Pengembangan Metode Quality Assurance Matrix untuk Meningkatkan Sensitivitas Penilaian Defect pada Proses Manufaktur

Faishal Arham Pratikno, Arini Anestesia Purba, Putri Gesan Prabawa Anwar

Abstract


Metode Quality Assurance Matrix (QAM) adalah sebuah alat kualitas yang didasarkan pada prinsip bahwa segala kegagalan terhadap proses manufaktur yang memengaruhi pelanggan (yang mungkin merupakan proses produksi selanjutnya atau pelanggan akhir) harus dihilangkan. Metode ini akan membentuk suatu peringkat, untuk cacat potensial yang ada dan batas keandalan sistem kontrol dalam proses produksi sehingga memungkinkan untuk memberikan langkah-langkah perbaikan yang diperlukan dan mencapai dengan tujuan kualitas. Pengukuran dalam QAM yang ada pada saat ini berbentuk skala 1/3/5 dan untuk menambah tingkat sensitivitas maka diusulkan skala yang lebih akurat yaitu 1/2/3/4/5. Dua kriteria yang dilakukan peningkatan sensitivitas adalah tingkat kritis dan tingkat kontrol dari masing-masing defect (kecacatan). Penentuan nilai jaminan kualitas dicapai jika jumlah nilai kontrolnya setidaknya sama dengan nilai tingkat kritisnya, dalam hal ini nilainya bertanda OK (jika tidak maka akan bertanda KO). Tahap terakhir dari metode QAM adalah mengurutkan defect yang bertanda KO berdasarkan defect rate/cost of quality. Peningkatan sensitivitas yang dilakukan diharapkan memperbaiki identifikasi permasalahan kualitas pada proses manufaktur.

 


Full Text:

PDF

References


R. S. Russell and B. W. Taylor, Operations Management: Creating Value Along the Supply Chain, 7th editio. Wiley, 2010.

M. Bui, “Visualize the quality of frozen fish using fluorescence imaging aided with excitation-emission matrix,” Opt. Express, vol. 26, no. 18, pp. 22954–22964, 2018, doi: 10.1364/OE.26.022954.

T. Alharthi, “Intrinsic detector sensitivity analysis as a tool to characterize ArcCHECK and EPID sensitivity to variations in delivery for lung SBRT VMAT plans,” J. Appl. Clin. Med. Phys., vol. 22, no. 6, pp. 229–240, 2021, doi: 10.1002/acm2.13221.

N. V. N. Madhusudhanaesty, “Evaluation and validation of IBA I’MatriXX array for patient-specific quality assurance of tomotherapy®,” J. Med. Phys., vol. 44, no. 3, pp. 222–227, 2019, doi: 10.4103/jmp.JMP_11_19.

S. Abolli, “Water safety plan: A novel approach to evaluate the efficiency of the water supply system in garmsar,” Desalin. Water Treat., vol. 211, pp. 210–220, 2021, doi: 10.5004/dwt.2021.26617.

L. Yan, “Inhibition monitoring in veterinary molecular testing,” J. Vet. Diagnostic Investig., vol. 32, no. 6, pp. 758–766, 2020, doi: 10.1177/1040638719889315.

S. Comero, “A conceptual data quality framework for IPCHEM – The European Commission Information Platform for chemical monitoring,” TrAC - Trends in Analytical Chemistry, vol. 127. 2020. doi: 10.1016/j.trac.2020.115879.

H. Agostini, “Experienced quality assurance in the IVOM structural contract of the AOK‑BW: A practical example,” Ophthalmologe, vol. 117, no. 4. pp. 331–335, 2020. doi: 10.1007/s00347-020-01067-9.

V. Nikolayevskyy, “Novel external quality assurance scheme for drug susceptibility testing of non-tuberculous mycobacteria: A multicentre pilot study,” J. Antimicrob. Chemother., vol. 74, no. 5, pp. 1288–1294, 2019, doi: 10.1093/jac/dkz027.

G. Peng, “Practical application of a data stewardship maturity matrix for the NOAA onestop project,” Data Sci. J., vol. 18, no. 1, 2019, doi: 10.5334/dsj-2019-041.

N. Dhafr, M. Ahmad, B. Burgess, and S. Canagassababady, “Improvement of quality performance in manufacturing organizations by minimization of production defects,” Robot. Comput. Integr. Manuf., vol. 22, pp. 536–542, 2006, doi: 10.1016/j.rcim.2005.11.009.

