Statistical Process Control

This paper aims at developing a regression residual control chart to economically detect the abnormal patterns of the stencil printing process (SPP), so as to predict significant deviations that might result in nonconforming products. The SPP is widely recognized as the main contributor of soldering defects in a surface mount assembly (SMA). The earlier those abnormal conditions can be detected in the SPP stage, the less expensive the defect correction costs. Shewhart control chart is frequently used to monitor the amount of solder paste volume. However, its results can be error-prone since the solder paste volume is significantly affected by other process factors. For developing the proposed control chart, a 3^8-3 experimental design was first conducted and validated to formulate the relationship between the control variables and the SPP response. Eight process factors for SPP were initially defined, including stencil thickness, component pitch, aperture area, snap-off height, sque...

This abstract presents a case study approach based on qualitative methods and data that aimed a detailed examination of an environment, a particular subject or situation. The objective of this work is to improve the painting process of an automotive company through the application of Statistical Process Control for the optimization of performance indicators quality and productivity. Statistical Process Control is applied to the monitoring of manufacturing industry processes, including automotive manufacturing. The implementation of the SPC in production environments is to improve and control real-time product and process characteristics, quality and productivity is an accepted manufacturing event.

Statistical process control (SPC) is widely used in process industries to monitor variations in process attributes. Typically, automatic devices capture a multitude of measurements on process and product characteristics every few seconds. Operators and engineers commonly monitor only a small subset of these. Multivariate SPC has been proposed to fully utilize the available data, however, interpretation of multivariate information is often too complex for most line operators. This paper describes the design and implementation of a real-time multivariate process control system that features a graphical user interface (GUI) and provides useful information for both line operators and engineers. The information system described in this paper should provide large-scale manufacturers with better access to information for identifying opportunities in continuing to improve processes performance and business competitiveness.

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 ...

Profile monitoring is referred to as monitoring the functional relationship between the response and explanatory variables. Traditionally, process control charts monitor the mean and/or the variance of a univariate quality characteristic. For several correlated quality characteristics, multivariate process control charts monitor the mean vector and/or the covariance matrix. However, monitoring the functional relationship between variables is sometimes more beneficial. One example is the power output of a Diesel engine and the amount of fuel injected should be related. In this paper, we propose a Kullback-Leibler information (KLI) control chart for linear profiles monitoring in Phase II. The plotted statistics of the KLI chart are calculated based on a backward procedure. The functional relationship is described by linear regression. The performance of the proposed KLI control chart is compared with those of other existing control charts, especially the Generalized Likelihood Ratio (GLR) chart for both KLI and GLR charts do not require design parameters. The results show that (1) the KLI 2 control chart is better than the GLR control chart in detecting the shift of variance in terms of Average Time to Signal, and (2) if the shift of the regression coefficient is small, the GLR chart outperforms the KLI chart, but if the size of shift is large, the KLI chart outperforms the GLR chart. The plotted statistics of KLI are compared to that of GLR. Their similarity is discussed.

This project work is carried out on Biggest-size Futa bread using process control technique to monitor the process for small shift in the weight of bread based on mixed EWMA-CUSUM (MEC) chart. Ten different bread chosen at random from the process was weighed and recorded for 60 days; the mean weight of bread per day is used as data points. The normality of the data was determined by using histogram and Anderson-Darling test of normality. Graphs are plotted for Exponentially weighted moving average (EWMA), Cumulative Sum (CUSUM) and Mixed Exponentially weighted moving average-Cumulative Sum (MEC) to show descriptive statistics of all numerical analysis performed on the data set. The mixed EWMA-CUSUM (MEC) control chart is compared with the Cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) which are calculated individually. The performance comparison shows that the mixed EWMA-CUSUM (MEC) control chart outperformed the other control charts for detecting small shift i.e. (CUCUM and EWMA control charts) in terms of out of control signal.

You yourself, or what is the same, your experience is such ``coin'' that, while you aren't questioned, it rotates all the time in ``free flight''. And only when you answer the question the ``coin'' falls on one of the sides: ``Yes'' or ``No'' with the believability that your experience tells you.

Kualitas produk merupakan hal terpenting yang diutamakan oleh perusahaan dalam proses produksi. Bila kualitas produk yang dihasilkan baik, dapat merepresentasikan bahwa suatu perusahaan telah berhasil melakukan proses produksi secara tepat. Sehingga menghasilkan produk yang akan tetap laku di pasaran dan banyak diburu oleh para konsumen. Di suatu perusahaan, pengendalian kualitas biasanya dilakukan oleh orang yang berkerja khusus di dalam bidang pengendalian kualitas. Untuk menganalisis dan memperbaiki proses produksi produk, tentunya harus memahami dan mengerti bagaimana kinerja dari proses tersebut. Biasanya orang yang berkerja dalam bidang pengendalian kualitas ini menggunakan suatu metode statistik yang dapat membantu di dalam melihat apakah suatu proses yang dilakukan di bawah kendali atau sebaliknya. Metode tersebut sering disebut dengan Statistical Process Control (SPC), yang di dalamnya terdapat tujuh alat kualitas dasar (seven basic quality tools). Tujuh alat kualitas dasar dari SPC seperti : histogram, check sheet, pareto chart, cause and effect diagram, control chart, scatter diagram, dan defect concentration diagram yang akan akan dibahas satu persatu melalui percobaan yang dilakukan dengan menggunakan kertas koran yang dibagi menjadi ukuran 2x2 cm sebanyak 40 buah lalu dilipat dan dipotong manual (dengan menggunakan tangan).

