Car Manufacturers Research Articles

BUSINESS STRATEGIES OF CAR BRANDS ON THEIR PATH TO SUSTAINABILITY

The article is dedicated to examining the structure of business strategies of several large car brands within the context of achieving sustainable development goals. To conduct this research, the authors have used information from annual, integrated, or sustainability reports of seven global car manufacturers for 2023. The authors disclose a reason for indicating automobile manufacturers as pivotal market actors in ensuring sustainability under the stable economic growth. The article outlines key elements of business strategies of these global car brands for the next decade. The authors determine the conformity between the considered strategic initiatives and the sustainable development goals. The authors ascertain what considered strategic initiatives aren’t directly connected to achieving sustainability. The article summarizes general tendencies in establishing development goals by the car manufacturers.

Class versus climate? Transformation conflicts in the automotive industry

ABSTRACT Major ecological threats such as climate change affect everyone, but do not level out social inequalities. The neglect of class in ecological politics means that measures against global warming often encounter social barriers and the social-ecological transformation becomes conflict-ridden. Based on in-depth empirical research at two German car manufacturers, the article shows how management and labour in a carbon industry deal with the transformation. With the help of concepts from class theory, the car plants analysed are described as class societies in miniature. They constitute social fields in which the transformation changes the rules of the game by which the company actors act. Company interest groups and trade union structures, as well as external veto players (climate movements, the far right) have a significant impact on conflict dynamics. In each case, it can be seen that institutionally contained class conflicts in co-determined companies are increasingly turning into social-ecological transformation conflicts. These are multi-level conflicts in which ownership-based decision-making power plays a central role. No ecological class is emerging beyond production. Instead, a transformation corporatism is asserting itself, which is coming up against class-specific limits in the decision-making monopoly over business models.

Classification of driver fatigue in conditionally automated driving using physiological signals and machine learning

Conditionally automated vehicles (Level 3 SAE) are emerging on the roads, but long periods without engaging in a non-driving-related task can reduce drivers’ vigilance. This study aims to determine whether driver fatigue can be accurately predicted using physiological signals and machine learning (ML) techniques in such a context. 63 young drivers completed two separate conditional automated drives of 30 min each, in either a rural or a urban area. Half of them had been mildly sleep-deprived the previous night (slept less than six hours). Electrocardiogram (ECG), electrodermal activity (EDA), and respiration were collected, along with subjective measures of sleepiness and affective state. Using ML, sleep deprivation, driving environment, and sleepiness could be predicted from physiological features with an accuracy of 99%, 85%, and 73% respectively. Signal segmentation increased model accuracy, and EDA features were the most predictive. The differences between the results obtained from statistical analyses of sleepiness measures and the accuracy achieved by ML models are discussed. The results of this empirical study indicate that even mild sleep deprivation affects the physiological state of drivers, which can have serious consequences when combined with long periods of inactivity. Car manufacturers and researchers should take this into account when designing intelligent systems capable of providing drivers with appropriate warnings before a critical situation arises.

Successful Foreign Direct Investment Through the Development of Parts Supply Industries in the Host Country: A Study of India's Automobile Manufacturing Sector

Several large multinationals and a few local assemblers dominate the Indian automobile industry. The largest multinational, Maruti Suzuki, entered the Indian automobile industry with a policy of developing local automotive parts manufacturers to achieve high local content ratio. This study seeks to determine the extent to which foreign direct investment (FDI) in host countries contributes to the growth of automobile parts manufacturers. We use district‐level panel data from 2000 to 2008 to assess the linkage between the production of major automobile assemblers and the development of automotive parts suppliers located in the same and adjacent districts to those assemblers. Our regression results show the heterogeneity of the linkages, suggesting that the formation of industrial clusters by local parts suppliers and the expansion of their production capacity are likely to be key determinants of the success of FDI in the Indian automobile industry, rather than relying on in‐house parts production or imports.

Investigation of Recycled Expanded Polyamide Beads through Artificial Ageing and Mechanical Recycling as a Proof of Concept for Circular Economy.

