BALANCING MATERIAL SUPPLY-DEMAND WITH ARIMA AND NEURAL NETWORKS

This study introduces a hybrid Autoregressive Integrated Moving Average Model-Back Propagation (ARIMA-BP) neural network model to improve the accuracy of production material demand forecasting amid growing market competition and diverse customer requirements. By integrating both linear and nonlinear elements, the model enhances efficiency in production planning, inventory optimization, and operational cost reduction. It explores novel methods to align supply and demand, optimizing the interplay of material procurement, product output, and inventory management. The study's key contribution is a forecasting approach that informs balanced production strategies, with significant implications for operational effectiveness and competitive advantage in manufacturing. (Received in July 2023, accepted in September 2023. This paper was with the authors 1 month for 2 revisions.)