MedTech Order Operations
AI-Powered Order Automation
- Client
- Medical Device Manufacturer
- Industry
- Healthcare
- Service
- AI Order Processing Platform|ERP Integration|Intelligent Document Parsing|Scalable Workflow Automation
Overview
A global medical device manufacturer processing over 50,000 B2B orders per month faced mounting inefficiencies from heavily manual order handling across fax, email, and portals. With 30+ staff dedicated to data entry and frequent delays caused by weekend backlogs, the company struggled to scale with rising demand. Sphere implemented an AI-powered order automation platform that transformed legacy workflows, dramatically cut labor costs, and unlocked sustainable growth—without expanding headcount.
Challenges
Manual, resource-heavy processes were no longer sustainable as order volumes climbed. The manufacturer faced a scalability ceiling — where growing demand meant growing headcount, driving up costs and straining operations.
- Weekend backlog: 24–48 hour delays on Mondays due to weekend order accumulation.
- Manual order processing: 30+ staff manually handled POs from fax, email, post, and portals, leading to delays and errors.
- Error-prone data entry: 12% of orders required rework due to mismatched customer/ERP data.
- High operational costs: Labor-intensive workflows required bloated teams to manage peak volumes.
Our Solution
Sphere deployed an AI Order Automation Platform using proprietary accelerator frameworks to:
- Automate multi-channel ingestion: AI parsed POs from any format (fax, email, scanned PDFs) into structured data.
- Auto-match and validate: Cross-referenced customer details with ERP records in real time.
- Flagged mismatches (e.g., outdated pricing, invalid shipping addresses) for human review.
- Seamless ERP integration: Auto-generated spreadsheets and fed validated orders into the Order Management System.
- Continuous learning: AI adapted to edge cases (e.g., handwritten POs) to improve automation rates.
Key Achievements
- Staff shifted to handling exceptions only, reducing manual work.
- Orders fulfilled in 2 hours vs. 24–48 hours post-weekend.
- 83% fewer rework cases (12% → 2% error rate).
- Preparing to automate 90% of orders by addressing edge cases with AI enhancements.
Result
By automating 75% of its order processing, the company reduced labor requirements from over 30 staff to just 8 — unlocking $750K in annual savings. Weekend backlogs were eliminated, fulfillment times improved, and the business scaled order volumes by 30% year-over-year without hiring additional personnel. The shift to AI-driven operations turned a cost center into a growth engine.
