The U.S. Automotive Industry: National and State Trends in Manufacturing Employment

[Excerpt] The U.S. motor vehicle manufacturing industry\u27 employs 880,000 workers, or approximately 6.6% of the U.S. manufacturing workforce, including those who work in the large motor vehicle parts manufacturing sector, as well as those who assemble motor vehicles. Since the beginning of the decade, the nation\u27s automotive manufacturing sector has eliminated more than 435,000 automotive manufacturing jobs (or an amount equal to about 3.3% of all manufacturing jobs in 2008). The employment level first dipped below one million in 2007 and fell to 880,000 workers last year. With the restructuring and bankruptcy of Chrysler and General Motors, and the ongoing recession in the auto sector, employment in the nation\u27s automotive manufacturing industry will most likely shrink in 2009 and 2010 as additional assembly, powertrain, and auto parts plants close. This report provides an analysis of automotive manufacturing employment, with a focus on national and state trends. The 111th Congress continues to be heavily engaged in oversight and legislative proposals in response to the unprecedented crisis of the domestic motor vehicle manufacturing industry. The Detroit-based automotive manufacturers (General Motors, Ford, and Chrysler) have suffered a series of setbacks in recent years with their share of the domestic market dropping from 64.5% in 2001 to 47.5% in 2008. As a consequence, the traditional auto states of Michigan, Indiana, and Ohio have been—and will continue to be—heavily impacted by the changes taking place in the automotive sector. Together, there are now 152,000 fewer automotive manufacturing jobs in these three states than there were five years ago. Recent automotive sales and production data indicate the enormous changes taking place in today\u27s motor vehicle manufacturing sector. For instance, automotive sales fell to 13.2 million units in 2008, down by 18% from 2007, and forecasts indicate U.S. consumers are expected to purchase fewer than 10 million cars and light trucks in 2009. There has also been a loss of market share by the Detroit 3 producers which has created gains for foreign-owned domestic manufacturers and imports. Some recent Detroit 3 automotive manufacturing employment losses are partially offset by new investments by foreign-owned manufacturers in the United States as they have open, or will open, new plants in states like Indiana, Georgia, and Tennessee. Many Members of Congress, and especially those members from the traditional auto belt states of Michigan, Indiana, and Ohio, have expressed their concerns about lost jobs in the automotive manufacturing sector. With the sale of GM assets to the U.S. government and Chrysler assets to Fiat, two new companies have emerged that will be substantially smaller than the companies that went into bankruptcy. As a consequence, the total level of motor vehicle manufacturing employment will be reduced, especially in locales where facilities have closed. The most recent automotive manufacturing employment data indicate that 42% of all persons in the industry work in one of the three traditional auto belt states, each of which at present employs more than 100,000 persons in the industry. Michigan alone has accounted for 40% of the net job loss in the industry since 2003. Losses in Ohio and Indiana have been less severe, offset somewhat by foreign investment. Alabama, with fewer total automotive manufacturing employees, has been the big job gainer, adding over 12,000 auto manufacturing jobs since 2003. Texas, now the eighth largest state by automotive employment, gained 5,200 jobs between 2003 and 2008. Auto industry states in the South including Kentucky, South Carolina, and Tennessee have lost jobs in recent years, but far fewer than in the traditional auto belt states

How is Big Data Transforming Operations Models in the Automotive Industry: A Preliminary Investigation

Over the years, traditional car makers have evolved into efficient systems integrators dominating the industry through their size and power. However, with the rise of big data technology the operational landscape is rapidly changing with the emergence of the “connected” car. The automotive incumbents will have to harness the opportunities of big data, if they are to remain competitive and deal with the threats posed by the rise of new connected entrants (i.e. Tesla). These new entrants unlike the incumbents have configured their operational capabilities to fully exploit big data and service delivery rather than production efficiency. They are creating experience, infotainment and customized dimensions of strategic advantage. Therefore the purpose of this paper is to explore how “Big Data” will inform the shape and configuration of future operations models and connected car services in the automotive sector. It uses a secondary case study research design. The cases are used to explore the characteristics of the resources and processes used in three big data operations models based on a connected car framework

Annual Report on the Big Data of New Energy Vehicle in China (2021)

This open access book, based on static indicators and dynamic big data from local electric vehicles, is the first New-Energy Vehicles (NEVs) research report on the Big Data in China. Using the real-time big data collected by China's National Monitoring and Management Platform for NEVs, this book delves into the main annual technological progress of NEVs, the vehicle operating characteristics, it also anticipates the trend of NEVs industry. Various graphs&charts, detailed data this book offers will familiarize readers with the operation characteristics and practical application of China's NEVs industry and popularize the concept of automobile electrification. Besides, this book also makes an objective evaluation of the current situation and technological improvement of China's NEVs industry, presenting sensible suggestions for the development of the industry. This book is written for government staff, researchers, college staff, and technical staff of automobile and spare parts enterprises, which serves as an important reference for the decision-making of government departments and strategic decisions of automotive companies

Digital Transformation of Primarily Physical Industries - Exploring the Impact of Digital Trends on Business Models of Automobile Manufacturers

