Impact of Artificial Intelligence's Part in Supply Chain Planning and Decision Making Optimization

Artificial intelligence (AI) is transforming supply chain planning and decision making, enabling organizations to tackle the complexities of modern supply chains. This article explores the various applications of AI in supply chain management, including demand forecasting, inventory optimization, transportation and logistics optimization, supplier selection and risk management, and predictive maintenance and asset management. AI-powered demand forecasting models analyze historical data and market trends to predict future demand accurately, while AI-driven inventory optimization considers factors such as lead times and demand variability to determine optimal inventory levels. AI can also optimize transportation routes, modes, and schedules, and assist in supplier selection and risk assessment. Predictive maintenance using AI helps reduce equipment downtime and maintenance costs. However, organizations must consider challenges such as data quality, algorithmic bias, interpretability of AI models, and ethical considerations when adopting AI in supply chain management. As AI technologies advance and integrate with other emerging technologies, the future of AI in supply chain management looks promising, offering organizations the potential to achieve greater efficiency, agility, and competitiveness.

This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges ofAI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use ofAI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.