Automobile Manufacturing Industry Research Articles

Characters of a Good Project Manager in the Automotive Industry

Project is a very basic concept, it runs through all walks of life, is the so-called "everything can be project". As the leader of the project and the main executor of the project management activities, the project manager has full control of the project-related matters, which directly determines the success or failure of the project. At the same time, different industry attributes have different characteristics for project managers. This paper attempts to discuss the characteristics of excellent project managers according to the requirements of project managers in the automobile manufacturing industry.

Two hybrid flow shop scheduling lines with assembly stage and compatibility constraints.

Two hybrid flow shop scheduling lines must be coordinated to assemble batches of terminated products at their last stage. Each product is thus composed of two jobs, each produced in one of the lines. The set of jobs is to be processed in a series of stages to minimize the makespan of the scheduling, but jobs forming a product must arrive at the assembly line simultaneously. We propose a mixed integer linear programming model. Then, based on the model, we propose a pull-matheuristic algorithm. Finally, we present two metaheuristics, a greedy randomized adaptive search procedure and a biased random key genetic algorithm, and compare all the methodologies with real-based instances of a production scheduling problem in the automobile manufacturing industry. The greedy algorithm yields high-quality solutions, while the genetic one offers the best computational times.

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Empirical Modeling and Analysis of Process Parameters in Laser Beam Cutting Process

The research has been originated based on the persuasive applications of Lasers in the field of various engineering industries, i.e., Aerospace, Automobile, Electronic and Heavy manufacturing industries to machine a variety of metals and alloys. Out of all the machining processes available, Laser Beam Machining is considered to be the best one, because of the following advantages, i.e., quick material removal, non-contact, non-wearing tool, involves highly localized heat input to the work piece, reduces distortion, and offers no tool wear, diminishes tendency of cracking. The present paper focused mainly on developing the empirical relationships between the input process parameters and the output responses in Laser beam cutting process. Laser power, Cutting Speed, Gas pressure and Focal distance are the input process parameters. The output responses considered are related to the quality of cut. Surface roughness is the first output responses considered and the second one is Burr height. Total number of experiments carried out is 27 based on the Taguchi Design of Experiments. Haste alloy C276 is the work material. Response Surface Methodology is applied for the Experimental data of 31 samples to derive the empirical relationships. Later ANOVA analysis is also carried out to check the adequacy of the derived equations. Based on the ANOVA analysis, the models are classified as significant or not significant. Further the significant models can be used for optimizing the Laser beam cutting process. Once the process is optimized, it can be automated, so that the process can be run very efficiently and economically.

Adoption and Design of Supply Chain Agility Model by Indian Automobile Manufacturing Industry for Enhancing Environment Sustainability <br>

Adoption and Design of Supply Chain Agility Model by Indian Automobile Manufacturing Industry for Enhancing Environment Sustainability <br>

Current Situation and Consequences of Environmental Information Disclosure in China's Automobile Manufacturing Industry --Taking SAIC Motor Corporation Limited as an Example

A sincere commitment to establishing a community of human destiny is made through achieving carbon peak and carbon neutrality. Following the pace of affluent nations, China has incorporated carbon peak and carbon neutrality into overall economic and social development. Environmental information disclosure is the most basic and major part of environmental accounting, following the sustainable development strategy because of daily accounting supervision and accounting. In this research, a case study of SAIC Group's environmental information disclosure in China from 2019 to 2022 is chosen, and analyzes SAIC Group's operating capacity, profitability, solvency and development capacity by combining information asymmetry theory and stakeholder theory. This paper investigates the roots of environmental information disclosure in the automobile manufacturing industry, chooses a representative SAIC Group to summarize its environmental information disclosure, assesses the current level of corporate environmental information disclosure, and provides relevant departments with pointers and relates for the development of an environmental information disclosure system for enterprises in this industry. Finally, optimization recommendations are offered for the growth of listed businesses' environmental information disclosure in China's vehicle manufacturing sector.

