A consumer electronics manufacturer sourcing components from 400+ suppliers across 12 countries experienced 23 supply disruptions in the prior year, causing $28M in lost production. Single-source dependencies existed for 35% of critical components.

Procurement was largely manual, with buyers spending 60% of their time on routine purchase orders. Supplier performance varied by 40% on key metrics, but no systematic evaluation drove vendor selection. Inbound logistics costs were 18% above benchmark due to fragmented shipping and lack of consolidation.

The manufacturer needed resilient, cost-optimized supply chain management with proactive risk monitoring.

The Solution

A 7-agent supply chain that forecasts, sources, procures, ships, inspects, and mitigates risk

Vijan.AI deployed 7 agents across the supply chain. The Demand Planner forecasts production needs from sales forecasts, current inventory, and lead times. The Supplier Evaluator scores all vendors across 15 criteria quarterly. The Procurement agent places orders via EDI with optimal vendor selection. The Logistics agent optimizes inbound shipping through consolidation and carrier selection. The Receiving agent validates incoming quality against specifications. The Risk Monitor tracks geopolitical events, weather, and financial health of suppliers. The Alternate Sourcer activates pre-qualified backup suppliers when disruptions are detected.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Visibility

Supply Chain Visibility Agent

Real-Time Network Visibility

Aggregates RFID, GPS, and IoT data across multi-tier supply network to provide real-time inventory positions and shipment tracking, autonomously detecting disruptions and escalating to logistics coordination.

Input

RFID scans, GPS coordinates, and warehouse status updates from network nodes

Output

Unified supply chain visibility dashboard with exception alerts

Queries RFID scanner API for material location updates across warehouses, in-transit, and production floors
Calls GPS tracker to monitor carrier locations and estimate time-to-delivery for inbound shipments
Autonomous decision: flag delays, reroute expedited shipments, or adjust safety stock triggers
Passes disruption alerts and capacity data to Logistics Coordination agent for mitigation

Step 2 · Logistics

Logistics Coordination Agent

Dynamic Route Optimization

Optimizes transportation routes and carrier selection using real-time traffic, weather, and capacity data, autonomously rerouting shipments to minimize delays and costs while coordinating with vendor management.

Input

Shipment requests with origin, destination, and time constraints

Output

Optimized routes with carrier assignments and delivery ETAs

Invokes route optimization API with current traffic, weather forecasts, and carrier availability constraints
Calls TMS to evaluate carrier rates, service levels, and on-time performance history
Autonomous decision: select carrier, consolidate shipments, or upgrade to expedited service
Sends vendor performance data and delivery schedules to Vendor Management agent

Step 3 · Sourcing

Vendor Management Agent

Strategic Vendor Selection

Evaluates vendor performance against quality, delivery, and cost KPIs, autonomously awarding purchase orders to optimal suppliers and managing contract compliance while informing inventory decisions.

Input

Procurement requests with specifications, volume, and delivery requirements

Output

Vendor selections with negotiated terms and contract assignments

Queries vendor database for scorecards ranking suppliers by quality, on-time delivery, and price competitiveness
Calls contract management system to verify terms, pricing agreements, and compliance status
Autonomous decision: single-source, dual-source, or spot-buy based on risk and cost optimization
Forwards purchase order confirmations and lead times to Inventory Manager for stock planning

Step 4 · Inventory

Inventory Manager

Multi-Echelon Inventory Optimization

Dynamically adjusts safety stock and reorder points across distribution network using demand variability and lead time uncertainty, autonomously triggering replenishment orders while feeding demand patterns forward.

Input

Vendor lead times, safety stock policies, and current inventory levels

Output

Optimized inventory positions with automated replenishment triggers

Calls WMS API to retrieve current stock levels, consumption rates, and stock-out risk scores
Executes safety stock calculator using service level targets and demand/lead time standard deviations
Autonomous decision: trigger replenishment, transfer between warehouses, or adjust safety buffers
Passes historical demand patterns and seasonality data to Demand Forecasting agent

Step 5 · Forecasting

Demand Forecasting Agent

AI-Powered Demand Forecasting

Generates rolling 12-month demand forecasts using machine learning models that incorporate seasonality, promotions, and market trends, autonomously adjusting procurement plans and validating spend patterns.

Input

Historical demand, promotional calendars, and market intelligence

Output

Probabilistic demand forecasts with confidence intervals by SKU

Invokes ML forecasting engine with time-series data, external regressors (promotions, GDP), and ensemble methods
Queries seasonality database to adjust forecasts for known cyclical patterns and holiday effects
Autonomous decision: adjust forecast based on new product launches, competitor activity, or economic indicators
Sends forecasted demand volumes to Procurement Spend agent for budget validation

Step 6 · Procurement

Procurement Spend Agent

Procurement Spend Optimization

Analyzes forecasted demand against procurement budgets, autonomously identifying cost-saving opportunities through volume discounts, payment terms optimization, and supplier consolidation while tracking COGS impact.

Input

Demand forecasts with SKU-level volume projections and pricing data

Output

Optimized procurement plans with cost savings initiatives

Calls procure-to-pay system to retrieve spend history, contract pricing, and volume discount tiers
Executes savings tracker tool to model scenarios for bulk ordering, early payment discounts, and consolidation
Autonomous decision: commit to volume agreements, negotiate terms, or switch suppliers for cost reduction
Forwards material cost data and procurement timing to COGS Analyzer for margin impact assessment

Step 7 · Costing

Cost of Goods Agent

Dynamic COGS Analysis

Calculates real-time cost of goods sold incorporating material costs, logistics expenses, and inventory carrying costs, autonomously identifying margin pressures and feeding insights back to demand forecasting for closed-loop optimization.

Input

Material costs, logistics expenses, and inventory carrying costs

Output

SKU-level COGS with margin analysis and profitability alerts

Invokes BOM engine to explode product structures and calculate rolled-up material costs from vendor pricing
Queries accounting ledger for freight, duty, and warehousing costs allocated by SKU
Autonomous decision: flag margin erosion, recommend price increases, or suggest product line rationalization
Feeds COGS variance data back to Demand Forecasting agent to refine SKU mix optimization

Results

Measurable impact within 90 days of deployment

22%

Procurement Cost Reduction

Total procurement costs reduced through vendor optimization, volume consolidation, and competitive sourcing.

Zero

Production Stoppages

Supply disruption-related production stoppages eliminated through proactive risk monitoring and alternate sourcing.

$12M

Annual Savings

Combined savings from procurement optimization, logistics consolidation, and eliminated disruption costs.

98%

Supplier On-Time Rate

Supplier on-time delivery improved from 82% to 98% through performance-based vendor selection.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

Ready to deploy autonomous agents for your use case?

Let's design an agentic AI solution tailored to your organization's workflows.

Get in touch Browse All Agents