Designing Cost-Effective, AI-Powered Demand Forecasting Models for the Manufacturing Sector

Abstract

AI has brought a sea of change in demand forecasting for the manufacturing sector, which had previously been an uncertain dream, making companies much more precise and effective at forecasts. This study, in the same line, deals with the cost-effective AI-driven demand-forecasting model of manufacturing.The model thus can predict demand trends, optimize inventories, and dynamically adjust the production schedules with advanced analytics coupled with machine learning algorithms. The study has emphasized the affordability of AI tools, which makes them accessible to SMEs while maintaining high accuracy in predictions. Various real-world case studies have underlined the benefits in terms of reduced operational costs, minimal stockouts,and improved customer satisfaction. The paper concludes with a discussion on challenges such as data quality, integration complexities, and ethical considerations and offers practical solutions for widespread adoption.