Infor M3 Workflow Examples

This guide outlines how to build automated workflows that connect Tekst AI with Infor M3 to process emails, create orders, and validate data without manual intervention. These examples show real-world automation scenarios for manufacturing and distribution operations.

Automated Email-to-Order Processing

This workflow converts incoming email purchase orders into confirmed M3 sales orders automatically.

Use Case

Your sales team receives 50+ email orders daily. Each requires manual data entry into M3, taking 15 minutes per order. This workflow automates the entire process.

Workflow Steps

1. Trigger: New email arrives in monitored inbox (e.g., orders@company.com)
2. Extract Data: Tekst AI identifies:
   • Customer VAT number or account code
   • Order date and requested delivery date
   • Line items with product codes, descriptions, and quantities
   • Special instructions or notes
3. Get Customer: Look up customer in M3 using VAT number
   • Input: VAT number from email
   • Output: Customer number (CUNO) for order creation
4. Validate Items: For each line item, use Get Item action
   • Input: Item number (ITNO) extracted from email
   • Output: Item details and availability confirmation
5. Create Order: Build M3 sales order
   • Company: "100" (or your company code)
   • Customer: CUNO from step 3
   • Order lines: Array of {ITNO, ORQT, delivery date}
6. Confirm Order: Update order status to confirmed (20-25)
7. Send Notification: Email customer with order confirmation and M3 order number (ORNO)

This workflow reduces order processing from 15 minutes to under 30 seconds, delivering instant confirmations to customers.

Configuration Tips

• Set up error routing: If customer or item lookup fails, route to manual review queue
• Add date formatting: Ensure extracted dates convert to YYYYMMDD format
• Handle attachments: Configure AI to extract order data from PDF or Excel attachments
• Log all orders: Store ORNO and email reference for audit tracking

Customer Validation Before Order Entry

Validate customer information exists in M3 before processing order requests, preventing errors and duplicate work.

Use Case

New customers often send orders before their account is set up in M3. This workflow catches missing customers early and routes requests appropriately.

Workflow Steps

1. Trigger: Email tagged as "Order Request"
2. Extract Customer Info: Pull VAT number, company name, or account code
3. Get Customer: Search M3 for customer record
   • Input: VAT number or customer code
   • Output: Customer details or "not found" error
4. Conditional Branch:
   • If customer exists: Proceed to order creation workflow
   • If customer not found: Route to sales team with email template: "New customer setup required for [Company Name]. Please create M3 account before processing order."
5. Track Status: Update CRM or ticketing system with customer validation result

This validation prevents incomplete orders and ensures your team sets up new customers correctly before processing their first order.

Multi-Line Order Processing with Batch Handling

Process orders with multiple line items efficiently, handling high-volume scenarios common in manufacturing.

Use Case

Large distributor receives orders with 20-50 line items. Manual entry is error-prone and time-consuming. This workflow handles complex orders automatically.

Workflow Steps

1. Trigger: Email with subject containing "BULK ORDER" or attachment with multiple items

2. Extract Line Items: Tekst AI builds array of all products:

   [
     {item: "SKU001", quantity: 100, delivery: "2025-02-15"},
     {item: "SKU002", quantity: 250, delivery: "2025-02-15"},
     {item: "SKU003", quantity: 75, delivery: "2025-02-20"}
   ]
   

3. Validate All Items: Loop through each item with Get Item action

   • Check item exists in M3
   • Verify availability if connected to inventory system
   • Flag items not found for manual review

4. Get Customer: Look up customer once for entire order

5. Create Order with All Lines: Build single M3 order with complete line item array

6. Confirm Order: Update to confirmed status

7. Generate Summary: Send detailed confirmation listing all items, quantities, and delivery dates

Enable batch processing settings in M3 integration for faster handling of orders with 10+ line items.

Error Handling

• If any item validation fails, pause workflow and notify team
• Create partial order with valid items if configured
• Log all validation failures for review and training improvement

Order Confirmation Notifications

Automatically send professional confirmation emails to customers after M3 order creation, improving customer experience.

Use Case

Customers expect immediate order confirmations with tracking details. This workflow delivers confirmations within seconds of order placement.

