Product Demand Forecasting For Inventory Management with Freight Transportation Services Index Using Advanced Neural Networks Algorithm

Purpose: Accurate demand forecasting is critical for optimizing inventory management, improving customer satisfaction, and maximizing profitability in the retail sector. Traditional forecasting models predominantly utilize micro-level variables, such as historical sales data and promotional activities, often neglecting the influence of macroeconomic conditions. Materials and Methods: This study addresses this gap by integrating Freight Transportation Services Index (TSI) which is an indicator for overall economic health with time series data of retail product sales. Utilizing an advanced neural networks model, we demonstrate that incorporating macroeconomic variables significantly enhances the model's predictive accuracy and explanatory power. Findings: The results reveal that the model with the TSI index outperforms conventional models, highlighting its potential for practical application in the industry. Implications to Theory, Practice and Policy: This approach offers a more comprehensive understanding of demand dynamics, enabling businesses to make more informed decisions, adapt to market fluctuations, and maintain a competitive edge.