S. M. R. Wille, “Liquid Chromatography High-Resolution Mass Spectrometry in Forensic Toxicology: What are the Specifics of Method Development, Validation and Quality Assurance for Comprehensive Screening Approaches?,” Current Pharmaceutical Design, vol. 28, no. 15. pp. 1230–1244, 2022. doi: 10.2174/1381612828666220526152259.

S. K. Dewi, “Minimasi defect produk dengan konsep six sigma,” J. Tek. Ind., vol. 13, no. 1, pp. 43–50, 2012.

H. M. Naeen, “Adaptive Markov-based approach for dynamic virtual machine consolidation in cloud data centers with quality-of-service constraints,” Softw. - Pract. Exp., vol. 50, no. 2, pp. 161–183, 2020, doi: 10.1002/spe.2764.

A. Munawaroh and M. L. Singgih, “Reduksi Produk Cacat pada Produksi Benang dengan Pendekatan Metode Lean Six Sigma,” J. Tek. ITS, vol. 6, no. 2, 2017.

H. J. He, “Multilaboratory Assessment of a New Reference Material for Quality Assurance of Cell-Free Tumor DNA Measurements,” J. Mol. Diagnostics, vol. 21, no. 4, pp. 658–676, 2019, doi: 10.1016/j.jmoldx.2019.03.006.

M. Ali, “Evaluation of water quality in the households of Baniyas Region, Abu Dhabi using multivariate statistical approach,” Sustain. Water Resour. Manag., vol. 5, no. 4, pp. 1579–1592, 2019, doi: 10.1007/s40899-019-00320-7.

J. Mantero, “Quality assurance via internal tests in a newly setup laboratory for environmental radioactivity,” J. Radioanal. Nucl. Chem., vol. 322, no. 2, pp. 891–900, 2019, doi: 10.1007/s10967-019-06769-2.

S. Eib, “Determination of detection thresholds of sinigrin in water-based matrix and allyl isothiocyanate in water- and oil-based matrices,” J. Sens. Stud., vol. 35, no. 4, 2020, doi: 10.1111/joss.12571.

L. Belova, “Ion Mobility-High-Resolution Mass Spectrometry (IM-HRMS) for the Analysis of Contaminants of Emerging Concern (CECs): Database Compilation and Application to Urine Samples,” Anal. Chem., vol. 93, no. 16, pp. 6428–6436, 2021, doi: 10.1021/acs.analchem.1c00142.

R. Srivastava, “Clinical experience using Delta 4 phantom for pretreatment patient-specific quality assurance in modern radiotherapy,” J. Radiother. Pract., vol. 18, no. 2, pp. 210–214, 2019, doi: 10.1017/S1460396918000572.

Ş. Luminiţa, B. Nadia, and B. M. Daniela, “Quality Assurance Matrix in Automotive Industry,” Ann. ORADEA Univ. Fascicle Manag. Technol. Eng., vol. XXI (XI), no. 2, 2012, doi: 10.15660/auofmte.2012-2.2674.

A. Boroiu, G. Jan, B. Ă. Lteanu, M. Bâldea, and A. Boroiu, “Possibilities to weighting the global indicators of quality determined with the Quality Assurance Matrix,” vol. 10, no. 10, pp. 154–156, 2015.

O. Andronic, “Biomarkers associated with idiopathic frozen shoulder: a systematic review,” Connective Tissue Research, vol. 61, no. 6. pp. 509–516, 2020. doi: 10.1080/03008207.2019.1648445.

H. Yin, “Optimal sensor placement based on relaxation sequential algorithm,” Neurocomputing, vol. 344, pp. 28–36, 2019, doi: 10.1016/j.neucom.2018.03.088.

S. R. Newton, “Examining NTA performance and potential using fortified and reference house dust as part of EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT),” Anal. Bioanal. Chem., vol. 412, no. 18, pp. 4221–4233, 2020, doi: 10.1007/s00216-020-02658-w.

W. Jeong, “Quality control for the geophone reorientation of ocean bottom seismic data using k-means clustering,” Geophys. Prospect., vol. 69, no. 7, pp. 1487–1502, 2021, doi: 10.1111/1365-2478.13127.

K. J. Nichols, “Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests,” Med. Phys., vol. 46, no. 1, pp. 262–272, 2019, doi: 10.1002/mp.13289.

A. D. Wait, “Forensic sampling practices for oil spills in the marine environment,” Environmental Forensics, vol. 21, no. 3. pp. 310–318, 2020. doi: 10.1080/15275922.2020.1806949.

