Analysis Of Bread Demand Forecasting Using Recurrent Neural Network (RNN) Method Based On Operational Delivery Data

Accurate demand forecasting plays a vital role in optimizing inventory and distribution planning, especially for perishable goods such as bread. This study develops a time series forecasting model using a Recurrent Neural Network (RNN) with a Sequential architecture to predict daily bread demand. Unlike previous research, this model is trained on two years of real operational delivery data (2023–2024), enabling it to capture actual consumption patterns more effectively. The model leverages a 7-day sequence window to predict the next day’s demand, reflecting weekly seasonality. Data preprocessing includes normalization and cleaning, followed by training with the Stochastic Gradient Descent (SGD) optimizer. The model achieved a Mean Absolute Percentage Error (MAPE) of 4.88% and an accuracy of 86.90%, demonstrating high predictive performance and robustness in handling fluctuating, real-world data. The implementation of this model provides a practical solution for improving production planning, reducing waste, and enhancing supply chain responsiveness. The findings confirm that RNN-based models are effective tools for demand forecasting in dynamic business environments. Keywords - Forecasting, Recurrent Neural Network (RNN), Demand Prediction, Operational Delivery Data, Bread Industry.