## 4 Findings Results
### Trajectory and Trends in ML and DL Applications  
#### 4.2.1 Thematic Trends and Patterns  
Thematic clustering and a trend analysis have revealed several key patterns illustrating how demand forecasting for supply chain management is evolving through artificial intelligence (AI) applications (Figure 11). There is a clear and consistent trend toward the greater adoption of AI technologies in supply chain management, driven by the need for more accurate and agile demand forecasting mechanisms. This shift enables better anticipation of demand changes, stock level optimization, and overall operational performance improvement for firms. Traditional forecasting practices are being reassessed with the integration of AI-driven models, which show significant accuracy advancements, as highlighted by [PERSON] [1]. The literature also indicates a noticeable shift towards deep learning models, such as long short-term memory (LSTM) networks, due to their unique ability to manage complex temporal patterns and non-linear relationships embedded within demand data. Advanced forecasting systems increasingly rely on deep learning models like LSTM as a foundational element. For example, according to [PERSON] and [PERSON] [8], these models are more accurate and adaptable, allowing supply chains to adjust their strategies to real-time market conditions. This adaptability results in reduced forecast errors and improved customer service levels. Additionally, traditional statistical methods are being combined with advanced AI techniques to create hybrid models, leveraging the strengths of both approaches to develop more robust and accurate forecasting solutions. One example is the SARIMA-LSTM-BP model, which combines the seasonal pattern detection of SARIMA with the temporal pattern learning capabilities of LSTMs and the optimization features of backpropagation. [PERSON] et al. [11] demonstrated that these hybrid models significantly outperform con ventional standalone AI algorithms, offering greater precision and reliability across various prediction scenarios. There is also a significant emphasis on implementing AI in decision-support systems in real-time within supply chain management. This approach allows for quick and accurate forecasting, even under changing conditions typical of fast-paced global value chains. Real-time AI-based decision-support systems enhance agile response capabilities, enabling supply chains to adapt quickly to shifts while making intelligent decisions within constrained time frames. Specifically, [PERSON] et al. [35] documented how SVM-based real-time forecasting systems have greatly improved decision-making, resulting in more agile and responsive supply chains. By examining how different keywords interrelate in the field of supply chain management through this thematic map, one can understand the terms clustered according to their frequencies and relevance in the existing literature. \"Supply chain management\", \"demand forecasting\", \"machine learning\", and \"big data\" are central topics in Cluster 1, reflecting core interests in the discipline. Terms like \"machine learning techniques\", \"neural networks\", and \"long short-term memory\" suggest a focus on advanced predictive and analytical methods. Cluster 2 includes keywords such as \"blockchain\", \"life cycle\", and \"behavioral research\", indicating an emerging interest in integrating blockchain technology into supply chain management and addressing global issues like COVID-19 and vaccine distribution. Cluster 3 covers methodological aspects, such as \"design/methodology/approach\", \"fuzzy neural networks\", and \"outsourcing\", which emphasize the design and optimization of supply chain processes. Cluster 4 highlights the role of AI in optimizing inventory management, focusing on themes like \"inventory control\", \"decision-making\", and \"artificial intelligence\". Terms such as \"predictive analytics\" and \"cost reduction\" reflect the ongoing need for efficiency and sustainability. Figure 11: Thematic clustering and trend.