The Compare feature in Numero enables users to analyze multiple transactional documents (such as purchase orders, invoices, or quotes) side by side, identifying matches, variations, and inconsistencies. Here’s a step-by-step guide on how to use this feature effectively:
Select Documents for Comparison: Choose multiple transactional documents (e.g., purchase orders, invoices, or quotes) from the repository for comparison. The system automatically checks for shared fields like SKU or quantity to ensure compatibility.
Note: Only documents with common comparison fields will proceed; mismatched or unsupported types (like SOWs) will be excluded.
Run the Comparison: Once the documents are selected, the tool dynamically compares the documents. Matches are consolidated into totals, while variations are displayed in structured categories. Users can drill down into differences or switch to the summary view for quick insights.
What Happens Next?The system dynamically analyzes and organizes the data into structured categories:
Matching values are aggregated and consolidated into totals.
Variations or mismatched values are displayed for easy review.
Navigate through the Summary, Comparison, and Detailed Comparison tabs to explore insights at different levels of granularity.
Export Results: Download the comparison output in Excel or CSV format for further analysis or record-keeping. Exported files maintain the structured format of matches and discrepancies, ensuring clarity.
Use Cases: Scenarios for Document Comparison
The Compare feature is designed to handle three key scenarios, providing flexibility for various types of document analysis:
Scenario | Description | Example |
Complete Match | When all fields in the selected documents align perfectly, the system seamlessly aggregates data. |
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Partial Mismatch | When certain fields match (e.g., SKUs) but others differ (e.g., unit prices or quantities), the system flags these variations for review. |
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Complete Mismatch | When no fields overlap between the selected documents, the system segregates the data into separate columns, clearly flagging differences. |
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Each scenario is explained in detail in the following articles.