To utilize the potential of machine learning and deep learning, enormous amounts of data are required. To find the optimal solution, it is beneficial to share and publish data sets. Due to privacy leaks in publically released datasets and the exposure of sensitive information of individuals by attackers, the research field of differential privacy addresses solutions to avoid this in the future. Compared to other domains, the application of differential privacy in the manufacturing context is very challenging. Manufacturing data contains sensitive information about the companies and their process knowledge, products, and orders. Furthermore, data of individuals operating machines could be exposed and thus their performance evaluated. This paper describes scenarios of how differential privacy can be used in the manufacturing context. In particular, the potential threats that arise when sharing manufacturing data are addressed. This is described by identifying different manufacturing parameters and their variable types. Simplified examples show how the differentially private mechanisms can be applied to binary, numeric, categorical variables, and time series. Finally, libraries are presented which enable the productive use of differential privacy.