Automotive Industry Data
Automotive industry worldwide – statistics & facts | Statista
Published by
I. Wagner,
Aug 12, 2021
The automotive market was on an upward trajectory throughout 2018 and had just entered a phase of stagnation in 2019 before the coronavirus crisis thrusted the world into turmoil. Between March and May 2020, global automotive sales contracted by around 15 percent globally. China was the first market to recover from the crisis, with automobile sales at pre-pandemic levels throughout the months following the outbreak. It is projected that trends such as electric vehicles, autonomous driving, and mobility services will continue to fuel the market, leading to an overall recovery in the coming quarters.
Dossier on the topic
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TOP SELLEROverview
Will the global automotive market continue to grow
It is projected that the global automotive industry will grow to just under nine trillion U. S. dollars by 2030. It is anticipated that new vehicle sales will account for about 38 percent of this value. Globally, Volkswagen Group and Toyota Motor are the leading carmakers in terms of revenue. The Japanese auto giant generated almost 250 billion U. dollars in revenue in 2020, while Volkswagen raked in a little more than 245 billion U. venue – automotive industry worldwide 2017-2030Revenue of leading carmakers worldwide 2020The leading global automotive suppliers based on revenue 2020Value of automotive products imports in key countries worldwide 2020Value of automotive products exports in key countries worldwide 2020Leading carmakers worldwide – global brand market share 2020Production
Global auto production slumps amid the pandemic
As our Statista Dossier on the impact of COVID-19 on the automotive industry intends to outline, the fate of the industry seems to rely on how fast production will be ramped up following the coronavirus outbreak. Amid the outbreak of the pandemic in China, many factories were closed, and no new vehicles were rolling off the assembly lines in Wuhan. Work stoppages resulting from outbreaks continue to affect the industry on a global scale, although factories have reopened in most markets. More recently, the coronavirus pandemic has also sparked a shortage of chips in many industries, including the auto sector. It is projected that on average, electronic systems will account for half of the total price of a new car by 2030.
Estimated worldwide automobile production from 2000 to 2020 (in million vehicles)
Worldwide motor vehicle production 2000-2020Worldwide motor vehicle production growth – 2015-2020Worldwide motor vehicle production by type 2017-2020Changes in worldwide vehicle production by region 2016-2020Passenger cars – major producing countries 2020Worldwide commercial vehicle production by region 2019-2020Sales
Global auto sales are poised for a post-pandemic recovery
Global sales of automobiles are forecast to fall to just under 70 million units in 2021, down from a peak of almost 80 million units in 2017. The auto industry’s most important industry segments include commercial vehicles and passenger cars. China is counted among the largest automobile markets worldwide, both in terms of sales and production. Car sales in China dipped for the first time in 2018 – the market cratered in February 2020 but bounced back shortly after. The global market is poised for a recovery in vehicle sales worldwide 2005-2020Motor vehicle sales growth worldwide 2015-2020Motor vehicle sales worldwide by type 2016-2020Worldwide car sales 2010-2021Largest automobile markets – new car registrations December 2020 YTDCommercial vehicles – worldwide sales 2005-2020Commercial vehicles – sales in selected countries 2019Leading manufacturers
Strong competition among manufacturers
Mass production of automobiles started in the early 1900s when Ford introduced assembly line car production to mass-manufacture the Model T. Today, the Ford Motor Company still ranks among the leading manufacturers of passenger cars, its most popular passenger light truck model being the Ford F-Series, which was also one of 2020’s best-selling light vehicles worldwide. The Toyota Motor Corporation is currently the best-selling manufacturer of motor vehicles, closely followed by the Volkswagen Group. Meanwhile, electric vehicle maker Tesla emerged as the most valuable automotive brand worldwide in 2021.
