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<p>Acceptance sampling plans serve as a valuable tool for verifying product quality, with the most fundamental plan being the single sampling plan (SSP). Numerous studies have recently explored acceptance sampling systems that combine two SSPs with distinct decision rules to enhance sampling inspection efficiency. The current literature categorises these systems into quick-switching sampling systems (QSSs) and two-plan sampling systems (TSSs). This study constructs a TSS based on the process capability index <i>C</i><sub><i>pmk</i></sub>. Compared to existing methods, including SSPs and QSSs, the proposed TSS demonstrates superior performance in terms of cost-effectiveness and discriminatory power regarding lot quality, significantly improving the efficiency of product quality verification. Finally, a practical case study illustrates the advantages of the proposed method, demonstrating that this method can reduce sample sizes by up to 71.05% compared to existing methods, offering a promising approach to advancing industrial practices.</p>

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Development of a cost-effective adaptive sampling system considering process yield and quality loss

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Chien-Wei Wu,
  • To-Cheng Wang

摘要

Acceptance sampling plans serve as a valuable tool for verifying product quality, with the most fundamental plan being the single sampling plan (SSP). Numerous studies have recently explored acceptance sampling systems that combine two SSPs with distinct decision rules to enhance sampling inspection efficiency. The current literature categorises these systems into quick-switching sampling systems (QSSs) and two-plan sampling systems (TSSs). This study constructs a TSS based on the process capability index Cpmk. Compared to existing methods, including SSPs and QSSs, the proposed TSS demonstrates superior performance in terms of cost-effectiveness and discriminatory power regarding lot quality, significantly improving the efficiency of product quality verification. Finally, a practical case study illustrates the advantages of the proposed method, demonstrating that this method can reduce sample sizes by up to 71.05% compared to existing methods, offering a promising approach to advancing industrial practices.