This research paper delves into the transformative intersection of data analytics and the Internet of Things (IoT) within logistics, focusing on their collective impact on demand forecasting accuracy. Investigating the challenges, methodologies, and outcomes associated with this integration, our study provides a comprehensive analysis of the contemporary logistics landscape. Utilizing a mixed-methods approach, we explore the diversity of data generated by IoT devices, ranging from location information and environmental conditions to machine health metrics, and evaluate various data analytics techniques, including machine learning algorithms and artificial neural networks. Real-world case studies illustrate the practical applications of data analytics in demand forecasting, showcasing tangible benefits such as optimized inventory management, reduced stockouts, and streamlined logistics operations. The findings underscore the broader impacts on supply chain efficiency, customer satisfaction, and cost-effectiveness, revealing a paradigm shift in traditional logistics practices. The integration challenges, including diverse data formats, security concerns, and scalability issues, are analyzed alongside potential solutions, emphasizing the need for standardized protocols, secure data handling practices, and scalable cloud-based platforms. Through a thorough examination of these challenges, our research positions data analytics and IoT as catalysts for innovation in logistics, paving the way for a future where data-driven decision-making redefines the industry.

KEYWORDS: Data Analytics, Internet of Things (IoT), Logistics, Demand Forecasting, Supply Chain Efficiency, Inventory Management, Machine Learning, Case Studies, Integration Challenges, Real-Time Processing.