LAUSR

Real time production performance evaluation plays a vital role in diagnosing manufacturing system health status and achieving productivity improvements. However, most existing studies on system performance evaluation are based on steady state analysis and focused on the production system with fixed cycle time machines. The real-time performance evaluation for a manufacturing system with variable cycle time machines, although typical for a large number of realistic scenarios, has been mostly ignored. The development of smart manufacturing and increasingly available sensor data have provided unprecedented opportunities to carry out thorough analysis on the real-time performance of such complex systems. In this letter, we developed a data-enabled methodology to efficiently identify and predict the real-time permanent production loss for a serial production line with variable cycle time machines. The concept and evaluation method of opportunity window are introduced to facilitate the permanent production loss estimation. Numerical case studies are presented to demonstrate the effectiveness of the proposed methods for opportunity window evaluation and production loss identification.