AI-Driven Forecasting of Supply Chain Shocks: Regulatory Determinants of B2B Fuel Trade Performance

Regulatory frameworks governing customs, environmental standards, tariffs, tax regimes, and product specifications can differ significantly from one jurisdiction to another. This creates both challenges and opportunities for fuel marketers operating on a global scale. This paper explores how these regulatory differences affect operational efficiency, decisions about entering new markets, pricing strategies, and overall sales performance in the B2B fuels sector worldwide. The research investigates the application of artificial intelligence (AI) forecasting models to quantify and predict the impacts and anticipate the effects of international tariff shocks on policies that influence B2B fuel sales in economies that rely heavily on imports. From a broader economic viewpoint, the analysis sheds light on how tariffs set by major fuel-exporting countries can send price shocks rippling through global and regional supply chains, hitting harder on vulnerable economies that lack sufficient domestic refining capabilities. By employing machine learning algorithms through recurrent neural networks (RNN), long short-term memory (LSTM) networks, and ensemble methods on historical data regarding trade flows, tariff changes, and energy price indices, the study uncovers complex relationships and dynamic lag effects between tariff events and pricing structures downstream. The findings reveal clear patterns of volatility over time, showing that AI-enhanced models are more effective than traditional econometric methods at predicting both short- and medium-term price changes. The implications for policy suggest that AI-driven forecasting tools can bolster regulatory readiness, minimize volatility, and lead to more flexible tariff and trade policies in the energy sector.