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Explanatory mixed-methods research analyzes reject rates using Pareto Analysis and Statistical Process Control, identifying key root causes.
This study aims to formulate a data-driven strategy to reduce high product reject rates at a B2B frozen food factory. High rejects threaten productivity and operational sustainability. This research employs an explanatory sequential mixed-methods design. The quantitative phase analyzes production and reject data from 2023-2025 using Pareto Analysis and Statistical Process Control (SPC) with a census sample. The qualitative phase uses interviews and a Focus Group Discussion (FGD) to explore root causes. Results show the process is statistically out of control, with 80.5% of rejects from 40 products. Key root causes are reactive maintenance, process bottlenecks, and a lack of formal operator competency standards.
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Wijaya et al. (2025) studied this question.
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