Search Results (40)

Supply chain risk management (SCRM) has become an increasingly relevant field of study, driven by operational challenges, global crises, and the increasing complexity of supply chains. Although the number of publications on the topic has increased significantly over the last two decades, empirical research aimed at practical applications is still limited. In this context, the present research aims to select and rank tools for use in SCRM in direct and indirect suppliers of the automotive industry. As a secondary objective, the research aimed to identify which risk management stages (identification, analysis, and assessment) are most relevant for SCRM. Methodologically, the research was conducted in two main stages, the first involving the Delphi method in twenty suppliers (Tier 2) to select and evaluate risk management tools and the second involving the AHP method in an auto parts manufacturer (Tier 1) to rank the tools raised in the previous step. The main results show that the risk identification phase is considered the most relevant in the SCRM process, being prioritized by 56% of the experts. Regarding the tools, the ISO 31010 techniques most suitable for SCRM in suppliers, in order of priority, are FMEA, SWIFT, and cause and consequence analysis, standing out for their ability to identify and prevent failures. Full article

In this study, we examine the supplier–buyer relationship based on resource dependence theory. When suppliers are asymmetrically dependent on buyers because of the industry structure, the suppliers are subject to the opportunistic behaviors of the buyers. In this industry setting, suppliers have less opportunity to sustain their profitability. We theoretically and empirically examine the conditions under which suppliers may overcome such conditions. Suppliers’ enhanced commitment to the asymmetric relationship can help them resolve problems associated with asymmetric dependence and thereby sustain profitability. This effect can be lessened when a buyer forms a new exchange relationship or magnified when a supplier forms a new exchange relationship. Suppliers’ industrial diversification and technological capability also affect the dynamics. We collected data from Korean auto parts suppliers between 1998 and 2007. Using the feasible generalized least squares regression model, most of our hypotheses were supported, except for the moderating effect of technological capability. These empirical results are also confirmed by random-effects model and fixed-effects model panel regressions. This study makes three distinctive contributions to the current research, suggesting the enhancement of commitment (dependence) as a strategic solution under conditions of asymmetric dependence, applying a dynamic perspective to resource dependence theory, and emphasizing the role of firms’ capability in circumstances characterized by asymmetric dependence. Full article

In the context of digital transformation, the varying dimensions of digital maturity significantly influence value creation enhancement for enterprises. Optimizing these dimensions to augment corporate value represents an urgent challenge for manufacturing enterprises. This study examines 355 listed automotive manufacturing enterprises (including auto parts and related businesses) through multi-case analysis, grounded theory, and QCA methodology to investigate the intrinsic mechanisms and pathways linking digital transformation with value enhancement in automotive manufacturing. The sample enterprises were categorized by industry type into capital-intensive, technology-intensive, and labor-technology-intensive manufacturers, and were then further segmented into complete vehicle manufacturers, component manufacturers, and related industry manufacturers. The selection criteria emphasized enterprises with explicit digital transformation strategies, sufficient transformation documentation, complete annual reports, stable core operations, and anomaly-free key data. The key findings include the following: (1) Grounded theory identified service digitalization, environmental digitalization, middleware digitalization, marketing digitalization, and R&D digitalization as critical variables, with enterprise value enhancement requiring multi-dimensional synergies rather than single-factor determinants. (2) Configuration analysis revealed that comprehensive empowerment type (consistency > 0.8, coverage 35.9%) drives high-value enhancement, while service-deficiency, R&D-deficiency, and marketing-deficiency configurations characterize non-high-value scenarios. Service, R&D, and marketing digitalization emerge as core-value-enhancing competencies (consistency 0.817, coverage 75.9%). (3) Heterogeneous driving forces were observed across vehicle manufacturers, component manufacturers, and related industry manufacturers, though service digitalization constitutes a common-value-enhancing element. This research provides theoretical insights into manufacturing digital transformation’s value creation mechanisms and strategic implications, addressing current academic gaps. However, the automotive industry focus limits generalizability despite its concrete exploration of industry-specific digital transformation. Future studies should expand industry coverage and conduct comparative analyses to enhance theoretical robustness. Full article

