Skip to main contentQuality Defect Assistant
Use Case: Product Defect Analysis
Scenario: Production finds batch of defective units
User Query: “We have 23 units with cracks in corner. Is this a known issue?”
Agent Response:
- Searches knowledge base for “corner cracks”
- Finds 3 past occurrences
- Reviews root causes (mold issue, temperature)
- Provides: “This matches defects from March (cause: mold temp too low) and July (cause: pressure too high)”
- Suggests: “Check mold temperature setting immediately”
- Recommends: “Create preventive maintenance task for mold calibration”
Result: Issue identified and solved in 5 minutes instead of hours
Safety Incident Responder
Use Case: On-Site Injury
Scenario: Minor injury in facility
User Query: “Employee cut hand on machinery. What’s the procedure?”
Agent Response:
- Automatically escalates to Safety Manager
- Retrieves safety procedures
- Provides:
- First aid instructions
- Incident report steps
- Notification requirements
- OSHA documentation needs
- Creates incident claim (Critical)
- Notifies executive (auto-escalation)
Result: Proper protocol followed, compliance documented, leadership notified
Maintenance Specialist
Use Case: Equipment Troubleshooting
Scenario: Production line down, technician needs help
Technician Query: “Hydraulic pump making noise and pressure dropping. What do I do?”
Agent Response:
- Searches past maintenance records
- Finds similar issues (3 times in past year)
- Provides step-by-step:
- Check pressure gauge (if below 300 bar, likely internal leak)
- Inspect hose connections (leaks)
- Listen to pump (bearing wear sound)
- Check oil level (low = air in system)
- References equipment manual
- Suggests part numbers if replacement needed
- Provides: “Had same issue Feb 2024, replaced seal kit #42857”
Result: Technician fixes problem in 30 minutes with guided help
Logistics Support Agent
Use Case: Supplier Delay Investigation
Scenario: Shipment delayed, impacting production
Logistics Query: “XYZ Supplier hasn’t shipped order #54321. What’s our history with them?”
Agent Response:
- Searches past logistics claims
- Finds 5 past delays from XYZ Supplier
- Analyzes patterns:
- 4 delays in last 6 months
- Average delay: 3 days
- Root cause: “Supplier capacity issues”
- References: “Used alternate supplier ABC before, worked well”
- Recommends:
- Call XYZ for status
- If >24 hour delay, switch to ABC
- Consider supplier diversification strategy
- Suggests preventive action: “Set up dual-source for this part”
Result: Alternative arranged, production continues
Manager Assistant
Use Case: Daily Standup
Manager Query: “Quick summary - what’s our status today?”
Agent Response (in seconds):
- Pulls today’s metrics:
- 8 new claims (average 10/day)
- 12 resolved (on pace)
- SLA compliance: 92% (good)
- 2 claims at-risk (list them)
- No overdue claims
- Workload status:
- Team average: 4 claims per person (good)
- John: 7 claims (needs help?)
- Sarah: 2 claims (can take more)
- Trends:
- Quality claims down 15% (good)
- Maintenance claims up 20% (check aging equipment?)
- Top issues today:
- 1 Safety claim (appropriate escalation)
- 2 Maintenance (John handling)
Result: Manager has complete picture for standup in 30 seconds
Executive Summary Agent
Use Case: Weekly Board Report
Executive Query: “How did we do this week?”
Agent Response:
- Volume: 47 claims (5% above normal)
- Speed: Average resolution 18 hours (vs 20 hours normal)
- Compliance: 91% SLA compliance (target 90%)
- Safety: 2 safety claims, both resolved
- Trends: Quality down 12%, maintenance up 8%
- Staffing: Quality team performing well, Maintenance may need help
- Action items: Consider additional maintenance hiring
- Recommendation: “Continue current operations, plan for maintenance expansion”
Result: Executive gets strategic insights for board meeting
Common Agent Interactions
Quick Lookup
User: “Show me all overdue claims”
Agent: Lists immediately with details
Pattern Analysis
User: “Why are we having so many equipment issues?”
Agent: Analyzes data, finds root cause, suggests prevention
Escalation Support
User: “This claim is complex, need help deciding what to do”
Agent: Provides context, similar past cases, recommendations
Training Support
User: “How do I calibrate the hydraulic press?”
Agent: Retrieves procedure, provides step-by-step guidance
Decision Making
User: “Should we buy replacement equipment or keep repairing?”
Agent: Analyzes repair cost history, failure patterns, recommendations
Agent Effectiveness Metrics
Track per agent:
- Usage frequency: How often called
- User satisfaction: Are responses helpful?
- Accuracy: Are suggestions correct?
- Time saved: How much faster with agent?
- Actions taken: Do users implement suggestions?
Adjust agents based on metrics:
- Low usage: Agent not solving real problem
- Low satisfaction: Improve system prompt or knowledge base
- High accuracy: Keep as is, share best practices
- High satisfaction: Consider expanding scope
Next Steps