Leading motor vehicle manufacturers worldwide in 2019 and 2020, based on sales worldwide (in million units)
Toyota net revenue 2012-2021Toyota motor vehicle sales by region 2017-2021Volkswagen AG – sales revenue 2006-2020Volkswagen – worldwide vehicle deliveries 2012-2020Renault Group – revenue 2010-2020Regional vehicle sales of Renault Group 2019 & 2020Leading suppliers
Leading suppliers
Bosch – revenue 2008-2020Continental AG’s revenue 1999-2020ZF Friedrichshafen AG sales revenue 2009-2020Denso – global revenue 2008-2021Magna International Inc. – global sales FY 2011-2020Outlook
Outlook
Light vehicle production worldwide forecast 2019-2025Worldwide light vehicle sales growth – outlook 2019-2023Electrified and battery electric vehicles – global sales 2020-2025Autonomous vehicle sales – globally by region 2040New car sales worldwide by autonomous vehicle level 2024Studies & ReportsInteresting Statista reportsContactGet in touch with us. We are happy to help.
How Does Big Data Impact the Automotive Industry?
How Does Big Data Impact the Automotive Industry?
What Is Big Data?
Today, whatever field we are in, we come across the term “big data” quite often. This leads us to the question: What exactly is big data? Let us try to thoroughly understand big data and its use in the automotive industry. Here is a fun fact: In 2013, Science Daily reported that 90% of total data in the world had been generated in the last two years! Here we are, seven years later, and the humongous amount of data generated since then is beyond our scope of imagination.
A major part of LHP’s business operations revolves around the automotive world, so let us understand big data by taking an example from this industry. The connected car has been in the limelight for a few years now. It has been the basis upon which many advancements have been made to cars, having a direct impact on how the automotive industry and the car itself functions. A connected car has bidirectional data sharing with devices inside the car as well as devices in the outside world. For a person inside the car, it is important to have access to data, such as news or just audio on the go, maps, traffic warnings, weather forecast and various other real-time information. Similarly, for the outside world, insurance companies for example, it is important to have access to data about the vehicle, such as mileage, and driver skills, such as acceleration, cornering and braking, to determine the insurance rates. The car might collect and share some other data which is also important for analysis, such as fuel consumption. Data about the vehicle itself, such as system and component data, are important for maintenance and warranty purposes. When collected and stored, this information can be crucial to safety. Accidents can be avoided, and the driver/owner of the vehicle can plan their trip accordingly. Most of all, analysis of the data generated by a car increases reliability and durability while providing comfort to the owner.
To summarize, huge amounts of constantly changing data that is difficult to handle but is of enormous value on the analysis of which we can provide new and improved user experience, is “big data. ” In the automotive industry, this data can improve driver safety and experience, and in short provide better and safer vehicle services.
Gartner defines big data as, “high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. ”
How is Big Data Collected?
Now, if we talk about the data gathering needed for this analysis, we dive deeper into the realm of big data. In general, data is collected in a number of different ways. Again, keeping the automotive industry in focus, various data-gathering tools, such as GPS, sensors and cameras, are installed in vehicles. Capturing real-time data insights from these tools, data is then extracted and combined together to provide services. These services allow telematic-service-providing companies, car insurers, and car leasing agencies to predict movement of cars. This critical information feeds into different business models and has helped companies to understand the demand-supply for their products and services. This gives companies’ customers more customized and personalized experiences. A whole new ecosystem, enhancing user experience, is created around the usage of this data. Doesn’t this reality give us a déjà vu of “The Jetsons”?
One of the most important forms of big data analytics is predictive analysis. Predictive analysis helps to predetermine the characteristics or behavior of a machine (also human being in some situations) in certain environments and situations. I would like to share here, an example from my Neural Networks class in engineering school. While studying the concept of Feedback Neural Networks, I came across an example of a self-driving car that would train based on the feedback provided by the passenger on each trip. The rating system was to determine how smooth the trip was based on driving skills, such as speeding, braking and cornering. The self-driving car would collect this information and analyze this data to improve. With every trip, the car would get better at driving skills. This is the power of data analysis and big data. We can extract value from the massive amounts of data around us and convert it into useful information.
What Are the Different Types of Big Data?
