We identify between 5% & 15% in potential savings at statistically significant level in our historic data.
Hear from Industry leaders
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
Evidence Based Practice
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.
Large System Cost Reduction
3 weeks
A large west coast not for profit hospital system
wanting to identify cost savings across selected
Purchased Services Categories.
wanting to identify cost savings across selected
Purchased Services Categories.
Business Challenge
The healthcare system was challenged in finding significant savings opportunities as a result of complex sourcing data.
This barrier was pervasive in Purchased Services and the data complexity prevented the effective utilization of GPO contracts.
This barrier was pervasive in Purchased Services and the data complexity prevented the effective utilization of GPO contracts.
Findings
- >$76mof IT spend with uncontracted
resellers - >30%price variations for same laptops
identified - >80laptop models and >120 configurations
identified - >85%of FnB suppliers are not contracted
Actions
- Review and consolidate purchases to
preferred VAR suppliers - Leverage preferred supplier consolidation
for Better Pricing and Terms - Optimize IT configuration matrix between
performance and price point - Streamline Food and Nutrition purchasing
and consolidate supply base for preferred
pricing options
Impact
- Optimal pricing savings of >$300k missed
- >$60k of savings identified at a commodity
level - Missed potential optimizing benefit of
>$130k - Optimal pricing savings foregone and
significant supplier 'tail' list to manage
Key Metrics
- $2.1bInvoice Spend
- >750kInvoices Analyzed Spend
- $1.2mCategorization Suppliers
- >3,400Identified Commodities
Process
Within 3 weeks of client data ingestion it was processed and analyzed to then be automatically loaded into standard reports that identified areas for substantial cost reduction. Utilizing original ERP PO, AP, and Invoice item datasets, previously misallocated or misrepresented spend was uncovered across all suppliers. Where commodity suppliers overlapped and price variations were apparent, consolidating technology reseller spend to selected key suppliers represents a significant opportunity with an annualized benefit.
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.
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.
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