Based on our implementation experience, here’s a comprehensive approach to integrating unstructured data into manufacturing planning dashboards:
Document Processing Pipeline: Implement automated document ingestion that monitors email systems, shared drives, and document management systems for relevant files. Use OCR for scanned documents and direct text extraction for digital formats. Apply document classification models to categorize incoming documents (engineering changes, quality reports, supplier communications) and route them to appropriate processing workflows.
JSON Schema Mapping: Design a unified JSON schema that accommodates both structured planning data and extracted document metadata. Key elements should include document type, source, timestamp, extracted entities (part numbers, dates, quantities), confidence scores, and relevance indicators. The schema should map to your Manhattan dashboard data model while maintaining flexibility for different document types. Create transformation layers that convert extracted document data into dashboard-compatible JSON structures.
Data Extraction Strategy: Implement hybrid extraction combining rule-based and ML approaches. For structured documents like engineering change notices with consistent formats, use template-based extraction rules. For variable formats like email communications and production notes, employ NLP models to identify entities and relationships. Extract actionable data points: affected part numbers, schedule impacts, material availability changes, quality issues, and deadline modifications. Link extracted information to existing planning entities in Manhattan using part numbers, work orders, and schedule IDs.
Unified Reporting Interface: Design dashboard components that present document-derived insights contextually alongside structured metrics. Create alert cards that highlight time-sensitive information like engineering changes affecting imminent production runs. Implement filtering by relevance, urgency, and document type. Use visual indicators to distinguish between core system data and document-extracted information, including confidence levels. Provide drill-through capabilities to access source documents when planners need full context.
This approach gives manufacturing planners unified visibility across structured planning data and critical unstructured information, improving decision-making without requiring them to search multiple systems. The key success factors are robust extraction accuracy, relevant information filtering, and intuitive presentation that doesn’t overwhelm users with document noise.