You must be wondering if big data can be categorized into different types for ease of handling. Yes, indeed! In general, there are three different types of big data – Structured, Unstructured and Semi-Structured. Let us understand each one of them.
Structured data is highly organized data that has a formal structure to it and can be stored, processed and retrieved seamlessly by simple search engine algorithms. Such data is usually stored and managed in a Relational Database Management System (RDBMS), where all the fields store length-delineated data, making it a simple matter to search. A very common example of such data is an employee structure in a company, where every employee’s information – from basic contact details to salary and bank account details to hierarchical details within the organization – is stored in a very well-ordered fashion.
Unstructured data is the type of data that doesn’t have a predefined schema or data model. This data can be in the form of text or images and can be human or machine generated. This type of data is usually stored in a non-relational database such as Not only Structured Query Language (NoSQL). Earlier, we said that managing and using structured data is pretty straight forward, but when it comes to unstructured data, the same is not true. Even though today there is more unstructured data (which makes up over 80% of enterprise data) than there is structured, there are many mature analytics tools available in the market for structured data, but analytics tools for mining unstructured data are nascent and developing. Commonly used examples of unstructured data are emails and other communications. However, since we are referring to the automotive industry, here’s another fun fact: Intel estimated that a car in its eight hours of operation/movement generates terabytes of data. This huge amount of data is collected from sensors, telemetry, accelerometers and many other devices, and has to be analyzed to perform calculations and adjustments required to safely navigate the car. It is wonderful how we are starting to make cars more and more intelligent, but that is a story for another day.
Then there is semi-structured data, which is a mix of structured and unstructured data. This type of data only makes up about 5%-10% of all the data. Modern databases can store both structured and unstructured data together, making them semi-structured. A few examples of semi-structured data are the Extended Markup Language (XML) and open standard JavaScript Object Notation (JSON).
In my day-to-day work life as a software developer working with LHP’s customers, I come across all three different types of big data and vote for structured data as the easiest form of data to work with. It has become a common practice to refactor and rework legacy systems to use a new structured form of data for more efficiency, better user experience, and most of all saving some big bucks. A structured form of data means clean, meaningful, reusable data that is easier to manage. It is more reasonable to adapt to storing information in a structured form than it is to make continuous attempts and spend a lot of money in trying to process, store and use unstructured and/or semi-structured forms of data.
How Is Big Data Processed?
Now that you are familiar with big data and its importance in the industry, we would like to give you some insight about how this data is processed to make it valuable. We will also discuss the tools available in the market, and which of those are better than others.
It is very hard to find one tool that fits all processing scenarios. Many enterprises in the industry are striving continuously to come up with an effective big data storage and processing tool which is also developer friendly. However, one of the key things that is usually done to make processing more manageable is breaking the data into smaller chunks and processing them in parallel. One of the most widely used techniques in computer science – divide and conquer – works well for big data processing too.
What Are Some Tools That Can Be Used for Processing and Analyzing Big Data?
Apache Hadoop’s Highly Distributed File System (HDFS™) runs on commodity hardware. It stores and manages data by partitioning it into small blocks and processing each of these blocks separately and in parallel. It uses a concept similar to the master and clients. It is used extensively in Business Intelligence (BI), data warehousing, and Information Technology (IT) analytics. Apache Hadoop is a very popular, efficient big data processing framework with many advantages. It is easy to use and can be scaled to work with different volumes of data just as efficiently. It is open source, making it cost effective. Hadoop is fault tolerant, meaning that in case of failure, data can be recovered, no problem! It is not tightly coupled to a single programming language and supports many languages like C, C++, Perl, Python, Ruby and Groovy. Hadoop can handle a variety of data from a variety of data sources. However, it has a few concerning failure points as well. It can handle small amounts of large files but fails while handling large amounts of small files. It handles data in batches instead of streams, due to which it cannot produce real-time output with low latency. Storage and network securities are also concerning. To sum it up, Hadoop is a framework that can be used in integration with other data analytics tools. It has more advantages than disadvantages. If you are looking for a cost-effective, efficient solution to store and analyze big data, Hadoop can suit your needs.
