Application of Process Failure Modes and Effect Analysis for Acceptance Quality Limit in Lot Sentencing
Authors: 
Nafadza Salsabila Reihana, Filscha Nurprihatin, Sri Susilawati Islam, Anak Agung Ngurah Perwira Redi, Michael Nayat Young, Yogi Tri PrasetyoAuthors Info & Claims
Nafadza Salsabila Reihana, Filscha Nurprihatin, Sri Susilawati Islam, Anak Agung Ngurah Perwira Redi, Michael Nayat Young, Yogi Tri PrasetyoAuthors Info & ClaimsPages 212 - 218
Abstract
The quality control process performs an acceptance sampling plan to determine if a batch of products meets quality specifications. Acceptance Quality Limit (AQL) defines the acceptable level of defects, but determining the appropriate AQL rate is challenging due to manufacturing process variations. This study utilized Process Failure Mode and Effect Analysis (PFMEA) to determine the AQL for acceptance sampling plans and evaluate its performance against the current method using Operating Characteristic Curve. The study identified eighteen failure modes from PFMEA, with severity level 7, occurrence level 3, and detection level 3. The results demonstrated that PFMEA can serve as a valuable reference for modeling acceptance sampling plans. Acceptance sampling with AQL effectively assesses defective batches compared to the current method. Incorporating PFMEA in acceptance sampling plans enables companies to enhance their understanding of process performance and potential risks, empowering them to make informed decisions and improve product quality.
References
[1]
Y. D. Regent Montororing and F. Nurprihatin, “Model of quality control station allocation with consider work in process, and defect probability of final product,” in Journal of Physics: Conference Series, IOP Publishing Ltd, 2021.
[2]
F. Nurprihatin, J. F. Andry, and L. Liliana, “Supply chain disruptions mitigation plan using six sigma method for sustainable technology infrastructure,” Management and Production Engineering Review, Jun. 2023.
[3]
I. Skrzypczak, “Concrete production - An application of OC curves in conformity control,” in E3S Web of Conferences, EDP Sciences, Aug. 2018.
[4]
D. C. Montgomery, Introduction to statistical quality control, Seventh. Arizona State University, 2013.
[5]
O. Haievskyi, V. Kvasnytskyi, and V. Haievskyi, “Development of a method for optimizing a product quality inspection plan by the risk of non-conformity slippage,” Eastern-European Journal of Enterprise Technologies, vol. 6, no. 3–108, pp. 50–59, 2020.
[6]
D. Plinta and A. Gren, “Application of the PFMEA for risk assessment in the production process,” in Advances in Manufacturing III, vol. 3, A. Hamrol, M. Grabowska, and D. Maletic, Eds., 2022, pp. 23–35.
[7]
B. Haughey, “Product and process risk analysis and the impact on product safety, quality, and reliability,” in Annual Reliability and Maintainability Symposium, 2019.
[8]
F. Nurprihatin, Y. N. Ayu, G. Dwinoor Rembulan, J. Fernandes Andry, and T. E. Lestari, “Minimizing product defects based on labor performance using linear regression and six sigma approach,” Management and Production Engineering Review, vol. 14, no. 2, pp. 1–11, 2023.
[9]
A. F. Fahad Halim, “Comparative study of the inspection parameters for AQL 2.5% and 1.5% in garments Manufacturing Process,” Journal of Textile Science & Fashion Technology, vol. 2, no. 4, May 2019.
[10]
M. Colledani, “Statistical process control,” in CIRP Encyclopedia of Production Engineering, G. Laperrière Luc and Reinhart, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2019, pp. 1150–1157.
[11]
D. Selvamuthu and D. Das, Introduction to statistical methods, design of experiments and statistical quality control. Springer Nature Singapore, 2018.
[12]
J. Lawson, “Quality management systems,” in An Introduction to Acceptance Sampling and SPC with, 1st ed., New York: Chapman and Hall, 2021, p. 20.
[13]
M. Saha, H. Tripathi, S. Dey, and S. S. Maiti, “Acceptance sampling inspection plan for the Lindley and power Lindley distributed quality characteristics,” International Journal of System Assurance Engineering and Management, vol. 12, no. 6, pp. 1410–1419, Dec. 2021.
[14]
ISO 2859, “ISO 2859-1:1999(en) Sampling procedures for inspection by attributes — Part 1: Sampling schemes indexed by acceptance quality limit (AQL) for lot-by-lot inspection.” Accessed: Feb. 08, 2023. [Online]. Available: https://www.iso.org/obp/ui/#iso:std:iso:2859:-1:ed-2:v1:en
[15]
A. Mascia, “A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories,” Accreditation and Quality Assurance, vol. 25, no. 5–6, pp. 311–321, Dec. 2020.
[16]
N. Sellappan, N. Deivanayagampillai, and K. Palanikumar, “Evaluation of risk priority number (RPN) in design failure modes and effects analysis (DFMEA) using factor analysis,” Int J Appl Sci Technol, vol. 3, no. 4, 2013.
[17]
F. Nurprihatin, M. Angely, and H. Tannady, “Total productive maintenance policy to increase effectiveness and maintenance performance using overall equipment effectiveness,” Journal of Applied Research on Industrial Engineering, vol. 6, no. 3, pp. 184–199, 2019.
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Published In
January 2024
235 pages
ISBN:9798400716652
DOI:10.1145/3647782
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Published: 06 May 2024
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ICCMB 2024
ICCMB 2024: 2024 7th International Conference on Computers in Management and Business
January 12 - 14, 2024
Singapore, Singapore
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