## 4 Findings Results
### Descriptive Findings
#### 4.1.8 Keywords and Subject Clusters within the Publications

Table 3: _Cont._  
Figure 9: The most globally cited documents [24; 25; 26; 27; 28; 29; 31; 32; 33; 34]. which research hotspots were at the forefront during each period. Researchers can trace these hotspots by sequentially examining keyword clusters, from 0 to 7, reflecting increased scholarly attention on forecasting demand using machine learning (ML) technologies (clusters 1 to 5). A cluster analysis revealed a variety of patterns within the field (see Table 4 and Figure 10). Cluster 1 is mainly devoted to supply chain management and review articles, which mostly appeared after 2019, indicating that foundational scholarship and critical evaluations were being enhanced during this period. Cluster 2 focuses on smart systems, covering vaccine supply chain management and the use of deep learning in other domains, with a strong emphasis on developing AI models for practical applications, particularly in response to global issues like vaccine allocation, which became prominent between 2020 \begin{table}
\begin{tabular}{p{28.5 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \# & **\# & **\# & **\# & **\# & **\# & **\# \\ \hline
1 & 18 & 0.988 & Supply chain management & Exploring application (6.19, 0.05) & Review (0.4) & 2019 \\ \hline
2 & 18 & 0.967 & Intelligent system & Vaccine supply chain management (6.19, 0.05) & Using deep learning technique (0.4) & 2020 \\ \hline
3 & 15 & 0.924 & Machine learning technique & Machine learning technique (6.62, 0.05) & Learning approach (0.34) & 2016 \\ \hline
4 & 14 & 1 & Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities & Supply chain demand forecasting (5.22, 0.05) & Supply chain demand forecasting (0.11) & 2020 \\ \hline
5 & 7 & 1 & Modeling wholesale distribution operations: an artificial intelligence framework & Modeling wholesale distribution operations (6.95, 0.01) & Modeling wholesale distribution operations (0.08) & 2021 \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the 5 largest clusters. Figure 10: The 5-subject cluster diagram. and 2022. Cluster 3 explores different machine learning techniques and innovative learning methods, yet it still aligns with the themes discussed in Cluster 2, thereby showing an expanding yet deepening understanding of various ML types during this period. In addition, Cluster 4 is primarily about forecasting using big data analytics for predicting demand within a supply chain, reaching its peak in 2016. This suggests that big data should be increasingly utilized for making predictions through improved techniques. This cluster also includes findings related to the creation and use of everyday prediction systems based on artificial neural networks for supermarket sales floors around 2012, implying early experimentation with neural networks in real-world forecasting scenarios during that time. Cluster 5 focuses on modeling wholesale distribution activities using intelligent artificial neural computer systems, with notable developments recorded in 2021. These developments highlight continued advancements in neural computing to improve complex distribution networks. All these clusters consistently show very high silhouette values, demonstrating coherence and high research quality. This comprehensive analysis of different research patterns, techniques, and time trajectories in thematic areas like supply chain management, intelligent systems, and advanced analytics provides valuable insights; thus, further study into what each distinct cluster uniquely contributes is necessary. The evolution from ML applications in demand forecasting to data-driven methodologies reflects how much this field has developed over time. Future research should build on existing frameworks to enhance methods and broaden their applications for anticipating demand. An analysis of keyword-rich content and the relationships between various topics generally shows how knowledge production has changed over time, the focus of studies, and the development of the field.