Comparing Fault Tree Analysis methods combined with Generalized Grey Relation Analysis: A new approach and case study in the automotive industry

The failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity.

The Supply Chain Advantages and Challenges of BYD in the New Energy Vehicle Industry: Implications for the Future

The new energy vehicle supply chain is evolving rapidly to meet growing market demand, and innovations in battery technology, motor manufacturing, and charging infrastructure, among others, are driving progress in this sector. This paper explores the supply chain strengths of BYD, a major new energy vehicle manufacturer in Chinas automobile manufacturing industry, and related issues. Through an in-depth analysis of BYDs history, the companys current status, and its innovative initiatives in the supply chain, the paper identifies its supply chain strengths in intelligence, sustainability, and innovation. However, some of the challenges BYDs supply chain faces, such as delivery issues, data security risks and low collaboration with suppliers, are also raised. Finally, the paper offers some recommendations, including actively collaborating with high-quality suppliers, strengthening data processing and management capabilities, and adhering to supply chain innovation. These recommendations can guide BYDs development and provide useful references for peer companies supply chain optimization.

The Knowledge Spillover Effect of Multi-Scale Urban Innovation Networks on Industrial Development: Evidence from the Automobile Manufacturing Industry in China

Multi-scale urban innovation networks are important channels for intra- and inter-city knowledge spillovers and play an important role in urban industrial innovation and growth. However, there is a lack of direct evidence on the impact of multi-scale urban innovation networks on industrial development. Drawing upon the “buzz-and-pipeline” model, this paper analyzes the impact of multi-scale urban innovation networks on industrial development by taking the automobile manufacturing industry in China’s five urban agglomerations as an example. Firstly, based on the Form of Correlation between International Patent Classification and Industrial Classification for National Economic Activities (2018) and co-patents, we construct urban innovation networks on three different geographical scales, including intra-city innovation networks, inter-city innovation networks within urban agglomerations, and innovation networks between cities within and beyond urban agglomerations. Then, we employ the ordinary least squares model with fixed effects at the urban agglomeration level to explore the impact of urban multi-scale knowledge linkages on the development of the automobile manufacturing industry and the results showed that urban innovation networks at three different geographical scales have different impacts on industrial development. Specifically, intra-city innovation networks have a facilitating effect on industrial development, while both inter-city innovation networks within urban agglomerations and innovation networks between cities within and beyond urban agglomerations have an inverted U-shaped impact on industrial development. The interactions between urban innovation networks on three different geographical scales have a negative effect on industrial development. Simultaneously, the agglomeration level of urban industry plays a positive moderating role in the impacts of multi-scale urban innovation networks on industrial development.

3D Printer Selection for the Sustainable Manufacturing Industry Using an Integrated Decision-Making Model Based on Dombi Operators in the Fermatean Fuzzy Environment

Three-dimensional printers (3DPs), as critical parts of additive manufacturing (AM), are state-of-the-art technologies that can help practitioners with digital transformation in production processes. Three-dimensional printer performance mostly depends on good integration with artificial intelligence (AI) to outperform humans in overcoming complex tasks using 3DPs equipped with AI technology, particularly in producing an object with no smooth surface and a standard geometric shape. Hence, 3DPs also provide an opportunity to improve engineering applications in manufacturing processes. As a result, AM can create more sustainable production systems, protect the environment, and reduce external costs arising from industries’ production activities. Nonetheless, practitioners do not have sufficient willingness since this kind of transformation in production processes is a crucial and irrevocable decision requiring vast knowledge and experience. Thus, presenting a methodological frame and a roadmap may help decision-makers take more responsibility for accelerating the digital transformation of production processes. The current study aims to fill the literature’s critical theoretical and managerial gaps. Therefore, it suggests a powerful and efficient decision model for solving 3DP selection problems for industries. The suggested hybrid FF model combines the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF–SWARA) and the Fermatean Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (FF–RAFSI) approaches. The novel FF framework is employed to solve a critical problem encountered in the automobile manufacturing industry with the help of two related case studies. In addition, the criteria are identified and categorized regarding their influence degrees using a group decision approach based on an extended form of the Delphi with the aid of the Fermatean fuzzy sets. According to the conclusions of the analysis, the criteria “Accuracy” and “Quality” are the most effective measures. Also, the suggested hybrid model and its outcomes were tested by executing robustness and validation checks. The results of the analyses prove that the suggested integrated framework is a robust and practical decision-making tool.

