## 4 Findings Results
### Descriptive Findings
#### 4.1.8 Keywords and Subject Clusters within the Publications
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Author(s) and** & **Research Goals** & **ML/DL Models** & **Dataset** & **Key Findings** & **Advantages** & **Limitations** \\
**Year** & **Research Goals** & **Used** & **Characteristics** & **DL models enhance & & \\
**[PERSON]**[26] & \begin{tabular}{c} To explore the role of AI \\ in optimizing demand \\ forecasting in operations \\ management \\ \end{tabular} & \begin{tabular}{c} Deep learning \\ models (CNN, \\ LSTM) \\ \end{tabular} & \begin{tabular}{c} Supply chain \\ operations data \\ \end{tabular} & \begin{tabular}{c} Supply chain \\ operations data \\ \end{tabular} & \begin{tabular}{c} DD models enhance \\ demand prediction \\ accuracy, especially \\ for volatile demand \\ patterns \\ \end{tabular} & \
\begin{tabular}{c} Handles large \\ datasets effectively \\ \end{tabular} &
\begin{tabular}{c} Long training times \\ and potential \\ overfitting \\ \end{tabular} \\ \hline
**[PERSON]**[27] & \begin{tabular}{c} To investigate process \\ optimization in supply \\ chains through AI-driven \\ demand forecasting \\ \end{tabular} & \begin{tabular}{c} Gradient boosting, \\ decision trees \\ \end{tabular} & \begin{tabular}{c} Manufacturing and \\ logistics datasets \\ \end{tabular} & \begin{tabular}{c} AI-driven models \\ increase efficiency in \\ supply chain \\ management by \\ optimizing \\ inventory levels \\ \end{tabular} & \
\begin{tabular}{c} Reduces human \\ intervention in \\ forecasting \\ \end{tabular} &
\begin{tabular}{c} Model \\ interpretability can \\ be challenging \\ \end{tabular} \\ \hline
**[PERSON]**[31] & \begin{tabular}{c} To evaluate traditional \\ and ML-based demand \\ forecasting methods \\ \end{tabular} & \begin{tabular}{c} Traditional methods \\ vs. ML models (e.g., \\ ARIMA, SVM) \\ \end{tabular} & \begin{tabular}{c} Historical sales and \\ demand data \\ \end{tabular} & \begin{tabular}{c} ML models provide \\ accuracy with better \\ scalability \\ \end{tabular} & \
\begin{tabular}{c} Balances traditional \\ and modern \\ approaches \\ \end{tabular} &
\begin{tabular}{c} ML models require \\ more data and \\ fine-tuning \\ \end{tabular} \\ \hline
**[PERSON]**[32] & \begin{tabular}{c} To implement ML \\ techniques in \\ design-support systems \\ for supply chain demand forecasting \\ \end{tabular} & \begin{tabular}{c} Ensemble learning \\ methods (e.g., \\ modeling, boosting) \\ \end{tabular} & \begin{tabular}{c} Ensemble methods \\ outperform single \\ chain datasets \\ \end{tabular} & \begin{tabular}{c} Ensemble methods \\ outperforms high demand \\ models in demand prediction \\ prediction \\ \end{tabular} & \
\begin{tabular}{c} Ensembles robustness \\ and accuracy \\ \end{tabular} &
\begin{tabular}{c} Requires more \\ computational \\ resources \\ \end{tabular} \\ \hline
**Hu H, 2023**[25] & \begin{tabular}{c} To explore cutting-edge \\ AI/ML techniques in \\ demand processing for dynamic supply chains \\ \end{tabular} & \begin{tabular}{c} Advanced DL \\ models (e.g., \\ models (e.g., \\ transformers)) \\ \end{tabular} & \begin{tabular}{c} Real-time and \\ historical data \\ integration \\ \end{tabular} & \
\begin{tabular}{c} Transformer models \\ capture temporal \\ dependencies better \\ than traditional \\ than traditional \\ methods \\ \end{tabular} &
\begin{tabular}{c} High accuracy in \\ real-time \\ high computational \\ cost \\ \end{tabular} \\ \hline
**[PERSON]**[33] & \begin{tabular}{c} To analyze data-driven \\ demand forecasting \\ using AI in supply chains \\ \end{tabular} & \begin{tabular}{c} Clustering, decision \\ trees \\ \end{tabular} & \begin{tabular}{c} Retail and \\ distribution data \\ \end{tabular} & \begin{tabular}{c} Approaches improve \\ demand forecasting \\ by identifying patterns \\ underlying patterns \\ \end{tabular} & \
\begin{tabular}{c} Effective in \\ managing large \\ datasets \\ \end{tabular} &
\begin{tabular}{c} May require \\ domain-specific \\ tuning \\ \end{tabular} \\ \hline
**Ablogbase**[34] & \begin{tabular}{c} To assess the impact of \\ AI on demand \\ forecasting in production \\ economies \\ \end{tabular} & \begin{tabular}{c} Neural networks, \\ regression models \\ \end{tabular} & \begin{tabular}{c} Production and \\ demand datasets \\ from various \\ industries \\ \end{tabular} & \begin{tabular}{c} All models lead to \\ timey predicates in \\ production planning \\ \end{tabular} & \
\begin{tabular}{c} Scalable across \\ industries \\ \end{tabular} &
\begin{tabular}{c} Requires continuous \\ model updating \\ \end{tabular} \\ \hline
**Shokouhif**[29] & \begin{tabular}{c} To incorporate \\ sustainability into \\ demand forecasting using AI \\ \end{tabular} & \begin{tabular}{c} DL models with \\ environmental \\ factors \\ \end{tabular} & \begin{tabular}{c} Supply chain data \\ with sustainability \\ metrics \\ \end{tabular} & \begin{tabular}{c} Sustainable AI \\ demand forecasting \\ with green supply \\ chain practices \\ \end{tabular} & \
\begin{tabular}{c} Balances economic \\ and environmental \\ goals \\ \end{tabular} &
\begin{tabular}{c} May face trade-offs \\ between accuracy \\ and sustainability \\ objectives \\ \end{tabular} \\ \hline \end{tabular}
\end{table}