Analysis of Sickle Product Defects Using Statistical Process Control in a Welding MSME
DOI:
https://doi.org/10.59890/ijist.v4i6.14Keywords:
Statistical Process Control, Product Defects, p-Control Chart, Pareto Diagram, Welding MSMEAbstract
Quality control is a critical aspect for the sustainability and competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in the manufacturing sector. This study aims to analyze product defects in sickle products manufactured by a welding MSME in Mataram City, West Nusa Tenggara, using the Statistical Process Control (SPC) method. A quantitative descriptive-analytical research approach was employed, with data collected through physical inspection and check sheet recording over 20 observation periods. A sample of 20 sickle units was drawn from each observation period, resulting in a total of 400 inspected units. Data analysis was conducted using four Statistical Process Control (SPC) tools : check sheet, Pareto diagram, p-control chart (p-chart), and cause-and-effect diagram (fishbone/Ishikawa). The results revealed that out of 400 inspected units, 32 defective units were identified, resulting in an overall defect rate of 8.00%. Based on the Pareto diagram analysis, the most dominant type of defect was Crack (43.75%), followed by Uneven Thickness (31.25%), and Dull Edge (25.00%). The p-control chart analysis indicated that all data points fell within the statistical control limits (CL = 0.0800; UCL = 0.2620; LCL = 0.0000), suggesting that the production process is in a state of statistical control. Nevertheless, the Defect Per Million Opportunities (DPMO) value of 26,666.7, equivalent to a sigma level of 3.43, indicates that process capability still requires substantial improvement. The fishbone diagram analysis identified defect-causing factors related to Man, Machine, Material, Method, and Environment, with the absence of a written Standard Operating Procedure (SOP) as the most fundamental contributing factor. This study recommends the implementation of a welding SOP, preventive machine maintenance, raw material standardization, operator training, and routine SPC monitoring as structured and continuous quality improvement measures.
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