Tableau is a good big data analytics tool, which provides visualization of data for better analysis and understanding. Human beings by nature are extremely visual. It is easier to understand something that you can see than something that requires exercising your imagination. If you want a data analytics tool that is not necessarily made for developers or programmers, Tableau is the way to go. It also provides mobile support, and apps for iOS and Android can be downloaded. However, the downside of Tableau is that since it is not made for programmers, there is no version control. Also, it is expensive, and pricing is inflexible.
Splunk is another great data analytics and visualization tool that can be used to collect and visualize machine data. It is an easy-to-deploy, easy-to-use software that is massively scalable and has real-time alerts to notify changes in data trends or activity. Splunk provides a dashboard for good visibility and also provides flexible filtering options. It can typically work with any type of data. It also has a very elegant report generation feature that can be highly beneficial based on your needs. Nevertheless, there are a few drawbacks of Splunk. It is expensive like many other tools. For complex operations and usage there is a steep learning curve, which makes it somewhat hard and time consuming to master. Reviews suggest that although data processing is fast, the interface itself is slow.
Zoho Analytics is the last data analytics tool we will talk about. This tool also focuses on data visualization, quick data insights, report generation and other functionality like the previously described tools. Zoho provides out-of-the-box integrations with several external software and applications that can be beneficial to many enterprises. This tool is less expensive compared to Tableau and Splunk. It also has a flatter learning curve when compared to the two. One of the downsides of Zoho includes slower execution of large and complex queries. Developers and users are of the view that there are areas in this software that can be worked on to make them better, such as dashboard design, handling documentation and media, communication, and collaboration.
These are just a few of the many tools available in the market today. Most of these tools perform operations and data processing on the underlying framework or database and export it in a nice package onto a good interface.
How Can LHP Help Your Business?
The importance of harvesting value from big data in the automotive world today cannot be overstated. As a leader in automotive engineering, LHP recognized this and has had a dedicated Data Analytics and IoT team since 2016. Paired with LHP’s deep expertise in the engineering realm, the Data Analytics team has seen exponential growth each year, with dozens of team members working not only in the global automotive space, but also in manufacturing, healthcare, finance, agriculture and more. With end-to-end support, the Data Analytics team takes a holistic approach to your toughest big data problems through careful application of people, processes and technology.
Here at LHP, we leverage both engineering and data analytics expertise seamlessly to provide several solutions that can be beneficial for companies to create a connected workspace and take into consideration results from complex data analysis while making business decisions. This is what makes us unique. We bring together Internet of Things (IoT) and big data to help solve business issues and provide options for better monitoring and service of intelligent applications. Feel free to contact us for more information on our products and services.
Interested in Learning More About Big Data for Your Organization? Contact Our Team Today!
Written by
Bhavana Shah is a software application engineer at LHP. She moved to the United States from India in 2015 to pursue her Master of Science in Computer Science degree from The University of Texas at Arlington. She worked in Atlanta for a brief period before joining LHP. Bhavana joined LHP in 2018 and is working as an on-site manager at one of our customer sites. Bhavana is a go-getter who is very passionate about her work.
