Control chart

This manuscript focuses on two areas: the review of the history and growth of the QC/sensory evaluation field, and the discussion of the new developments and the future opportunities of this field. This discussion applies to programs worldwide, for companies with established and sound programs, and for companies with either no program or one needing improvement. From this author's point of view, the areas or growth and opportunities include: (1) awareness of the importance of and support for QC/sensory programs within an organization, (2) increased involvement of sensory scientists in in-plant QC/sensory programs, (3) establishment of new or improved QC/sensory training programs, (4) establishment of improved sensory specifications (in techniques and the integration of consumer/management input), (5) Quality Control/sensory evaluations focused on ingredients and in-process products, (6) understanding and use of the diverse applications of product variability studies, (7) use of new or improved sensory methods, (8) collaboration between R&D and QC functions to produce high and consistent quality, (9) continued use of instrumental measures: their establishment and validation, (10) assuring minimal requirements for QC/sensory programs, (11) global Quality Control, (12) development of QC/sensory programs in the personal care, pharmaceutical, household and paper industries, (13) use of multivariate QC/sensory data analyses and control charts, (14) Internet applications in the QC/sensory field, (15) the ultimate goal: a more effective approach to deliver consistent quality products

Selling means convincing one or more people to do a job and operate in today's competitive marketplace, Today industries sell their products as commodities or groups of commodity, so in general it can be said that sales management is one of the most complex and challenging business topics. Because profitability in the market is one of the main goals of manufacturing industries and one of the important factors in assessing the growth rate and profitability of manufacturing industries is their sales. In this article, with the help of mean control chart and the concepts of Six Sigma, which are topics in the field of quality control, we seek to create a control chart to investigate the sale of commodity or commodity group. With the method proposed and prove the scenarios in this article, It is possible to provide a control method with diagrams to control the sales of goods or goods in the product group in all manufacturing industries, that This will provide the desired results for the manufacturing industry.

Two approaches for constructing control charts to monitor multivariate attribute processes when data is presented in linguistic form are suggested. Two monitoring statistics T \(^{\rm 2}_{f}\) and W 2 are developed based on fuzzy and probability theories. The first is similar to the Hotteling’s T 2 statistic and is based on representative values of fuzzy sets. The W 2 statistic, being a linear combination of dependent chi-square variables, its distribution is derived by Satterthwaite’s approximation. Resulting multivariate control charts are compared based on the average run length (ARL). A numerical example is given to illustrate the application of the proposed multivariate control charts and the interpretation of out-of-control signals.

In some real case problems, the relationship between a response variable and one or more explanatory variables called as profile should be monitored over time instead of the quality characteristic itself. Profile monitoring is used in such instances. Many researches have been done in the area of profile monitoring but in most of them it is assumed that the response variable follows normal distribution. In recent years Yeh et al.(2009) proposed 5 T 2 based methods for monitoring logistic profiles in which response variable is binary. In ...

Traditionally, process improvement is considered a defect prevention effort. Current cost models consider the coupled effect of both prevention and appraisal costs on the cost of failure. This paper proposes a new model for the cost of quality, which captures the value of ...

