Transform Snowflake Data into Inventory Insights with Autonmis

In today's competitive retail landscape, effective inventory management is crucial for business success. With Autonmis, you can leverage the power of Snowflake's sample data (TPCDS_SF100TCL) to gain deep insights into inventory operations, product performance, and sales patterns. This guide demonstrates how Autonmis empowers businesses to analyze inventory metrics, helping decision-makers optimize stock levels and maximize operational efficiency.

What is Autonmis?

Autonmis is an AI-enhanced data platform designed for businesses to make data-driven decisions. The platform integrates multiple data sources into a unified warehouse, featuring:

• SQL Editor with AI Copilot for custom queries
• Interactive Dashboards for real-time monitoring
• Notebooks to save and share analysis
• Dynamic Charts for metric visualization
• AI-powered insights generation

These capabilities allow users to explore complex relationships across inventory, sales, and product data effortlessly.

Step 1: Setting Up the Analysis with Key Questions

Our analysis focuses on three critical aspects of inventory management:

1. How do stock levels vary across warehouses and products?
2. What is the sales velocity of different products?
3. How do seasonal trends and promotions impact inventory turnover?

Step 2: Exploring the Data Schema

This analysis utilizes tables from the Snowflake TPCDS_SF100TCL schema:

• INVENTORY: Tracks stock levels across warehouses
• STORE_SALES: Records in-store transactions
• WEB_SALES: Captures online purchases
• ITEM: Contains product details and costs
• PROMOTION: Stores promotional campaign information
• DATE_DIM: Enables time-based analysis

Step 3: Running Key Queries for Comprehensive Insights

Let's explore several SQL queries in Autonmis that reveal insights into inventory levels, product performance, and sales trends.

Query 1: Current Stock Levels by Warehouse

1  sql
2  SELECT 
3      w.warehouse_name,
4      i.item_id,
5      SUM(inv_quantity_on_hand) as total_stock
6  FROM 
7      TPCDS_SF100TCL.INVENTORY inv
8      JOIN TPCDS_SF100TCL.WAREHOUSE w ON inv.warehouse_id = w.warehouse_id
9  GROUP BY 
10      w.warehouse_name, i.item_id
11  ORDER BY 
12      total_stock DESC;

Output Interpretation: This query provides visibility into current stock distribution across warehouses, helping identify potential imbalances or stockout risks.

Query 2: Sales Velocity Analysis

1  sql
2  SELECT 
3      i.item_id,
4      i.item_desc,
5      COUNT(*) as sales_count,
6      SUM(ss.quantity) as total_quantity_sold,
7      SUM(ss.quantity)/30 as daily_sales_velocity
8  FROM 
9      TPCDS_SF100TCL.STORE_SALES ss
10      JOIN TPCDS_SF100TCL.ITEM i ON ss.item_id = i.item_id
11  WHERE 
12      ss.sale_date >= DATEADD(day, -30, CURRENT_DATE())
13  GROUP BY 
14      i.item_id, i.item_desc
15  ORDER BY 
16      daily_sales_velocity DESC;

Output Interpretation: By calculating daily sales velocity, teams can optimize reorder points and ensure adequate stock levels for fast-moving items.

Query 3: Inventory Turnover by Category

1  sql
2  SELECT 
3      i.category,
4      SUM(ss.quantity) as total_sold,
5      AVG(inv.inv_quantity_on_hand) as avg_stock_level,
6      SUM(ss.quantity)/NULLIF(AVG(inv.inv_quantity_on_hand), 0) as turnover_rate
7  FROM 
8      TPCDS_SF100TCL.STORE_SALES ss
9      JOIN TPCDS_SF100TCL.ITEM i ON ss.item_id = i.item_id
10      JOIN TPCDS_SF100TCL.INVENTORY inv ON ss.item_id = inv.item_id
11  GROUP BY 
12      i.category
13  ORDER BY 
14      turnover_rate DESC;

Output Interpretation: This analysis reveals which categories have healthy turnover rates and which might need attention to prevent excess inventory or stockouts.

Query 4: Promotional Impact on Sales

1  sql
2  SELECT 
3      p.promo_name,
4      COUNT(DISTINCT ss.item_id) as items_promoted,
5      SUM(ss.quantity) as total_quantity_sold,
6      SUM(ss.net_paid) as total_revenue
7  FROM 
8      TPCDS_SF100TCL.STORE_SALES ss
9      JOIN TPCDS_SF100TCL.PROMOTION p ON ss.promo_id = p.promo_id
10  GROUP BY 
11      p.promo_name
12  ORDER BY 
13      total_revenue DESC;

Output Interpretation: Understanding promotional impact helps teams prepare inventory levels for future promotional events and optimize campaign strategies.

Key Insights and Applications

By running these queries in Autonmis, teams can gain actionable insights into various aspects of inventory management:

1. Stock Level Optimization Identify warehouses with excess or insufficient stock Balance inventory across locations Set optimal reorder points based on sales velocity
2. Category Performance Track high-turnover categories requiring closer monitoring Identify slow-moving inventory for potential promotions Optimize category-specific stock levels
3. Promotional Planning Measure promotion effectiveness on inventory movement Plan stock levels for upcoming promotional events Identify optimal timing for promotions based on historical data

Conclusion

With Autonmis, businesses can transform raw inventory data into actionable insights. This example demonstrates how Autonmis enables comprehensive inventory analysis, empowering businesses to:

• Optimize stock levels across warehouses
• Improve inventory turnover rates
• Enhance promotional effectiveness
• Reduce carrying costs while maintaining service levels

The platform's ability to handle complex queries and generate visualizations makes it an invaluable tool for modern inventory management.