Implementing a Real-Time Data Analytics Platform for Coffee & Tea Supply Chain Optimization

As a growing SME in the Coffee & Tea industry, we face challenges in managing a dynamic and complex supply chain, often resulting in inefficiencies and increased costs. To combat this, we aim to develop a real-time data analytics platform that provides continuous insights into our supply chain operations. The project will involve deploying Apache Kafka for real-time data streaming and Spark for data processing, integrated with a data mesh architecture to ensure data democratization across our organization. Additionally, using tools like Airflow for orchestrating data workflows, dbt for data transformation, and Snowflake or BigQuery for data warehousing will ensure a seamless flow of information. By focusing on data observability and employing MLOps practices, we will maintain data quality and leverage predictive analytics to forecast demand more accurately. This initiative will not only streamline operations but also enhance decision-making, ultimately leading to better customer service and reduced operational costs.