Integrating Statistical Quality Control and Root-Cause Analysis in Marble Manufacturing for Sustainable Process Improvement

Subject Areas : Statistical Quality Control

Nurhayati Rauf ^{1 *} , Ahmad Padhil ²

1 - Department of Industrial Engineering, Faculty of Industrial Technology, Muslim University of Indonesia, Makassar, Indonesia
2 - Department of Industrial Engineering, Faculty of Industrial Technology, Muslim University of Indonesia, Makassar, Indonesia

Received: 2025-04-12 Accepted : 2025-07-09 Published : 2025-08-14

Keywords: Statistical Quality Control, Marble manufacturing, Defect analysis, P-chart, Fishbone diagram, Sustainable production, Predictive maintenance. ,

Abstract :

The marble manufacturing industry often faces quality control challenges due to the natural variability of raw materials and inconsistencies in human-machine interactions. These issues can result in high defect rates, production inefficiencies, and customer dissatisfaction. Therefore, a systematic and data-driven approach is essential to monitor, evaluate, and improve product quality. This study aims to analyze the quality performance of marble tile production by identifying dominant defect types and assessing process stability using Statistical Quality Control (SQC) techniques. Specifically, the research applies Pareto analysis to determine the most frequent defect categories and constructs a p-chart to evaluate the temporal variation in defect proportions over time. The methodology involved daily defect recording over 30 production days, resulting in a dataset of 16,015 marble units. Defect categories included cracks, breaks, discoloration, chipping, and misalignment. Pareto analysis revealed that over 99% of all defects were concentrated in cracks and breaks, confirming the effectiveness of prioritizing these two categories for corrective action. Meanwhile, the p-chart indicated a process mean (CL) of 0.10607, with control limits at 0.11343 (UCL) and 0.09871 (LCL). Of the 30 observations, 27 were within control limits, suggesting the presence of common-cause variation. However, three outlier points on Days 7, 15, and 24 exceeded the UCL, signaling assignable causes. These anomalies coincided with peak equipment usage and unexpected operator reassignments, highlighting the interplay of technical and human factors. The findings, supported by visual representation in Figure 3, suggest that while the process is generally stable, it is vulnerable to short-term disruptions. Implementing predictive maintenance and ergonomic scheduling is recommended to enhance long-term process capability and product consistency.

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