Abstract:
Effective inventory management is essential for businesses in today’s competitive global
market. By accurately forecasting demand, companies can determine optimal stock levels,
enhancing both efficiency and profitability. However, the Addis Machine Spare Part and
Manufacturing Industries (AMSPMI’s) outdated manual inventory management system
struggles to keep up with market demands, leading to frequent overstock and understock of
products, disrupting production and customer satisfaction. This challenge hinders the
company’s ability to compete globally. To address these logistical challenges, this paper offers
a comparative analysis of four leading time series demand forecasting models aimed at
improving inventory management: Artificial Neural Network (ANN), Recurrent Neural
Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The
models' accuracy was evaluated using Mean Squared Error and Root Mean Squared Error.
Based on the results of the models, the LSTM model outperformed with the highest prediction
accuracy of 99.95 % with the least square error of 0.05 %. According to the analysis, using the
recommended strategy would be advantageous since it would significantly reduce the issues
related to excess stock and stock outs. By managing both surplus stock and stock-outs,
businesses may achieve a more balanced and efficient inventory management and improve
their overall financial performance, operational efficiency, and customer satisfaction.
Therefore, the study highly recommends that this manufacturing company manage its
inventory using the LSTM model
