A growing number of machine learning (ML) projects in manufacturing require the collaboration of various experts. In addition to data scientists, stakeholders with production engineering knowledge have to specify and prioritize individual project tasks. Data engineers prepare input data, while machine learning operations (MLOps) engineers ensure that trained models are deployed and monitored within IT landscapes. Existing project management approaches, e.g., Scrum, have problems for ML projects, as they do not consider various expert roles or ML project stages. We propose a project management approach defining a Kanban workflow by readjusting stages of ML development lifecycles, e.g., CRISP DM. This makes it possible to map expert roles to stages of the Kanban workflow. An adapted Kanban board allows visualizing and reviewing the status of all project tasks. We validate our approach with specific use cases, showing that it facilitates ML project management in manufacturing.