Manufacturing
Production Planning & Scheduling
4 autonomous agents optimize production scheduling and adapt to disruptions in real-time. 25% throughput increase.
Agentic AI Workflow
4 autonomous agents maximize production efficiency in parallel
The Challenge
Manual scheduling couldn't keep up with changing demand and frequent disruptions
A food and beverage manufacturer with 6 production lines and 200+ SKUs created weekly production schedules in spreadsheets. Changeover times between products averaged 45 minutes, and suboptimal sequencing wasted 15% of available production time on unnecessary changeovers.
When disruptions occurred (equipment failures, material shortages, rush orders), rescheduling took 4-6 hours of manual replanning. During that time, lines ran suboptimally or sat idle. On-time delivery was 78%, and expediting costs consumed $2.1M annually.
The manufacturer needed automated scheduling that optimized sequences and adapted to disruptions in minutes, not hours.
The Solution
Agents that plan, schedule, optimize sequences, and replan in real-time
Vijan.AI deployed 4 agents. The Demand Intake agent pulls sales orders, forecasts, and inventory positions to determine what needs to be produced. The Capacity Planner evaluates machine availability, labor schedules, and material readiness. The Scheduler agent optimizes production sequences using constraint solvers that minimize changeover time, balance workload, and meet delivery commitments. The Real-Time Adjuster monitors MES feedback and replans within minutes when disruptions occur, reassigning orders across lines to maintain throughput.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Scheduling
Production Scheduling Agent
Master Production Scheduling
Generates optimal production schedules by balancing customer due dates, changeover costs, and equipment availability, autonomously sequencing work orders to minimize tardiness while operating in parallel with capacity planning.
Input
Customer orders with quantities, due dates, and priority levels
Output
Sequenced production schedule with start times and resource assignments
- Calls MES API to retrieve current work-in-process status and equipment availability windows
- Invokes advanced planning system to optimize job sequencing using constraint-based scheduling algorithms
- Autonomous decision: prioritize high-margin orders, batch similar SKUs, or split jobs across shifts
- Operates in parallel with Capacity Planner to ensure schedule feasibility against resource constraints
Step 2 · Capacity
Capacity Planner
Dynamic Capacity Planning
Analyzes equipment capacity, labor availability, and material constraints to validate production feasibility, autonomously identifying bottlenecks and recommending capacity expansions in parallel with scheduling lane.
Input
Production schedule proposals with resource requirements
Output
Capacity validation reports with bottleneck alerts and mitigation options
- Queries ERP system for machine capacity rated speeds, planned maintenance windows, and shift patterns
- Executes bottleneck detection tool to identify constraint resources limiting throughput
- Autonomous decision: approve schedule, recommend overtime, or flag need for additional equipment
- Runs in parallel with Master Scheduler to iteratively refine plans for capacity-feasible solutions
Step 3 · Resources
Resource Optimizer
Multi-Resource Allocation
Optimally allocates labor, tooling, and materials to scheduled production runs, autonomously balancing skill requirements and material availability while coordinating with OEE optimization in parallel.
Input
Validated schedules with resource demand profiles
Output
Detailed resource assignments with material release schedules
- Calls labor management system to match worker skills and certifications to job requirements
- Queries materials database to verify component availability and trigger just-in-time kitting
- Autonomous decision: assign cross-trained workers, expedite materials, or adjust job priorities
- Operates in parallel with OEE Tracker to ensure resource allocation supports performance targets
Step 4 · OEE
OEE Optimization Agent
OEE Performance Tracking
Monitors overall equipment effectiveness metrics in real-time, autonomously detecting deviations from target availability, performance, and quality rates and feeding insights back to scheduler for continuous improvement.
Input
Real-time production data with downtime, speed losses, and defect counts
Output
OEE scorecards with improvement recommendations and trend analysis
- Queries OEE dashboard for live availability, performance efficiency, and quality yield percentages
- Calls analytics engine to trend OEE components over time and identify chronic loss categories
- Autonomous decision: flag underperforming equipment, recommend process improvements, or adjust standards
- Feeds execution feedback to Master Scheduler to refine future planning with actual performance data
Results
Measurable impact within 90 days of deployment
Throughput Increase
Production throughput increased 25% on the same equipment through optimized sequencing and reduced changeovers.
Less Changeover Time
Total changeover time reduced 55% through intelligent product sequencing that minimizes cleaning and setup.
On-Time Delivery
On-time delivery improved from 78% to 96%. Expediting costs reduced by 80%.
Replan Time
Schedule adjustments for disruptions generated in under 10 minutes vs. 4-6 hours of manual replanning.
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
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