PT. Mayora Indah Tbk merupakan perusahaan yang bergerak dalam sector indusutri makanan dan minuman. Dalam menjalankan kegiatan produksinya, perusahaan selalu berupaya untuk menghasilkan produk yang baik dan menekan terjadinya reject. Penelitian ini bertujuan untuk mengetahui faktor-faktor penyebab terjadinya reject hasil biskuit roma sandwich dan bagaimana pengendalian kualitas produk biskuit roma sandwich di PT. Mayora Indah Jatake 2 dengan menggunakan metode statistik. Analasis pengendalian kualitas dilakukan menggunakan metode statistik berupa check sheet, histogram, peta kendali, diagram sebab-akibat dan metode Failure Mode Effect Analysis (FMEA). Hasil analisis menggunakan checksheet menunjukkan bahwa dalam proses produksi masih terdapat reject biskuit yang tinggi sebesar 19,28%, kemudian hasil dari peta kendali menunjukan bahwa adanya titik berfluktuasi sangat tinggi dan tidak beraturan yang menunjukkan  bahwa proses produksi masih mengalami penyimpangan, oleh sebab itu masih diperlukan analisia lebih lanjut dengan menggunakan diagram sebab-akibat (fishbone diagram). Dari analisis sebab-akibat dapat diketahui faktor penyebab penyimpangan/reject berasal dari faktor manusia, metode, dan material. Selanjutnya berdasarkan analisis menggunakan metode FMEA prioritas utama perbaikan berdasarkan nilai RPN tertinggi yang harus dilakukan oleh PT. Mayora Indah diantranya yaitu Membuat jalur biskuit dan menurunkan packing table lebih rendah dari output mesin sandwiching dengan nilai RPN sebesar 168, Membuat standar diameter teflon dan revisi pengecekan kondisi teflon di setiap minggu dengan nilai RPN sebesar 105, dan Merubah jalur stacking dari yang tidak berlawanan arah menjadi berlawanan arah nilai RPN sebesar 72

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Pengendalian kualitas produksi roma sandwich menggunakan metode statistik quality control (sqc) dalam upaya menurunkan reject di bagaian packing

Calibration of storage tanks is an exact means of determining the accurate capacity of storage tanks at a
given incremental level. When calibrating a certain number of oil storage tanks having the same dimensions, it is
assumed that if the specified tank dimensions are within the statistical control limits, a calibration chart generated
for one tank can be used for the other tanks. This study used X-bar and R-control chart to investigate the stability of
the tank calibration process for some fabricated oil storage tanks. The method of control chart was used to check if
the process is under control or not. The variables of the calibration process are Circumference of each course shell,
Height of each course shell and Elevations for the bottom profile. Results revealed that the calibration process was
statistically stable and under control with no special or assignable cause of variation. Process capability conducted
also showed that the calibrated tanks met the pre-set limits.

/pdf/application-of-statistical-quality-control-sqc-in-the-kr4o8dzfv6.pdf

Application of statistical quality control (sqc) in the calibration of oil storage tanks

Implementation Statistical Quality Control (SQC) and Fuzzy Failure Mode and Effect Analysis (FMEA): A Systematic Review

The Fuzzy Failure Mode and Effect Analysis (FMEA) Method to Improve Roofing Product’s Quality (case study : XYZ Company)

Stainless steel welded pipe is manufacture by various methods like as shearing, bending, partial welding, full welding, nitriding, final length, expanding and buffing. This research is about to implement statistical quality control tools in pipe manufacturing industries. There are various defect found in stainless steel welded pipe manufacturing, due to these defects, pipes are rejected lot wise, and that is very unproductive result for each and every industries. Statistical quality control (SQC) tools, such as x bar chart, R-chart, C-chart, P-chart had been utilized to measure the variability in the process. We observed good result about the process, whether it is in control or not. These types of charts will also provide the fast result that will be used to take action immediately. In pipe manufacturing industries, rejection reduction is possible by proper shearing process, appropriate welding line, maintaining expanding process. This research helps to decide the process within the control limit or not, that would be decided by various control charts. This is also used for quality improvement. In S.Q.C, there are number of tools used such as Frequency distribution, Control charts, acceptance sampling, and analysis of the data. From all of these tools, we have to concentrate only on control charts. Ultimately, It has been used for any manufacturer to solve the rejection problem, enhancing the productivity and quality of the product. In apurvi industries, out of control process has been detected by Xbar and R chart and root cause analysis used for defect detection. C chart has also been used for depicted the number of defects in the particular lot. Using the C chart it can be easily decided whether the particular lots are accepted or not. Flow of this research is the beginning with the introduction, Literature review regarding the research area then Methodology followed by the result and discussion. Finally future scope of the research is presented.

/pdf/implementation-of-statistical-quality-control-s-q-c-in-3q4tmyfti1.pdf

Implementation of statistical quality control (s.q.c.) in welded stainless steel pipe manufacturing industry

The 6061 T6 Aluminum alloy(Al 97.5%, Cr 0.1%, Cu 0.15%, Fe 0.5%, Mg 0.8%, Mn 0.15%, Si 0.5%, Ti 0.15%, Zn 0.15%) round bar of 40 mm o size has been used as a work piece material for the present experiments with tungsten carbide single roller burnishing tool[6]. Al 6061 T6 is special hot-worked aluminum with good hardness and toughness properties. It is used for extreme load conditions such as hot-work forging, extrusion etc. The working life and dimensional accuracy of Al 6061 T6 can be improved with suitable heat treatment. A. Machining Parameters and Experimental Design The three machining parameters considered for this study were Spindle speed (RPM), feed (mm/rev) Burnishing force (kgf), No of tool pass. The parameters were set at five levels each. The summary of the parameters are shown in Table 1.

Amit Patel

Papers

Implementation of statistical quality control (s.q.c.) in welded stainless steel pipe manufacturing industry

Effect Evaluation of Roller Burnishing Process on Surface Roughness of 6061 T6 Al Alloy Using Response Surface Methodology