Supply Chain Use Case

AI: Smarter Buying Decisions for Supply Chain

This supply chain distributor used AI to optimize supplier selection based on price, delivery performance, and reliability — cutting procurement costs while increasing order fulfillment speed.

Procurement Teams Were Losing Margin to Inefficiency

A mid-sized wholesale distributor managing hundreds of SKUs and vendor relationships was facing margin pressure. As demand fluctuated and vendor pricing shifted, their buyers struggled to keep up.

Purchasing decisions were based on outdated spreadsheets, email threads, and experience — not real-time data. Despite having years of PO history, delivery performance, and pricing trends, none of it was being used effectively.

They needed to bring structure, insight, and speed to procurement — without changing their ERP or disrupting fulfillment.

No centralized visibility into supplier cost, lead time, or service levels

Buyers relied on habit or "last vendor used" to place orders

Missed opportunities to consolidate spend or switch to better-performing vendors

Margins eroded by late deliveries and non-optimized purchasing decisions

Vendor Selection Dashboard

1
Supplier A - Best Price
2
Supplier B - Fastest Delivery
3
Supplier C - Highest Reliability
4
Supplier D - Strategic Partner
5
Supplier E - Regional Option

Steps to Success

Atlas initiated the engagement with a Procurement Optimization Workshop, bringing together purchasing leaders to identify pain points and opportunities. Vendor selection stood out as a high-impact, automation-ready use case.

01

Pilot Scope & Use Case Definition

Atlas collaborated with the client's purchasing and category managers to define a clear pilot objective: Help buyers select vendors based on best total value — not just lowest price.

Identify categories with high price and lead time variation
Evaluate vendors by actual performance (not just quoted specs)
Recommend smarter supplier options directly within the PO process
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Pilot Scope & Use Case Definition
02

Data Aggregation & Cleansing

We integrated and cleansed the following to build a clean data model to support cross-vendor comparison by SKU, category, and performance tier:

PO history and item-level pricing from the ERP
Supplier delivery logs and exception notes
Vendor profiles including lead times, MOQs, and fulfillment accuracy
Notes and override logs from buyers
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Data Aggregation & Cleansing
03

AI Model Development & Insights

Atlas developed a multi-factor decision engine. The engine generated ranked supplier recommendations per SKU and category — with justifications visible to each buyer:

Predictive models to estimate actual delivery performance
Optimization algorithms balancing price, lead time, and vendor reliability
Custom business logic to respect MOQs, regional availability, or strategic partners
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AI Model Development & Insights
04

Buyer Review & Validation

We collaborated with procurement leads to validate the AI recommendations. Buyers began trusting the AI — especially when it uncovered high-margin opportunities in low-volume categories.

Compared suggested vendors against real PO outcomes
Highlighted cost savings and service improvements across fast-moving SKUs
Fine-tuned the model with buyer rules, overrides, and feedback
Pilot Development
Buyer Review & Validation
05

Procurement Workflow Integration

Once validated, the solution was integrated into the client's ERP procurement workflow:

Buyers creating a PO now see AI-ranked supplier recommendations
Each vendor comes with a scorecard: cost, reliability, lead time, and risk
Buyers can accept or override with reasoning — creating a loop for continuous learning
Full Agentic AI Implementation
Procurement Workflow Integration
Results Dashboard

From Vendor Guesswork to Strategic Sourcing

AI helped this distributor move from reactive, price-only buying to data-backed vendor optimization — all without disrupting procurement operations.

6-10%
Cost savings in key categories
22%
Reduction in fulfillment delays
100%
Improved on-time inventory availability
Clear, trusted insights for buyers
AI-ranked supplier recommendations in every PO
Vendor scorecards with cost, reliability, and risk
Continuous learning from buyer feedback

Frequently Asked Questions

Common questions about AI-driven procurement optimization

Success Stories

Real Results, Real Impact

See how leading companies have transformed their operations with ATLAS.

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Featured Case Study
TC

TechCorp

SaaS Company • 200+ employees

“ATLAS completely transformed our revenue operations. What used to take our team 40 hours per week now takes just 10. The AI handles all the repetitive follow-ups, data entry, and lead enrichment.”

75%
Less Admin Time
3x
Meeting Bookings
$2M+
Pipeline Generated
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BuildRight Construction

Construction

Automated vendor follow-ups and RFQ tracking across all projects.

20 hrs
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Enriched leads instantly and automated personalized outreach sequences.

3x
pipeline velocity
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MedHealth Solutions

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Streamlined patient onboarding and reduced administrative overhead.

50%
faster onboarding
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Client Reviews

What Our Clients Say

Hear directly from the leaders who've transformed their businesses with ATLAS.

It feels like having an extra teammate that never gets tired. Our follow-up rate went from 40% to 95%. We’re closing deals we would have lost before.

SJ

Sarah Johnson

VP Sales, TechCorp

We stopped losing deals to slow follow-ups. ATLAS keeps every opportunity moving without us having to chase. Our team can focus on closing, not admin.

MR

Mike Rodriguez

CEO, BuildRight Construction

The ROI was obvious within 30 days. We’re booking 3x more meetings with the same team size. ATLAS pays for itself many times over.

AL

Amanda Lee

Head of Growth, Finova Group

We tried three other automation tools before ATLAS. Nothing else could handle our complex workflows. This is the real deal.

JP

James Park

COO, MedHealth Solutions

Ready to Optimize Your Procurement?

From vendor guesswork to data-driven sourcing — let's build your intelligent procurement system.