The phenomenon of digital transformation received some attention in previous literature concerning industries such as media, entertainment and publishing. However, there is a lack of understanding about digital transformation of primarily physical industries, whose products cannot be completely digitized, e.g., automotive industry. We conducted a rigorous content analysis of substantial secondary data from industry magazines aiming to generate insights to this phenomenon in the automotive industry. We examined the impact of major digital trends on dominant business models. Our findings indicate that trends related to social media, mobile, big data and cloud computing are driving automobile manufactures to extend, revise, terminate, and create business models. By doing so, they contribute to the constitution of a digital layer upon the physical mobility infrastructure. Despite its strong foundation in the physical world, the industry is undergoing important structural changes due to the ongoing digitalization of consumer lives and business

Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Abstract. Insights gained from social media platforms are pivotal for businesses to understand their products’ present position. While it is possible to use consulting services focusing on surveys about a product or brand, such methods may yield limited insights. By contrast, on social media, people frequently express their individual and unique feelings about products openly and informally. With this in mind, we aim to provide rigorous methodologies to enable businesses to gain significant insights on their brands and products in terms of representations on social media. This study employs conjoint analysis to lay the analytical groundwork for developing positive and negative sentiment frameworks to evaluate the brands of three prominent emerging automotive companies in Indonesia, anonymized as “HMI,” “YMI,” and “SMI.” We conducted a survey with a sample size of n=67 to analyze the phrasings of importance for our wording dictionary construction. A series of data processing operations were carried out, including the collection, capture, formatting, cleansing, and transformation of data. Our study’s findings indicate a distinct ranking of the most positively and negatively perceived companies among social media users. As a direct management-related implication, our proposed data analysis methods could assist the industry in applying the same rigor to evaluating companies’ products and brands directly from social media users’ perspective. Keywords:  Brand image, social media, data analytics, sentiment analysis, conjoint analysi

Annual Report on the Big Data of New Energy Vehicle in China (2021)

This open access book, based on static indicators and dynamic big data from local electric vehicles, is the first New-Energy Vehicles (NEVs) research report on the Big Data in China. Using the real-time big data collected by China's National Monitoring and Management Platform for NEVs, this book delves into the main annual technological progress of NEVs, the vehicle operating characteristics, it also anticipates the trend of NEVs industry. Various graphs&charts, detailed data this book offers will familiarize readers with the operation characteristics and practical application of China's NEVs industry and popularize the concept of automobile electrification. Besides, this book also makes an objective evaluation of the current situation and technological improvement of China's NEVs industry, presenting sensible suggestions for the development of the industry. This book is written for government staff, researchers, college staff, and technical staff of automobile and spare parts enterprises, which serves as an important reference for the decision-making of government departments and strategic decisions of automotive companies

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest. This is attributed to the wide range of big data analytics in supply chain management, in addition to demand forecasting, and behavioral analysis. In this article, we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications, identify gaps, and provide ideas for future research. Algorithms will then be classified and then applied in supply chain management such as neural networks, k-nearest neighbors, time series forecasting, clustering, regression analysis, support vector regression and support vector machines. An extensive hierarchical model for short-term auto parts demand assessment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series. The concept of extensive relevance assessment was proposed, and subsequently methods to reflect the relevance of automotive demand factors were discussed. Using a wide range of skills, the factors and cofactors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components. Then, it is compared with the existing data and predicted the short-term historical data. The result proved the predictive error is less than 6%, which supports the validity of the prediction method. This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers

The application of big data and AI in the upstream supply chain

The use of Big Data has grown in popularity in organisations to exploit the purpose of their primary data to enhance their competitiveness. In conjunction with the increased use of Big Data, there has also been a growth in the use of Artificial Intelligence (AI) to analyse the vast amounts of data generated and provide a mechanism for locating and constructing useable patterns that organisations can incorporate in their supply chain strategy programme. As these organisations embrace the use of technology and embed this in their supply chain strategy, there are questions as to how this may affect their upstream supply chains especially with regards to how SME’s may be able to cope with the potential changes. There exists the opportunity to conduct further research into this area, mainly focusing on three key industry sectors of aerospace, rail and automotive supply chains.N/

The application of big data and AI in the upstream supply chain

The use of Big Data has grown in popularity in organisations to exploit the purpose of their primary data to enhance their competitiveness. In conjunction with the increased use of Big Data, there has also been a growth in the use of Artificial Intelligence (AI) to analyse the vast amounts of data generated and provide a mechanism for locating and constructing useable patterns that organisations can incorporate in their supply chain strategy programme. As these organisations embrace the use of technology and embed this in their supply chain strategy, there are questions as to how this may affect their upstream supply chains especially with regards to how SME’s may be able to cope with the potential changes. There exists the opportunity to conduct further research into this area, mainly focusing on three key industry sectors of aerospace, rail and automotive supply chains.N/

Enabling Big Data Analytics at Manufacturing Fields of Farplas Automotive

Digitization and data-driven manufacturing process is needed for today's industry. The term Industry 4.0 stands for today industrial digitization which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer's high-quality expectations. However, due to the increase in the number of connected devices and the variety of data, it has become difficult to store and analyze data with conventional systems. The motivation of this paper is to provide an overview of the understanding of the big data pipeline, providing a real-time on-premise data acquisition, data compression, data storage and processing with Apache Kafka and Apache Spark implementation on Apache Ha-doop cluster, and identifying the challenges and issues occurring with implementation the Farplas manufacturing company, which is one of the biggest Tier 1 automotive supplier in Turkey, to study the new trends and streams related to topics via Industry 4.0.Comment: 8 page