Managing inventory in retail can be a balancing act. Retailers need to meet customer demands while avoiding stockouts and overstocking, both of which can negatively impact profit and customer satisfaction. This project aims to develop a Retail Inventory Management and Forecasting System to streamline inventory tracking, monitor sales, and accurately forecast future stock requirements
The system leverages SQL databases and cloud services, such as Azure, to deliver real-time inventory visibility and demand forecasting, empowering retailers to make informed decisions.
Design a robust, structured database schema tailored to the needs of the project. Populate the database with sample data to validate its functionality. Write SQL queries to provide insights that address critical business questions.
Build a centralized data warehouse to consolidate data from multiple sources. Implement automated ETL processes to efficiently manage data flow into the warehouse. Clean and preprocess raw data to prepare it for deep analysis.
Develop a forecasting model that leverages historical sales and inventory data to predict future demand. Utilize Azure services for data storage and analysis to extract actionable insights. Test and validate the forecasting model rigorously to ensure accuracy and reliability.
Use MLflow to track and manage machine learning models throughout their lifecycle. Deploy the forecasting model using Azure Machine Learning for automated MLOps. Design an intuitive dashboard to visualize inventory predictions and trends.
Refaat Mohamed Mohammed Mokhtar Mahgoub Hany Eyad Medhat Mohamed Mohsen Amr Khaled