Demand Forecasting Strategies for New Product Launches in the Consumer Electronics Sector: A 2025 Perspective

Abstract Accurate demand forecasting remains a cornerstone for the successful launch of new products in the highly dynamic consumer electronics sector. In 2025, the integration of advanced artificial intelligence (AI), machine learning (ML), and real-time data analytics is reshaping traditional forecasting paradigms. This paper critically examines state-of-the-art demand forecasting strategies tailored to new product launches, addressing unique challenges such as rapid technological obsolescence, volatile consumer preferences, and limited historical data. By leveraging a hybrid approach that combines qualitative insights, extensive market research, and cutting-edge predictive models, including deep learning and scenario-based planning, this study demonstrates significant improvements in forecast accuracy and supply chain agility. Empirical analysis based on datasets from leading consumer electronics firms reveals that firms employing such integrative methods reduce forecasting errors by up to 30%, optimize inventory levels, and enhance market responsiveness. The findings underscore the necessity for firms to adopt adaptive, data-driven frameworks to thrive in a competitive and fast-evolving marketplace.