# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 6 Conclusions  
In conclusion, this research makes a substantial contribution to supply chain management by leveraging advanced machine learning techniques for demand prediction. The study follows a systematic approach, starting with the meticulous gathering and cleaning of input data from the supply chain, ensuring accuracy and relevance. The focus on retail-specific features, extracted through probabilistic and Apriori approaches, adds a layer to the analysis. The incorporation of statistical and time series feature extraction methods, such as mean, variance, log-log linear regression, and two-level linear models, further enhances the model's capability to capture temporal patterns in \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Damand Forecasting Dataset**} \\ \cline{2-11}  & \multicolumn{8}{c}{**Delay 25**} & \multicolumn{8}{c}{**K-Fold 10**} \\ \cline{2-11}  & **MAE** & **MSE** & **RMSE** & **RMSE** & **R-Squared** & **MAE** & **MSE** & **RMSE** & **RMSE** & **R-Squared** \\ \hline RF Regressor & 1.88 & 8.73 & 2.95 & 0.029 & 0.80 & 1.67 & 6.87 & 2.62 & 0.025 & 0.84 \\ \hline Ensemble & 1.95 & 9.66 & 3.11 & 0.030 & 0.81 & 1.77 & 7.07 & 2.66 & 0.026 & 0.87 \\ \hline ETS & 1.90 & 9.49 & 3.08 & 0.030 & 0.82 & 1.70 & 6.97 & 2.64 & 0.026 & 0.88 \\ \hline LSTM Grid Search & 1.86 & 8.55 & 2.92 & 0.028 & 0.82 & 1.66 & 4.83 & 2.20 & 0.021 & 0.90 \\ \hline Recurrent BiLSTM & 1.85 & 8.14 & 2.85 & 0.028 & 0.84 & 1.32 & 4.59 & 2.14 & 0.021 & 0.94 \\ \hline NARX & 1.82 & 7.92 & 2.81 & 0.027 & 0.89 & 1.31 & 3.82 & 1.96 & 0.019 & 0.95 \\ \hline UDAF & 1.73 & 4.14 & 2.03 & 0.020 & 0.94 & 1.22 & 1.16 & 1.08 & 0.010 & 0.96 \\ \hline \hline \end{tabular}
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Table 1: Demand forecasting dataset comparison for delay and K-fold. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Daily Demand Forecasting Orders**} \\ \cline{2-11}  & \multicolumn{8}{c}{**Delay 25**} & \multicolumn{8}{c}{**K-Fold 10**} \\ \cline{2-11}  & **MAE** & **MSE** & **RMSE** & **RMSE** & **R-Squared** & **MAE** & **MSE** & **RMSE** & **RMSE** & **R-Squared** \\ \hline RF Regressor & 5.38 & 12.32 & 3.51 & 0.034 & 0.70 & 2.80 & 8.21 & 2.87 & 0.043 & 0.85 \\ \hline Ensemble & 5.36 & 11.56 & 3.40 & 0.033 & 0.74 & 2.90 & 8.41 & 2.90 & 0.039 & 0.85 \\ ARIMA-NN & 5.36 & 11.56 & 3.40 & 0.033 & 0.74 & 2.90 & 8.41 & 2.90 & 0.039 & 0.85 \\ \hline ETS & 5.18 & 10.23 & 3.20 & 0.031 & 0.76 & 2.83 & 8.31 & 2.88 & 0.035 & 0.86 \\ \hline LSTM Grid Search & 5.12 & 8.21 & 2.86 & 0.027 & 0.78 & 2.80 & 6.17 & 2.48 & 0.035 & 0.86 \\ \hline Recurrent BiLSTM & 3.46 & 8.05 & 2.84 & 0.027 & 0.80 & 2.45 & 5.94 & 2.44 & 0.034 & 0.88 \\ \hline NARX & 3.20 & 7.92 & 2.81 & 0.027 & 0.87 & 2.45 & 5.16 & 2.27 & 0.032 & 0.89 \\ \hline UDAF & 2.95 & 7.85 & 2.80 & 0.026 & 0.90 & 2.35 & 2.50 & 1.58 & 0.031 & 0.90 \\ \hline \hline \end{tabular}
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Table 3: Daily demand forecasting orders dataset comparison for delay and K-fold. demand. The application of a recurrent BiLSTM network, combined with an NARX model, reflects a sophisticated approach to demand forecasting that considers both historical data and external factors. The fusion framework, which merges predictions from two models, seeks to enhance the precision and dependability of demand forecasts within the constantly changing supply chain context. The uni-regression deep approximate forecasting model achieves the lowest values of MAE, MSE, RMSE, RMSSE, and R-squared, specifically 1.73, 4.14, 2.03, 0.020, and 0.94, respectively. These low error values demonstrate the model's precision in forecasting demand. This study not only enhances academic comprehension of demand forecasting but also offers valuable practical guidance for supply chain professionals seeking more robust and effective prediction methods to optimize their operations and reduce excess inventory costs while meeting customer demand.