A Tableau-based analysis identifying stockout risks, overstock conditions, and lost sales to improve inventory decision-making.
This project analyzes inventory and demand data to identify supply chain inefficiencies, including stockout risks, overstock conditions, and lost sales. The goal is to provide data-driven insights that support better inventory planning, reduce risk, and improve operational efficiency.
- Built an interactive Tableau dashboard to analyze inventory risk and demand patterns
- Developed KPIs including Stockout Rate, Overstock Rate, and Estimated Lost Sales
- Identified inventory imbalances across products and warehouses
- Delivered actionable recommendations to reduce lost sales and improve stock allocation
Dashboard Features:
- KPI summary of inventory performance
- Distribution of inventory risk categories
- Lost sales analysis by product and warehouse
- Demand vs stock comparison for decision-making
Organizations often struggle to balance inventory levels with fluctuating demand. Poor inventory management can result in:
- Stockouts → Lost revenue and missed customer demand
- Overstock → Increased holding costs and tied-up capital
- Inefficient distribution → Imbalance across warehouses
This project aims to identify these issues and provide actionable recommendations.
Effective inventory management directly impacts revenue and cost. This project demonstrates how data analytics can be used to:
- Reduce lost sales from stockouts
- Minimize excess inventory and holding costs
- Improve supply chain efficiency
- Tableau – Data visualization and dashboard development
- Excel – Data preparation and structuring
- Data modeling concepts
- Stockout Rate – Percentage of products at risk of stockout
- Overstock Rate – Percentage of excess inventory
- Estimated Lost Sales – Revenue impact from stock shortages
- Joined inventory and demand datasets using an inner join in Tableau
- Created calculated fields for:
- Stockout Rate
- Overstock Rate
- Estimated Lost Sales
- Developed an Inventory Status classification:
- Stockout Risk
- Overstock
- Balanced
- Built interactive dashboard components:
- KPI cards
- Product-level analysis
- Warehouse-level analysis
- Demand vs Stock scatter plot
- Applied filters for dynamic exploration (warehouse, category)
- A significant portion of products fall into the stockout risk category, contributing to measurable lost sales and highlighting gaps in inventory planning
- Overstock conditions exist across multiple products, indicating inefficient inventory allocation and potential holding costs
- Lost sales are concentrated in specific products and warehouses, suggesting opportunities for redistribution and improved supply chain coordination
- Demand and stock levels are misaligned, with high-demand products understocked and low-demand products overstocked
- Stockout conditions lead directly to revenue loss
- Overstock increases storage costs and reduces capital efficiency
- Warehouse-level imbalances reduce operational effectiveness
- Improved inventory planning can significantly enhance profitability
- Implement demand-driven inventory planning
- Rebalance stock across warehouses based on demand patterns
- Introduce automated alerts for stockout and overstock conditions
- Continuously monitor KPIs to improve decision-making
- Estimated Lost Sales is an analytical estimate, not actual recorded revenue
- Inventory thresholds are rule-based and may vary by business context
- Supplier lead times and demand variability were not included
- Analysis is based on available dataset and assumptions
| Field Name | Description |
|---|---|
| product_id | Unique product identifier |
| warehouse | Warehouse location |
| stock_level | Current inventory on hand |
| daily_demand | Average daily demand |
| reorder_point | Minimum threshold before reorder |
- Stockout Rate: Percentage of products below reorder point
- Overstock Rate: Percentage of products exceeding demand thresholds
- Estimated Lost Sales: Revenue impact from unmet demand
This project demonstrates how data analytics can be used to identify operational inefficiencies, reduce inventory risk, and support data-driven decision-making in supply chain management.
- GitHub: https://github.com/Richie-Rokka
- LinkedIn: https://www.linkedin.com/in/abodunrin-oketade-579aa331
Abodunrin Oketade
Aspiring Data Analyst | Operations & Supply Chain Analytics
Aspiring Data Analyst | Operations & Supply Chain Background