Dalam perkembangan sistem ekonomi Indonesia, persaingan antar perusahaan pada era globalisasi semakin ketat dan menjadi hal yang wajar. Daya saing perusahaan sendiri tidak hanya terjadi dalam keunggulan produk yang ditawarkan, melainkan kualitas dan keamanan produk bagi kesehatan juga turut dipertimbangkan. Perusahaan “X” merupakan salah satu pabrik yang bergerak di industri kosmetik, utamanya produksi parfum remaja. Dengan target pasar remaja, dimana remaja lebih sensitif terhadap zat-zat kimia, perusahaan “X” menjaga kualitas produknya dengan menetapkan spesifikasi untuk karakteristik kualitas dalam produk parfum yang diproduksi. Pengendalian kualitas statistik merupakan teknik penyelesaian masalah yang digunakan untuk memonitor, mengendalikan, menganalisis, mengelola, dan memperbaiki produk dan proses menggunakan metode-metode statistik. Pada praktikum ini, akan dibahas mengenai pengendalian kualitas terhadap karakteristik kualitas pada parfum remaja yang diproduksi oleh Perusahaan “X” yang diukur dengan pH dan massa jenis menggunakan peta kendali Generalized Variance dan T2 Hotelling. Data yang dianalisis harus memenuhi asumsi dependen dan berdistribusi normal multivariat, dan selanjutnya akan dilakukan analisis menggunakan peta kendali Generalized Variance dan T2 Hotelling yang kemudian dilanjutkan dengan analisis kapabilitas proses. Dari pengujian yang dilakukan, diketahui bahwa data telah memenuhi asumsi dependen dan berdistribusi normal multivariat. Peta kendali Generalized Variance menunjukkan bahwa variabilitas proses produksi parfum remaja produksi perusahaan “X” telah terkendali secara statistik atau sudah stabil sehingga dapat dilanjutkan menganalisis dengan peta kendali T2 Hotelling. Pada peta kendali T2 Hotelling terdapat satu pengamatan yang out of control sehingga rata-rata proses produksi parfum remaja oleh perusahaan “X” belum terkendali secara statistik atau belum stabil. Pengamatan yang out of control tersebut adalah pengamatan ke-3 dan setelah diidentifikasi, diketahui penyebabnya adalah kedua karakteristik kualitas, yaitu pH dan massa jenis. Namun, pada praktikum ini diasumsikan telah in control. Pada keadaan in control diperoleh MCp sebesar 1,74 dan MCpk sebesar 1,08 sehingga dapat disimpulkan bahwa produk parfum remaja memiliki variabilitas rendah dan telah sesuai spesifikasi perusahaan yang artinya kualitas produk parfum remaja produksi perusahaan “X” telah baik.

Control chart is a well-known tool for monitoring the performance of an ongoing process. The variability of a process is an important parameter that may deteriorate the process performance if it is not taken care on time. In this study, we have proposed some new auxiliary information-based exponentially weighted moving average (EWMA) charts for improved monitoring of process variability. We employed auxiliary information in some useful forms including ratio, regression, power ratio, ratio exponential, ratio regression, power ratio regression, and ratio exponential regression estimators. The performance of the newly developed charts is evaluated and compared with some existing charts (viz., the NEWMA, the Improved R, the Synthetic R, and the classical R charts), using some useful measures such as average run length (ARL), extra quadratic loss, and relative ARL. The comparative analysis revealed that the proposed charts outperform their counterparts, especially when there is a strong relationship between the study and the auxiliary variables. Finally, an illustrative example is provided for the monitoring of air quality data. K E Y W O R D S auxiliary variables, average run length, control charts, dispersion parameter, EWMA chart, extra quadratic loss 1 | INTRODUCTION Statistical process monitoring is a powerful mechanism that is used to characterize a process and to further monitor its stability over time. A stable process contains only the natural variations, while an unstable process has an additional source of variation, known as unnatural variation. These two types of variations may be differentiated using an important process-monitoring tool called control chart. A control chart is a graphical display that traditionally uses three lines to monitor process behavior. The conventional charts used for detecting assignable causes are Shewhart, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts. Shewhart charts perform well for the detection of large shifts, while CUSUM and EWMA charts are good at detecting small and persistent shifts. Over the years, new control charting structures have been developed to enhance the performance of these conventional charts. The use of auxiliary information is popular, especially, when the information on the auxiliary variable is readily available and is highly correlated with the study variable (cf. Chaudhuri 1). The ratio and regression methods of estimation are common in practice to improve the efficiency of the estimators of population mean and variance. The auxiliary information based control charts are quite efficient at detecting process shifts. Ahmad et al. 2 proposed several Shewhart

