Abstract
Even though the automotive industry was among the key players of the industrial revolution in the last century, striking transformations experienced in other sectors did not have significant repercussions on this industry until a few years ago. However, general advancements in technology and Industry 4.0 have presented new opportunities for the reconfiguration of the business environment. Developments in cryptocurrencies such as bitcoin, in particular, have attracted the attention to what is known as blockchain technology. Several successful examples of blockchain applications in different industries have tempted the automotive industry to be rapidly involved with efforts in this direction. As a consequence, the application of the blockchain technology to highly diverse areas in the automotive industry was set in motion. The purpose of this chapter is to explore the application of blockchain technology in the automotive industry, to analyse its advantages and disadvantages, and to demonstrate its successful in general.
TopIntroduction
The Blockchain (BC) technology has revolutionized several areas of society including manufacturing, commerce, banking, healthcare, automotive, and supply chain, to name but a few. The innovative approach of the BC which is based on trust and value is one of the primary attractive points of this revolution which offers new ways of doing business. Even though the automotive industry did not get acquainted with full digitalization until recently, it had and still has great potential for the future of personal mobility (Colonna, 2018). Today, along with the advancement of technology, the automotive industry has transformed from internal combustion engine vehicles to hybrid and electric automobiles. By virtue of the use of Industry 4.0 technologies such as Internet of Things (IoT), Automated Guided Vehicles (AGV), Cloud Computing, Big Data Analytics (BDA), Robotics, Blockchain and Mobile Services and Technologies in the industry, the automotive industry began to become one of the most technologically advanced industries alongside the transformation of conventional vehicles to autonomous (self-driving) automobiles in line with its adaptation to the changing innovations. The fourth industrial revolution represents a new level of organization and control of the entire value creation chain across the life cycle of products. This cycle typically addresses the increasingly individualized customer demands, that range from the initial purchase order, to the development & production, to the delivery of a product to the end-user and, in the end, to the recycling process including the relevant services (Çetin Gerger, 2019; Kern & Wolff, 2019; Fraga-Lamas & Fernández-Caramés, 2019; Reinsel, Gantz, & Rydning, 2018).
As the automotive sector started to place the focus on autonomous vehicles, a very large amount of data is getting to be produced. To illustrate this point, the amount of data produced by an autonomous automobile is typically approximately 1 gigabyte every second. When this figure is benchmarked against data produced by all the autonomous vehicles used in a single metropolis alongside the growing number of autonomous automobiles with each passing day and against similar sources of data, a figure hugely enormous in size comes into view. Along with the generation of such an enormous amount of data, the need not only for safely storing data but also for statistically analysing and interpreting it comes to the forefront. As per the estimates of International Data Corp (IDCC), the amount of data generated across the globe, currently, is expected to reach around 175 zettabytes (ZB) by 2025 whilst it was 33 ZB in 2018. In view of the fact that 1 ZB is 1 trillion gigabytes, that is, 1021 (1,000,000,000,000,000,000,000) bytes, its manipulation requires new methods for analytics (Gerger, 2020; Gerger, 2019a; Gerger, 2019b; Pepper, 2012), and that is where Big Data techniques comes in.
As a result of the digitalization of the automotive sector together with Industry 4.0, cyber-attacks, unwanted losses, accidents, high costs, and operational inefficiencies are giving rise to inflated prices for parts and services and related security challenges. Such problems experienced in the sector are currently transferred to different and heterogeneous stakeholders positioned in the life cycle of vehicles production; the stakeholders being individual and corporate vehicle owners, service users, customers of logistics businesses or end-users. Industry 4.0 contributes to advancement in multiple domains which allow the positioning of sensors in large numbers. Some of them are as listed below (Fraga-Lamas & Fernández-Caramés, 2019):
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Implementation of big data techniques
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Improvements in connectivity and computational power
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Appearance of machine learning approaches
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Development of new computing paradigms (e.g. cloud, fog, mist and edge computing)
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Human-machine interfaces (HMI)
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Development of IoT and the use of robotics
Key Terms in this Chapter
Blockchain (BC): This is an open, distributed ledger that can record transactions between two or more parties. more efficiently, securely, and permanently.
Automated Guided Vehicles (AGV): Automated Guided Vehicles are transport vehicles which automatically guide themselves without direct propulsion, as these are equipped with self-propulsion. These vehicles are autonomous vehicles which are employed for towing and/or carrying raw materials to. For example, warehouses.
Big Data: Big data refers to large amounts of heterogeneous data that cannot be easily processed using conventional database techniques. Big data characteristics include volume, variety, and velocity of data. This data is generated by many sources including smart sensors, smartwatches, wristbands, medical equipment/devices, tablets, mobile phones, web usage records, social media, and simulation.
Internet of Thing (IoT): It refers to a network of inter-connected intelligent objects (computers, sensors, devices that have embedded processors, smart phones, etc.). These objects communicate with each other and transmit data between the connected smart devices.
Robotics: This refers to the use of machine learning, artificial intelligence and use of automation that employ these technologies; mainly to perform complex operations without much need of human intervention.
Big Data Analytics: This refers to analysis of data clusters to gain business intelligence and determine logic and trends that exist in the data.
Industry 4.0: Industry 4.0 emerged in Germany in 2011 to adopt digital technologies into development, production, and manufacturing. It involves automation, use of machine leaning and all forms of emerging technologies to achieve speed, efficiency and effectiveness of operations.
Cloud Computing: This refers to a distributed environment that houses networks, servers, storage spaces, software applications and virtualized hardware, which can be remotely accessed, utilized and released with minimum management effort, Environment acts as customizable common pool of data processing resources and used for providing on-demand network access.
Statistics: Statistics is the science involved in the study of development of methods for collecting, analysing, interpreting and presenting data. Statistics is an interdisciplinary field, used in almost all scientific fields. Research questions in various scientific fields contribute to the development of new statistical methods and theories.