Workflow Steps

1. Trigger: Order successfully confirmed in M3 (status 20-25)

2. Gather Order Details: Retrieve from previous workflow steps:

   • Order number (ORNO)
   • Customer name and email
   • Line items and quantities
   • Expected delivery date
   • Order total if available

3. Format Email: Use template with M3 data:

   Subject: Order Confirmation #[ORNO]
   
   Dear [Customer Name],
   
   Your order has been confirmed and will be processed shortly.
   
   Order Number: [ORNO]
   Order Date: [Date]
   Expected Delivery: [Delivery Date]
   
   Items:
   - [ITNO]: [Quantity] units
   - [ITNO]: [Quantity] units
   
   Thank you for your business.
   

4. Send Email: Deliver to customer's email address

5. Log Communication: Record confirmation sent in CRM or M3 notes

Instant confirmations reduce "where's my order?" inquiries and improve customer satisfaction scores.

Exception Routing for Manual Review

Handle edge cases and exceptions gracefully by routing complex orders to human reviewers when automation confidence is low.

Use Case

Some orders contain ambiguous data, special pricing, or unusual requests that require human judgment before processing.

Workflow Steps

1. Trigger: Email classified as "Order Request"
2. Extract with Confidence Scoring: Tekst AI assigns confidence scores to extracted data:
   • High confidence (>90%): Customer, items clearly identified
   • Medium confidence (70-90%): Some ambiguity in item codes or quantities
   • Low confidence (<70%): Missing data or unclear intent
3. Conditional Routing:
   • High confidence: Proceed with automated order creation
   • Medium confidence: Create draft order and flag for quick review
   • Low confidence: Route to sales team inbox with highlighted extraction issues
4. Manual Override: Sales team can approve, modify, or reject flagged orders
5. Learning Loop: Feed approved exceptions back to AI training to improve future accuracy

Exception routing ensures automation handles 80-90% of orders fully, while maintaining quality control for complex cases.

Multilingual Order Processing

Process orders from international customers in their native languages, extracting data accurately across 20+ languages.

Use Case

European distributor receives orders in Dutch, French, German, and English. This workflow handles all languages uniformly.

Workflow Steps

1. Trigger: Email from international inbox
2. Detect Language: Tekst AI automatically identifies email language
3. Extract Data: Process order details regardless of language:
   • French: "Quantité: 50, Référence: ABC123"
   • German: "Menge: 50, Artikelnummer: ABC123"
   • Dutch: "Aantal: 50, Artikelnummer: ABC123"
   • All extract to: quantity=50, item=ABC123
4. Validate in M3: Use language-neutral item codes and customer numbers
5. Create Order: Process identically to single-language workflow
6. Respond in Original Language: Send confirmation email in customer's language using templates

Train Tekst on multilingual product catalogs and common phrases in each market to improve extraction accuracy for regional SKU aliases.

Integration with Infor Data Lake

For high-volume operations, connect M3 workflows to Infor Data Lake for real-time data synchronization and analytics.

Use Case

Manufacturing enterprise processes 1,000+ orders daily and needs real-time visibility into order status, inventory, and customer data.

Workflow Enhancement

1. Enable Data Lake Sync: Configure M3 integration to use Data Lake endpoints
2. Real-Time Lookups: Customer and item validations query live data instead of cached records
3. Batch Optimization: Process multiple orders simultaneously with improved performance
4. Analytics Integration: Export order data to BI tools for process mining and ROI tracking

Best Practices

Testing Workflows

• Start with test inbox and sample emails before production deployment
• Use M3 test environment if available to avoid impacting live data
• Validate all extraction and creation steps with known-good orders
• Monitor first 50-100 automated orders closely

Training for Accuracy

• Provide historical emails representing your typical order volume (500-1,000 examples)
• Include edge cases: rush orders, returns, special pricing, multi-language
• Update training quarterly as product catalogs and customer base evolve
• Leverage free workshops (Enterprise plans) for custom terminology training

Monitoring and Optimization

• Track automation rate (% of orders processed without manual intervention)
• Monitor average processing time per order (target: <1 minute)
• Review exception routing patterns to identify training opportunities
• Measure ROI using time saved × volume × hourly cost
• Set up alerts for connection failures or high error rates

Customers typically achieve 90%+ automation rates within 6 weeks of deployment with proper training and workflow optimization.