Pengendalian Kualitas Proses Produksi Pupuk Urea di PT. Pupuk Sriwidjaja Palembang

PT. Pupuk Sriwidjaja Palembang merupakan salah satu perusahaan produsen pupuk urea di Indonesia. Untuk memenuhi kebutuhan konsumen, PT. Pupuk Sriwidjaja Palembang sangat mengutamakan kualitas pupuk urea yang baik sesuai dengan standar yang ditetapkan perusahaan. Beberapa karakteristik kualitas yang sangat diperhatikan konsumen dan perusahaan adalah Nitrogen, Biuret, Moisture, dan Mesh -6+18. Oleh karena itu perlu dilakukan pengendalian kualitas pupuk urea dengan memonitor proses produksi. Pada penelitian ini digunakan diagram kontrol kombinasi MEWMA yaitu dengan diagram kontrol M2Z2 untuk memonitor variabilitas proses dan diagram kontrol MZ untuk memonitor target proses. Hasil monitoring fase I pabrik 1B menunjukkan bahwa variabilitas proses dan target proses sudah terkendali secara statistik baik menggunakan batas kontrol dengan kriteria ATS maupun ARL, dan pada fase II menunjukkan variabilitas proses dan target proses belum terkendali statistik atau belum stabil baik menggunakan batas kontrol dengan kriteria ATS maupun ARL. Pada pabrik 3 fase I, variabilitas proses dan target proses sudah terkendali secara statistik, serta pada fase II variabilitas proses dan target proses belum terkendali secara statistik jika menggunakan batas kontrol dengan kriteria ARL. Sedangkan jika menggunakan kriteria ATS, proses belum stabil dari segi variabilitas dan sudah stabil dari segi target proses. Kapabilitas proses secara multivariat pada pabrik 1B dan pabrik 3 menunjukkan bahwa akurasi dan presisi proses belum kapabel karena nilai indeks proses MPp dan MPpk kurang dari 1. =============================================================================================================== PT. Pupuk Sriwidjaja Palembang is one of the company that produce urea prill in Indonesia. In order to meet consumer needs, PT. Pupuk Sriwidjaja Palembang give priority for quality of urea prill that is appropiate with company’s specifications. Some of characteristic qualities of urea prill are Nitrogen, Biuret, Moisture, and Mesh -6+18. So, it is necessary to control the quality of urea prill with monitor the production process. In this study used M2Z2 control chart to detect changes in process variability. While the process target if production process is monitored by using MZ control chart. Both of these control chart are types of combination MEWMA. At factory 1B, the result of both process variability and target in phase I have been statistically controlled using control limit with ATS and ARL criteria, while in phase II both process variabilily and target have not been statistically controlled using control limit with ATS and ARL criteria. At factory 3, both process variability and target in phase I have been statistically controlled, In phase II both process variability and target have not been statistically controlled using contol limit with ARL criteria. While using ATS kriteria, process variability is not stable or have not been statistically controlled and process target have been controlled statistically or stable. The process capability analysis multiariately using MPp and MPpk shows the process has not been good capability both factory 1B and factory 3. MPp and MPpk value less than 1 indicates that the precision and accuracy of the process has not been good

Efficient Auxiliary Information Based Exponentially Weighted Moving Coefficient of Variation Control Chart using Hybrid Estimator : An Application to Monitor NPK Fertilizer

In this era, manufacturing sectors should ensure the quality of their production process and products. They must reduce the variability that occurs in their operation. Coefficient variation control charts have become important statistical Process Control (SPC) tools for monitoring processes when the process mean linear function with the standard deviation. In recent years, auxiliary information-based-CV control charts using memory type structure have been investigated to enhance the sensitivity of control charts. Auxiliary information is selected when the variable remains stable during the monitoring period. Nevertheless, the AIB statistic is constructed based on lognormal transformation, and no research investigated the memory type CV chart using estimator of AIB-CV from the combination of ratio and regression form called hybrid form. This research proposes a hybrid auxiliary information-based exponentially weighted moving coefficient of variation (Hybrid AIB-EWMCV) control chart for detecting small to moderate shifts in the CV process. The Average Run Length (ARL) simulation shows that increasing the level of correlation and sample sizes enhances the detection ability of the control chart. Also, the proposed chart performs well than existing chart. A real dataset from fertilizer manufacturing is implemented to explain the condition of the process by using a Hybrid AIB-EWMCV control chart

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

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. =================================================================================================== 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