Car manufacturers are currently challenged with increasing the sustainability of their products and production to comply with sustainability requirements and legislation. One way to enhance product sustainability is by reducing the carbon footprint of fossil-based plastic parts. Particle foams are a promising solution to achieve the goal of using lightweight parts with minimal material input. Ongoing developments involve the use of expanded particle foam beads made from engineering plastics such as polyamide (EPA). To achieve this, a simulated life cycle was carried out on virgin EPA, including mechanical recycling. The virgin material was processed into specimens using a steam-free method. One series was artificially aged to replicate automotive life cycle stresses, while the other series was not. The mechanical recycling and re-foaming of the minipellets were then carried out, resulting in an EPA particle foam with 100% recycled content. Finally, the thermal and chemical material properties were comparatively analysed. The study shows that the recycled EPA beads underwent polymer degradation during the simulated life cycle, as evidenced by their material properties. For instance, the recycled beads showed a more heterogeneous molecular weight distribution (an increase in PDI from two to three), contained carbonyl groups, and exhibited an increase in the degree of crystallization from approximately 24% to 36%.

Multi-parameter similarity model-based lightweight design of battery electric vehicle body-in-white

The bending stiffness, torsional stiffness, and low-order modal frequencies of the body-in-white (BIW) are crucial factors in structural lightweight design. This paper introduces a BIW optimization approach for electric vehicles (BEVs) that integrates comprehensive sensitivity analysis and multi-parameter similarity models. The aim is to efficiently balance the BIW’s stiffness performance with minimal weight, while also managing potential manufacturing cost increases from design modifications. Firstly, the validity of the computational model is demonstrated by the BIW bending and torsion stiffness test and modal test. On this basis, the integrated sensitivity evaluation indexes of the structural member thickness parameters to the stiffness, weight and low-order modal frequency response of the BIW were calculated. The sheet metal thickness parameters of 17 structural members were preferred as design variables, and a multi-parameter similarity prediction model for BIW weight reduction design was constructed. Minimum weight, maximum stiffness and higher modal frequency of the BIW are defined as the optimization objectives. The optimal solution was finally solved using a multi-objective optimization algorithm. The results indicate a 1.582% reduction in weight of the BIW compared to the pre-optimization BIW. This paper delves into the intricate interplay between the parameters of the BIW’s structural components using simulation optimization techniques alongside experimental validation. This study not only proposes a new idea for the problem of how to efficiently and accurately determine the best optimization object from a large number of structural components in the development of vehicle structural safety, but also provides a research basis for automobile manufacturers to implement further weight reduction and optimization work on the BIW at the later stage of BEV development.

Navigating Uncertainties: A Comparative Exploration of Risk Management Strategies in The Indian Auto Finance Industry Among Selected Companies

The automotive financing business in India is an essential component of the nation's economic progress. It offers customers and car manufacturers the essential financial support they require to fulfill their financial obligations. Nevertheless, this industry is confronted with a plethora of uncertainties, such as shifting market needs, shifting regulatory requirements, unstable economic conditions, and changing customer habits. Through the analysis of case studies of renowned companies, the research identifies significant risk factors and analyzes the efficiency of various approaches for mitigating those risks. The research makes use of a mixed-methods approach, including qualitative insights from industry professionals through interviews and surveys in addition to quantitative data analysis. The findings shed light on a variety of techniques, including credit risk assessment models, the diversification of loan portfolios, the adoption of sophisticated analytics, and the deployment of rigorous compliance frameworks. Furthermore, the research sheds light on the role that technology advancements, such as blockchain and artificial intelligence, play in boosting the capacities of risk management. The comparison highlights considerable disparities in the efficiency of strategies, which are driven by factors such as the size of the organization, its position in the market, and its acceptance of technical advancements. This study makes a contribution to the current body of research by offering insights that can be put into practice by industry practitioners and policymakers in order to improve the resilience and sustainability of the Indian auto financing sector in the face of uncertainty. The findings that were obtained are intended to provide assistance to businesses in the process of refining their risk management frameworks, therefore generating a financial climate that is more stable and secure.

