Demand Forecasting in Logistics : Supply Chain Management Strategies and Trends

Abstract

In today's complex and ever-evolving global commerce landscape, the importance of demand forecasting in supply chain management (SCM) cannot be ignored. As organizations face everlasting challenges such as geopolitical conflicts, the COVID-19 pandemic, and climate-related disruptions, the critical role of accurate demand forecasting is more apparent than ever. This study explores the dynamics of demand forecasting and its impact on effective supply chain management. By adopting a qualitative approach, the study offers an in-depth analysis of diverse perspectives and strategies from various companies and business houses emphasizing the role of predictive analytics and machine learning in enhancing demand forecasting accuracy.

In order to proactively manage supply chain risks and optimize inventory levels, the study highlights the significance of utilizing modern technologies for real-time data analysis and flexible forecasting models. Adopting AI and machine learning technologies, integrating IoT for real-time data collecting, and using big data analytics for improved visibility and decision-making are suggested ways. The thesis states that effective methods of demand forecasting are essential for companies to thrive in the competitive global market. It offers insightful information that advances our understanding of demand forecasting in supply chain management (SCM) and offers helpful advice on how to handle interruptions, make business operation stable, and competitive.

Keywords: Demand forecasting, Logistics Management, supply chain management, predictive analytics, machine learning, real-time data, inventory optimization, risk mitigation strategy, AI in logistics, IoT in supply chain, big data analytics.