A Review and Benchmark on State-of-the-Art Steel Defects Detection

Steel, a critical material in construction, automobile, and railroad manufacturing industries, often presents defects that can lead to equipment failure, significant safety risks, and costly downtime. This research aims to evaluate the performance of state-of-the-art object detection models in detecting defects on steel surfaces, a critical task in industries such as railroad and automobile manufacturing. The study addresses the challenges of limited defect data and lengthy model training times. Five existing state-of-the-art object detection models (faster R-CNN, deformable DETR, double head R-CNN, Retinanet, and deformable convolutional network) were benchmarked on the Northeastern University (NEU) steel dataset. The selection of models covers a broad spectrum of methodologies, including two-stage detectors, single-stage detectors, transformers, and a model incorporating deformable convolutions. The deformable convolutional network achieved the highest accuracy of 77.28% on the NEU dataset following a fivefold cross-validation method. Other models also demonstrated notable performance, with accuracies within the 70–75% range. Certain models exhibited particular strengths in detecting specific defects, indicating potential areas for future research and model improvement. The findings provide a comprehensive foundation for future research in steel defect detection and have significant implications for practical applications. The research could improve quality control processes in the steel industry by automating the defect detection task, leading to safer and more reliable steel products and protecting workers by removing the human factor from hazardous environments.

Application of DC-DC power module in electric vehicle system

DC-DC power module has been increasingly widely used in car manufacturing industry and studied by scientists due to its crucial role in electric vehicles. It has two major functions. One is to provide electric power that motivates the power steering system, air conditioning, and other auxiliary equipment; the other is to operate in a hybrid electric source system by connecting with super-capacitors in series in a circuit, which performs a function of regulating power source output, as well as stabilizing the DC voltage bus. This review aims to investigate the fundamental principle of DC-DC converter, to comprehensively review the different types of DC-DC power modules and their characteristics, as well as their advantages and limitations. In addition, to discuss the major challenges existing in this system currently, and the potential it has for future developments, which refers to the power output efficiency of the system when it is applied in electric vehicles. Finally, to demonstrate some real-life applications of this module, the representative examples of firms successfully applying this power module into production. This paper has a guiding significance for acknowledging the current state of development of DC-DC power modules in the electric vehicle system, and for studying some related research fields.

Optimisation of laser welding parameters to maximise the joint strength of laser weld stainless steel joints using a GA for geothermal plants

ABSTRACTLaser beam welding is vital in high-temperature fields such as the aerospace, automobile, and gas turbine manufacturing industries. In this article, parametric studies are carried out on Nd:YAG laser weldments that influence stainless steel 430 L weldment quality. A series of trial runs are organised to investigate input parameters such as peak power, frequency, speed, and time that impact output responses such as yield strength, ultimate tensile strength, shear strength, and elongation simultaneously. Trail runs on martensitic stainless steel AISI 430 L are conducted based on Taguchi experiments, and a regression equation is established. To yield optimum results, the process parameters are optimised by using the genetic algorithm optimisation technique. By conducting experimentation with optimum parameters, the output responses are compared with the optimum results 95% confidence level was obtained. The optimum levels for process variables are identified for quality weld peak power 160 kW, frequency 9 Hz, speed 3.33 mm/ms, and time 2.53 ms. Optical microscopy reveals that refined grains are formed in the weld zone with a grain size of 2.909 µm, and a scanning electron microscopy test is performed to study the morphology of AISI 430 L weldment specimen failure takes place at the interface of the weld metal.

