Neural Network-Based Forecasting of Inventory Risk Level for Spare Parts
Rong Du · Zhongguo guanli kexue · 2008
This paper proposes a neural network-based classification approach to inventory risk level of spare parts.Firstly a fuzzy evaluation of spare parts is made in terms of their availability of suppliers,importance,predictability of failure,specificity and lead time.Then a multi-layer feed forward neural network model is established. The Back Propagation(BP)algorithm for training a neural network is used to decide the weights to connections in the model.Choosing a sample of historical data of 100 spare parts and undertaking a BP training stimulation, the model is used to predict the inventory risk levels of 60 spare parts for a well-logging service firm.The forecasting reliability reaches 84%.