# Application of Neural Networks to Explore Manufacturing Sales Prediction
## 4 Results and Discussion
### Artificial Neural Network Prediction  
Study parameters were established from previous ANN studies. [PERSON] et al. [47] showed that a single hidden layer was sufficient for most nonlinear problems, and [PERSON] and [PERSON] [48] showed there was no standard for setting the number of hidden layer nodes, but recommended to use n, i.e., the column length or number of variates, as the number of nodes in the input layer to test the number of hidden layer nodes. Previous studies considering generally considered setting the learning rate for predictive and estimated problems between 1-10, with subtypes were set between 0.1-1.0 [PERSON] [49] indicated there was no absolute rule to determine network parameters, and parameter settings providing improved results could only be found through trial and error. Therefore, we set BPN within Neuro Solutions 7. The network has one hidden layer, with its number neurons as twice the input layer, whereas one neuron in the outer layer is used. The conversion function used is sigmoid, with learning rate, training iterations and error targets as 0.01-1.0, 2000 epochs, and MSE \(\leq\) 10.5, respectively. The BPN operation steps are as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Indicator** & **Pearson** & **Hypothesis** \\  & **Correlation** & **Test** \\ \hline X11 & Manufacturing industry sales volume index & 0.381 & **0.000** \\ X12 & Customs export values (SNTD Billion) & 0.339 & **0.000** \\ X13 & Import value of machinery and electrical equipment (SNTD Billion) & 0.273 & **0.003** \\ X14 & Lagging indicator composite index & 0.116 & 0.21 \\ X15 & Leading indicator excluding trend index & 0.076 & 0.411 \\ X16 & Unemployment rate (\%) & \(-\)0.115 & 0.214 \\ X17 & Manufacturing unit output labor cost index & \(-\)0.236 & 0.010 \\ X18 & Financial industry overnight interest rate & \(-\)0.031 & 0.741 \\ X19 & Loan and investment for all financial institutions (SNTD Billion) & 0.009 & 0.925 \\ X20 & Manufacturing inventory (10\({}^{3}\)) & 0.243 & 0.008 \\ X21 & Consumer confidence index & 0.225 & 0.014 \\ X22 & National income & 0.011 & 0.902 \\ X23 & Consumer price index & 0.042 & 0.652 \\ X24 & Industrial production index & 0.306 & **0.001** \\ X25 & Manufacturing industry production index & 0.297 & **0.001** \\ X26 & Plastic products manufactured per worker per month & 0.288 & **0.002** \\ X27 & Business turnover & 0.368 & **0.000** \\ X28 & Labor productivity index for plastic products manufacturing & 0.151 & 0.102 \\ X29 & General price index & 0.323 & **0.000** \\ X30 & Export orders (machinery) & 0.278 & 0.002 \\ X31 & The total value of trade imports (SNTD) & 0.441 & 0.000 \\ X32 & Commercial container unloading capacity (equivalent to 20 containers) & \(-\)0.075 & 0.419 \\ X33 & Freight tonage (metric ton) & 0.254 & 0.005 \\ X34 & SUSD spot exchange rate—exchange rate between bank and customer—buy (SNTD) & \(-\)0.430 & 0.000 \\ X35 & SUSD spot exchange rate—exchange rate between bank and customer—sell (SNTD) & \(-\)0.430 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **X6** & **X8** & **X11** & **X12** & **X27** & **X29** & **X31** \\ \hline Pearson correlation & 0.399 & 0.339 & 0.381 & 0.339 & 0.368 & 0.323 & 0.441 \\ \(p\)-Value & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Pearson correlation results. 1. Set the number of input, hidden, and output layers; inertia coefficient, learning rate, learning cycles and other relevant parameters. 2. Randomly determine initial BPN weights. 3. Transfer to the BPN to start calculation. 4. Correct join and bias weights according to errors. 5. If not yet converged, repeat steps 3-5. After selecting the relevant factors, they were input into the BPN learning using 7/8 of the available data as the training set with the remaining 1/8 served as the test set.