Risk Management Strategies for Chip Supply Chain Disruption: Case Study of R Automotive Manufacturing Company

This article addresses the chip supply chain problems faced by the global automobile industry, takes R automotive manufacturing company as a case study, and thoroughly researches the risk of the out-of-supply of the key raw material, chips, in the automobile manufacturing industry and its impact on the production of the enterprise. The article explains the importance of supply chain management in modern enterprises and points out supply chain risks. Taking the automotive manufacturing industry as the background, the article discusses in depth the importance of micro-control units as the electronic control units of automobiles, analyses the competitive advantages of R enterprises in the new energy vehicle market, analyses the risk characteristics of chip out-of-supply as well as the challenges faced by R enterprises under the influence of the global epidemic and market uncertainty, and proposes the strategies for R enterprises to cope with the risk of chip out-of-supply, including the strengthening of supply chain cooperation, formulating a reasonable procurement mechanism, establishing a good supplier relationship, and promoting independent research and development of chips. Finally, the findings of this paper are summarized, and relevant suggestions are provided for automotive manufacturing enterprises to avoid the risk of chip supply shortage.

How policies and actor strategies affect electric vehicle diffusion and wider sustainability transitions.

Implementing electromobility is a central component in the de-carbonization of personal mobility. In recent years, the absolute number of electric vehicles (EVs) and their market share has increased sharply in many countries. This paper focuses on Norway, a pioneer market for EVs that other countries can learn from. The analysis highlights how a combination of local and national policies over a 30-y period, which targeted both industry development and vehicle demand, were important drivers of this development. It also highlights the importance of advocacy groups and strong networks in promoting EVs, as well as changes in user preferences. The paper demonstrates how the EV diffusion has been driven by alignments of multiple processes across different levels, involving interactions between multiple actors and social groups with different interests and views about desirable futures as described by the multi-level perspective (MLP). Building on the MLP, the study of EV diffusion in Norway illustrates how niches are often sustained through demonstrations, experimentation, strategic alliances, and actors securing favorable political and economic conditions. Further, it shows how local or national niches may depend on international regime actors, such as the car manufacturing industry and policies developed abroad. The paper also explores how the introduction of EVs has opened for wider effects, including innovation within production-consumption systems beyond mobility. Based on this analysis, we argue for a nuanced perspective on the relationship between incremental, regime-internal innovation, and wider transformative changes, where the merits of societal learning and experience with battery electricity for transportation are highlighted.

Analysis of M&A in Vehicle Industry: Evidence from Geelys Acquisition of Lotus

With the continuous improvement of Chinas domestic economic level, the automobile manufacturing industry is developing rapidly, the market competition is fierce, and the automobile market is gradually becoming saturated. The reduced market demand has led to higher and higher requirements for the technical level of domestic automobile manufacturers. In 2013, China proposed the Belt and Road initiative, which brought new opportunities to some domestic auto companies. Geely Group seized the opportunity to acquire Lotus. This paper mainly analyzes the reasons for Geelys acquisition of Lotus, and this paper analyzes Geelys acquisition of Lotus through two aspects of finance and motivation. In terms of motivation, this paper analyzes policy, society, external environmental factors of Geely Group through economy and technology through P EST model. Meanwhile, SWOT is used to analyze the advantages, disadvantages, opportunities, and internal factors of Geelys acquisition of Lotus. On the financial side, through the analysis of each financial indicator, trend, causality, growth, and revenue. According to the analysis, after the acquisition of Lotus, Geelys enterprise strength has been improved to a certain extent, and the future development prospects are expected.

Research on the Digital Transformation Business Model of the Automobile Manufacturing Industry

With the implementation of Internet Plus and Made in China 2025 plans, Chinas digital technology has developed rapidly, and the transformation to a digital direction is imminent for Chinas manufacturing industry. In response to the national strategic deployment, many automobile enterprises have begun to carry out digital transformation. This article selects three representative Chinese automobile manufacturing enterprises and studies their business models through case analysis. This paper summarizes automobile enterprises' digital transformation business model from six aspects: product production, user service, marketing methods, and business processes. This paper has promoted the theoretical development of the digital transformation of the manufacturing industry, especially the automobile manufacturing industry. At the same time, it provides practical business model suggestions for the digital transformation of Chinese automobile manufacturing enterprises.

