Data Analytic approach to Aviation: The Sri Lankan Context

Sri Lanka had been recalled to be the epicentre of International trade in the Silk Route due to its strategic positioning geographically. The geo-political factors surrounding the modern context of Sri Lanka, has had major revelations on developing the 21st century Silk route or the Trans-Asia highway network opting to derive a considerable amount strategic advantage to Asia over the western trade. Major strategic planning had been performed, although for effective implementation, similar to which Sri Lanka had experienced with transforming major airports and ports into transport hubs with global accessibility, essentially revolving around adopting the models of major hub cities such as Singapore and Dubai. Despite the fact of a booming tourism industry in the post-conflict era, Sri Lanka has not yet seems to have exploited the strategic movements having reciprocal effect on the economic development in the country. Concerning aviation, as one of the dominant factor in the said domain, Sri Lanka have handled 2,050,332 passengers in total at BIA in 2016, which also recorded 604,953 passengers, a 3.4% growth by month of March in 2017, with respect to the preceding years statistic by the same month according to CAA. Therefore, basic strategic and policy planning in Sri Lanka had been demanding a suitable framework for both complement and supplementary services in terms of infrastructure, systems, knowledge in their respective areas, being ‘Aviation’ in the context of this article.

According to statistics, despite Sri Lanka handling approximately 52,000 scheduled and 3,400 unscheduled flights annually, these had been predominantly tourists ‘specifically’flying in and out of the country for holiday. In addition, 20,000 flights fly across the airspace with over 8 million passengers on board. Therefore, potential revenue streams and massive amounts of data had existed around Sri Lanka, in a way as if the country is being in a glass sphere, not being able to grab the opportunity even though they are clearly visible.

Exploiting large amounts of data

The said massive number of passengers would potentially generate TBs of data, only through social network platforms daily in an hour in addition to the large amounts of operational data, flight data and maintenance records from which only a mere portion the data set is being utilized by airline operators, governments, airports etc. Hence, with the emergence of data intensive science as the forth scientific paradigm, usage of data intensive applications particularly in industrial context for customer relations, market analysis has been crucial in order to enhance or develop competitiveness in the market which the aviation industry have lacked to cope with, especially considering the regional airlines including Sri Lankan air lines.

Big Data

Big data is a term commonly being used to refer large volumes of diverse and sophisticated data, also mistaken to be referred strictly with Web technologies. In fact, big data has various definitions and explanations. Doug Laney (2011) explained big data using 3V: Volume, Variety (structured, semi-structured and unstructured data originating from different sources of heterogeneous nature) and Velocity (continuous high speed data streams). This is also associated with another V, often relating to value, virtual or veracity (uncertain and unverified nature of data).

The collective features of big data, having high diversity demands high computation intensive state-of-the-art storage and processing mechanisms and hence, traditional off-the-shelf techniques such as relational database management systems are not able to manage the operational complexity of the process. For example, in most proposed architectures, including that of Sujie Li et.al in “Civil Aircraft Big Data Platform” (2017), a hybrid storage module adopting “virtualized storage” in order to facilitate the variety of data in order to overcome what is known as the “bottle neck of aviation applications” that arise as a result of independent and heterogonous nature of data (Data in aviation are generated from different sources such as aircrafts, air traffic controllers, maintenance centres, airports, operators, passengers using different equipment of different manufacturers which are developed on different protocols and formats).

General Aviation Platforms

Although, manufacturers have shown great interest in the modern era with research being undertaken to adopt modern trends in technology such as Big Data and analytics especially toexploit this large, heterogeneous data of high velocity generated by avionics, flight recorders and sensors in order to improve efficiency, eco friendliness, cost reduction and resource optimisation in the forms of “Predictive Maintenance” and “Aircraft Health Management”. This in fact has had a duplex effect on both the manufacturers and operators in a favourable manner. In the study by Sujie Li et.al (2017) proposed civil air craft big data platform with a three tier architecture that integrates different operational and maintenance aspects of aviation using real time and archived data sources, based on similar work been performed by the industrial researchers. General Electrics, proposed the concept of “Industrial Internet of Things” (IIoT) as the infrastructure for their cloud based big data platform “Predix” for analysing over 35,00 of their engines along with a “Fuel forecasting model” enabling abnormal alarming and fuel savings for customers. GE has recently introduced the GEnx series of engines, the fully functional engines utilizing Big Data for real time performance optimisation and predictive fault diagnosis. Of similar nature, Pratt and Whitney deployed the Advance Diagnostics and Engine Maintenance (ADEM) tool, with the Geared Turbofan (GTF) engine, opted to manage real time aircraft performance data, transmitted to ground 24 hours a day, providing the ability to constantly monitor and observe operational patterns and maintenance issues early. In similar instances, big data platforms have been comprehensive in uplifting safety and essentially reduce costs and improve fuel efficiency by 10-15%.

