Real Time Deamand Forecasting and Its Role in Inventory Optimization in Manufacturing

Real-time demand forecasting, Inventory optimization, Manufacturing sector, Artificial intelligence (AI), Machine learning (ML), Internet of Things (IoT), Forecast accuracy, Operational efficiency, Inventory turnover rates, Supply chain management,Data integration, Technological innovation, Competitive advantage, Production alignment, Manufacturing agility

Authors

Vol. 12 No. 03 (2024)
Engineering and Computer Science
March 29, 2024

Downloads

Real-time demand forecasting has emerged as a transformative tool in the manufacturing sector, enabling businesses to align production with market needs dynamically. Traditional demand forecasting methods often rely on historical data and static models, which fail to adapt to rapid market fluctuations. Real-time forecasting leverages advanced technologies such as artificial intelligence (AI), machine learning (ML), and IoT sensors to provide accurate and timely insights into consumer demand. This research investigates the integration of real-time demand forecasting with inventory optimization strategies to reduce costs, minimize waste, and enhance operational efficiency. Through case studies and simulation models, we demonstrate significant improvements in forecast accuracy and inventory turnover rates. The findings underscore the potential of real-time systems to revolutionize inventory management and suggest practical implementation strategies for manufacturers aiming to stay competitive in a volatile market.