A Fortune 500 manufacturer processes thousands of purchase orders daily for highly configurable industrial products. Each order contains detailed specifications, custom configurations, and multi-page document attachments that need to be verified, entered into SAP, cross-referenced against product catalogs, and synced to Salesforce.

Before Automat, this was a manual operation consuming dozens of FTE hours daily. The team copy-pasted between email, PDFs, SAP, and Salesforce. Peak demand periods created backlogs. Errors in product configuration went undetected until downstream in the production process, causing rework and delays.

The problem

Three specific challenges made this workflow resistant to traditional automation:

1. Document variability: Purchase orders arrived in mixed formats (typed PDFs, scanned documents, Excel attachments, emails with inline specs). Each customer used different templates, field names, and layouts. No two POs looked the same.
2. SAP configuration complexity: Products were highly configurable with interdependent options. A valid configuration in one product line might be invalid in another. Catching conflicts required cross-referencing specs against internal catalogs that lived in SAP.
3. System fragmentation: Data needed to flow between SAP (ERP), Salesforce (CRM), Oracle (supply chain), and Microsoft tools (communication). No single API connected them. Manual work was the integration layer.

Previous attempts with traditional RPA failed because SAP GUI's native Windows interface didn't expose the stable selectors that UiPath and similar tools require. The bots broke every time SAP was updated or a supplier changed their PO format.

The solution

Automat deployed AI agents that handle the full purchase order lifecycle:

1. Document intake and extraction

Incoming POs (email attachments, portal downloads, EDI) are processed by vision language models that extract line items, quantities, product codes, specifications, and special instructions. No template training per supplier. The AI reads documents the way a procurement analyst would: understanding context, layout, and field relationships.

2. Configuration validation

Extracted specs are cross-referenced in real-time against internal product catalogs in SAP. The system detects configuration conflicts (incompatible options, discontinued components, quantity limits) and flags them for human review before data entry, preventing downstream production issues.

3. Multi-system data entry

Validated orders are entered into SAP (creating sales orders, updating inventory), synced to Salesforce (customer records, pipeline tracking), and logged in Oracle (supply chain coordination). The AI agents navigate each system visually, handling custom screens, mandatory fields, and approval workflows.

4. Exception handling

When ambiguities arise (unclear specifications, missing fields, new product codes), the system routes to human reviewers with context: what it extracted, where the uncertainty is, and suggested resolutions. Decisions are fed back to improve future processing.

Results

• Volume: Thousands of POs processed daily with a one-hour SLA from receipt to system entry
• Speed: Processing time per order dropped from 15-20 minutes (manual) to under 2 minutes
• Accuracy: Configuration conflicts caught automatically. Manual errors eliminated from the data entry step.
• Scale: Peak demand periods handled without additional headcount. The system scales with volume, not with FTEs.
• Integration: SAP, Salesforce, Oracle, and Microsoft tools connected through visual automation rather than custom API development

Why traditional RPA couldn't solve this

This workflow had been attempted with selector-based RPA tools previously. The failures were structural:

• SAP GUI has no DOM. Traditional RPA tools need HTML selectors or accessibility tree access. SAP GUI is a native Windows application that doesn't expose these. Attempts to use image recognition were fragile and broke with font/theme changes.
• Document formats are unpredictable. Template-trained OCR required per-supplier setup and broke when suppliers changed their PO layouts. With hundreds of suppliers, the maintenance burden exceeded the automation benefit.
• Multi-system workflows need visual navigation. Each system (SAP, Salesforce, Oracle) has a different interface architecture. Building selector-based integrations across all three, plus maintaining them through quarterly updates, required a full-time team of 3-4 RPA developers.

Automat's vision-based approach solved all three: it navigates SAP without scripting, reads documents without templates, and operates across systems through visual interaction that adapts when interfaces change.

Technical details

• Deployment time: Initial PO processing workflow live in production within 3 weeks
• Systems integrated: SAP ERP, Salesforce CRM, Oracle Supply Chain, Microsoft 365
• Document types handled: PDF, scanned images, Excel, email bodies with inline specifications
• SLA: One-hour from PO receipt to complete system entry
• Uptime: Operates continuously including off-hours and weekends
• Maintenance: Self-healing. Supplier portal changes and SAP updates are handled automatically without developer intervention.

See how Automat can automate your manufacturing workflows | More on Automat for Manufacturing

Frequently asked questions

Can this work with our SAP configuration?

Yes. Automat operates SAP visually, meaning it works with any SAP version, custom T-codes, Z-transactions, and company-specific screen configurations. No SAP scripting permissions are required.

What happens when a supplier changes their PO format?

The AI adapts automatically. Because it reads documents through visual understanding rather than template matching, layout changes, new field positions, and format variations are handled without manual updates or retraining.

How does this handle exceptions and edge cases?

Ambiguous or unclear inputs are routed to human reviewers with full context: what was extracted, where the uncertainty is, and suggested resolutions. The system learns from these decisions over time, reducing exception rates continuously.

What's the typical deployment timeline for manufacturing PO automation?

Initial workflows go live in 2-3 weeks. Full production deployment with all system integrations and edge case handling typically completes within 4-6 weeks. This compares to 4-6 months for equivalent UiPath implementations in manufacturing environments.