# Safety Maintains Lean Sustainability and Increases Performance through Fault Control
## 6 Sustainable Lean Safety Performance Enhancement  
The improvement was done by tracking the activities in time-at-risk cases during the studying interval. This work resorted to using an optimization tool such as Artificial Neural Networks (ANN) because there are no linear dependencies between input and output data (i.e., evaluate all possible values of a certain \"unknown\" function) by solely establishing the nonlinear relations between input or output datasets, based on the learning process itself. The ANN has the ability to force using the Simple Moving Average (SMA) to monitor the VA and NNVA activities with time. Finally, at the end of the run, will obtain the array of SMA values for each time-cost at a moment \(t\). Table 18 illustrates the Neural Network input data. The number of neurons is 21, while the second layer of network models has 19 neurons. The regression analysis was implemented on a specific training data set loaded on the local dataset to determine highly accuracy running performance with correlation coefficient R, which approximates a value of 0.999. The performance of tracking the faults interval via MSE of 0.027 at Epoch 3 and the R between the target and output for validation data was 0.9744. The results of testing for ANN used in this work illustrated in Figure 11, where the convergence becomes valid when the R between standard values calculated from Table 13 and predicted output is \(>\) 80%, to reduce the defects related with faults similar to the [PERSON] et al. approach to reach zero faults [47]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**LSPL** & \(\mathbf{x^{2}}\) & **d.f** & \(\mathbf{x^{2}/d.f}\) & **GFI** & **AGFI** & **CFI** & **SRMR** & **RMSE** \\ \hline Measurement values & 562.175 & 237 & 2.030 & 0.831 & 0.820 & 0.974 & 0.071 & 0.074 \\ Recommended values & & \(\leq\) 0.30 & \(\geq\) 0.90 & \(\geq\) 0.90 & \(\geq\) 0.90 & \(\leq\) 0.08 & \(\leq\) 0.08 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline
**GFI:** Goodness of Fit Index [0:1], AGFI: Adjusted Goodness of Fit Index [0:1], CFI: Comparative Fit Index. SRMR: Standardized Root Mean Square Residual, RMSEA: Root Mean Square Error of Approximation.  
\end{table} 
Table 16: Results of fit indices for CFA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LSPL Stages** & \begin{tabular}{c} **Identify and** \\ **Planning** \\ **Stage** \\ \end{tabular} & \begin{tabular}{c} **Design Stage** \\ **Instruments** \\ \end{tabular} & \begin{tabular}{c} **Tracking and** \\ **Test Stage** \\ \end{tabular} & \begin{tabular}{c} **Work Safety** \\ **and Control** \\ **Performance** \\ \end{tabular} & \begin{tabular}{c} **Expected Lean** \\ **Performance** \\ \end{tabular} \\ \hline Identify and Planning & 0.881 & & & & & \\ Design stage & 0.608 ** & 0.787 & & & \\ instruments & 0.534 ** & 0.693 ** & 0.819 & & \\ Work safety and control & 0.397 ** & 0.657 ** & 0.638 ** & 0.949 & \\ \
\begin{tabular}{c} Expected Lean \\ performance \\ \end{tabular} & 0.429 ** & 0.449 ** & 0.540 ** & 0.640 ** & 0.851 \\ CR & 0.946 & 0.919 & 0.924 & 00.974 & 0.913 \\ AVE & 0.777 & 0.620 & 0.671 & 0.902 & 0.725 \\ \hline \hline \end{tabular}
\begin{tabular}{c} The second derivative of the variance, where ** means \(p\leq\) 0.01, While ** means \(p>0.05\). \\ \end{tabular}
\end{table} 
Table 17: Correlation matrix and average variance extracted (AVE). Figure 12 illustrates the standard value deduced from Table 13 vs. the output plot for the trained ANN simulated by all training dataset stored on the ERP's enterprise system via running the code in Appendix A. The performance of the network can be improved if training data increasingly take into account the effect of Fault Tree Analysis (FTA), as discussed by [PERSON] [48], where authors suffered from collecting the data, where it is collected in the manufacturing environment, not a laboratory environment.