The Ultimate Supply Chain & Logistics Dataset Generator
How do you measure a supplier’s true performance or find the hidden costs in your shipping network? Answering these critical business questions requires a dataset that connects procurement, inventory, and transportation into a single, cohesive story. Finding realistic practice data that models this entire lifecycle is nearly impossible.
That’s why we built the PNRao Supply Chain & Logistics Data Generator.
This powerful tool creates a complete, enterprise-grade supply chain dataset with a single click. Whether you’re an operations analyst practicing how to calculate inventory turnover, a student learning about global logistics, or a manager building a business case for a new warehouse, this generator provides the data you need. Stop working with simple purchase order lists and start analyzing the end-to-end supply chain.
Why This Dataset is a Powerful Learning Tool
Learning supply chain analytics with abstract numbers can be challenging. This generator provides a realistic context that makes complex concepts easier to grasp.
- Builds a Narrative: You can step into the role of a Supply Chain Analyst for the PNRao Company. Your task could be to identify which suppliers have the best on-time delivery rates, analyze the shipping costs for different carriers, or forecast raw material needs for the next quarter. This narrative-driven practice helps solidify your understanding.
- Makes Concepts Concrete: When you see a
Purchase Orders
table linked toShipments
andInventory
, abstract concepts become tangible. This makes metrics like On-Time Delivery (OTD), Inventory Turnover, and Landed Cost easier to understand because you can see how they are built from the ground up. - Encourages Realistic Analysis: The rich context inspires more creative and insightful analysis. You might build a dynamic supplier scorecard in Power BI or create a report in Excel to track warehouse stock levels. This allows you to practice not just the technical skills, but also the art of data storytelling.
Designed for Real-World Operations Analysis
This dataset isn’t just a random collection of tables. It has been carefully crafted to mirror the data structures you will find in actual Enterprise Resource Planning (ERP) and Warehouse Management Systems (WMS), making it the perfect tool to develop practical, job-ready skills.
- Reflects Core Supply Chain Operations: Every manufacturing and retail business deals with
suppliers
,purchase orders
,inventory
, andshipments
. The tables in this generator represent the core of what you will encounter professionally. By understanding how these tables relate to each other, you are learning the fundamental blueprint of supply chain data. - Focus on a High-Demand Skillset: Supply chain analysis is a critical function for improving efficiency and reducing costs. The skills you build here—analyzing supplier performance, tracking inventory levels, and optimizing logistics—are directly transferable to the tasks and challenges you will face in a real data analytics or operations job.
- Covers a Wide Range of Concepts: The structure of this data is intentionally designed to be a comprehensive playground for learning. With this single resource, you can practice:
- Excel/Sheets Functions: Use VLOOKUP or XLOOKUP to connect purchase orders with supplier details, SUMIFS and COUNTIFS to create summary reports, and PivotTables to analyze spending by vendor.
- Inventory Management: Use the data to practice calculating safety stock, reorder points, and inventory turnover ratios.
- Data Cleaning: Use the “messy data” option to practice cleaning and validating shipping logs and purchase order data.
- Advanced Analysis: The interconnected tables are perfect for practicing relationship-building in Power BI or writing SQL
JOIN
queries to combine data from multiple sources for a complete analysis.
In short, learning with this dataset ensures you are not just practicing abstract concepts, but preparing yourself with the practical knowledge and experience valued by employers.
Tables Overview
The generator produces a wide array of datasets across different supply chain functions. Each dataset has a unique structure, as detailed below.
Procurement & Suppliers
Table Name | Description | Columns |
---|---|---|
Purchase Orders | The main fact table for all purchase orders placed. | PO_ID, SupplierID, OrderDate, ExpectedDeliveryDate, Status, TotalCost |
Purchase Order Lines | The detailed line items for each purchase order. | POLineID, PO_ID, MaterialID, Quantity, UnitCost |
Supplier Master | A list of all raw material suppliers. | SupplierID, SupplierName, Country |
Raw Materials Master | A list of all raw materials purchased. | MaterialID, MaterialName, Category, UnitCost |
Inventory & Warehouse
Table Name | Description | Columns |
---|---|---|
Warehouse Inventory | Current stock levels for each material at each warehouse. | InventoryID, MaterialID, WarehouseID, QuantityOnHand, LastStocktakeDate |
Stock Movements | A log of all inventory movements (inbound and outbound). | MovementID, MaterialID, WarehouseID, MovementType, Quantity, Date |
Warehouse Master | A list of all warehouse facilities. | WarehouseID, WarehouseName, Location |
Transportation
Table Name | Description | Columns |
---|---|---|
Shipment Tracking | Tracking data for inbound and outbound shipments. | ShipmentID, PO_ID, CarrierID, Origin, Destination, ShipDate, ActualDeliveryDate, Status |
Carriers Master | A list of all shipping carriers. | CarrierID, CarrierName |
Order Fulfillment
Table Name | Description | Columns |
---|---|---|
Customer Sales Orders | A log of incoming sales orders from customers. | SalesOrderID, OrderDate, ProductID, ProductName, Quantity, CustomerID |
Fulfillment Status | Links sales orders to their corresponding shipments. | FulfillmentID, SalesOrderID, ShipmentID, Status |
Performance
Table Name | Description | Columns |
---|---|---|
Supplier Performance | Key performance indicators for each supplier. | SupplierID, SupplierName, OnTimeDeliveryRate, QualityScore, AverageLeadTime_Days |
How to Use This App
- Select a Table to Preview: Use the “Select Table to Preview” dropdown menu to choose the type of data you want to inspect.
- Specify the Number of Purchase Orders: Enter the desired number of purchase orders. This is the main driver; a higher number will generate more related records in all other tables.
- Configure Advanced Options (Optional): Click on Advanced Options to expand the menu.
- Date Range: Select a Start Date and End Date to constrain the generated data to a specific time period.
- Data Quality: Choose the quality of your dataset (Clean, Slightly Messy, or Very Messy).
- Generate the Data: Click the “Generate All Datasets” button. The application will process your request and display a preview of your selected table.
- Download Your Data:
- Preview: To quickly download just the data shown in the preview table, click “Download Preview (CSV)” or “Download Preview (Excel)”.
- All Related Tables: Click “Download All Tables (Excel)” to download a single Excel file with every table on a separate, clearly named sheet. This is the best option for relational analysis.
How to Use This Data
- For Data Analysts & BI Professionals:
- Supplier Scorecards: Use the
Supplier Performance
andPurchase Orders
tables to build a dashboard in Tableau or Power BI that ranks vendors by performance. - Inventory Analysis: Analyze the
Inventory
andMovements
tables to calculate inventory turnover and identify slow-moving stock. - Practice SQL: Load the tables into a database and practice writing complex
JOIN
queries to track a material from purchase order to final shipment.
- Supplier Scorecards: Use the
- For Students & Educators:
- Create realistic case studies for supply chain management, logistics, and operations courses.
- Use the data as a foundation for assignments on calculating key supply chain metrics like on-time delivery rate, inventory holding costs, and lead time variance.
Feedback & Suggestions
This tool is built for you. If you have an idea for a new supply chain table or an improvement, please leave your feedback in the comments section below.
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