The global automotive motors market size is projected to grow from USD …
NEW YORK, Aug. 17, 2020 /PRNewswire/ — The growth of the automotive motors market can be attributed to significant R&D investments in electric and hybrid vehicles and more automatic comfort features in upcoming the full report: global automotive motors market size is projected to grow from USD 20, 321 million in 2020 to USD 25, 719 million by 2025, at a CAGR of 4. 8%. The advent of fuel efficient technology will have a significant impact on hybrid electric and battery electric vehicles, which, in turn, will drive the automotive motors focus of automobile manufacturers has shifted from fuel efficiency, performance, driver safety, and stability of the vehicle to additional features such as vehicle connectivity and electrification of change in focus has created a massive opportunity for traction, induction, and brushless creasing consumer focus on driving comfort will encourage OEMs to include more automotive motors for proper automotive motors supporting all features in the mid and lower segment vehicles would boost the demand for during the forecast initiatives could pose a major revenue opportunity for automotive motors increasing electric power steering application would drive the automotive motors market in the forecast. Electric power steering motor accounts for the largest growing adoption of these motors can be attributed to the demand for safety and rrently, safety and convenience features are not limited to premium vehicles but are also provided in mid-level and entry-level, the adoption of comfort systems such as power steering is fostering the requirement for automotive electric power steering from this, the penetration of electric power steering motors is not only limited to passenger cars but also increasing in commercial vehicles as Pacific is expected to be the largest and the fastest-growing market during the forecast periodThe Asia Pacific region is projected to account for the largest share of the global automotive motors market during the forecast plementing new technologies, setting up more manufacturing plants, and creating a value-added supply chain between the manufacturers and material providers have created a vast opportunity for growth of the automotive motors market in the mpanies such as Aisin Seiki, Denso, Johnson Electric, Mitsubishi, Hitachi, and Mikuni have a dominant presence in the automotive industry in Asia Pacific is expected to see significant growth in the next few automotive motors market is expected to grow proportionally to the regional automotive reover, the rising adoption of EVs in Asia Pacific is expected to drive the production of automotive motors. In Asia Pacific countries, attractive government incentives to popularize electromobility and increased investments by automakers are expected to drive the America is expected to be the second-fastest market in the forecast periodThe North American automotive industry is one of the fastest-growing sectors adoption of LCVs and the electrification of automotive applications in the US market mainly influence the North American automotive political conditions in the US directly affect the North American auto leading market players such as Infineon, Cummins, Gates Corporation, and TI Automotive in the automotive motors market are based in the the US is the largest market for premium cars in North America, it is also the largest market for advanced automotive OEMs in North America have focused on the development of fuel-efficient vehicles to meet federal fuel efficiency mandates. Furthermore, several OEMs are planning to set up new production facilities in Mexico and Canada, which, in turn, would drive the demand for automotive interviews were conducted with CEOs, marketing directors, other innovation and technology directors, and executives from various key organizations operating in this market. • By Level: Tier I – 31%, Tier II – 48%, and OEMs – 21%• By Designation: Directors – 35%, C Level Executives- 40%, and Others – 25%• By Region: North America – 30%, Europe – 50%, Asia Pacific – 15%, and RoW-5%The automotive motors market comprises major companies such as Robert Bosch (Germany), Nidec Corporation (Japan), Continental (Germany), Johnson Electric (Hong Kong), and Denso Corporation (Japan). Research Coverage:The market study covers the automotive motors market size and future growth potential across different segments such as by motor type, function, vehicle type, electric vehicle type, EV motor type, application, and region. The study also includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market Benefits of Buying the Report:• The report will help market leaders/new entrants in this market with information on the closest approximations of revenue numbers for the overall automotive motors market and its subsegments. • This report will help stakeholders understand the competitive landscape and gain more insights to better position their businesses and plan suitable go-to-market strategies. • The report also helps stakeholders understand the pulse of the market and provides them information on key market drivers, restraints, challenges, and the full report: Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need – instantly, in one place. __________________________ Contact Clare: [email protected] US: (339)-368-6001 Intl: +1 339-368-6001
Frequently Asked Questions about automotive industry data
What is big data in automotive industry?
To summarize, huge amounts of constantly changing data that is difficult to handle but is of enormous value on the analysis of which we can provide new and improved user experience, is “big data.” In the automotive industry, this data can improve driver safety and experience, and in short provide better and safer …Sep 9, 2020
How big is the automotive industry 2020?
The global automotive motors market size is projected to grow from USD 20,321 million in 2020 to USD 25,719 million by 2025, at a CAGR of 4.8%Aug 17, 2020
What is automotive data?
Automotive Data is similar to Telecom Data, AI & ML Training Data, Research Data, Cyber Risk Data, and IoT Data. These data categories are commonly used for Tire Condition Analysis.
