Part 8/11:

• Building classifiers trained on such scenarios to identify potential phantom inventory situations

By examining features like transit stock, corrections, and sales patterns, the system detects anomalies with about 80% confidence, enabling targeted audits and reducing unnecessary automatic replenishments.

Continuous Improvement and Future Directions

The system is designed to evolve:

• Regularly retraining models using recent data

• Incorporating new signals, like store opening times and dynamic inventory changes

• Enhancing data collection methods, potentially integrating more real-time sensors or video feeds selectively

• Planning deeper integration with supply chain systems for faster response times