Case Study — Nexora
End-to-End Supply Chain Visibility From Supplier to Customer in 14 Weeks
$5.2M inventory reduction through real-time multi-tier supply chain intelligence
Industry
Global Manufacturing
Timeline
14 weeks
Team
5 engineers
Tech
REST integrations + PostgreSQL + React
The Challenge
A global manufacturer with 180 suppliers across 12 countries and 3 distribution centers had no real-time visibility into inventory positions, supplier lead times, or delivery risk. Procurement teams placed orders based on historical averages. Excess inventory was tying up $8M in working capital while stockouts were still causing $2M in expedite costs annually.
Our Approach
How We Solved It
Supplier Network Integration
Built a supplier portal and API integration layer that collects real-time inventory positions, production schedules, and shipment tracking from all 180 suppliers — reducing dependency on manual purchase order confirmation emails.
Multi-Tier Inventory Visibility
Unified inventory positions across raw materials at suppliers, WIP at production facilities, finished goods at distribution centers, and in-transit shipments into a single real-time inventory intelligence layer.
Lead Time Intelligence Engine
Replaced static lead time assumptions with a dynamic lead time model that learns actual supplier lead time distributions from historical shipment data, flagging suppliers whose performance is trending worse.
Demand-Driven Replenishment
Connected real-time inventory visibility with ERP demand signals to generate dynamic reorder recommendations based on actual inventory levels and current lead time risk — not static safety stock rules.
Engineering Process
How We Built It
Supplier API Tier Architecture
Suppliers were tiered by capability: Tier 1 (API integration), Tier 2 (supplier portal data entry), Tier 3 (EDI parsing). Each tier has a different integration depth but contributes to the same unified visibility layer.
Shipment Tracking Aggregation
Integrated 7 freight carriers and 2 ocean tracking providers into a unified shipment event stream, normalizing carrier-specific status codes into a standard lifecycle model.
Risk Signal Engine
Continuous monitoring of supplier financial signals, geopolitical risk indices, and weather data generates a supplier risk score that proactively flags at-risk suppliers 30 days before delivery disruption.
Architecture Decisions
Key Technical Choices
Portal Over EDI for Supplier Onboarding Speed
EDI integrations take 8-16 weeks per supplier. The supplier portal allowed 140 of 180 suppliers to begin sharing inventory data in week 3, accelerating time-to-value significantly.
Probabilistic Lead Time Distributions
Storing supplier lead time as a distribution (mean + standard deviation) rather than a single value enabled safety stock calculations based on actual service level targets rather than rule-of-thumb days.
Event Sourcing for Inventory State
Inventory positions are derived from an event log (receipts, shipments, adjustments) rather than a mutable quantity field — enabling point-in-time inventory queries and full reconciliation audit trails.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- API integrations (40 suppliers)
- Supplier portal (140 suppliers)
- EDI / email parsing
- Multi-tier inventory unification
- Probabilistic lead time engine
- Supplier risk scoring
- Dynamic reorder recommendations
- Risk-based exception alerts
- ERP demand plan integration
Lessons Learned
What We Improved
Supplier Adoption Requires a Value Exchange
Suppliers were reluctant to share inventory data until we gave them a portal view of our demand forecasts. The data exchange became mutual, and adoption went from 30% to 92% in 6 weeks.
Carrier Data Quality Is Worse Than Expected
30% of shipment tracking events from carriers were malformed, duplicated, or out of sequence. Building a robust event deduplication and ordering layer took twice as long as estimated.
Safety Stock Rules Don't Survive First Contact With Real Data
When procurement teams saw actual lead time distributions for their top 50 suppliers, they voluntarily revised safety stock levels — the data itself drove the behavior change.
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