AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand

Abstract

The advent of artificial intelligence (AI) has ushered in a new era of efficiency and accuracy across various industries, with inventory management and demand forecasting being at the forefront of these advancements. Traditional inventory management techniques, often reliant on historical data and simple statistical models, fall short in addressing the dynamic and complex nature of contemporary markets (Chopra & Meindl, 2016). AI, with its advanced algorithms and machine learning capabilities, offers a transformative approach to these critical business functions. This paper explores the integration of AI technologies in optimizing inventory management and predicting customer demand. AI-enhanced inventory management involves the application of various AI technologies such as machine learning, natural language processing (NLP), computer vision, and robotics process automation (RPA) (Ivanov et al., 2017). Machine learning algorithms analyze vast amounts of historical data to identify patterns and trends, enabling more accurate predictions and adjustments in inventory levels. NLP processes unstructured data from sources like social media and customer reviews to provide deeper insights into market trends and customer preferences (Cambria & White, 2014). Computer vision technologies assist in real-time monitoring of inventory levels and identifying discrepancies through visual data, while RPA automates repetitive tasks like order processing and inventory tracking, thereby reducing human error and increasing efficiency (Aguirre & Rodriguez, 2017). This paper highlights significant improvements in forecast accuracy and inventory turnover rates achieved through AI implementation and discusses future implications for supply chain management.