The field of machine learning (ML) is of specific interest for production companies as it displays aperspective to handle the increased complexity within their production planning and control (PPC) processesin an economic and ecologic effective as well as efficient way. Several studies investigate applications ofML to different use cases. However, the research field lacks in research on industry case studies. A broadunderstanding from a practical perspective and in this context, an evaluation from a data mining and businessstandpoint is key for gaining trust in ML solutions. Therefore, this paper gives a comprehensive overviewof evaluation dimensions and outlines the current state of research in ML-PPC by conducting a systematicresearch overview. First, the present work provides key dimensions of business and data mining objectivesas evaluation metric. Business objectives are clustered into economic, ecological and social objectives anddata mining objectives are grouped into prediction accuracy, model’s explainability, model’s runtime, andmodel’s energy use. Secondly, the systematic literature review identifies 45 industry case studies in MLPPC from 2010-2020. The work shows that the scientific publications only rarely reflect in detail on a widerange of evaluation metrics. Instead, researchers mainly focus on prediction accuracy and seldom investigatethe effect of their results to a business context. Positively, some papers reflect on further aspects and caninspire future research. This resulting transparency supports decision makers of companies in theirprioritization process when setting up a future ML-roadmap. In addition, the research gaps identified hereininvite researchers to join the research field.