Artificial intelligence (AI) has become a revolutionary tool in the automotive sector, specifically in quality management and issue identification. This article presents a systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 and 5.0. The main methods analyzed are deep learning, artificial neural networks, and principal component analysis, which improve defect detection, process automation, and predictive maintenance. The manuscript emphasizes AI’s role in live auto part tracking, decreasing dependance on manual inspections, and boosting zero-defect manufacturing strategies. The findings indicate that AI quality control tools, like convolutional neural networks for computer vision inspections, considerably strengthen fault identification precision while reducing material scrap. Furthermore, AI allows proactive maintenance by predicting machine defects before they happen. The study points out the importance of incorporating AI solutions in actual manufacturing methods to ensure consistent adaptation to Industry 5.0 requirements. Future investigations should prioritize transparent AI approaches, cyber-physical system consolidation, and AI material enhancement for sustainable production. In general terms, AI is changing quality assurance in the automotive industry, improving efficiency, consistency, and long-term results. Full article

Big data analytics capabilities (BDACs) are strategic capabilities that expedite decision-making processes, empowering organizations to mitigate the impacts of supply chain disruptions. These capabilities enhance the ability of companies to be more proactive in detecting and predicting disruptive events, increasing their resilience. This study analyzed the effects BDACs have on firms’ reaction time and the effects companies’ reaction time has on their resilience. The research model was assessed with 263 responses from a survey with professionals of auto-parts companies in Brazil. Data were analyzed with the Partial-Least-Squares—Structural Equation Modeling method. Cluster analysis techniques were also applied. This study found that BDACs reduce reaction time, which, in turn, improves firms’ resilience. We also observed greater effects in first-tier and in companies with longer Industry 4.0 journeys, opening further perspectives to investigate the complex mediations of digital readiness, reaction time, and organizational resilience performance of firms and supply chains. Our research builds upon the dynamic capabilities theory and identifies BDACs as dynamic capabilities with the potential to enhance resilience by reducing data, analytical, and decision latencies, which are recognized as core elements of the reaction time concept, which is particularly crucial during disruptive supply chain events. Full article

The spatial heterogeneity of potentially toxic elements (PTEs) in a typical green tea-producing area in Zhejiang was investigated with application of geostatistics. The positive matrix factorization (PMF) was conducted for analysis of pollution sources and risk assessment of the soil of the tea garden. The results revealed that 93.52% of the study area did not exceed the PTEs risk screening value in the soil pollution risk control standard of agricultural land. The results of the spatial heterogeneity analysis showed that Cd and Pb had moderate spatial auto-correlation, exhibiting similar spatial distribution patterns. The high-value locations were distributed in the southeast of the study area, while low-value locations were distributed in the southwest of the study area. The Cr, As, and Hg had strong spatial auto-correlation, while Cr and As had similar spatial distribution patterns whose high-value areas and low-value areas were concentrated in the west and center of the study area, respectively. The Cd, Pb, and As originated from the agricultural source, transportation source, and industrial source, respectively, while Cr and Hg were from the natural source on the basis of the results of the PMF model. The results of a potential ecological risk assessment revealed that five PTEs in the study area were of low potential risk. The single-factor ecological risk ranking was Cd > As > Hg > Cr > Pb. The overall ecological risk in the study area was slight. The human health risk model indicates that there was a non-carcinogenic risk for children in the study area, and the high-value area was concentrated in the northwest of the study area. It is concluded that emphasis shall be given to excessive Cd caused by agricultural sources in the southeast of the study area, and control and monitoring will be strengthened in the northwestern part of the study area. The relevant measures for prevention of soil pollution must be conducted. Full article

This study focuses on how global automotive suppliers manage innovation by analyzing keywords in CEO messages. Given that CEOs significantly shape innovation strategy, the intricate dynamics of open innovation and the role of CEO characteristics in its adoption warrant further investigation. Accordingly, the research unfolds in three stages: (1) extracting keywords related to innovation highlighted in CEO communications, (2) contrasting the deployment of these keywords between high-performing and low-performing companies, and (3) deciphering the nuances of innovation management by interpreting the underlying meaning and structure of these keywords. This comparative analysis between top and bottom performers underscores stark contrasts in keyword emphasis. Through eigenvector centrality, mapping open innovation’s success factors pinpointed provision of resources and governance as pivotal in top-performing firms. Notably, the preferred keywords among leading firms reflect their current challenges and innovative management direction. Thus, to embody agile and visionary leadership in open innovation, CEOs should strategically incorporate and highlight keywords aligned with critical factors of open innovation in their communications. These insights offer valuable benchmarks for less successful firms aiming to refine their approaches to innovation management, vision, and strategy. Full article

