Diagram Kontrol Generally Weighted Moving Coefficient of Variation (GWMCV) & Auxiliary Information Based-Generally Weighted Moving Coefficient of Variation (AIB-GWMCV)

Nuriman, Muhammad Alifian (2021) Diagram Kontrol Generally Weighted Moving Coefficient of Variation (GWMCV) & Auxiliary Information Based-Generally Weighted Moving Coefficient of Variation (AIB-GWMCV). Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Statistical Process Control (SPC) merupakan metode yang dapat digunakan untuk memonitor proses produksi. Salah satu alat SPC yang dapat digunakan adalah diagram kontrol yang dapat mendeteksi assignable causes pada waktu tertentu. Secara umum, diagram kontrol digunakan untuk memonitor mean proses dan standar deviasi proses. Akan tetapi, ketika standar deviasi proses proporsional dengan mean proses dan mean proses tersebut berfluktuasi antar waktu, diagram kontrol berbasis koefisien variasi (CV) lebih efektif digunakan untuk memonitor variabilitas proses. Pada penelitian ini diusulkan diagram kontrol Generally Weighted Moving Coefficient of Variation (GWMCV) dengan transformasi log-normal 3 parameter yang merupakan generalisasi dari diagram kontrol EWMCV. Hasil studi simulasi menunjukkan bahwa diagram kontrol GWMCV lebih sensitif mendeteksi pergeseran proses kecil ke moderat dibandingkan dengan diagram kontrol EWMCV. Jika parameter ω = 1, diagram kontrol GWMCV memiliki kinerja yang sama dengan diagram kontrol EWMCV. Untuk menambah sensitivitas pada diagram kontrol GWMCV, pada penelitian ini juga diusulkan diagram kontrol Auxiliary Information Based-Generally Weighted Moving Coefficient of Variation (AIB-GWMCV) dengan transformasi log-normal 3 parameter Hasil studi simulasi menunjukkan bahwa diagram kontrol AIB-GWMCV memiliki kinerja lebih baik dibandingkan dengan diagram kontrol GWMCV dalam mendeteksi pergeseran koefisien variasi proses yang kecil ke besar. Kedua diagram kontrol yang diusulkan selanjutnya diaplikasikan untuk memonitor proses produki pupuk NPK di PT Pupuk Sriwidjaja Palembang. Hasil monitoring menunjukkan bahwa proses belum terkontrol secara statistik dan diagram kontrol AIB-GWMCV lebih sensitif dalam mendeteksi pergeseran proses dibandingkan dengan diagram kontrol GWMCV.
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Statistical Process Control (SPC) is a method used to monitor the production process. One of the tools in SPC which can be used is a control chart that can detect assignable causes at a certain time. Generally, control charts are utilized to monitor the mean process and standard deviation process. However, if the standard deviation process is proportional to the mean process and the mean process itself fluctuates from time to time, a control chart based on the coefficient of variation (CV) is more effective to be used to monitor the process of variability. In this study, a generally weighted moving coefficient of variation (GWMCV) control chart with three-parameter lognormal transformation as a generalization of the EWMCV control chart, is proposed to monitor the variability process. The simulation studies show that the GWMCV control chart is more sensitive in detecting the shifts of small to moderate processes compared to the EWMCV control chart. Both GWMCV and EWMCV have the same performance when parameter ω = 1. To enhance the sensitivity of the control chart, Auxiliary Information Based-Generally Weighted Moving Coefficient of Variation (AIB-GWMCV) control chart with three-parameter lognormal transformation is also proposed. The simulation studies show that the AIB-GWMCV control chart performs better than the GWMCV control chart in detecting the shifts of small to large CV processes. Furthermore, both control charts are applied to monitor the production process of NPK fertilizer at PT Pupuk Sriwidjaja Palembang. The results of the monitoring show that the process is under out-of-control and the AIB-GWMCV control chart is more sensitive to detect shifts process than the GWMCV control chart.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	CV, GWMCV, AIB-GWMCV, ARL, Three-Parameter Lognormal Transformation, CV, GWMCV, AIB-GWMCV, ARL, Transformasi Log-Normal 3 Parameter
Subjects:	T Technology > TS Manufactures > TS156 Quality Control. QFD. Taguchi methods (Quality control)
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49101-(S2) Master Thesis
Depositing User:	Muhammad Alifian Nuriman
Date Deposited:	13 Mar 2021 01:59
Last Modified:	13 Mar 2021 01:59
URI:	http://repository.its.ac.id/id/eprint/84173

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