Influencing factors of digital platform construction of automobile manufacturing industry based on ISM-MICMAC

Intelligent interconnection and big data will be the core content of the future competition, and a unified digital platform construction of the automobile manufacturing industry will inevitably become an important support for the future development from large to strong. Using literature research and expert consultation, 14 influencing factors of digital platform construction in the automobile manufacturing industry were sorted out. this study uses ISM (Interpretative structural modelling) model to stratify the influencing factors of digital platform construction of the automobile manufacturing industry, draw a multi-layer hierarchical structure diagram of influencing factors, and uses the MICMAC (Matrix impacts cross-reference multiplication applied to a classification) method to analyze the dependence and driving force of the main influencing factors. The results show that 14 factors are more scientific and reasonable as influencing factors of digital platform construction in the automobile manufacturing industry. A1, A3, B1, B3, C3, D2, D3 are the top-level influencing factors. C1 and C2 are the bottom influencing factors, highlighting that technical factors are still the fundamental factors affecting the digital platform of the automobile manufacturing industry. C1, D1 and C2 are autonomous factors with a high driving force and play an important role in promoting digital platform construction in the automobile manufacturing industry. The research results have important reference value for accelerating the digital transformation of the automobile manufacturing industry, enhancing the core competitiveness of automobile industry enterprises, and improving the monitoring degree of operation status of the automobile industry market.

A proposal of analytical formulations to calculate safety lead times under demand variability. A case study

This paper deals with the issue of safety lead time (SLT) calculations in production-inventory systems in presence of both demand and replenishment lead time variability. We provide some formulations of the SLT and numerically show their performance as compared to a benchmark in the literature. Thus the main objective of this paper is to provide analytical formulations to calculate the SLT that contemplate demand variability. To this end, a literature review was done to analyze the approaches and justifications of the different revised research works to identify reference formulations according to the objectives of this work. A supply chain from the automotive sector was used as the study frame and to validate the proposed formulations. This supply chain involved two companies: a car manufacturer and a first-tier supplier. In order to compare the proposed formulations with one another, and with that currently used by the first-tier supplier and is the study object, three parameters were used: safety stock, the number of times stockout occurs and the mean stock. They allowed the final selection of the most suitable SLT formulation for each case study.

Green fabrication for ultra-lightweight polyethersulfone/epoxy foam with excellent thermal insulation

High-performance polymers find extensive applications in heat-resisting materials, aerospace, and automobile manufacturing industry. However, due to their challenging processability, achieving low-density high-performance polymer foams for thermal insulation materials remains a big challenge. Furthermore, the preparation of lightweight special engineering plastic foams has garnered substantial attention in current research. Herein, we have developed polyethersulfone (PES)/bisphenol A epoxy resin (EP) blend foams with ultra-low density and good thermal insulation properties using a facile and environmentally friendly supercritical CO2 (ScCO2) foaming approach. The inherent compatibility between PES and EP resin enhanced the free volume of molecular chain in the matrix, subsequently improving the solubility and diffusion rate of carbon dioxide within the PES matrix. Consequently, in contrast to the maximum expansion ratio of 5.5 observed in pure PES, the PES/EP blend foams achieved an expansion ratio of up to 26 times, along with an improvement of the cell structure of PES and increasing cell density from 2.55 × 109 to 2.96 × 1010 cells/cm3. Furthermore, the thermal conductivity of PES-based foams notably decreased from 0.0667 to 0.0348 W/(m·K) with the addition of EP, possibly resulting from the low density (0.05 g/cm3) and high open-cell content (79.9%) of PES/EP blend foam. Our work presents a novel strategy for producing special engineering plastic foams with ultra-low density for thermal insulation, thereby expanding the potential application of special engineering plastic foam in areas such as biological scaffolds, absorbent material applications, and automotive interior industry.

A Comprehensive Analysis of Corporate Tax Norms in the Indian Automobile Manufacturing Sector

A Comprehensive Analysis of Corporate Tax Norms in the Indian Automobile Manufacturing Sector

Conformalized prescriptive machine learning for uncertainty-aware automated decision making: the case of goodwill requests

