Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction
problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers
of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to
determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were
trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network
was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product.
Hence, the prediction result could facilitate production planners in managing assembly processes on the production line