Project Highlights
- Data Integration and Forecasting: Consolidate sales, inventory, and market data, applying machine learning for demand forecasts.
- Real-Time Visualization: Enable dynamic, real-time data visualization and scalable system deployment to handle growing data requirements.
About Client
Industry:Logistics
Location:Australia
Client Challenges
Our client aims to develop a system using machine learning and historical data. Key challenges include:
- Ineffective Demand Forecasting: Lacking a unified data system leads to inaccurate demand predictions, resulting in stockouts or excess inventory that negatively impacts revenue and client satisfaction.
- Inventory Accuracy: Applying advanced technologies to enhance inventory precision and achieve an increase in stock levels.
Solutions
- Data Integration & Forecasting: Use Azure Data Factory and Synapse to unify data from multiple sources, and apply machine learning models with Azure Machine Learning for accurate demand forecasting.
- Real-time Visualization: Implement Power BI for dynamic visuals and deploy on Azure Kubernetes with RabbitMQ for efficient communication between components.
Benefits
- Enhanced Inventory Management: Accurate demand forecasting minimizes the risk of stockouts and excess inventory, leading to optimized stock levels.
- Faster Decision-Making: Real-time visualization through Power BI enables quicker insights, allowing managers to make informed decisions promptly.
- Scalability: The use of Azure Kubernetes ensures the system can scale to meet growing demands without performance degradation.
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