Machine learning (ML) is a form of artificial intelligence (AI) algorithm adopted by manufacturing organisations to aid systems in learning and to improve based on past experiences without explicit programming. It is important to research the field of ML in manufacturing to uncover the range of benefits and how they affect manufacturing firms. This dissertation systematically reviews the existing literature concerning ML in the manufacturing sector. The methodology of this study searched for articles systematically using a specific search string across three databases, filtered the studies based on the inclusion and criteria, removed duplicate articles, removed articles with a title and abstract review, carried out a full-text analysis, and backward and forwards searched the articles. A total of 26 articles were narrowed down that qualified for data extraction.

The results of this study indicate that ML offers a wide range of benefits in the field of manufacturing. The identified benefits of ML include faster processing of data, greater accuracy in tasks compared to human effort, the ability to solve complex problems, and greater control and flexibility in manufacturing practices. Challenges identified amongst ML in the manufacturing field included employee skill, data quality, and information security. The conclusion can be drawn that ML plays a significant role in the manufacturing sector across an extensive range of applications.