Introduction

A pharmaceutical company faced challenges in optimizing their drug distribution network across various depots. They needed a smart solution to reduce safety stock while ensuring consistent drug availability at clinical sites. Our team developed an AI-powered system to optimize inventory routing and management based on predicted demand patterns.

Challenges

1. Inventory Management: Maintaining optimal safety stock levels across multiple locations
2. Demand Prediction: Accurately forecasting drug requirements at different clinical sites
3. Supply Chain Efficiency: Balancing operational costs with service level agreements
4. Resource Optimization: Minimizing wastage while ensuring drug availability

Objectives

• Develop AI-powered inventory optimization system
• Implement smart routing algorithms for drug distribution
• Reduce operational costs while maintaining service levels
• Improve inventory accuracy and prediction capabilities

Implementation

1

AI Model Development

Tools Used: Dataiku Python scikit-learn

• Demand Prediction: Built machine learning models for accurate demand forecasting
• Route Optimization: Developed algorithms for optimal drug distribution paths
• Pattern Recognition: Implemented historical data analysis for trend identification
• Model Training: Utilized real supply chain data for accurate predictions

2

Supply Chain Integration

Tools Used: Plotly Dash REST APIs Docker

• System Integration: Connected with existing supply chain management systems
• Real-time Updates: Implemented live inventory tracking and updates
• API Development: Created interfaces for data exchange and system communication
• Containerization: Deployed scalable microservices architecture

3

Monitoring System

Tools Used: Spotfire REST APIs

• Performance Tracking: Real-time KPI monitoring and visualization
• Inventory Tracking: Dynamic stock level monitoring across locations
• Analytics Dashboard: Interactive reporting and analysis interface
• Alert System: Automated notification system for inventory management

Results

• Improved Accuracy: Achieved over 95% accuracy in safety stock inventory management
• Cost Reduction: Decreased operational costs by 30%
• Enhanced Efficiency: Optimized drug distribution routes and delivery times
• Better Compliance: Maintained service level agreements while reducing inventory levels

Conclusion