Real-Time Inventory Optimization for Grocery & Supermarkets

In the competitive grocery and supermarkets industry, efficient inventory management is critical to maintaining profitability and customer satisfaction. Our enterprise seeks a data engineering solution to transform our traditional inventory processes into a real-time, predictive system. We plan to implement a data mesh architecture to decentralize data ownership and enhance scalability. This project involves setting up real-time event streaming using Apache Kafka to ensure timely data updates across various departments. By integrating Spark for large-scale data processing and Snowflake for scalable cloud data warehousing, we will enable comprehensive data analysis and storage. Furthermore, we aim to incorporate MLOps practices to automate machine learning workflows, ensuring that our predictive models are continuously updated and optimized based on new data inputs. The project will also include setting up data observability tools to monitor data quality and performance across the pipeline. This initiative will significantly reduce wastage, optimize stock levels, and increase customer satisfaction by ensuring product availability and freshness.