AI-driven demand forecasting: Enhancing inventory management and customer satisfaction

Abstract

This study explores the implementation of AI-driven demand forecasting to enhance inventory management and customer satisfaction. Traditional forecasting methods often fail to predict consumer demand accurately, leading to either excess inventory or stockouts, both of which are detrimental to business performance. Excess inventory ties up capital and increases holding costs, while stockouts result in missed sales opportunities and diminished customer satisfaction. By employing advanced AI algorithms and machine learning models to analyze historical sales data, market trends, and external factors such as seasonality and promotions, we aim to generate precise demand forecasts. The integration of these models into existing inventory management systems automates replenishment processes, ensuring stock levels align closely with anticipated demand. Our results indicate significant improvements in inventory optimization, cost reduction, and customer satisfaction. Specifically, the neural network model outperformed other models, achieving the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), highlighting the effectiveness of incorporating external factors into the forecasting process (Brown & White, 2020). This study underscores the potential of AI-driven demand forecasting to transform inventory management practices, ultimately contributing to more efficient operations and enhanced customer satisfaction.