This study we observed the application of variable control charts on the data from a sugar factory, to test the quality of the sugar`s color property and compute factory`s capability. The samples are selected at systemic random sample covered all the season. Variables control charts are applied using Mintabe software program, x-bar chart, R chart, x-bar with R chart and x- bar with S chart. R chart, x- bar chart and xbar with S chart shows that the dispersion of the point are random within control limits, chart pattern seem to be of cyclic pattern, the process was in statistical control, x-bar with R chart shows that there is one point outside the lower control limit. np chart was applied to display where the defect that leads to the nonconforming a point , np chart clearly showed that the test failed at points 15,2 and 9 is grown on same said of center line and test failed at points 9,10,11,12. Process Capability was computed to monitor effect of variables, for this Factory the process uses up about 98% of the specification band. The study recommends the use of control charts to monitor the processes of the product lines and can be used as one of the tools to measure variation in the quality of specifications in factories

Dengan menempuh pendidikan di perguruan tinggi, maka mahasiswa bisa mendapatkan tambahan ilmu serta wawasannya yang dapat digunakan di kehidupan serta untuk mempersiapkan masa depannya. Selain itu, dengan menempuh pendidikan di perguruan tinggi mahasiswa bisa mendapatkan suatu gelar yaitu gelar sarjana. Untuk mendapatkan gelar sarjana tersebut, maka mahasiswa harus memenuhi salah satu persyaratannya yaitu menulis tugas akhir. Dalam praktikum ini peneliti ingin mengetahui bagaimana pengendalian kualitas statistika terhadap kesalahan maupun kecacatan dari hasil tugas akhir mahasiswa Departemen Statistika ITS tahun 2015. Kesalahan maupun kecacatan diklasifikasikan menjadi lima jenis yaitu kesalahan format (bold, italic, underline, dan kapital), kesalahan penulisan bahasa asing, kesalahan penulisan kata (typo), tinta memudar saat pencetakan, dan kesalahan spasi. Hasil dalam praktikum ini adalah bahwa jumlah kesalahan paling banyak dari setiap bab oleh 10 TA 2015 yang diamati terdapat pada Bab IV dimana terdiri dari 28 kesalahan baik dari kesalahan penulisan (bold, italic, underline, dan kapital), kesalahan ejaan bahasa asing, kesalahan penulisan huruf (typo), tinta pudar maupun kesalahan pemberian spasi. Rancangan sampling yang digunakan dalam praktikum ini untuk adalah sampling acak sederhana, hasil uji kecakan dari data kesalahan maupun kecacatan pada hasil tugas akhir mahasiswa telah acak, uji binomial untuk data kesalahan maupun kecacatan pada hasil tugas akhir tidak berdistribusi binomial namun diasumsikan telah berdistribusi binomial, diagram Pareto diperoleh komponen yang menyebabkan kesalahan dalam penulisan tugas akhir mahasiswa adalah penulisan (bold, italic, underline, dan kapital) karena indikator tersebut memiliki pengaruh paling besar pada kualitas ketelitian TA 2015, peta kendali p diperoleh bahwa kerusakan kursi di Departemen Statistika ITS belum terkendali, peta kendali np diperoleh bahwa kerusakan kursi di ruang F08 dan T103A sudah terkendali, dan berdasarkan analisis kapabilitas proses binomial dapat dikatakan bahwa rata-rata proporsi kondisi tidak teliti pada TA 2015 adalah 0,0638 dimana proses masih berada di luar batas kendali.

Industrial manufacturing processes can experience a variety of changes to important quality characteristics as a result of tool breakage, tool wear, introduction of new raw materials, and other factors. Statistical process control charts are often used to monitor for changes in quality characteristics for manufacturing processes. The control chart computes a statistic based on measured observations of the process and

In dealing with positively-skewed distributed data, the direct logarithmic transformation may result in a control chart with inappropriate control parameters in the application of quality control. When a specific interval for the lognormal mean is given, a new method is introduced to set up two control charts and these two charts can monitor a process for which the underlying distribution of the quality characteristic is lognormal.

Mohd. Rohani, Jafri and Mohd. Yusof, Sha'ri and Mohamad, Ismail (2007) Application of statistical process control in manufacturing company to understand variability. In: 2nd International Conference on Mathematical Sciences 2007 (ICoMS 2007), 2007, Johor Bahru.