operations Research in Airline Planning, Marketing and Distribution,” Interfaces, Vol. 31, No. 2, pp. 37–55.
• Stone, D. (2017, May 16). The Hidden Costs of Flying. Retrieved December 13, 2017, from https://www.nationalgeographic.com/environment/urban-expeditions/transportation/urban-expeditionsgraphic- V21/
• Stone, D. (2017, May 16). The Hidden Costs of Flying. Retrieved December 11, 2019, from https://www.nationalgeographic.com/environment/urban-expeditions/transportation/urban-expeditions-graphic-V21/.
• Su, M., Hu, B., Luan, W., & Tian, C. (2022). Effects of COVID-19 on China's civil aviation passenger transport market. Research in Transportation Economics, 96, 101217.
• Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 1-23.
• Tian, H., Presa-Reyes, M., Tao, Y., Wang, T., Pouyanfar, S., Miguel, A., ... & Iyengar, S. S. (2021). Data analytics for air travel data: a survey and new perspectives. ACM Computing Surveys (CSUR), 54(8), 1-35.
• Trochim, W. M., & Donnelly, J. P. (2001). Research methods knowledge base.
• Wu, C., & Truong, T. (2014). Improving the IATA delay data coding system for enhanced data analytics. Journal of Air Transport Management, 40, 78-85.
• Yalcin, A. S., Kilic, H. S., & Delen, D. (2022). The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review. Technological Forecasting and Social Change, 174, 121193.
• Yilmaz, M. K., Kusakci, A. O., Aksoy, M., & Hacioglu, U. (2022). The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach. Applied Soft Computing, 119, 108620.
• Yu, K. D. S., & Aviso, K. B. (2020). Modelling the economic impact and ripple effects of disease outbreaks. Process Integration and Optimization for Sustainability, 4(2), 183-186.
• Zheng, Y., Lai, K. K., & Wang, S. (2018). Forecasting air travel demand: Looking at China. Routledge.