An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production

The determination of completion time to produce a new product is one of the most important indicators for manufacturers in delivering goods to customers. Failure to fulfil delivery on-time or known as tardiness contributes to a high cost of air
shipment and production line down at other entities within the supply chain. The uncertainty of completion time has created a big problem for manufacturers of audio speakers which involved semiautomatic production lines. Therefore, the main
objective of this research is to develop an integrated model that enhances the artificial neural networks (ANN) and system dynamics (SD) methods in estimating completion time focusing on the cycle time. Three ANN models based on multilayer
perceptron (MLP) were developed with different network architectures to estimate cycle time. Furthermore, a proposed momentum rate equation was formulated for each model to improve learning process, where the 3-2-1 network emerged as the best network with the smallest mean square error. Subsequently, the estimated cycle time of the 3-2-1 network was simulated through the development of an SD model to evaluate the performance of completion time in terms of product quantity, manpower fatigue and production workload scores. The success of the proposed integrated ANNSD model also relied on a proposed coefficient correlation of causal loop diagram (CLD) to identify the most influential factor of completion time. As a result, the proposed integrated ANNSD model provided a beneficial guide to the company in determining the most influential factor on completion time so that the time to complete a new audio product can be estimated accurately. Consequently, product delivery was smooth for on-time shipment while successfully fulfilling customers’ demand.