AbstractDue to the inherent presence of uncertainty in machine learning (ML) systems, the usage of ML is until now out of scope for many critical (financial) business processes. One such process is goodwill assessment at car manufacturers, where a large part of goodwill cases is still assessed manually by human experts. To increase the degree of automation while still providing an overall reliable assessment service, we propose a selective uncertainty-aware automated decision making approach based on uncertainty quantification through conformal prediction. In our approach, goodwill requests are still shifted to human experts in case the risk of a wrong assessment is too high. Nevertheless, ML can be introduced into the process with reduced and controllable risk. We hereby determine the risk of wrong ML assessments through two hierarchical conformal predictors that make use of the prediction set and interval size as the main criteria for quantifying uncertainty. We also utilize conformal prediction’s property to output empty prediction sets if no prediction is significant enough and abstain from an automatic decision in that case. Instead of providing mathematical guarantees for limited risk, we focus on the risk vs. degree of automation trade-off and how a business decision maker can select in an a posteriori fashion a trade-off that best suits the business problem at hand from a set of pareto optimal solutions. We also show empirically on a goodwill data set of a BMW National Sales Company that by only selecting certain requests for automated decision making we can significantly increase the accuracy of automatically processed requests. For instance, from 92 to 98% for labor and from 90 to 98% for parts contributions respectively, while still maintaining a degree of automation of approximately 70%.

Mixed-model sequencing with stochastic failures: A case study for automobile industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the extensive equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20% for both small- and large-sized instances.

The Effect of Changing the Angle of the Passenger Car Seat Backrest on the Head Trajectories of the 50th Percentile Male Dummy.

The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.

Peran Public relations dalam Membangun Ekuitas Merek Mobil Listrik Wuling Air EV di Indonesia

This research employs a quantitative research methods to analyze the important role of Public relations (PR) in building brand equity and the existence of Wuling Air EV electric car products in Indonesia. In an era that increasingly emphasizes environmental issues, the automotive industry is turning to environmentally friendly vehicles such as electric cars. Wuling, as a newcomer in this market, faces challenges in building consumer trust and competing with established brands. Through effective PR strategies, such as media relations, digital campaigns, and active participation in social media, Wuling strives to increase brand awareness, positive image, and customer loyalty. With a focus on educating consumers about the benefits of electric vehicles, Wuling Air EV is expected to maintain and strengthen its position in the competitive automotive market.

ANALYSIS OF THE DEVELOPMENT OF GONDOLA CAR BODY STRUCTURES

The article is devoted to the analysis of promising directions for the development of gondola car body structures. It considers the history of the emergence of gondola cars as a separate type of railway rolling stock. The authors systemise the design of gondola cars and discuss the advantages and disadvantages of various designs. The study has shown that the manufacture of the two-axle gondola cars began in the second half of the 19th century. They were designed mainly for transporting coal and iron ore, with their bodies made of wood. Further design improvement led to the appearance of four-axle gondola cars with increased load capacity. Rolled metal began to be used in body structures to improve strength. Particular attention in the article goes to analysing the results of innovative design and technological solutions intended to enhance the efficiency and reliability of body structures. The research indicates that attempts to replace steel with aluminium alloys in manufacturing bodies did not yield results. The reason is the high cost of aluminium and the labour-intensive manufacturing. A promising direction is the use of low-alloy steels 10KhSND and 10KhNDP. For many years, there has been a trend in the railcar-building industry toward producing higher-load gondola cars. It requires increasing the axle load to 25 tons. However, implementing such cars necessitates a substantial restructuring of the railway infrastructure. We consider the design features of innovative domestic gondola cars in detail. The latest technical solutions for the body frame, efficient materials, and components with improved performance have doubled the overhaul mileage. The article analyses the North American experience of gondola car construction and operation. It shows that some models of American gondola cars use longitudinal cylindrical boxes to increase the body volume and reduce the car’s centre of mass. They are on both sides of the girder beam and occupy the space between the carts. Aluminium alloys are also widely used: the frame and lower part of the body are steel, and the side and end walls are aluminium alloy. Keywords: gondola car, body, girder beam, pivot beam, side walls, hatch cover, reliability.

Proactive product warranty service planning and control: unravelling the boons of customer-generated content and multi-frequency analyses

