Manufacturers employ AI for monitoring vehicle mileage, inspecting components, and scheduling maintenance. Past studies underscore the need for auto-related plans to prioritize environmental protection, concentrating on AI-driven environmental solutions promoted by AI for Good. AI enhances brand success by improving investment, technology, and promotional capabilities. This study emphasizes consistency in AI application across the automotive value chain for brand sustainability. A web-based poll surveyed 120 AI users in marketing, HR, sustainability, as well as 180 sustainability specialists and regulators. The primary goal is to assess, via structural model evaluation, how extraneous variables affect the development of AI-powered brand sustainability strategies. The study highlights AI's sustainability benefits in the automotive industry improving transportation safety, forecasting maintenance, and creating eco-friendly vehicles. However, challenges involve over-reliance on AI, predicting human behavior, and addressing sustainability threats. AI development should consider regional differences, prioritizing openness, policy harmony, and consumer agency. These findings aid marketing and HR professionals in devising customer-centric long-term plans.

The MEWMA chart is one of the traditional multivariate charts which are widely employed in inspecting the quality of manufacturing and services. This chart is created through monitoring the small shifts of mean vectors of variable quality characteristics. Often in practice, the measurement of a quality characteristic produces uncertain, incomplete values, so that ambiguous numbers are obtained. In this condition, a neutrosophic-based control chart can overcome the problem resulting from the ambiguous data. The paper’s objective is to construct a new multivariate monitoring scheme based on a neutrosophic chart, namely the neutrosophic Multivariate EWMA (NMEWMA). Furthermore, the performance of the new multivariate monitoring scheme is evaluated in detecting process shifts employing the Average Run Length (ARL) and Standard Deviation Run Length (SDRL). This control chart is an innovation in the quality monitoring of uncertain data. The research result obtained indicates that the NMEWMA chart performs better than the MEWMA in finding the small mean shifts as well as in the real case application.

In connection with the global financial and economic crisis, the leading countries have made adjustments to the economic strategies and policies to stabilize the post-crisis economy. The goal of transnationalization is, on the one hand, to reduce quickly the technological gap between domestic producers and their Western competitors, and on the other hand, to win more dynamic demand at lower costs. Ownership structure and market positioning have become two factors that determine the strategic motivation of Transnational corporations (TNCs) and the ways of their expansion. The aim of the study is to analytically generalize the globalization trends of TNCs in the field of production, taking into account industry definitions and point determination of factors influencing the effectiveness of its adaptation in industry. The profiles of adaptation of transnationalization in the sphere of production of Germany, France and the Netherlands were singled out in separate analytical sets, which allowed to reveal heterogeneity of economic practices and branch changes in the sphere of production and to determine the degree of influence of array of variables (factors): Production value and Gross operating surplus profitability of multinational companies, in the sectoral context (domestic production, ISIC Rev 4, 21 industry), as a “basket” of reserve instruments to increase the profitability of production in TNCs.