# Data-Driven Optimization of CNC Manufacturing Using Simulation and DOE Techniques
## 3 Literature Review  
A key component of manufacturing system optimization, especially in settings with CNC machining, is the examination of part flow in a production process. Minimal delays, a decrease in work-in-progress (WIP), and optimal machine utilization are all guaranteed by efficient part flow. A crucial tool for examining and visualizing part movements in a variety of systems is discrete event simulation (DES). Due to its versatility in simulating large systems with numerous process stages, decision points, and rework loops, Arena simulation software is one of the most used DES tools. Manufacturers can experiment with various layouts and flow techniques without interfering with real-world production by using simulation, according to [4].  
With precise and sequential operations like turning, milling, drilling, and inspection, CNC machining cells are frequently at the center of the production flow in modern manufacturing. Flow modeling becomes more complex when parts undergo various operations because of their dynamic behavior, particularly when rework is included. Accurate simulation of such systems can reveal information about performance measures like throughput, queue length, cycle time, and bottlenecks, according to research by [5; 6]. For simulation models to accurately depict system behavior, rework--a major hindrance to smooth component flow--must be included. Rework loops, according to [4], result in longer cycle times, congestion at crucial equipment, and decreased overall efficiency, particularly when machines such as CNC turning are overworked because of the repeated re-entry of defective parts.  
Furthermore, process timings can be modeled using suitable probability distributions by statistical tools such as the Arena Input Analyzer, which improves part flow simulation accuracy [7; 8; 9]. Modeled part behavior is guaranteed to represent actual variability in processing times and inspection findings using statistical fits, such as uniform, beta, or Poisson distributions. Accurate flow analysis also helps in identifying bottlenecks and resource constraints, aligning with [PERSON]'s Theory of Constraints,which states that system throughput is determined by the slowest or most burdened process [10, 11, 12]. As a result, part flow analysis through simulation becomes a crucial method for enhancing production planning, layout design, and quality control strategy decision-making [13, 14, 15].  
[PERSON]. [16] used OptQuest and Arena to search for the optimal supply chain network decisions under three levels of uncertainty. Similarly, [PERSON]. [17] used Arena and OptQuest to determine the best number of workstations in the garment assembly line. By contrast, the authors of [18] demonstrated the possibility of integrating Arena and CPLEX software tools for simulation-based optimization. Evidence from the previous studies shows an integration of Arena and Opt Quest as the most used simulation-based optimization software tool [19]. However, in any design problem, the selection of software to be used for simulation studies is very important and is primarily based on the number of criteria, including the ease of use, animation capability, model development, and input category [20, 21, 22, 23, 24, 25].