Proactive warranty service problems planning and control serve a crucial role in service continuity and sustainability. In this regard, for the majority of manufacturers, predicting warranty claims for complicated goods is a reliability problem. The prediction of warranty claims is a complex task that also inherently possesses ambiguity, uncertainty, and volatility due to various factors. This study tries to overcome these challenges by providing a unique hybrid forecasting technique and experimenting with the use of social media data to improve the accuracy of warranty claims forecasts. We have established a collaborative partnership with a provider of automobile product services to curate a comprehensive dataset comprising two distinct types of data: (1) internal operational data and (2) social media data sourced from Twitter and Facebook. The proposed algorithm exhibits a high level of proficiency in predicting warranty claims and demonstrates robustness in effectively handling various forms of time series data that display multifarious statistical characteristics. Empirical Wavelet Transform (EWT), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and a Bayesian optimisation algorithm for optimising the shaped Deep LSTM-CNN hyperparameters make up the proposed approach. It is shown that using EWT to provide multi-frequency analysis, LSTM-CNN combination to capture both short- and long-term memory of the data, and the utilisation of social media data significantly improve the efficacy of out-of-sample predictions and counteract the impact of the “warranty data maturation” phenomenon. The suggested algorithm exhibits a decrease in Root Mean Square Error (RMSE) ranging from 29.13% to 90.64% compared to other advanced benchmark models.

A Comparative Study of Traffic Signal Control Based on Reinforcement Learning Algorithms

In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. Reinforcement learning methods have demonstrated considerable potential for addressing complex traffic signal control problems with multidimensional states and actions. In this research, the team propose Q-learning and Deep Q-Network (DQN) based signal control frameworks that use variable phase sequences and cycle times to adjust the order and the duration of signal phases to obtain a stable traffic signal control strategy. Experiments are simulated using the traffic simulator Simulation of Urban Mobility (SUMO) to test the average speed and the lane occupancy rate of vehicles entering the ramp to evaluate its safety performance and test the vehicle’s traveling time to assess its stability. The simulation results show that both reinforcement learning algorithms are able to control cars in dynamic traffic environments with higher average speed and lower lane occupancy rate than the no-control method and that the DQN control model improves the average speed by about 10% and reduces the lane occupancy rate by about 30% compared to the Q-learning control model, providing a higher safety performance.

Noise Analysis and Structural Optimization of Automobile Scroll Compressor Air Valve

The air conditioning compressor is a critical component in automobile heating, ventilation and air conditioning systems. However, compressor noise has long been a problem for automobile manufacturers. In recent years, the development and application of automobile air conditioning scroll compressors has increased significantly due to their low mechanical vibration and noise. However, their limitations in terms of airflow pulse and noise cannot be ignored, especially in low speed and high load conditions where the noise generated has a negative impact on driving and passenger experience. Noise and airflow pulses are important considerations that cannot be ignored. This study innovatively modifies the end cap structure of the scroll compressor, using the principles of expansion muffler and insertion tube structure, with the aim of improving the acoustic quality of the scroll compressor. The results show that the novel valve construction can significantly reduce the sound pressure level of the scroll compressor noise to a maximum of 75.20 dBA. The results of this study provide a theoretical basis and practical technical applications for future research and development in the automobile industry.

SUBSIDI PEMBELIAN MOTOR LISTRIK RODA DUA DALAM UPAYA MENINGKATKAN DAYA BELI MASYARAKAT

The Indonesian Government's commitment to entering the Industrial Revolution 4.0 era is demonstrated by the launch of the Making Indonesia 4.0 movement. Making Indonesia 4.0 has focused on five manufacturing sectors, including the food and beverage industry, textiles and clothing, chemicals, automotive and electronics. In the automotive and electronics sector, we strive to become the largest car manufacturer in ASEAN and develop the capabilities of the domestic electronics sector industry. As a form of effort to accelerate the formation of an electric vehicle ecosystem, the Government issued a policy regarding the expansion of recipients of the assistance program for purchasing two-wheeled electric motorbikes. This policy was marked by the issuance of Minister of Industry Regulation number 21 of 2023 concerning Amendments to Minister of Industry Regulation number 6 of 2023 concerning Guidelines for Providing Government Assistance for the Purchase of Two-Wheeled Battery-Based Electric Motorized Vehicles. This research uses a descriptive qualitative method, which will discuss providing subsidies for the purchase of two-wheeled electric motorbikes to increase people's purchasing power. This research is supported by literature data related to subsidies for electric motorbikes and sales of electric motorbikes, as well as various policy documents, regulations and legislation related to the provision of subsidies, as well as data related to the use of two-wheeled electric motorbikes in Indonesia. With the issuance of a policy from the Ministry of Industry, this research can answer the problem that we want to research, namely that providing subsidies can increase the selling value of electric motorbikes so that they can compete in the market.