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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Optimizing inventory in real-time through Azure demand forecasting solution