Anomaly detection plays a critical role in industrial quality control, ensuring defect-free production in sectors like automotive, electronics, and manufacturing. Traditional anomaly detection methods rely on supervised learning, which requires large datasets with labeled anomalies—often impractical in real-world scenarios. Unsupervised methods such as PatchCore have gained attention for their ability to detect anomalies without labeled defect data, making them particularly valuable for automated inspection systems in factories. Despite its high accuracy, PatchCore has limitations that hinder its deployment in real-time edge devices like smart cameras used in industrial environments. The memory bank storing patch-level embeddings grows exponentially with dataset size, making it computationally expensive and requiring large storage capacity.