Prediction on Demand for Storage by Using Artificial Neural

Keywords: Artificial Neural Networks, Prediction, Invetory, Optimal Quantity, Back Propagation Algorithm


In this research, neural networks with back-propagation of error were used for the purpose of predicting time series for one of the warehouses belonging to the Ministry of Defense, which is the Babylon Supply warehouse, for a period of five months, with the aim of finding the optimal storage size for some dry livelihoods. The results were analyzed and tested to find out and determine the appropriate storage model, and since the coefficient of variation of the data it is less than 20%, so the model is specific and it is a model of purchasing without a deficit. The WQSB.V2 program was used to obtain the results for the mathematical model used for the different materials in storage, which are flour and rice, and for different months of the year. The results were obtained with the lowest percentage of error and to determine the optimal size of storage and the economic quantity. Optimum, safety stock, reorder period, safety period, and total cost for each material. The majority of materials had a reorder period of up to 14 to 24 days and a safety period of up to 7 days.


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