Design and development of a knowledge discovery system in inventory management

To manage the inventory efficiently, it is necessary to have accurate forecasting. To extract and deploy the knowledge associated with forecasting attracts the attention of both academic and practitioners. Knowledge is regarded as a valuable asset for enterprises and it can be manipulated through intelligence techniques like artificial neural networks (ANN). ANN has the special ability to learn facts about one knowledge domain by inputting data obtained from observations. This study focuses on exploring how ANN learns and analyzes different types of ANN and ANN architectures used in the demand forecasting. The feasibility of the proposed approach to the demand forecasting issue is demonstrated with numeric data. The significance of this study is to adopt ANN as a knowledge discovery system thereby enhancing the inventory management.