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Abstract
Traditional spare parts demand forecasting techniques concentrate on
traditional forecasting techniques. These single mathematical function-based
forecasting techniques, although they have achieved a certain degree of success in spare parts forecasting, are unable to represent the relationship of demand for spare parts as accurate as a multiprocessing node-based feed-forward network. Artificial Neural Network technology has been adopted because of its ability to learn complex and non-linear relationships that are difficult to model with conventional techniques. Neural Networks can be trained to solve problems that are difficult for conventional computers or human beings.
This research investigates the applicability of neural networks in spare parts demand forecasting by incorporating the back-propagation learning process into historical data of parts demand.
Empirical results indicate that utilizing a back-propagation neural network outperforms conventional forecasting techniques in terms of forecasting accuracy.