Similar research by Dr.Tulinda Larsen in the paper “Cross platform aviation analytics using Big data methods” (2013),Anuj Singh and AkhilKaushik on “Knowledge based retrieval scheme form big data for aviation industry” (2015) and Jin Chen et.al on “A Big Data Analysis and Application Platform for Civil Aircraft Health Management”(2016) provides the generic framework in terms for effectively handling the sophisticated big data as the platforms having very high potential that could benefit a larger group of stakeholders of aviation industry both directly and indirectly.

Using Social media as an insight for Airlines and Airports

Social media has been a dominant media in sharing and as well retrieving information on the market, the latter being known as “Business Intelligence”: the process of using large cooperate data in order to generate useful insight. In the case of KLM’s “Meet and Sit” service which integrates Facebook and LinkedIn profiles to the flight booking system, enabling passengers network prior to the flight.

In a comprehensive research by Sien Chen et.al (2016) based on the data acquired from SinaWiebo, China’s largest social network platform, on passengers of China Southern Airlines researchers were able to develop a model on customer social media value and perform semantic analysis on the newsfeed. The model could be applied on mapping passenger networks on social media: passengers’ relationship with airliners and other passengers to identify the association with events in the industry with respect their influence; and identify the customers’ perspective on the airliner.

Suggestive Use Cases in Sri Lanka

Data analytics could be used as one of the supplementary tool, which especially was lacking in management decision support. The national carrier Sri Lankan could especially be benefited by “predictive maintenance” and similar technologies, given that the airliner is operating with GE manufactured engines with the introduction of A320neo and A330-300s, in order to reduce ground time for maintenances, avoid unscheduled repairs and most importantly reduce fuel costs.

Adopting sematic analysis techniques to analyse the more lucrative European and North American markets, with a recommendation feed through social media platforms could potential- ly have supplementary favourable effect on the independent campaigns been undertaken by the Tourism promotions board. Sri Lankan airlines should be capitalizing on providing competitive offerings to the customers especially in a period conceiving losses by devising passenger oriented services, based on market intelligence on regular outbounddestinations from Europe and North America.

In addition, Sri Lankan could potentially identify the value added features from passenger sentiments in social media, allowing community detection and influence propagation through the passenger networks. Considering the areas that have been covered by other researches, determining the lucrativeness of air routes and planning airport operations using big data has had less work been performed. Despite commercial services such as OAG Analytics Connection Analyser which provide evaluations on air routes based on current air traffic information, a comprehensive connection analyser could effectively be utilized for airports such as BIA. Therefore, it is proposed, in using Social media statuses generated by passengers, especially “travelling” statuses in Facebook with an appropriate Prediction model for air route passenger flow, such as being suggested by Liu Xia et.al (2016) using Grey prediction model could essentially generate knowledge how to concentrate or at least deviate a significant portion of the passengers to use BIA as a hub. In simple words, by identifying the alternative routes taken by passengers whom could have been catered by using BIA as the transit hub, and evaluating profitability of frequent destinations through those alternative routes, management decisions on route management and device competitive offerings to uplift value or the profitability air routes arriving at BIA could be made. Hence, serving the purpose of transforming BIA as the transport hub in Asia.

In conclusion, it is pertinent to state that introducing Data analytics techniques or at least taking services for civil aviation purposes will open up new dimensions for air operations in Sri Lanka while augmenting the potential for economic development and trade capability of the island nation as Transport hub in Asia. As well providing customer oriented airline services could improve the profitability of the national carrier.

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