Recently, as ESG management has become an important issue, major companies in the automotive parts manufacturing industry are conducting ESG evaluations of their suppliers for the purpose of supply chain management. The results of these evaluations are being incorporated into contractual agreements. However, many small and medium-sized enterprises(SMEs) are lacking in their capacity and resources to effectively respond to ESG evaluations. Furthermore, existing ESG management guidelines do not provide an industry-specific guidance, making it necessary to establish industry-specific guidelines that SMEs can refer to. Therefore, in this study, the evaluation Indicators of ESG supply chain assessments are surveyed, which is conducted by domestic major automotive parts companies and global automobile manufacturers. Then 56 supply chain ESG evaluation Indicators are derived. Also, ESG management indicators for SMEs is analyzed through the Importance-Performance Analysis(IPA), based on an interview of expert groups. Therefore, this study could propose industry-specific ESG guidelines, based on the results of the derived indicators, which reflects the need for SMEs to practice ESG management within certain boundaries.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

Price quotations for SOR / RFQ from OEM clients is a very important process in the automotive parts industry. However, OEM clients are demanding a price quote on short duration but it takes long delivery time due to sales, research and development, purchasing, production and cost management departments role and jobs focused on detail and responsibility. And to provide a reasonable alternative with eliminating the waste of non-value processes is to achieve OEM clients satisfaction through standardized and parallel processing, IT system based on the systems and processes of global benchmark companies.

In recent years, Prognostic Health Management (PHM) has caught the attention of lots of researchers in a wide range of applications. Thus, many researchers from research institutes, universities and companies have published and patented their findings in various fields of PHM. PHM is a method or technique that can be used to detect performance degradation and forecast remaining life of a system based on the health monitoring information. A number of review papers discussing on PHM methods and techniques have been presented by other researchers but mostly in maintenance of manufacturing equipments, aerospace systems and structural monitoring. This paper focuses on the trend and direction of PHM especially in the automotive industry through published research papers and also patents. The purpose of this paper is to recognize the trend and to provide suggestions for potential future research in PHM for automotive. Accordingly, this paper should offer opportunity for the manufacturers or researchers to get information on current trends and also potential areas that can be further explored in PHM for automotive.

The study aims to investigate corporate social responsibility (CSR) best practices of the world automotive industry - Peugeot, BMW, Ford, Hyundai and Toyota among others – and recommend that they plan their business strategies and managerial responses accordingly. Based on the comparative research and case studies, this research finds that all five automobile manufacturers have taken very similar measures and actions in order to establish and maintain a high level of CSR practices. Sustainability was a core value in all five companies and served as a guiding principle in every aspect and approach of their business. This study finds that all five companies have CSR strategies in place to increase energy efficiency as well as reduce the usage and wastage of water in their production and plants. This research also finds that all companies monitor their suppliers and their own production process to ensure that they maintain their CSR standards. More impressively, this sustainable management practice is transferred along the companies’ supply chain through education and training. Their suppliers and business partners are closely monitored to make sure that their high CSR standards are respected and followed. However, we find that there also are some differences in terms of their CSR deliveries and activities.