Research on the Forecasting of Inventory Risk Management of Spare Parts: A Neural Network Model

摘要

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 multilayer 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 welllogging service firm. The forecasting reliability reaches 84%.