Online lead time prediction supporting situation-aware production control

Cyber-Physical Production Systems (CPPS) appeared already in recent manufacturing environments, and they are capable of providing detailed data about the products, processes and resources in near real-time. Various analytics techniques are available to exploit such technology related data in decision making, however, these tools typically act in the fields of maintenance and quality. Only a few approaches target production control, while the effectiveness of related processes is of crucial importance from overall performance’s viewpoint. In the paper, a new production data analytics tool is presented, applying machine learning techniques to proactively predict manufacturing lead times to make decisions by implementing a closed-loop production control. The proposed method applies regression techniques, and based on that, it supports job priorization to be utilized in dispatching decisions. The efficiency of the proposed method is analyzed and presented by numerical results of a case study