AI: Smarter Buying Decisions for
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Purchasing decisions were based on outdated spreadsheets, email threads, and experience — not real-time data.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.
Key Challenges:
- 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
Steps to Success
The engagement started with a focused AI pilot to evaluate historical supplier performance and PO data across top-moving SKUs. Once proven, Captivix helped automate intelligent vendor recommendations — all within the client’s existing procurement flow.
1. Pilot Scope & Use Case Definition
Captivix 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.
Pilot Objectives:
- 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
2. 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
3. AI Model Development & Insights
Captivix developed a multi-factor decision engine using the following. 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
4. 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
5. 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
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.
- Achieved 6–10% cost savings in key categories
- Reduced fulfillment delays by 22% from poor-performing vendors
- Improved on-time inventory availability for high-demand SKUs
- Empowered buyers with clear, trusted insights — not just experience