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Ed Marx, Chief Executive Officer Divurgent, and former Chief Information Officer (CIO) for Cleveland Clinic talks spend transparency and supply chain disruption with Jeff Heenan-Jalil, CEO & Founder of hunterAI.
Case Studies
Discover how ChristianaCare recovered $1.9 million in just two months.
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OEM Rebate Case Study
3 weeks
A large east coast university affiliated hospital system wanting to validate OEM rebates within their IT expenditure.
Business Challenge
The healthcare system was experiencing difficulty in checking and reconciling the completeness of OEM rebate reports against their purchasing records.
The business lacked highly detailed visibility into their procurement of EUC models and configurations, in both internal and OEM data.
The business lacked highly detailed visibility into their procurement of EUC models and configurations, in both internal and OEM data.
Actions
- Reconcile OEM report and approach manufacturer for rebate correction
- Ensure all OEM purchases are done through OEM to maximise rebate opportunity
- Approach OEMs to reconcile report and advise on missing invoices with the goal of obtaining further rebates
- Review configurations and propose standard suite of laptop configurations across the organisation
Findings
- >$8mOEM spend not identified on OEM Report
- >$2mOEM spend identified with resellers Invoices missed from OEM Rebate report
- >100laptop models
- >300laptop's configurations purchased
Key Metrics
- $23mInvoice Spend
- >4kInvoices Analyzed
- 12Suppliers Identified
Impact
- Missed Rebate opportunity of >$200k
- Increased support requirements and
missed volume pricing
Process
Hospital system data is ingested, cleansed, optimized, and automatically loaded into standard reports to identify rebate savings opportunities. OEM spend that is hidden through miscategorization or allocation is then highlighted as a discrepancy against the OEM rebate reports.
Utilizing original ERP PO, AP and Invoice item datasets alongside external unstructured data files, hunterAI technology identified and substantiated the gap in the OEM rebates being received. This highlights the opportunity within the hunterAI process to analyze and review structured ERM data with external unstructured datasets in a quick and efficient manner. This is an added service opportunity beyond the standard package of insights.
Hospital system data is ingested, cleansed, optimized, and automatically loaded into standard reports to identify rebate savings opportunities. OEM spend that is hidden through miscategorization or allocation is then highlighted as a discrepancy against the OEM rebate reports. Utilizing original ERP PO, AP and Invoice item datasets alongside external unstructured data files, hunterAI technology identified and substantiated the gap in the OEM rebates being received.
This highlights the opportunity within the hunterAI process to analyze and review structured ERM data with external unstructured datasets in a quick and efficient manner.This is an added service opportunity beyond the standard package of insights.
Utilizing original ERP PO, AP and Invoice item datasets alongside external unstructured data files, hunterAI technology identified and substantiated the gap in the OEM rebates being received. This highlights the opportunity within the hunterAI process to analyze and review structured ERM data with external unstructured datasets in a quick and efficient manner. This is an added service opportunity beyond the standard package of insights.
Hospital system data is ingested, cleansed, optimized, and automatically loaded into standard reports to identify rebate savings opportunities. OEM spend that is hidden through miscategorization or allocation is then highlighted as a discrepancy against the OEM rebate reports. Utilizing original ERP PO, AP and Invoice item datasets alongside external unstructured data files, hunterAI technology identified and substantiated the gap in the OEM rebates being received.
This highlights the opportunity within the hunterAI process to analyze and review structured ERM data with external unstructured datasets in a quick and efficient manner.This is an added service opportunity beyond the standard package of insights.
ChristianaCare Health System
2 months
A nationally recognized healthcare system.
Business Challenge
ChristianaCare partnered with hunterAI to deploy AI to analyze “every item purchased” from their historical accounts payable (AP) data and identify previously undetectable invoice anomalies. Despite existing financial systems, workflow tools, and finance service providers already in place, hidden inefficiencies remained — costing millions in leakage.
Findings
- >$3.2min Invoice Anomalies
- $1.9mrecovered cash / credits
- $500kfuture forecast working capital improevement
Actions
- hunterAI’s AnomalyHunt solution was implemented
- Audit five years of accounts payable transactions
- $1.9M was recovered through direct cash refunds and supplier credits
Impact
- Significant recoveries in 2 months
- Process improvement opportunities uncovered
- Scalable data foundation for future value-creation initiatives
- Strategic platform for continuous financial optimization across the health system.
Key Metrics
- $1.2bAnnual AP Spend
- $2M+Transactions Analyzed
- $3.2mTotal Anomalies Detected
- $1.9mTotal Cash & Credit Recovered