## 4 Discussion
### Inventory Management Implications  
In terms of the implementation ease, the EOQ model has the upper hand. As one of the most classical inventory management methods, EOQ can be readily implemented using basic mathematical calculations and does not require specialized software. However, its assumptions of constant demand and non-deteriorating items may limit its effectiveness in our scenario of constant-deteriorating items with time-varying demand. Conversely, the Q-Learning technique, being a type of reinforcement learning algorithm, necessitates an advanced understanding of ML and AI. However, it is potentially more suited for our scenario, given its ability to adapt to variable demand and optimize the total profit. The \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Item-1 (cV = 36.94)**} \\ \hline
**KPI** & **FR (\%)** & **AIL (qty.)** & **THC** & **\# of Stockouts** & **MAE** & **MAPE (\%)** & **RMSE** \\ \hline SES & \(89.3\) & \(-87\) & \(0\) & \(12\) & \(88.5\) & \(1107\) & \(104.57\) \\ \hline ARIMA & \(98.56\) & \(\mathbf{12.25}\) & **61,200** & \(6\) & \(\mathbf{73.91}\) & \(1016\) & \(\mathbf{94.56}\) \\ \hline ANN & \(66.46\) & \(-315\) & \(0\) & \(12\) & \(106.25\) & \(710.28\) & \(138.21\) \\ \hline LSTM & \(\mathbf{100}\) & \(308\) & \(441,529\) & \(\mathbf{0}\) & \(173.91\) & \(3795\) & \(208.39\) \\ \hline SVR & \(15.42\) & \(-797\) & \(0\) & \(12\) & \(125.66\) & \(\mathbf{82.84}\) & \(164.69\) \\ \hline RF & \(92.69\) & \(-17.5\) & \(20,519\) & \(6\) & \(100.25\) & \(1232\) & \(116.39\) \\ \hline W-ANN & \(96.24\) & \(14.5\) & \(81,124\) & \(5\) & \(99\) & \(1212\) & \(127.09\) \\ \hline W-LSTM & \(\mathbf{100}\) & \(150\) & \(216,290\) & \(\mathbf{0}\) & \(127.08\) & \(1496\) & \(152.94\) \\ \hline \multicolumn{6}{c}{**Item-2 (cV = 64.95)**} \\ \hline SES & \(63\) & \(-107\) & \(0\) & \(12\) & \(\mathbf{17.5}\) & \(204.18\) & \(29.5\) \\ \hline ARIMA & \(73.85\) & \(-83\) & \(0\) & \(12\) & \(19.16\) & \(271.80\) & \(30.5\) \\ \hline ANN & \(83.35\) & \(-30\) & \(210,023\) & \(10\) & \(19.75\) & \(282.87\) & \(29.91\) \\ \hline LSTM & \(52.06\) & \(-117\) & \(0\) & \(12\) & \(21.83\) & \(169.93\) & \(\mathbf{28.5}\) \\ \hline SVR & \(90.14\) & \(373\) & \(15,490,057\) & \(\mathbf{2}\) & \(90.16\) & \(836.03\) & \(130.57\) \\ RF & \(62.23\) & \(-111\) & \(0\) & \(12\) & \(\mathbf{17.5}\) & \(221.79\) & \(30.87\) \\ \hline W-ANN & \(92.57\) & \(\mathbf{5.75}\) & \(\mathbf{946,825}\) & \(6\) & \(34.58\) & \(\mathbf{129.85}\) & \(41.12\) \\ \hline W-LSTM & \(\mathbf{98.42}\) & \(18\) & \(960,597\) & \(\mathbf{2}\) & \(30.91\) & \(275.46\) & \(39.62\) \\ \hline \multicolumn{6}{c}{**Item-3 (cV = 44.91)**} \\ \hline SES & \(71.61\) & \(-27,103\) & \(0\) & \(12\) & \(4543\) & \(31.4\) & \(5644\) \\ \hline ARIMA & \(69.52\) & \(-28,458\) & \(0\) & \(12\) & \(5094\) & \(35.82\) & \(6188\) \\ \hline ANN & \(61.90\) & \(-41,384\) & \(0\) & \(12\) & \(4794\) & \(33.22\) & \(6485\) \\ \hline LSTM & \(96.85\) & \(2508\) & \(\mathbf{14,395}\) & \(5\) & \(\mathbf{3669}\) & \(\mathbf{30.53}\) & \(\mathbf{4055}\) \\ \hline SVR & \(90.56\) & \(16,679\) & \(26,270\) & \(7\) & \(9905\) & \(90.62\) & \(13,307\) \\ \hline RF & \(57.58\) & \(-39,752\) & \(0\) & \(12\) & \(6283\) & \(45.03\) & \(7338\) \\ \hline W-ANN & \(93\) & \(\mathbf{1742}\) & \(15,985\) & \(4\) & \(5562\) & \(43.38\) & \(5768\) \\ \hline W-LSTM & \(\mathbf{100}\) & \(16,016\) & \(60,543\) & \(\mathbf{0}\) & \(11,A34\) & \(83\) & \(12,611\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Implication of forecasting under different KPIs based on the original data. resources required for implementing each method vary significantly. The EOQ model, due to its simplicity, has minimal resource requirements. It can be calculated using basic tools and does not demand a high level of technical expertise. In contrast, the Q-Learning model requires substantial computational resources and data science expertise.