Transfer learning is a transformative technique in manufacturing that accelerates the modeling process by using pre-trained models as foundational building blocks, benefiting areas such as production optimization, defect detection, predictive maintenance, and quality control. By allowing models to leverage prior knowledge, transfer learning enhances efficiency, accuracy, and reduces data requirements, although it presents challenges like domain mismatch and overfitting. The complexity and resource demands of transfer learning require careful implementation and monitoring, with best practices including selecting relevant pre-trained models, ensuring domain similarity, fine-tuning, performance evaluation, and addressing computational constraints. Despite potential drawbacks such as limited interpretability and high problem dynamics, a strategic approach to transfer learning can lead to operational excellence and a competitive edge in the manufacturing industry.