Artificial poka-yoke increases process reliability via a hybrid of the neural network with arima results

A product's sustainability fails if its standard function deviates from the desired working conditions (i.e., by making an unscheduled stop), which makes less competitive. This study aims to develop a smart autonomation system via closer monitoring of the time series' forecasting model of deviation behavior to enable it to stop for maintenance when needed. The proposed methodology (Artificial Poka-yoke; APY) achieves the objective via two sequential phases focusing on improving the operation’s performance, (deviation behavior to stop the machine before antagonizing HBL’s essential elements). The methodology alters the ARIMA's trajectory and its error, as a Neural-Network (NN) inputs model is used to simulate them in the second phase to enhance its accuracy by comparing minimized MAE and RMSE values, through 72 trials and revealing the Poka-yoke index error. An APY approach is adopted for an electrical combustion engine to enhance its reliability level, based on intake control, unloaders valve and rifle drilled. The proposed methodology demonstrates its ability to forecast energy consumption related to that actually generated effectively and accurately. The methodology guarantees a reduction in downtime and waste by 0.71% as a profit saving via ARIMA (2,1,1) x(0,1,1)6 supported with a neural network to create smart operation.