Supplier Relationship Management (SRM) plays a crucial role in enhancing the resilience of global supply chains. This paper investigates how SRM strengthens supply chain stability and agility by fostering better coordination, transparency, and responsiveness. It highlights essential factors such as power dynamics, mutual trust, supplier performance monitoring, and long-term strategic collaboration. As global markets face increasing uncertainties—from geopolitical risks to environmental challenges—organizations rely more heavily on robust SRM frameworks to manage supplier risks and ensure continuity. In recent years, companies have also faced growing pressure to comply with ethical standards, labor regulations, and environmental laws, elevating the importance of SRM as a governance mechanism for ethical sourcing and sustainability. The research incorporates a case study of Walmart, demonstrating how structured SRM practices can help mitigate supply risks, increase supplier responsiveness, and maintain cost-effectiveness under dynamic conditions. It further reveals that companies integrating SRM into broader strategic planning not only reduce their vulnerability to disruption but also build competitive advantage by fostering supplier innovation and flexibility. Overall, the paper concludes that SRM has evolved beyond an operational necessity into a key enabler of long-term organizational resilience, sustainability, and strategic alignment in a rapidly changing global business environment.

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Machine learning, as a data-driven algorithm, has become increasingly prevalent in option pricing. Building on prior research, we integrated outputs from various models, including the Black-Scholes model and Random Forest model, as input parameters for a feed forward neural network model (FFNN) and a convolution neural network model (CNN). This study also improves the model architecture by including a volatility encoder based on historical stock prices and rudimentary volatility estimates to forecast bid/ask prices. The results from all tested models were promising, especially CNN, underscoring the effectiveness of CNN. Moreover, by including extra regularization terms, significant enhancements are observed in model performance.

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This paper explores the effect of the European Union (EU)’s recently (2024/7/5) imposed tariffs on imported Chinese Electric Vehicles (for short CEVs). The EU made the policy decision after a long anti-subsidy investigation into CEV manufacturers that are competitive and gradually gaining market share in the European market, and so the tariff is for protecting the business of domestic EV manufacturers. Based on the conceptual framework of the supply-and-demand model of international trade that is commonly used in microeconomics, I hypothesize that the tariffs will cause CEVs’ sales to decline. However, after analyzing the monthly sales of selected European and Chinese EV brands using line diagrams and calculating the percentage fall in July 2024’s sales roughly after the tariff, the results are not in alignment with the hypothesis backed by the conceptual framework. Further research that is able to find out the natural up-and-down cycle of the European EV market is required to isolate the effect of the tariff. Or, it might be that the policy’s effects have not become obvious enough to be observed yet.

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ESG rating is a key link in the development of ESG, while the current global ESG rating agencies are numerous in number, with very different backgrounds and large rating divergences, and it is still difficult to generate a consensus on the rating of the same subject. This paper examines the impact and causes of ESG divergence in detail. It is pointed out that ESG divergence mainly stems from the differences in evaluation systems, data sources, and the extent of information disclosure. At the same time, it has an impact on investors, corporations and intermediaries. Finally, we propose a variety of possible approaches to investors and researchers to help them do better under ESG divergence conditions.

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