An Empirical Research of the Relationship Between ESG Performance and Firm Value

In the context of the continuous promotion and implementation of ESG concepts around the world, this paper aims to study the correlation between ESG scores and the share prices of companies in different regions and industries. Based on the stock price data and ESG scores of the top 30 companies by market capitalization in the semiconductor industry, car manufacturing industry, and financial services industry, as well as the stock price data and ESG scores of the top 30 companies by market capitalization in the United States, China, and Japan, the linear fit model is constructed to analyze the correlation and intercept. It is found that the ESG score is positively correlated with company stock price in most situations, which means that the improvement of the ESG score often implies the improvement of the companys share price. According to the economic environment of different industries and countries, the correlation and intercept are different. Usually, the correlation or intercept is more evident in high-end industries or regions with better economies, also the ESG level is better.

How the Terminator might affect the car manufacturing industry: Examining the role of pre-announcement bias for AI-based IS adoptions

The steep development of artificial intelligence (AI) is accompanied by a completely new set of challenges for information systems (IS) research and practice, especially in the area of individual-level technology adoption. In this article, we elaborate on the important role that biases play regarding the adoption of AI-based IS by individuals in a work environment and for which, in addition, an alarmingly early occurrence within the overall process can be reported. Based on an exploratory case study within three different German car manufacturers, we credit this work with a valuable threefold contribution. First, it introduces and substantiates knowledge and theory on the pre-announcement phase of individual-level IS adoption. Second, it is one of the very few works that thoroughly conceptualize and analyze the role of biases in user decision making resulting from the individuals’ cognitive limitations. And third, it indicates a notable spillover effect of experiences and opinions on AI from the users’ private lives to their professional ones, which stands in clear contrast to what usually is reported in IS research. The article thereby discusses general implications for IS theory but also elaborates on the AI-specific elements regarding IS implementations in a professional environment.

Prevalence of work-related musculoskeletal disorders among workers in the automobile manufacturing industry in China: a systematic review and meta-analysis

ObjectivesWork-related musculoskeletal disorders (WMSDs) have become one of the major occupational health problems. Lots of auto workers in China are exposed to WMSDs. However, there are few systematic review and meta-analysis about WMSDs in this field. This study aimed to evaluate the prevalence of WMSDs among these workers.MethodsThis study was carried out using the Systematic Reviews and Meta-analyses method under the most up to date PRISMA guidelines. A literature search was conducted for studies on the epidemiology of WMSDs among auto workers in China from inception to August 2022, using English and Chinese databases (China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine Disc, China Science and Technology Journal Database, PubMed, and Web of Science). All statistical analyses were performed using STATA V.16.0.ResultsOut of the 849 references identified, 26 articles were were eligible for inclusion, of which 12 reported the overall 12-month prevalence of WMSDs, while 14 stated the 12-month prevalence of WMSDs on body regions. The overall 12-month prevalence rate of WMSDs among workers was 53.1% [95% Confidence Interval (CI) = 46.3% to 59.9%]. The lower back/waist was the body region affected most (36.5%, 95%CI = 28.5% to 44.5%). The definition on WMSDs of “Chinese version” resulted in a high prevalence of WMSDs. Obesity, high educational level, long job tenure, female, logistic workers, and foundry workers are factors that led to a high prevalence rate of WMSDs in the lower back/waist.ConclusionsThis study showed a high prevalence rate of WMSDs among auto workers in China. Thus, it is necessary to pay particular stress to them. Several effective measures should be taken to prevent these workers from WMSDs.Trial registrationThis review was registered on PROSPERO (registration number CRD42023467152).