A Data-Driven AI and Generative AI Approach to Improve Supply Chain Service Level Performance

This project aims to develop an advanced predictive framework that leverages both traditional AI and Generative AI (Gen-AI) to enhance supply chain service level performance. The core objective is to use Gen-AI to coordinate different subfunctions within the supply chain, intelligently collate outputs, and gain deeper insights into user demands. By integrating these technologies, the project seeks to improve intelligent responses to disruptions and visualize results in a more interactive and user-friendly manner.
The framework includes specialized components:
• Data Ingestion and Preprocessing Component: Collects and preprocesses time-series and real-time data from inventory levels, orders, production schedules, customer feedback, and other accessible company data sources to ensure consistency and accuracy.
• AI-Driven Coordination Module: Utilizes Generative AI models to coordinate various supply chain subfunctions such as procurement, production, logistics, and distribution. This module intelligently collates outputs to provide a holistic view of the supply chain.
• Intelligent Response System towards Higer Service Level Performance: Employs AI algorithms to better understand user needs and improve responsiveness. It analyzes patterns and trends to proactively identify potential bottlenect and recommend actionable strategies to achieve higher service level improvement.
• Interactive Visualization Dashboard: Displays real-time forecasts, supply chain trends, and risk assessments. The dashboard uses interactive elements to enhance user engagement and facilitate quick decision-making.