Building Trust:
Rethink the Processes:
Science fiction narratives based on fear of a dystopian future have narrated various fictional contents, where Intelligent robots take over the human race and enslave all of humanity. Though the technology is not quite at the superhuman level of robots as portrayed in the science fiction stories, artificial intelligence tools have already had a significant impact on our day to day lives. May it be a simple google search where search results displayed as per RankBrain, walking into your office where your identity is recognized by image / facial recognition software, interaction with a chatbot where Natural Language Processing (NLP) is at work or an email spam filter. Artificial Intelligence (AI) is already assisting the humans whether known or unknowingly.
Artificial intelligence is a branch of computer science that endeavours to replicate human intelligence into machines so that machines can perform specific tasks that require human intelligence or AI can also be defined as the ability of a computer to mimic human intelligence. Artificial intelligence is not a new invention, this subject has been an area of research and education in academics for the last 70 years, and remained in academic laboratories. Now, due to the advancement in the processing power, statistical techniques and tools have supported this field of science to innovate new applications for real-world problem/scenarios and also commercially viable. This article is intended to give a high-level view of artificial intelligence in a simple language for the understanding of the aviation community.
As we have already seen that, AI technologies endeavours to replicate the human intelligence in machines and based on this characteristic, AI can be classified into two types, Type 1 is based on capability (to the extent by which AI is capable to replicate Human Intelligence) & Type 2 is based on functionality (performing simple basic operations to advanced level conscious decisions). Tuning test is the methodology used to determine the capability of artificial intelligence.
Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) are the types of AI-based on capability. Reactive machines, limited memory machines, the theory of mind, and self-aware AI are classifications based on functionality. The table below shows the types of AI, its attributes and real-world examples or applications.
The moment a search for any content related to AI is made on the web, it will be flooded with various technical jargons such as machine learning, deep learning, data science, computer vision, Robot Process Automation (RPA) and so on, let’s understand in a structured way.
Artificial Intelligence is like a brain (manmade thinking power) for perceiving, learning, language understanding, reasoning, and solving problems. Data is like the energy that supports the activity of the brain through data science, machine learning techniques and algorithms to make decisions. Robotic process automation is like hands, i.e., the tools to execute the decisions of AI. The tools can be soft tools (e.g. software, chatbots) or hard tools (e.g. robots, intelligent machines). Today's AI is probabilistic and based on machine learning, which means it is no longer based on conventional “if - then - else if” rules. Since, digitalization and AI rely on data, both the quantity and the quality of data are of critical importance to support the successful implementation of these technologies. Computer vision to help computers “see” and understand the content of digital images such as photographs and videos.
How does the AI Impact Aviation Industry?
Artificial intelligence and digitalization is impacting the commercial aviation industry in a huge way and has the potential to disrupt the aviation industry and its entire value chain. AI capabilities can build new services, new business capabilities and models or can enhance the existing airline business capabilities such as customer touchpoints, operational, support, management processes. The business establishment believes that investing in AI strategically can position them better in the marketplace. A few use cases where AI got its place are revenue management, targeted advertising, ticketing kiosks, passenger identification, assisting customers, baggage screening, maintenance prediction, predictive analytics, pattern recognition, auto-scheduling, customer feedback etc. This use cases will not only provide better customer experience but also at the same time will reduce the operational and labour cost for airlines.
It can be very well concluded that AI and digitalization are game-changers in aviation, as in every other sector. The use of AI and digitalization technologies allows for more safety, adaptability, optimization, efficiency, capacity and support to all aviation stakeholders.
AI-led growth can impact the economy in three ways, creating new virtual workforce, enhancing the existing skills and tasks (as a complement system) and driving innovations. The AI in the aviation market is likely to reach USD 2,222.5 million by 2025, at a CAGR of 46.65%. However, the limited number of experts in AI is restraining the growth of AI in the aviation market.
The aviation industry is a safety-sensitive / critical industry, as such application of AI in the industry should have due consideration on safety-sensitive and non-safety sensitive aspects. There is considerable work ongoing in industry and academia in the area of “Provable AI” and “Trustable AI” which is required to bring AI into the safety-critical operational domains. These new technologies will contribute to the future of aviation and will redefine the core competencies of aviation professionals.
