:30.0 The development of machine learning technologies are broadly changing how humans interact with their environments across all sectors. In industrial settings, this is referred to as the fourth industrial revolution, Industry 4.0, and encompasses several technologies that are pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial Injection Molding Machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines nearest neighbors classification and nearest neighbors optimization methods to effectively optimize an Injection molding process. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and machine learning methods in a real-world setting to improve the overall performance regarding production time, energy cost, and production quality.