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This study aims to analyze the quality control of car tire production at Company X using the Statistical Process Control (SPC) method. The data used in this research are secondary data obtained from GitHub, consisting of weekly production and defect data observed over one year with a total of 52 observations. The analysis was carried out using descriptive statistics, histograms, P-charts, and cause-and-effect (Ishikawa) diagrams. The histogram results indicate that the distribution of defects is right-skewed, showing that most defects occur at a moderate level, although several extreme values are present. The initial P-chart analysis revealed several points below the lower control limit, indicating the presence of special cause variation and suggesting that the production process was not fully stable. After removing out-of-control data and recalculating the control limits, the revised P-chart showed that all data points were within the control limits, indicating that the production process was statistically under control. Furthermore, the Ishikawa diagram identified potential causes of defects related to human factors, machines, methods, materials, and the working environment. Overall, the results demonstrate that SPC is an effective tool for monitoring production stability and providing a systematic basis for improving quality consistency in car tire manufacturing processes.

This study aims to analyze the quality control of white crystal sugar production at ABC Sugar Factory using the Statistical Quality Control (SQC) method. The research employed a descriptive quantitative approach with a case study design. The primary data consisted of production volume and defective product data during the 2024 production period, while supporting data were obtained through observation and interviews with the Quality Control department. The analytical tools applied included check sheets, histograms, Pareto diagrams, p control charts, and fishbone diagrams. The results show that from a total production of 190,745.89 tons, defective products amounted to 66.70 tons, representing 0.33 percent of total output. The identified defects consisted of wet sugar at 45 percent, brownish sugar color at 30 percent, and oversized sugar crystals at 25 percent. Defective products occurred only during the first to third production periods, while no defects were observed from the fourth to seventh periods. The p chart analysis indicates that the production process was statistically out of control in the early periods but became stable and controlled in the subsequent periods. From a managerial perspective, these findings provide practical guidance for improving manufacturing quality through enhanced process control, equipment maintenance, and workforce capability development.

This study analyzes the quality control of lamb rendang products at CV. Mitra Tani Farm using three Statistical Quality Control (SQC) tools: the checksheet, Pareto diagram, and Fishbone diagram. The research was conducted to identify major product defects and their root causes in the canned lamb rendang production process. A descriptive quantitative method was applied through direct observation, interviews, and documentation. Data were collected using a checksheet to record defect types and frequencies, then analyzed with Pareto and Fishbone diagrams to determine dominant issues and underlying factors. The results showed that labeling errors were the most frequent defect, followed by inaccurate sterilization and machine overload. These problems were mainly caused by limited operator skills, non-standardized procedures, and inadequate machine performance. The findings indicate that improving operator training, refining standard procedures, and maintaining equipment are essential to enhance product quality and consistency.  

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in Indonesia’s economy, including in the garment industry. The subject of this study is MSME X, located in Cimahi City, which specializes in the production of knitted clothing. This research aims to identify production problems, analyze the causes of defects, and design improvement strategies using the Six Sigma method with the DMAIC (Define, Measure, Analyze, Improve, Control) approach. In the Define stage, the main problems are identified through the Critical to Quality (CTQ) approach, which reveals three major types of defects: button defects (39%), stitching defects (32%), and fabric or pattern defects (29%). Next, the Measure stage is conducted to assess process performance using quality indicators. The calculation results show a Defects per Million Opportunities (DPMO) value of 10,968.95 and a Sigma Level of 3.79, indicating room for improvement. The Analyze stage utilizes Pareto and Fishbone diagrams to identify root causes. The Improve stage applies Failure Mode and Effect Analysis (FMEA) to determine improvement priorities. The Control stage involves the use of Statistical Process Control (SPC), regular inspections, and an incentive system. This approach is expected to reduce defect rates and sustainably improve production efficiency.