Aircraft Recycling and End of life have come into focus very recently. After accumulating thousands of flight hours, every aircraft will have to retire from the service due to various reasons as technical or economical. Here in this article we may focus on aircraft retirement and end of life due to economical reasons and provides an overview of aircraft recycling and end of life management.
An aircraft’s life cycle consists of different phases, a generally agreed way of categorizing is into five to seven phases such as material, design, supply chain, manufacture, transportation, aircraft operation, and end-of-life.
The end of life typically is the last phase of Aircraft life cycle management. It starts with the operator’s decision to withdraw the aircraft from the active fleet and followed by the decision of the aircraft owner to disassemble and dismantle the aircraft, as opposed to the other alternatives. The decision to dismantle mostly depends upon whether the value of aircraft in the dismantled condition is higher than it is in the flying condition.
The end-of-life stage of the aircraft’s life cycle was neglected for a long time. Thousands of retired aircraft have been stored in so-called aircraft graveyards, Planes were dismantled in huge areas and landfilling does not seem to be a suitable long-term solution for handling aircraft at their end-of-life stage, as there was an increased worldwide demand for raw material and secondary material. The amount of retired aircraft each year is increasing and landfilling does not seem to be a sustainable end-of-life alternative. Thus there was a need for ecological End of life management for aircraft, which on its objective is to avoid discard or reduce landfill.
The "End of life hierarchy" model is a heuristic approach widely accepted among practitioners. At the top of the hierarchy having waste prevention as the most favored option followed by re-use, recycling, recovery (e.g. energy recovery), and finally, discard (landfill of non-recovery waste) at the least favored option.
The End Of Life (EOL) process is divided into two phases, the first being removal of aircraft parts for reuse while aircraft having its certification and while the second phase is recycling and dismantling of aircraft once it loses its certification. EOL phases can also be approached as product recycling level and material recycling level. In this article, the approach on the basis of certification is preferred. The components/parts removed while having their certification status enters into the aviation supply chain as used parts. Other materials that were removed may get into the non-aviation business domain or declared as waste. This implies there is an involvement of three business domains (Aviation business, Non-aviation business, and Waste business).
The average retirement age of passenger aircraft around the world is 25 years. Recycling an airplane can avoid parking costs, minimize environmental impacts, make money from a part out and metal sales, and create new business opportunities. According to estimates, by 2030 the total number of aircraft that need to be recycled is between 12000 to 17000 airplanes, which will account for up to 45% (approx) of the global fleet. In line with this, the recycling business market is expected to reach USD 5.40 Billion by 2027 from 4.70 billion in 2019. The number of aircraft retirements will range from 700 to 2000 aircraft per year and depends on various factors including economic indicators.
Despite the lack of regulations or standards available to approach End of life, Original Equipment Manufacturers (OEMs) and associations have over period of time has introduced best practices in this business domain. Briefly mentioned, in the late ‘90s and early 2000s, Airbus and Boeing have introduced alternative approaches namely PAMELA (Process for Advanced Management of End-of-Life Aircraft) and AFRA (Aircraft Fleet Recycling Association) respectively. Today, it is generally recognized that 80% to 95% of an aircraft can be recycled. Between 40% and 50% of the weight of most dismantled aircraft finds its way back to the parts distribution pipeline and engines makes almost 80% to 90% of the value. As an approach towards the circular economy, manufacturers and operators are working towards the development of the 100% recyclable airplane.
By way of acknowledging the potential business scope in this segment, the government of India has recently revised the Foreign Exchange Management Act (FMEA) regulation, which now allows the export of planes in complete knocked-down or partial knocked-down conditions. This has paved the seed for a new business in India. Now, India can be a hub for aircraft recycling, a place for storage or resale of parts of dismantled aircraft, but it requires an industrial ecosystem to be in place to achieve it.
With all this, the important thing is that the industry needs to continue research on developing and implementing sustainable management practices to reduce environmental impacts and to create a sustainable industrial ecosystem.
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