Inventory performance evaluation based on demand forecast with Neural Networks (MLP) approach
Subject Areas : مدیریت
1 - M.A., Department of Industrial Management, Farabi Campus, University of Tehran, Tehran, Iran (Correspondence)
Keywords: Neural network, Forecast, Inventory management, Artificial Intelligence,
Abstract :
Proper management and better control of inventory of food items are one of the most essential and important objectives of food store managers. The store inventory management to improve performance, increase customer service and reduce the deficit in the following days will be. The purpose of this paper is to provide a prediction model for demanding meat products from Gorgan ETKA chain stores in order to improve inventory performance. In this study, the ANNmlp model has been used to predict the meat market demand of this store and is also compared with ARIMA and 14-day moving average to understand the accuracy of prediction. For this purpose, the code coding of this model was used in MATLAB software and time series data for meat products demand from the beginning of 1392 to the 12th week of 1395, which was received weekly. The results of the research showed that ANN5-8-1 model is the best model for predicting the demand for this product. The prediction model provided by the inventory control period has led to a reduction in the number of days facing the shortage and increased customer service levels.
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