Osha Snack UMKM faces issues related to product defects, which are suspected to arise due to a lack of quality control in the production process. This study aims to identify the causes of product defects, provide improvement suggestions, and determine effective ways to control product quality. Data were collected through interviews with relevant parties and field surveys to gather information on production volume, product defects, and production activities. The data were processed by organizing it into check sheets, performing regression calculations, and analyzing it using scatter diagrams and fishbone analysis. The Statistical Process Control (SPC) method was applied to analyze, control, and improve the production process. The results show a significant relationship between production volume and the number of defective products, with the regression equation Y = 0.0764X and a coefficient of determination R² = 0.9625. The most common type of defect is broken snacks before packaging. In addition, waste products also come from product returns by buyers, which leads to raw material waste and hinders the achievement of production targets. The analysis results suggest that the UMKM should implement quality control measures in the form of process improvements, enhancement of work skills, proper machine maintenance, implementation of SOPs, and improvement of working conditions.

Didik Bag Convection is a manufacturing business specializing in bag production. However, its production activities often encounter issues related to product quality, such as defects caused by employees' lack of understanding of new sewing machines, the absence of standard operating procedures (SOPs), and an unsupportive work environment. To address these issues, quality control measures are necessary, which can be analyzed using Statistical Process Control (SPC) to identify the root causes of defects in product quality and production processes. The quality control analysis techniques employed include Check Sheets, Pareto Diagrams, Control Charts (P-Charts), and Fishbone Diagrams (Cause-and-Effect Diagrams). The results of this study indicate that the quality control at Didik Bag Convection is within acceptable limits as it falls within normal control boundaries. The number of defective products remains within a reasonable threshold. Based on the Pareto Diagram, damaged stitching defects account for the highest proportion of defects, making up 43.28% of the total defects. Improvements to address stitching defects are needed, which can be achieved using the Fishbone Diagram by focusing on the factors of Man (human resources), Material (raw materials), Machine, Method, and Environmental indicators that influence product defects during production.

PT. XYZ is an international company operating in the energy sector, especially oil and gas, with a focus on fabrication and construction activities for large-scale projects. One of the main production processes that is very influential is welding. To ensure welding quality, PT. XYZ uses the Non-Destructive Testing – Ultrasonic Testing (NDT-UT) method. However, during the inspection process, problems are often found in the form of rejects due to defects that do not meet welding quality standards. Some types of defects detected include LOF (Lack of Fusion), LOP (Lack of Penetration), and ESI (Excessive Spatter Inclusion). This research aims to determine the percentage of the most dominant welding defects, identify factors that cause defects, and provide recommendations for improvements to improve welding quality. The method used is Statistical Quality Control (SQC) with the help of tools such as check sheets, histograms, Pareto diagrams, scatter diagrams, control charts and fishbone diagrams. Based on the data collected, a LOF defect of 5,434 mm was found, an ESI defect of 1,990 mm, and an LOP defect of 1,030 mm. SQC analysis shows that the dominant defect is LOF with a percentage of 64%. The main causal factors come from methods, humans, machines and materials. Therefore, companies need to make improvements and developments to reduce product defects so that they can improve welding quality.

The problem faced by the Bmrsign Wallgallery company is that products are found that do not match the specifications set by the company. The types of product damage are wrong size frames, broken frames, and uneven corners. To solve this problem, the SQC method is used to reduce the causes of defects in the product. The purpose of this method is to reduce the risk of damage to the product. The object studied was Bmrsign Wallgallery with a sample of 29 days. The use of the SQC method is accompanied by analysis tools in the form of control charts (p-charts), Pareto diagrams, cause and effect diagrams (fishbone). Based on the results of data analysis implementing the Statistical Quality Control (SQC) method using the P-Chart control map, the largest value of the Upper Control Limit (UCL) can be shown as 0.272, while the Lower Control Limit (LCL) is -0.013. The results show that the number of defective products during the frame making process is still within normal control limits. The analysis using the Pareto diagram shows that the types of frame defects are dominated by broken with a percentage of 35% or a total of 66 pcs, inappropriate size shapes as much as 33% or a total of 62 pcs, and uneven corners as much as 32% or a total of 60 pcs. Based on the results of the fishbone diagram analysis, defective products are caused by workers who are not focused, work standards are not adhered to, machine maintenance is not given attention, and there is a decrease in the quality of raw materials.