Methods and Techniques of Effective Management of Complexity in Aviation

Methods and Techniques of Effective Management of Complexity in Aviation

Maksym Yastrub (National Aviation University, Belgium), Mario Boyero Pérez (EUROCONTROL, Belgium) and Svetlana Kredentsar (National Aviation University, Ukraine)
DOI: 10.4018/978-1-7998-1415-3.ch019

Abstract

This chapter presents the use of enterprise architecture to manage the growing complexity in aviation. Any aviation organization or air traffic management system can be considered as a complex enterprise which involves different stakeholders and uses various systems to execute its business needs. The complexity of such an enterprise makes it quite challenging to introduce any change since it might have an impact on various stakeholders as well as on different systems inside of the enterprise. That is why there is a need for a technique to manage the enterprise and to anticipate, plan, and support the transformation of the enterprise to execute its strategy. This technique can be provided by enterprise architecture, a relatively new discipline that focuses on describing the enterprise current and future states as well as providing a holistic view of it. The authors describe the modern enterprise architecture frameworks and provide an example of an application of one of them (European ATM Architecture framework) to identify and manage changes in aviation.
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Introduction

Due to economic globalization and growing demand for air transportation, the level of air traffic has been increasing over the past years. In addition, it is forecasted that the volume of the air traffic will at least double in the next 15 years, with the annual growth of around 4.4 – 4.6 per cent per year (Industry High Level Group, 2017a). On the other hand, the capacity of the current air traffic management (ATM) systems is limited and is approaching, or even has reached, the ceiling to handle more traffic.

Thus, there is a vital need to modernize and enhance the current air traffic management systems to accommodate and support the growing demand for air traffic in a safe and sustainable way. Therefore, a few research and development initiatives were launched worldwide to change air traffic management systems, for example, Single European Sky (SES) initiative and its technological pillar – Single European Sky ATM Research (SESAR) in Europe, Next Generation Air Transportation System (NextGen) in the United States, Collaborative Actions for Renovation of Air Traffic Systems (CARATS) in Japan, etc. These programs and initiatives, in general, are not just single projects but rather a combination of many different projects that contribute to the evolution of the air traffic management systems. (Yastrub & Kredentsar, 2018).

However, any air traffic management system or aviation organization (e.g. aircraft manufacturer, airline, air navigation service provider, airports, network manager, military, etc.) can be considered as a complex enterprise, which involves different stakeholders and uses various systems to execute its business needs. The complexity of such an enterprise makes it quite challenging to introduce any change since it might have an impact on various stakeholders as well as on different systems inside of the enterprise. Besides that, the research programs launched worldwide (e.g. SESAR, NextGen, etc.) consider different aspects of the ATM systems developing various solutions to improve the overall performance. This brings or will bring a number of changes into the current ATM systems that have an impact on different systems and stakeholders. That is why the research programs have used enterprise architecture to describe their ATM systems and to provide a common reference for the research and development activities to ensure the solutions developed by different projects are consistent and interoperable.

In addition, a number of researches have emerged over the past years to address different challenges in aviation using artificial intelligence, machine learning or deep learning, e.g. aircraft design (Tan Wei Min, Sagarna, Gupta, Ong, & Keong Goh, 2017b), trajectory predictability, an efficiency of airport operations (SESAR Joint Undertaking, 2018), etc.

A number of projects have been started in the past years in Europe with the aim to solve ATM-related issues with the help of artificial intelligence, for example:

  • Automatic speech recognition to convert speech of air traffic controllers into text and add a new input to an air traffic control system and therefore improve the safety and efficiency of airport operations.

  • COPTRA project focused on predicting a trajectory of an aircraft closer to the take-off time or during the flight to predict more precisely when the aircraft will enter a particular part of airspace.

  • BigData4ATM project investigating patterns in the behavior of passengers, door-to-door travel time and choice of travel mode using big data and machine learning (SESAR Joint Undertaking, 2018).

The objectives of this chapter are the following:

  • Describe what is enterprise architecture and enterprise architecture framework.

  • Expose the relationship between artificial intelligence and enterprise architecture.

  • Provide an overview of the most relevant enterprise architecture frameworks and their application.

  • Look at a practical application of enterprise architecture in aviation – European Air Traffic Management Architecture.

  • Give an example of the use of enterprise architecture in aviation based on European Air Traffic Management Architecture.

  • Identify the potential elements of the enterprise architecture model where artificial intelligence could be implemented.

Key Terms in this Chapter

Enterprise: A system of one or more organizations and the solutions they use to pursue a shared set of common goals.

Enterprise Architecture Framework: A set of models, methods and principles that can be applied to define and implement enterprise architecture.

Architecture: Fundamental concepts or properties of a system in its environment embodied in its elements, relationships, and in the principles of its design and evolution.

Artificial Intelligence: Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

Enterprise Architecture: A well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a holistic approach at all times, for the successful development and execution of strategy.

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