## 3 Methodology
### Comparison with Other Inventory Forecasting Models  
In this section, we compare the prediction results with various AI models for inventory forecasting [45; 46]. At the beginning of the investigation, the parameter settings for each model were firstly determined. The parameter settings for each AI model are summarized in Table 4, determined based on the results of the lowest RMSE. \begin{table}
\begin{tabular}{c c} \hline \hline
**Inventory Forecasting Method** & **Parameter Setting** \\ \hline Last-value method & None \\ Simple averaging method & Average days = 14 \\ Moving averaging method & Average days = 14 \\ Weighted moving average method & Average days = 14 \\ Multiple linear regression method & Fit intercept = True \\ Ridge linear regression method & Alpha = 0.5 \\ Lasso linear regression method & Alpha = 0.5 \\ Random forest regression method & Number of estimators = 70, Random state = 666 \\ XGBoost method & Learning rate = 0.1, max depth = 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for each AI model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SKU ID** & **Week1** & **Week2** & **Week3** & **Week4** \\ \hline
0 & SK014 rnU5 & 1.210902 & 1.574173 & 2.058533 & 2.701825 \\
1 & SK014 TYJ & 12.672414 & 16.474139 & 21.543105 & 28.275325 \\
2 & SK017 ChA & 0.794154 & 1.032401 & 1.350062 & 1.771957 \\
3 & SK017 ZaL & 1.512163 & 1.965812 & 2.570677 & 3.374014 \\
4 & SK018 hXR & 0.889771 & 1.156702 & 1.512611 & 1.985302 \\... &... &... &... &... \\
158,184 & SKZzwsYm & 0.238165 & 0.309615 & 0.404880 & 0.531406 \\
158,185 & SKZzwXlc & 0.302657 & 0.393454 & 0.514516 & 0.675302 \\
158,186 & SKZZxför & 1.643016 & 2.135921 & 2.793127 & 3.665979 \\
158,187 & SKZzY1 AJ & 1.070267 & 1.391347 & 1.819453 & 2.388032 \\
158,188 & SKzZYFOC & 0.556377 & 0.723290 & 0.945841 & 1.241417 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inventory forecasting results over four weeks. Table 5 summarizes the results of different inventory forecasting models in terms of the RMSE and the computation time. The results revealed that the XGBoost Method achieved the lowest error compared with other algorithms. The RMSE was approximately 1.4-times better than the second-best AI model, random forest regression, in which the last-value method achieved the lowest accuracy but the fastest computation time among the other algorithms. The computation time of the XGBoost method is approximately the median of other algorithms. To better compare the capabilities of different forecasting methods, Figure 2 presents the graphical results of different algorithms based on the accuracy and computation time. The \(x\)-axis is 1/RMSE, which means that a higher value shows a better computation accuracy, while the \(y\)-axis refers to the computation time of the algorithm. The results revealed that that the inventory forecasting method can be basically divided into four groups. The worst group, including lasso linear regression, multiple linear regression, and ridge linear regression models, has the lowest computation efficiency and computation accuracy. The second group, including the last value, weighted moving average, simple average, and moving average method, has the highest computation efficiency, but the computation accuracy is the lowest. The third group is the random forest regression method, which has average computation efficiency and good accuracy. The last group is the XGBoost method, which demonstrated the highest accuracy and average computation efficiency. In this study, the training parameters including learning rate, maximum depth, etc. were defined by referring to the related research by [PERSON] and [PERSON] [47]. Figure 2: Graphical representation of the computation efficiency and accuracy for various forecasting models.