Artificial Neural Network Model For Optimization of Forecasting Material Inventory

The increasing competition in the fast-moving consumer goods (FMCG) industry leads to demand fluctuations, negatively impacting the accuracy of demand forecasts and determining optimal lot sizes in material inventory planning. Many companies struggle to adopt appropriate forecasting models, resulting in poor accuracy and higher material costs. This study aims to develop an integrated model for forecasting and material planning using simulation. The artificial neural network (ANN) method is proposed to improve forecasting accuracy, with performance evaluated through mean percentage error (MAPE), mean absolute deviation (MAD), and mean squared error (MSE). The forecast results are then applied to optimize material inventory using the economic order quantity (EOQ) model, considering warehouse capacity constraints. The EOQ model is applied to adjust lot sizes under time-varying demand. The findings highlight the importance of integrating forecasting with inventory planning to provide accurate demand predictions and optimal lot sizing, ultimately minimizing material costs in the FMCG industry. This research contributes to better decision-making in supply chain management by enhancing forecasting accuracy and inventory optimization.