AI Solution for Pallet Inspection System
Business Problem
Implementing an AI-based pallet inspection system to detect damaged pallets accurately before they enter inventory, thereby reducing unnecessary travel costs and improving customer satisfaction by ensuring only quality pallets are shipped.
Objectives
- Maximize Damaged Pallet Detection: Identify and classify pallets into Good, Repair, and Dismantle (BBR).
- Reduce Manual Effort: Automate the inspection process to minimize human intervention.
- Minimize Unnecessary Travel Costs: Prevent the shipping of damaged pallets to customers.
Success Criteria
1. Business Success Criteria
Increase damaged pallet detection by 95%.
2. ML Success Criteria
Achieve an accuracy of at least 95% in pallet damage detection.
3. Economic Success Criteria
Achieve annual savings of 30%.
Key Features
- Damage Detection & Classification: Use AI to detect and classify pallets into Good, Repair, and Dismantle categories.
- Automated Inspection: Implement automated systems to reduce manual effort.
- Logistics Optimization: Improve logistics by ensuring only quality pallets are shipped.
Benefits
- Improved Detection Accuracy: High precision in identifying damaged pallets.
- Cost Savings: Significant reduction in unnecessary travel and return costs.
- Customer Satisfaction: Enhanced satisfaction by ensuring only quality pallets are delivered.