## 4 Model (SIRPM)
### Characteristics and Benefits of the Model (SIRPM)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Technology** \\ **Integration** \\ **(RPA, Machine** \\ **Learning, IoT)** \\ \end{tabular} & \begin{tabular}{c} **Method/** \\ **Algorithm** \\ **Algorithm** \\ \end{tabular} & \begin{tabular}{c} **Intelligent** \\ **Automation** \\ \end{tabular} & \begin{tabular}{c} **Continuous** \\ **Learning** \\ \end{tabular} & \begin{tabular}{c} **Flexibility** \\ **and** \\ **Scalability** \\ \end{tabular} & \
\begin{tabular}{c} **Real-Time** \\ **Monitoring** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Efficiency and** \\ **Sustainability** \\ \end{tabular} \\ \hline
[43] [PERSON]; [PERSON]; [PERSON] (2021) & \
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} &
\begin{tabular}{c} Random Forest, \\ SVM \\ \end{tabular} & x & Moderate & & \\ \hline
[44] [PERSON]; \\ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2020) & AI & Bayesian Networks & x & Moderate & x & \\ \hline
[45] [PERSON]; \\ [PERSON]; \\ [PERSON] (2022) & \
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} &
\begin{tabular}{c} Random Forest, \\ Decision Trees \\ \end{tabular} & x & Moderate & Limited & \\ \hline
[46] [PERSON]; [PERSON] (2021) & RPA &
\begin{tabular}{c} Rule-Based \\ Automation \\ \end{tabular} & x & & High & Moderate \\ \hline
[47] [PERSON]; \\ [PERSON]; [PERSON] (2020) & AI &
\begin{tabular}{c} Search Algorithms, \\ Dynamic \\ Programming \\ \end{tabular} & & Moderate & x & \\ \hline
[48] [PERSON]; [PERSON]; [PERSON] (2021) & RPA &
\begin{tabular}{c} Rule-Based \\ Automation \\ \end{tabular} & & & High & & \\ \hline
[49] [PERSON]; [PERSON]; [PERSON] (2021) & IoT, Machine &
\begin{tabular}{c} Random Forest, \\ Decision Trees, \\ KNN \\ \end{tabular} & x & Moderate & Moderate & \\ \hline
[50] [PERSON]; [PERSON]; [PERSON] (2021) & \
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} &
\begin{tabular}{c} Random Forest, \\ Decision Trees \\ \end{tabular} & x & Moderate & & \\ \hline
[51] [PERSON]; [PERSON]; [PERSON] (2021) & AI &
\begin{tabular}{c} Reinforcement \\ Learning \\ Algorithms \\ \end{tabular} & & Moderate & & \\ \hline
[52] [PERSON]; [PERSON]; [PERSON]; [PERSON] (2023) & AI, IoT &
\begin{tabular}{c} Reinforcement \\ Learning \\ Algorithms \\ \end{tabular} & x & Moderate & x & Moderate \\ \hline
[53] [PERSON]; \\ [PERSON]; \\ [PERSON]; [PERSON] (2023) & RPA &
\begin{tabular}{c} Rule-Based \\ Automation \\ \end{tabular} & & High & & \\ \hline
[54] & & & & & & \\ [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; \\ [PERSON] [PERSON]; [PERSON] (2020) & \
\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} &
\begin{tabular}{c} Random Forest, \\ Decision Trees \\ \end{tabular} & x & Moderate & & \\ \hline
[55] [PERSON], [PERSON], [PERSON]; [PERSON] (2023) & Deep Learning, IoT &
\begin{tabular}{c} Deep Neural \\ Networks (DNNs) \\ \end{tabular} & x & High & x & x \\ \hline
[56] [PERSON]; [PERSON]; [PERSON] (2024) & RPA, AI &
\begin{tabular}{c} Rule-Based \\ Automation, UI \\ Automation \\ \end{tabular} & x & High & x & Moderate \\ \hline
[57] [PERSON]; \\ [PERSON]; \\ [PERSON]; \\ [PERSON]; [PERSON]. (2023) & RPA &
\begin{tabular}{c} Rule-Based \\ Automation \\ \end{tabular} & x & High & x & Moderate \\ \hline \hline \end{tabular}
\end{table} 
Table 12: _Cont._The comparative analysis between existing studies and the proposed SIRPM model clearly demonstrates the distinct advantages that our model offers. Table 12 shows that while many previous models integrate technologies such as machine learning, the IoT, or RPA in isolation or with varying levels of automation, the SIRPM model stands out for its advanced combination of RPA and machine learning, providing intelligent automation and continuous learning. In addition, SIRPM stands out for its flexibility and scalability, allowing for efficient adaptations and expansions according to the company's needs.