Optimisation of Economic Order Quantity Using Neural Networks Approach

In this paper, a Back Propagation-Artificial Neural Network (BPANN) has been adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. The conventional approach to determine the parts requirements is the Economic Order Quantity (EOQ) method. The ability of neural models to learn, particularly their capability of handling large amounts of data simultaneously as well as their fast response time, are the characteristics desired for predictive and forecasting purposes. Here, the actual data obtained from a major auto parts supplier chain, involving a multi-layer system of supplying auto parts to car dealers, have been used to optimise and develop a BP-ANN model. The model has shown promising results in predicting parts orders with high degree of accuracy.