How This Workflow Works
This workflow processes historical inventory stockout data stored in Snowflake, aggregates monthly stockouts by warehouse location and item category, and visualizes trends over time. It also predicts stockouts for the current month, helping teams make faster and more informed inventory decisions.
Key Features:
- Load and extract inventory stockout data from Snowflake
- Perform in-database aggregations of stockout events by warehouse location and product category
- Predict likely stockouts for the current month to support proactive planning
- Visualize stockout patterns for easier interpretation and reporting
Step-by-step:
1. Extract Stockout Data from Snowflake and Aggregate It:
The workflow connects to a Snowflake database and retrieves stockout records. It performs in-database processing to parse city information from warehouse locations, and aggregates monthly stockout counts by warehouse and item category. This enables users to easily compare stockout frequencies across different locations and categories.
2. Enable Interactive Selection and Filtering:
Users can select specific warehouses and item categories to focus the analysis. The workflow dynamically applies in-database filters based on these selections, ensuring that subsequent insights and predictions are relevant to the user's needs.
3. Predict Current Month Stockouts:
Using historical data, the workflow applies a predictive model to estimate stockout counts for the current month. This supports proactive planning and helps teams anticipate potential shortages.
4. Visualize and Share Insights:
The workflow visualizes results using line plots and bar charts to display monthly stockout quantities, trends and predictions. These visualizations turn data into easy-to-understand insights that support decision-making for supply chain and business teams.