The integration of Artificial Intelligence (AI) into Supply-Chain Management (SCM) has revolutionized operations, offering avenues for enhanced efficiency and decision-making. AI has become pivotal in tackling various Supply-Chain Management challenges, notably enhancing demand forecasting precision and automating warehouse operations for improved efficiency and error reduction. However, a critical debate arises concerning the choice between less accurate explainable models and more accurate yet unexplainable models in Supply-Chain Management applications. This paper explores this debate within the context of various Supply-Chain Management challenges and proposes a methodology for developing models tailored to different Supply-Chain Management problems. Drawing from academic research and modelling, the paper discusses the applications of AI in demand forecasting, inventory optimization, warehouse automation, transportation management, supply chain planning, supplier management, quality control, risk management, and customer service. Additionally, it examines the trade-offs between model interpretability and accuracy, highlighting the need for a nuanced approach. The proposed methodology advocates for the development of explainable models for tasks where interpretability is crucial, such as risk management and supplier selection, while leveraging unexplainable models for tasks prioritizing accuracy, like demand forecasting and predictive maintenance. Through this approach, stakeholders gain insights into Supply-Chain Management processes, fostering better decision-making and accountability.

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few studies benchmark model performance across the diverse operational demands of global pharmaceutical supply chains. Predictive setbacks contribute to financial losses, reduced supply chain efficacy, and potential adverse health consequences, yet understanding these failures offers firms opportunities to refine strategy and strengthen resilience. Drawing on 1.2 million shipments spanning 39 countries, we compare traditional statistical models (ARIMA), ensemble methods (random forests, gradient boosting), and deep neural networks (LSTM, GRU, CNN, ANN) across pricing, demand forecasting, vendor management, and shipment planning. Gradient boosting produced the strongest pricing performance, while ARIMA delivered the lowest demand-forecasting errors but with limited explanatory power; neural networks captured nonlinear demand shocks and achieved superior maintenance-risk classification. We also identified three vendor performance clusters—high-performing, cost-efficient, and mixed-reliability vendors—enabling firms to better align shipment criticality with vendor capabilities by prioritizing high performers for urgent deliveries, leveraging cost-efficient vendors for non-urgent volumes, and managing mixed performers through targeted oversight. These insights highlight the value of our evidence-based roadmap for selecting algorithms in high-stakes healthcare logistics, in rapidly evolving, technologically complex global contexts where increasing algorithmic sophistication elevates the standards for safer, smarter pharmaceutical supply chains.

Considering the United Nations Climate Change Accord and insights from the OECD Global Material Resources Outlook to 2060, this study explores the intricate interrelationships of population growth, economic expansion, energy consumption, and carbon emissions in key OECD and BRICS countries. With the global economy heavily reliant on fossil fuels—the primary drivers of carbon emissions—we examine historic and projected energy use trends in developed and emerging economies. Through a combination of exploratory data analysis and ARIMA-based statistical forecasting, we investigate the relationships among GDP growth, energy use, and emissions, drawing distinctions between OECD and BRICS nations. Our findings reveal that, while developed economies demonstrate declining energy use, emerging markets show an upsurge in usage tied to economic growth. This research presents a compelling case for transitioning to a low-carbon future, drawing on renewable energy sources and proposing a roadmap to achieve both economic resilience and environmental sustainability. Our work serves as a call to action for policy-driven, cleaner energy investments to curb emissions and safeguard the planet.

Marine pollution incidents pose significant threats to marine ecosystems and coastal communities across Pacific Island nations, necessitating advanced predictive capabilities for effective environmental management. This study analyzes 8133 marine pollution incidents from 2001–2014 across 25 Pacific Island nations to develop predictive models for pollution type classification, hotspot identification, and seasonal pattern forecasting. Our analysis reveals Papua New Guinea as the dominant pollution hotspot, experiencing 51.9% of all regional incidents, with plastic waste dumping comprising 78.8% of pollution events and exhibiting pronounced seasonal peaks during June (coinciding with critical fish breeding periods). Machine learning classification achieved 99.1% accuracy in predicting pollution types, with material composition emerging as the strongest predictor, followed by seasonal timing and geographic location. Temporal analysis identified distinct seasonal dependencies, with June representing peak pollution activity (755 average incidents), coinciding with vulnerable marine ecological periods. The predictive framework successfully distinguishes between persistent geographic hotspots and episodic pollution events, enabling targeted conservation interventions during high-risk periods. These findings demonstrate that pollution type and location are highly predictable from environmental and temporal variables, providing marine conservationists with tools to anticipate when and where pollution will most likely impact fish populations and ecosystem health. The study establishes the first comprehensive baseline for Pacific Island marine pollution patterns and validates machine learning approaches for proactive pollution monitoring, offering scalable solutions for protecting ocean ecosystems and supporting evidence-based policy formulation across the region.

Income inequality has emerged as a defining challenge of our time, particularly in advanced economies, where the gap between rich and poor has reached unprecedented levels. This study analyzes income inequality trends from 2000 to 2023 across developed countries (the United States, the United Kingdom, Germany, and France) and developing nations using World Bank Gini coefficient data. We employ comprehensive visualization techniques, Pareto distribution analysis, and ARIMA time-series forecasting models to evaluate the effectiveness of the Kuznets curve as a predictor of income inequality. Our analysis reveals significant deviations from the traditional inverse U-shaped Kuznets curve across all examined countries, with persistent volatility rather than the predicted decline in inequality. Forecasts using ARIMA and neural networks indicate continued fluctuations in inequality through 2030, with the U.S. and Germany showing upward trends while France and the UK demonstrate relative stability. These findings challenge the conventional Kuznets hypothesis and demonstrate that contemporary inequality patterns are influenced by factors beyond economic development, including technological change, globalization, and policy choices. This research contributes to the literature by providing empirical evidence that the Kuznets curve has limited predictive power in modern economies, informing policymakers about the need for targeted interventions to address persistent inequality rather than relying on economic growth alone.