Purchase Order Automation Processing 500 POs Daily Across 3 Manufacturing Plants
A food manufacturer's procurement team manually created, routed, and confirmed 500 purchase orders daily across 3 plants. We automated the end-to-end PO workflow — reducing processing time by 90%.
About the Client
The Challenge
A food manufacturer's procurement team manually created, routed, and confirmed 500 purchase orders daily across 3 plants. We automated the end-to-end PO workflow — reducing processing time by 90%. The organization had reached an inflection point — production efficiency metrics were tracked in spreadsheets updated after each shift — by which time the data was already stale. Quality issues were discovered at end-of-line inspection, not during the process where they could be corrected. Supply chain visibility ended at the factory gate.
The manufacturing industry added specific complexity. OSHA safety regulations, ISO 9001/14001 standards, and FDA compliance for pharmaceutical manufacturing demanded auditable processes and governance. Any technology initiative needed to maintain compliance continuity while delivering measurable improvement. Previous attempts had stalled because vendors didn't understand these industry-specific constraints.
The executive sponsor set clear expectations: demonstrate measurable impact within one quarter. No 18-month roadmaps. No theoretical architectures. Working software, real data, measurable results — or the budget moves elsewhere. They needed a partner who could deliver rpa solutions with manufacturing domain expertise from day one.
Our Approach
We designed a phased approach optimized for speed-to-value while maintaining OSHA safety regulations, ISO 9001/14001 standards, and FDA compliance for pharmaceutical manufacturing continuity:
Process Discovery & ROI Analysis (Weeks 1-2)
Mapped current manual processes with process mining. Identified automation candidates ranked by volume, error rate, and business impact. Built ROI model for each opportunity.
Bot Design & Development (Weeks 2-5)
Designed automation workflows with exception handling paths. Built bots using UiPath for complex multi-system processes and Power Automate for Microsoft-ecosystem tasks.
Testing & Validation (Weeks 4-6)
Tested with production-volume data. Validated exception handling — ensuring bots escalate correctly for unexpected scenarios. Parallel-run with manual process for reconciliation.
Deployment & Monitoring (Weeks 5-8)
Deployed to production with monitoring dashboards tracking processing volume, error rates, and exception queues. Established SLA metrics and escalation procedures for bot failures.
Optimization & Expansion (Weeks 7-10)
Analyzed bot performance data. Optimized processing paths for speed and reliability. Identified adjacent processes for automation expansion. Trained internal team on bot management and extension.
Solution Architecture
Platform: UiPath + Power Automate hybrid automation
Architecture: Attended bots for exception handling + unattended bots for high-volume straight-through processing
Monitoring: Real-time dashboard for bot health, processing volumes, error rates, and exception queues
Results
Technologies Used
Key Takeaways
If your organization is facing a similar challenge, here's what we learned:
Industry context eliminates weeks of discovery. Understanding manufacturing terminology, OSHA safety regulations, ISO 9001/14001 standards, and FDA compliance for pharmaceutical manufacturing, and operational workflows meant we skipped the "teach us your business" phase. Our rpa team brought domain context from the first workshop.
Phased delivery maintains executive sponsorship. By delivering measurable results in 8-12 weeks, the sponsor had proof for their next board meeting. This is critical in manufacturing organizations where budget cycles are tight and competing priorities are constant.
User adoption is the real success metric. Technology implementations fail when users don't adopt. We designed the solution around existing manufacturing workflows — not the other way around. The system met users where they already worked, driving 80%+ adoption within the first month.
Ongoing governance prevents value decay. We established review cadences, defined data ownership, and built monitoring dashboards that make issues visible early. The platform continues to deliver value because governance is sustained — not because the initial deployment was perfect.
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We deliver rpa solutions for manufacturing organizations — with measurable outcomes typically within 8-12 weeks.