This paper investigates the application of Transformer-based learning algorithms within the domain of manufacturing scheduling. As Transformer models gain prominence in various fields due to their sophisticated handling of sequential data and long-range dependencies, their potential in manufacturing processes remains largely uncharted. Our research aims at identifying the industries and specific scheduling problems where these models are being implemented as well as at characterizing its applications. Through a detailed analysis of relevant literature, we assess the current landscape of Transformer applications in manufacturing, highlighting both the methodologies used for data extraction and the insights gained from these studies. Preliminary findings suggest that though the use of Transformer models in manufacturing scheduling is emergent, it presents substantial opportunities for future research. The study reveals a promising yet underexplored area ripe for innovation and practical application in industrial operations management.