A model on forecasting safety stock of ERP based on BP neural network

Li Zhang · Dan Wang · Chang Lu

15 citations0/0 files storedIEEE International Conference on Management of Innovation and Technology (ICMIT)

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Supply Chain and Inventory Management Forecasting Techniques and Applications Evaluation and Optimization Models

Abstract

Safety stock is one of the important parts of logistics management in ERP. This paper attempts to use artificial neural networks to forecast safety stock. Based on the index system of safety stock, this paper constructs a three-level BP neural network to analyze the principle and model of safety inventory. By using the samples to train and inspect the BP neural network, we conclude that the application of BP neural networks is an effective method to forecast safety stock, and can also be used to find the key factors for enterprises to improve their logistics management level.

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References

2004Enterprise resource planning 2002Classifying inventory using an artificial neural network approach 2001Coordinating supply chain inventories through common replenishment epochs 2007An advanced supply chain management tool based on modeling and simulation 2007A neural network evaluation model for ERP performance from SCM perspective to enhance enterprise competitive advantage

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