Data-Driven Optimization of CNC Manufacturing Using Simulation and DOE Techniques

In the highly competitive manufacturing environment of today, operational success depends on increasing efficiency and cutting waste. The goal of this research is to use Arena simulation software to model a CNC production system to assess and improve system performance. Three different parts are processed by the model, which also includes rework loops in which a portion of faulty products are sent back for further processing. Finding bottlenecks, evaluating important performance metrics such as output, queue lengths, waiting times, and machine utilization, and testing improvement scenarios are the primary goals. Study findings indicate that waiting times were greatly shortened and resource usage was balanced in alternative scenarios, which were accomplished by shifting workloads, line balancing, and modifying inter-arrival durations. The results demonstrate how well simulation can represent and resolve inefficiencies in intricate industrial systems. Manufacturers may optimize manufacturing processes without interfering with ongoing operations thanks to this method, which encourages educated decision-making.