Our client belongs to a large enterprise that deals in the sales and services of sports commodities. The client faced persistent inventory management issues due to the seasonality of certain products. For instance, cricket equipment demand dropped during rainy seasons, making year-round stocking inefficient and costly. The absence of a centralized, data-driven system led to overstocking, understocking, and overall inefficiencies in store operations.
Centralized Data Warehousing
The client was using multiple disparate platforms to manage day-to-day operations—ranging from inventory and invoicing systems to CRM tools. Data existed in various formats such as CSV files, relational databases, and Microsoft Dataverse. To bring structure and consistency, a data warehouse was built to consolidate and store all operational data.
Additionally, historical weather data was integrated via APIs to understand seasonal patterns across regions—critical to optimizing stock levels of weather-sensitive sports gear.
Data Modelling and Machine Learning
Once the data was centralized, it was cleaned, structured, and modelled to serve as input for predictive analytics. A machine learning model was trained to forecast inventory needs based on product category, seasonal trends, historical sales, and regional weather patterns.
Azure Machine Learning (Azure ML) was used as the platform for training, tuning, and deploying the model, ensuring scalability and integration within the existing Microsoft ecosystem.
Visualization Through Power BI
To make insights accessible to business users, a Power BI dashboard was created. It allowed stakeholders to:
Results & Impact
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