AI-Enabled Sustainable Manufacturing: Intelligent Package Integrity Monitoring for Waste Reduction in Supply Chains

Despite advances in automation, the global manufacturing sector continues to rely heavily on manual package inspection, creating bottlenecks in production and increasing labor demands. Although disruptive technologies such as big data analytics, smart sensors, and machine learning have revolutionized industrial connectivity and strategic decision-making, real-time quality control (QC) on conveyor lines remains predominantly analog. This study proposes an intelligent package integrity monitoring system that integrates waste reduction strategies with both narrow and Generative AI approaches. Narrow AI models were deployed to detect package damage at full line speed, aiming to minimize manual intervention and reduce waste. Using a synthetically generated dataset of 200 paired top-and-side package images, we developed and evaluated 10 distinct detection pipelines combining various algorithms, image enhancements, model architectures, and data processing strategies. Several pipeline variants demonstrated high accuracy, precision, and recall, particularly those utilizing a YOLO v8 segmentation model. Notably, targeted preprocessing increased top-view MobileNetV2 accuracy from chance to 67.5%, advanced feature extractors with full enhancements achieved 77.5%, and a segmentation-based ensemble with feature extraction and binary classification reached 92.5% accuracy. These results underscore the feasibility of deploying AI-driven, real-time QC systems for sustainable and efficient manufacturing operations.