Enhancing quality in Lot reception: A comparative analysis of innovative attribute acceptance sampling plans
Abstract
Purpose: This study aims to enhance production quality by applying Quality Control (QC) principles through acceptance sampling, specifically analyzing the efficacy of attribute acceptance sampling plans in final lot receptions.
Design/methodology/approach: Through a comprehensive review and critical evaluation of various sampling methodologies found in literature, this paper assesses their efficiency under distinct administrative and operational conditions. It emphasizes the comparison of different attribute acceptance sampling plans by examining variations in parameters and key performance indicators, such as Average Outgoing Quality (AOQ) and inspection time allocation percentage. Furthermore, it proposes a model for Continuous Sampling Plans (CSP) to evaluate these plans' performance in response to operational characteristic variations.
Findings: The analysis reveals that the selected methodologies significantly aid in decision-making processes for lot acceptance, utilizing non-conforming rates depicted by the Average Quality Level (AQL). This provides a robust framework for improving Quality Control strategies, demonstrating the potential of these methodologies to optimize production quality through strategic lot acceptance.
Practical implications: This paper outlines a practical approach for industry practitioners to enhance decision-making in lot acceptance, offering a method to balance quality control with operational efficiency effectively.
Originality/value: By comparing a wide range of attribute acceptance sampling plans and introducing a novel CSP model, this research contributes valuable insights into the optimization of QC strategies. It offers a unique perspective on enhancing production quality, marking a significant advancement in the field of Quality Control and management.
Keywords
Full Text:
PDFDOI: https://doi.org/10.3926/jiem.7491
This work is licensed under a Creative Commons Attribution 4.0 International License
Journal of Industrial Engineering and Management, 2008-2024
Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008
Publisher: OmniaScience