A Data-Driven Research on Optimizing Supply Chain Logistics for Manufacturing Companies: A Predictive Modeling Approach

This research paper aims to explore the application of data-driven predictive modelling techniques to optimize supply chain logistics for manufacturing companies. The study focuses on harnessing the power of data analytics, machine learning, and artificial intelligence to develop accurate and efficient predictive models that enhance decisionmaking processes within the supply chain domain. By analyzing historical data and key performance indicators, this research seeks to identify factors influencing supply chain efficiency, such as demand forecasting, inventory management, transportation planning, and distribution network optimization. The paper emphasizes the importance of leveraging advanced analytics to improve the overall performance of manufacturing supply chains, reduce costs, minimize lead times, enhance customer service, and enable a competitive advantage in a dynamic and complex business environment. The proposed predictive model aims to bridge the gap between theory and practice, offering actionable insights to industry professionals and decision-makers, while also contributing to the body of knowledge in the field of supply chain management.