AI-Based Defect Detection

Client Overview

Our client is a mid-sized FMCG company specializing in the production and distribution of packaged food products. Operating in a highly competitive and regulated market, the company emphasizes strict adherence to quality control and brand reputation. With high-speed production lines and a diverse range of SKUs, the client was looking for a scalable solution to ensure consistent packaging quality and reduce the risk of product defects reaching the end consumer.

Challenge

Manual quality inspection methods were proving insufficient for the client’s growing production demands. Their quality control team was facing several recurring issues:

• Difficulty detecting subtle packaging defects such as print misalignment, incorrect labeling, and seal integrity failures
• High error rates and subjectivity in manual inspections leading to inconsistent results
• Increased costs due to product recalls triggered by defects slipping through undetected
• Time-consuming inspection process that created bottlenecks and affected overall production efficiency

The client needed a more reliable, real-time solution to reduce human dependency and improve inspection accuracy across their production lines.

Solution

Fx31 Labs collaborated with the client to design and implement an AI-powered Quality Control & Defect Detection system. This solution leveraged a combination of computer vision, deep learning models, and real-time analytics to identify packaging anomalies with high precision and speed.

Key highlights of the solution included:

• Training of deep learning models on a robust dataset of historical defect images, combined with real-time production data
• Deployment of high-resolution cameras along the production line to continuously monitor packaging units
• Real-time analytics dashboard for tracking inspection results, flagged defects, and generating reports
• Custom alert system to notify operators immediately upon detecting a defect, allowing for quick intervention

A proof of concept (POC) was successfully conducted, demonstrating the model’s ability to accurately identify packaging inconsistencies — even at high production speeds — and significantly reduce false positives and negatives.

Impact

• Enhanced accuracy in defect detection, significantly reducing manual inspection errors
• Real-time quality monitoring eliminated bottlenecks in the production process
• Lower product recall rates, preserving brand integrity and customer trust
• Reduced reliance on manual inspections, freeing up skilled personnel for higher-value tasks
• Scalable solution ready for multi-line deployment across various product categories

By integrating AI-driven automation into their quality control workflow, the client achieved consistent packaging standards, faster production cycles, and a noticeable improvement in customer satisfaction.