The purpose of this study was to propose an interactive computer system that utilises the MATLAB Fuzzy Logic Designer to measure the level of implementation of SDG 8, which focuses on sustainable decent work (SDW) and economic growth. This study used policies and laws as parameters to determine the presence or absence of SDW. The fuzzy method was implemented in car windshield manufacturing in the auto parts industry as a case study to define and quantify work conditions and to determine the level of sustainable decent work (SDWL). The study described environmental conditions, such as noise, lighting, and heat stress; ergonomic factors, such as exposure time, the mass of the object manipulated, and lifting frequency; and organisation at work, such as workplace violence, salary, and workday, as linguistic variables. The level of the presence or absence of SDW was defined as their membership functions. The resulting vectors determined the absence of SDW with a score of 1.5 in two linguistic variables: environmental conditions and ergonomic factors. Some features of SDW in the linguistic variable organisation at work had an SDW score of 5. The SDWL vector determined a final score of 1.24, indicating the absence of decent work in production areas. This study found that the workers suffer a lack of long and healthy lives and a bad standard of living without economic growth due to work-related musculoskeletal disorders and work illnesses, increasing their out-of-pocket spending and catastrophic health expenses. As a CSR approach, assessing SDWLs helped managers improve policies and work conditions. Full article

Optical brighteners (OBs) have become an integral part of our daily lives and culture, with a growing number of applications in various fields. Most industrially produced OBs are derived from stilbene, which has been found in environmental matrices. The main objectives for this work are as follows: first, to identify protein targets for DAST, FB-28, and FB-71, and second, to assess their effects in some behaviors physiologic of Caenorhabditis elegans. To achieve the first objective, each OB was tested against a total of 844 human proteins through molecular docking using AutoDock Vina, and affinities were employed as the main criteria to identify potential target proteins for the OB. Molecular dynamics simulations took and validated the best 25 docking results from two protein databases. The highest affinity was obtained for the Hsp70-1/DAST, CD40 ligand/FB-71, and CD40 ligand/FB-28 complexes. The possible toxic effects that OBs could cause were evaluated using the nematode C. elegans. The lethality, body length, locomotion, and reproduction were investigated in larval stage L1 or L4 of the wild-type strain N2. In addition, transgenic green fluorescent protein (GFP) strains were employed to estimate changes in relative gene expression. The effects on the inhibition of growth, locomotion, and reproduction of C. elegans nematodes exposed to DAST, FB-71, and FB-28 OBs were more noticeable with respect to lethality. Moreover, an interesting aspect in OB was increased the expression of gpx-4 and sod-4 genes associated with oxidative stress indicating a toxic response related to the generation of reactive oxygen species (ROS). In all cases, a clear concentration-response relationship was observed. It is of special attention that the use of OBs is increasing, and their different sources, such as detergents, textiles, plastics, and paper products, must also be investigated to characterize the primary emissions of OBs to the environment and to develop an adequate regulatory framework. Full article

The auto industry aims to deliver cost-effective, efficient vehicles to meet customer needs. They are utilizing aluminum to lower expenses, enhance durability, and lighten vehicles. Currently, the industry is developing a high-speed blow forming (HSBF) technique—a faster version of the aluminum thermoforming process, superplastic forming (SPF). HSBF allows the rapid creation of aluminum bodywork or structural parts at high temperatures using pressurized gas. It can produce up to 25 parts per hour, significantly faster than SPF, which only produces 4 parts per hour. The primary objective of this project is to select and characterize a seal that can increase the production rate to 120 parts per hour by allowing the sheet to flow into the mold, especially during the initial stages of the forming process, where most of the deformation occurs. Several test benches were developed to assess the performance and durability of the selected high-temperature seals under conditions that imitate the HSBF process. During the tests, low air pressures are applied to a gasket-enclosed cavity and the resulting mass-flow leakage is measured. The temperature of the mold is kept constant at approximately the superplastic temperature of the aluminum alloy. Through testing, we derived leakage mass flow curves based on cycle count, showcasing the superior sealing ability and longevity of packing seals in HSBF conditions. The seals displayed good durability and sealing performance under HSBF operational conditions, sustaining over 3000 cycles. Moreover, the seals attained a leakage mass flow rate of around 0.3 g/s·m·bar, nearly ten times below the target application limit of 2 g/s·m·bar, confirming their superior performance. Full article

Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts. Full article

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing risks and determining insurance premiums for personal vehicles. However, the app is not limited exclusively to actuaries. Other individuals or entities, such as insurance companies, researchers, or analysts, who have access to the necessary data and require insights or analysis related to personal auto insurance, can also benefit from using the app. It is the first web-based application of its kind that is free to use and deployable from the personal computer or mobile device. AutoReserve is a software solution that caters to the needs of insurance professionals where only a few existing web-based applications are available. The application is divided into three parts: a summary of the loss data, a classical loss reserving tool, and a machine learning loss reserving tool. Each component of the application functions differently and allows for inputs from the user to analyze the provided loss data. The user, in other words, individuals or entities who utilize the Auto Reserve application, can then use the outputs for these three sections to improve his or her risk management or loss reserving process. AutoReserve is unique compared to other loss reserving tools because of its ability to employ both traditional, spreadsheet-based and modern, machine-learning-based loss reserving tools. AutoReserve is accessible on the web. The app is currently usable and is still undergoing frequent updates with new features and bug fixes. Full article

The growing trend toward vehicles being connected to various unidentified devices, such as other vehicles or infrastructure, increases the possibility of external attacks on“vehicle cybersecurity (VC). Detection of intrusion is a very important part of network security for vehicles such as connected vehicles, that have open connectivity, and self-driving vehicles. Consequently, security has become an important requirement in trying to protect these vehicles as attackers have become more sophisticated in using malware that can penetrate and harm vehicle control units as technology advances. Thus, ensuring the vehicles and the network are safe is very important for the growth of the automotive industry and for people to have more faith in it. In this study, a machine learning-based detection approach using hybrid analysis-based particle swarm optimization (PSO) and an adaptive genetic algorithm (AGA) is presented for Android malware detection in auto-driving vehicles. The “CCCS-CIC-AndMal-2020” dataset containing 13 different malware categories and 9504 hybrid features was used for the experiments. In the proposed approach, firstly, feature selection is performed by applying PSO to the features in the dataset. In the next step, the performance of XGBoost and random forest (RF) machine learning classifiers is optimized using the AGA. In the experiments performed, a 99.82% accuracy and F-score were obtained with the XGBoost classifier, which was developed using PSO-based feature selection and AGA-based hyperparameter optimization. With the random forest classifier, a 98.72% accuracy and F-score were achieved. Our results show that the application of PSO and an AGA greatly increases the performance in the classification of the information obtained from the hybrid analysis. Full article

In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day and night to reduce cost, duration, and complexity in all application areas. It provides tremendous benefits, such as sustainability, welfare improvement, cost-effectiveness, user-friendliness, and adaptability. However, it faces many challenges in making critical decisions during motion, which requires a long training period and intelligent motion planning. These include obstacle avoidance during movement, intelligent control in hazardous situations, and ensuring the right measurements. Following up on these issues requires a sophisticated control design and a secure communication link. This paper proposes a control design to normalize the integration process and offer an auto-MERLIN robot with cognitive and sustainable architecture. A control design is proposed through system identification and modeling of the robot. The robot control design was evaluated, and a prototype was prepared for testing in a hazardous environment. The robot was tested by considering various parameters: driving straight ahead, turning right, self-localizing, and receiving commands from a remote location. The maneuverability, controllability, and stability results show that the proposed design is well-developed and cost-efficient, with a fast response time. The experimental results show that the proposed method significantly minimizes the obstacle collisions. The results confirm the employability and sustainability of the proposed design and demonstrate auto-MERLIN’s capabilities as a sustainable robot ready to be deployed in highly interactive scenarios. Full article

The automotive and auto parts industries are important economic sectors in Thailand. With rapidly changing technology, every organization should understand what needs to be improved clearly, and shift their strategies to meet evolving consumer demands. The purpose of this research is to develop a Business Intelligence system for a brake pad manufacturing company in Thailand. By analyzing the relationship between market demand and supply components of the company through regression analysis and the principles of the marketing mix, we develop a product lifecycle curve for forecasting product sales. The developed system increases the workflow efficiency of the case study company, being able to simplify the traditional data preparation process that requires employees to collect and summarize data every time a request is made. An intelligence dashboard is subsequently created to help support decision-making, facilitate communication within the company, and eventually improve team efficiency and productivity. Full article