Client Overview:

The client is a prominent brewery looking to optimize its supply chain management by implementing a recommendation engine to forecast SKU (Stock Keeping Unit) volumes based on historical data. They seek to improve inventory management, reduce wastage, and enhance customer satisfaction by ensuring adequate stock levels.

Challenges:

1. Forecasting Accuracy: The client needed accurate predictions of SKU volumes to avoid stockouts or excess inventory.

2. Scalability: The solution should be scalable to handle varying data granularities across different regions or countries.

3. Integration: Seamless integration with existing infrastructure and tools such as Azure Blob Storage, Azure Databricks, and Visual Studio Code was crucial.

4. Collaborative Development: The team required a collaborative development environment with version control to accommodate multiple contributors.

Solution Overview:

To address the client's requirements, we developed a recommendation engine utilizing Holt-Winters Exponential Smoothing model, structured in Python leveraging libraries like statsmodels, numpy, and pandas. The solution was designed with object-oriented principles, enabling flexible training and forecasting at different data granularities. We also integrated Data Version Control (DVC) for efficient data management and collaborated using Git with branching mode for simultaneous development.

Technical Implementation:

1. Model Selection: Holt-Winters Exponential Smoothing was chosen for its ability to capture both trend and seasonality in time-series data, crucial for SKU volume forecasting.

2. Code Structuring: The codebase was structured into atomic and pooled model classes, allowing for easy training and forecasting at dynamic granular levels. Object-oriented programming principles were employed for modularity and reusability.

3. Integration with Azure: DVC with Azure Blob Storage facilitated versioning and tracking of datasets, while Azure Databricks provided a scalable environment for model training and execution.

4. Collaborative Development: Git with branching mode enabled concurrent development by multiple team members, ensuring code integrity and seamless integration of features.

5. Packaging: The code was packaged and integrated into an in-house auto-ML library alongside other models, enabling easy deployment and maintenance.

6. Documentation: Detailed documentation accompanied by demo videos was prepared, covering model architecture, usage instructions, and integration with client systems.

Results:

1. Improved Forecasting Accuracy: The implemented recommendation engine provided accurate SKU volume forecasts, reducing instances of stockouts and overstocking.

2. Scalable Solution: The solution proved scalable, accommodating varying data granularities across different regions or countries.

3. Integration Success: Seamless integration with Azure services and existing tools streamlined the client's workflow and enhanced operational efficiency.

4. Efficient Collaboration: Git-based collaborative development facilitated efficient teamwork, enabling the timely delivery of features and enhancements.

5. Streamlined Deployment: Packaging the solution into an auto-ML library simplified deployment and maintenance, reducing overhead costs.

Conclusion:

The implementation of the recommendation engine empowered the brewery client to optimize inventory management and enhance customer satisfaction through accurate SKU volume forecasts. By leveraging advanced forecasting techniques and integrating with cloud services and collaborative development tools, the solution offered a scalable, efficient, and seamlessly integrated solution tailored to the client's needs.