Success Story

reading time: 3 min

Manufacturing

Material Industries

Visual Inspection

Anomaly Detection

Industrial Software

Context

A leading industrial manufacturer faced challenges in ensuring the accuracy and quality of their packaging processes. Packaging errors, such as incorrect labels, missing components, damaged packaging, or misprinted barcodes, could lead to costly recalls, compliance issues, and customer dissatisfaction. Manual inspections were slow, prone to human error, and inefficient, especially when scaled across production lines.

Challenge

The client struggled to maintain consistent quality in packaging while dealing with frequent errors during manual inspections. The errors led to potential product recalls, customer dissatisfaction, and non-compliance with regulatory standards, which posed a significant risk to the company’s reputation and bottom line.

Assignment

We proposed implementing navio VISION, an AI-powered visual anomaly detection system, to inspect each package in real-time on the production line. The system integrates with the existing ERP system to flag defective packages and ensure accurate labeling and contents.

Solution

navio VISION is deployed on the production line, utilizing high-resolution cameras to capture images of every package. The machine learning models in navio VISION analyze these images for packaging defects, including incorrect labels, missing components, and damaged packaging. The system is seamlessly integrated with the client's ERP system, automatically flagging any defective packages for removal or correction. This integration ensures that the quality control process is efficient, and actionable insights are provided in real-time.

The implementation of navio VISION significantly improves quality control, reducing the risk of costly recalls by 55% and accelerating package inspection by 72%. The system’s integration with the ERP system ensures that packaging standards and regulations are met, increasing overall throughput while maintaining high-quality standards.

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