Integration of Statistical Process Control and Artificial Intelligence to Improve Steering System Process Capability in the NANO Model - Bagus Pamungkas; Nailla Dwi Rizkia; Ridzqi Alfauji; Ahmad Syarifudin; Yudi Prastyo (Lentera Ilmu Nusantara, 2026)
The high Defects per Unit (DPU) in the NANO model, mainly caused by handlebar effort problems, indicates process variation in the steering system that affects product quality. This study aims to evaluate the stability and capability of the steering system production process and identify process parameters that contribute to handlebar effort through the integration of Statistical Process Control (SPC) and Artificial Intelligence (AI). Data were collected from measurements of steering system component dimensions and assembly process parameters on the production line. The analysis used X̄-R control charts, process capability indices, and a machine learning-based AI approach to detect abnormal patterns among process variables. The results show that the process is generally close to statistical control, but special cause variations are still found. Process capability analysis indicates that the process has not consistently met specifications, as shown by Cp = 0.87 and Cpk = 0.74. The main parameters affecting process performance are steering stem height 1 and head pipe height, both of which show high variation and low process capability. AI analysis also identified abnormal multivariate patterns related to these parameters. Therefore, improvements should focus on standardizing work procedures, calibrating measuring instruments, controlling materials, maintaining jig fixtures, and improving operator competence. The integration of SPC and AI supports more predictive quality control and helps reduce defects in the steering system production process.