C. Schmidt, “Artificial intelligence for non-destructive testing of CFRP prepreg materials,” Prod. Eng., vol. 13, no. 5, pp. 617–626, 2019, doi: 10.1007/s11740-019-00913-3.

A. Saha, “Determination of critical trace impurities in ‘uranium silicide dispersed in aluminium’ nuclear fuel by inductively coupled plasma mass spectrometry (ICP-MS),” J. Anal. At. Spectrom., vol. 36, no. 3, pp. 561–569, 2021, doi: 10.1039/d0ja00391c.

G. M. Sicoe, N. Belu, N. Rachieru, and E. V. Nicolae, “Improvement of the customer satisfaction through Quality Assurance Matrix and QC-Story methods: A case study from automotive industry,” IOP Conf. Ser. Mater. Sci. Eng., vol. 252, no. 1, 2017, doi: 10.1088/1757-899X/252/1/012045.

F. Yu, “Current Status of Metallurgical Quality and Fatigue Performance of Rolling Bearing Steel and Development Direction of High-End Bearing Steel,” Jinshu Xuebao/Acta Metallurgica Sinica, vol. 56, no. 4. pp. 513–522, 2020. doi: 10.11900/0412.1961.2019.00361.

S. Bandyopadhyay, “Application of statistical and machine learning approach for prediction of soil quality index formulated to evaluate trajectory of ecosystem recovery in coal mine degraded land,” Ecol. Eng., vol. 170, 2021, doi: 10.1016/j.ecoleng.2021.106351.

L. Y. Hsu, “A high-capacity QRD-based blind color image watermarking algorithm incorporated with AI technologies,” Expert Syst. Appl., vol. 199, 2022, doi: 10.1016/j.eswa.2022.117134.

C. Meyer-Frießem, “Between clinical practice, teaching and research – a project report on the development and implementation of a career mentoring curriculum for female clinician scientists,” GMS J. Med. Educ., vol. 39, no. 3, 2022, doi: 10.3205/zma001556.

B. Nadia, M. Agnieszka, and I. Laurentiu Mihai, “Quality Assurance Matrix as the advanced generation of quality control,” no. Icemi, pp. 251–256, 2016, doi: 10.2991/icemi-16.2016.3.

B. Crawford, “A Bayesian belief approach to quality control of resin transfer molding process,” Int. J. Adv. Manuf. Technol., vol. 109, no. 7, pp. 1953–1968, 2020, doi: 10.1007/s00170-020-05715-x.

Y. K. Kim, “Natural fibre composites (NFCs) for construction and automotive industries,” Handbook of Natural Fibres: Volume 2: Processing and Applications. pp. 469–498, 2020. doi: 10.1016/B978-0-12-818782-1.00014-6.

A. C. Taylor, “Applications for Passive Sampling of Hydrophobic Organic Contaminants in Water—A Review,” Critical Reviews in Analytical Chemistry, vol. 51, no. 1. pp. 20–54, 2021. doi: 10.1080/10408347.2019.1675043.

I. Vanany, “Application of multi-based quality function deployment (QFD) model to improve halal meat industry,” J. Islam. Mark., vol. 10, no. 1, pp. 97–124, 2019, doi: 10.1108/JIMA-10-2017-0119.

S. O’Driscoll, “An evaluation of ten external quality assurance scheme (EQAS) materials for the faecal immunochemical test (FIT) for haemoglobin,” Clin. Chem. Lab. Med., vol. 59, no. 2, pp. 307–313, 2021, doi: 10.1515/cclm-2020-0210.

S. S. E. Ali, “An Efficient Quality Inspection of Food Products Using Neural Network Classification,” J. Intell. Syst., vol. 29, no. 1, pp. 1425–1440, 2020, doi: 10.1515/jisys-2018-0077.




DOI: http://dx.doi.org/10.24014/sitekin.v20i1.20263

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 SITEKIN: Jurnal Sains, Teknologi dan Industri




Editorial Address:
FAKULTAS SAINS DAN TEKNOLOGI
UIN SULTAN SYARIF KASIM RIAU

Kampus Raja Ali Haji
Gedung Fakultas Sains & Teknologi UIN Suska Riau
Jl.H.R.Soebrantas No.155 KM 18 Simpang Baru Panam, Pekanbaru 28293
Email: sitekin@uin-suska.ac.id
© 2023 SITEKIN, ISSN 2407-0939

SITEKIN Journal Indexing:

Google Scholar | Garuda | Moraref | IndexCopernicus | SINTA


Creative Commons License
SITEKIN by http://ejournal.uin-suska.ac.id/index.php