Logistics Container ID Fuzzy Matching with Approximate String Matcher Node

<p>📦 <strong>Logistics Container ID Fuzzy Matching with Approximate String Matcher Node</strong></p><p>In real-world logistics, data entry errors are inevitable — a single mistyped character in a container ID can prevent records from matching correctly.</p><p><br>This workflow demonstrates how to overcome such issues by combining <strong>approximate string matching</strong> with <strong>data cleaning and joining</strong> in KNIME.</p><p><strong>What This Workflow Does</strong></p><ol><li><p><strong>Load Data</strong> – Import two datasets:</p><ul><li><p><strong>Port Arrival Data</strong> – IDs recorded upon ship arrival.</p></li><li><p><strong>Warehouse Intake Data</strong> – IDs scanned upon warehouse receipt.</p></li></ul></li><li><p><strong>Prepare Reference IDs</strong> – Extract unique container IDs from the Port Arrival Data to avoid duplicate comparisons.</p></li><li><p><strong>Fuzzy Match IDs</strong> – Use the <strong>Levenshtein distance</strong> to identify matches even when IDs have minor typos.</p><ul><li><p>Distance = 0 → Exact match</p></li><li><p>Distance ≤ 1 → Likely typo (e.g., swapped characters, missing digit)</p></li></ul></li><li><p><strong>Join on Best Match</strong> – Link each Port Arrival record to its closest warehouse record based on the cleaned, matched container IDs.</p></li></ol><p><strong>Key Benefits</strong></p><ul><li><p>Handles <strong>typos and small formatting differences</strong> automatically.</p></li><li><p>Ensures accurate matching without losing valid records.</p></li><li><p>Easily adjustable <strong>distance threshold</strong> to control match tolerance.</p></li></ul><p>💡 <strong>Use Case</strong>: Ideal for logistics, inventory tracking, or any scenario where IDs must match despite human input errors.</p>