Anomaly detection in complex industrial scenarios requires models that characterize normal patterns and maintain high sensitivity to subtle anomalies. Generative Adversarial Networks (GANs) have attracted increasing attention for their strong representation and generation capabilities. Among them, GANomaly adopts an encoder-decoder-encoder architecture and computes anomaly scores via latent vector discrepancies, providing a solid baseline. However, its scoring mechanism remains limited in capturing pixel-, feature-, and distribution-level anomalies. To address this, we propose MemCLR-GANomaly, which integrates multiple collaborative modules. We introduce SimCLR-based contrastive pre-training for better feature discriminability, a multi-scale region error module for local anomaly perception, and a dynamic memory bank to model normal distributions. An adaptive fusion network integrates these indicators. On MVTec AD, our method achieves 0.919 mean AUC, best in 13/15 categories, improving over GANomaly and Skip-GANomaly by 25.9% and 9.2%, respectively. On a custom Grinding Wheel dataset, it achieves 0.982 AUC, improving over Skip-GANomaly by 9.4%.