Predicting the Efficiency of Inventory Management Using Artificial Neural Networks

The purpose of this study is to design a model to predict the efficiency of inventory management to help creditors and actual and potential investors and other stakeholders to avoid major losses in the capital market. For this reason, 137 companies listed on the Tehran Stock Exchange during the 10-years period 2012-2021 were examined. In this study, the predicting variables of institutional ownership, managerial ownership, corporate ownership, ownership concentration, board size, percentage of non-executive board members, and duality of CEO (Chief Executive Officer) role have been used. The efficiency of inventory management was predicted using a three-layer perceptron artificial neural network with the Backpropagation of Error algorithm. Finally, a network with the mean squared error of 0.360, 0.428, 0.261 and 0.353, respectively for training data, validation, test and total data and a coefficient of determination of more than 72%, as the best network Selected.

Predicting the Efficiency of Inventory Management Using Artificial Neural Networks Keywords:

Inventory management efficiency , Predictive variables , CEO , Artificial Neural Networks , Backpropagation of Error algorithm