Statistical Process Control (SPC) charts are simple graphical tools that allow monitoring of process results. They are used to describe the type of variance that exists within the process. They highlight areas where further investigation could be needed. The purpose of the study was to use statistical control charts to examine the production process of buff sausages in the food manufacturing industry. The mean (X-bar) chart and range (R) chart were used in the study to assess if the production process was under control. The weight of 100 packets of buff sausages was measured (in grams) using a weighing scale. In order to collect information about the manufacturing process of the buff sausage, the interview of operations manager was followed by the observation of the buff sausage production plant. The result of mean chart shows that the within subgroup variation is consistent because all the sample points fall within the three-sigma upper and lower control limits. The range chart displays that all the sample points are within the two-sigma control limits. The buff sausage manufacturing process is therefore in a state of statistical control. However, the production unit can still seek to further improve performance to maintain consistent quality of the product.

Oleh: Muhammad Taurif Rasyidin Sampling Plan Sampling plan adalah prosedur yang digunakan dalam mengambil keputusan terhadap produk-produk yang datang atau yang sudah dihasilkan perusahaan. Sampling plan tidak digunakan untuk memperkirakan keputusan penerimaan atau penolakan saja. Sampling plan juga bukan merupakan alat pengendalian kualitas, namun merupakan alat untuk memeriksa apakah produk atau bahan baku yang datang keperusahaan atau produk yang telah dihasilkan perusahaan tersebut telah memenuhi spesifikasi. Sampling plan digunakan sebagai suatu bentuk dari inspeksi antara perusahaan dengan pemasok, antara pembuat produk dengan konsumen, atau antar divisi dalam perusahaan. Sampling plan tidak melakukan pengendalian atau perbaikan kualitas proses, melainkan hanya sebagai metode untuk menentukan disposisi terhadap produk yang datang (bahanbaku) atau produk yang telah dihasilkan (barang jadi). Sebagai contoh dalam 1 lot pengiriman barang yang berisi 100 unit, konsumen ingin melakukan inspeksi dalam memeriksa 1 lot pengiriman barang terebut dengan pengambilan sampel sebanyak 5 yang dilakukan secara random (untuk banyaknya pengambilan sampel tentunya telah sepakati pihak produsen maupun konsumen) dengan asumsi bahwa misalnya dalam 5 sampel tersebut jika salah satunya terdapat kerusakan atau cacat (jumlah cacat bisa juga tergantung oleh kesepakatan antara konsumen dan produsen) maka 1 lot pengiriman tersebut akan dikembalikan oleh konsumen dan produsen melakukan perbaikan kualitas barang tersebut. Terdapat berbagai metode perencanaan sampel antara lain adalah singel, double, multiple dan sequential sampling plans. Namun pada kesempatan kali ini akan dibahas adalah kinerja Double, Multiple dan Sequantial Sampling Berdasarkan OC curve. Double Sampling Parameter yang digunakan oleh rencana double sampling adalah sebagai berikut: n 1 = Ukuran sampel pada sampel pertama c 1 = Ukuran toleransi penerimaan pada sampel pertama n 2 = Ukuran sampel pada sampel kedua c 2 = Ukuran toleransi penerimaan pada sampel kedua

In today's world, the amount of available data is steadily increasing, and it is often of interest to detect changes in the data. Statistical process monitoring (SPM) provides tools to monitor data streams and to signal changes in the data. One of these tools is the control chart. The topic of this dissertation is a special control chart: the exponentially weighted moving average (EWMA) chart.

A control chart plots the data togther with two control limits. A control chart signals a (possible) change when the plotted data exceeds the control limits. A control chart performs well if it signals changes in the data quickly, without triggering frequent false alarms.

Before a control chart can be set up, estimates of the process parameters are needed. To this end an initial data set is collected. In practice this data set often contains outliers, recording errors, and other data quality issues. These so-called `contaminations' are problematic as they influence the parameter estimates. We investigate robust estimation methods to ensure accurate estimation of the process parameters. We propose a new estimation method based on screening and show that this new method outperforms existing estimation methods, when the type of contaminations is unknown.

In the second phase of this dissertation we study the effect of estimation on the performance of the EWMA chart and give recommendations regarding its design. We show that traditionally designed charts have very variable performance. We study an alternative design procedure based conditional performance which provides control over the variability in performance.

– Most of the modern industrial processes are naturally multivariate. Multivariate control charts are supplanted univariate control charts, as it takes into account the relationship between variables and identifies the real process changes, which are undetectable by univariate control charts. In practice, the basic assumption that the measurements are independently and identically distributed about a target value is not always valid. Violation of this assumption increases the False Alarm Rate (FAR) and deteriorates the separation of assignable causes from common causes. This paper presents the application of Multivariate Statistical Process Control (MSPC) charts (e. g., Hotelling , MEWMA) to monitor the flare making process in a straight fluorescent light bulb industry. Furthermore, it develops the appropriate procedure for monitoring a multivariate autocorrelated data variable (i. e., dynamic behavior) by using Autoregressive Integrated Moving Average (ARIMA) models. Univariate SPC charts and decomposition approach are used to identify the out-of-control signals that are generated from multivariate SPC charts. Software packages such as Minitab 17 and Statgraphics Centurion XVI are used to construct the control charts.

INFO ARTIKEL ABSTRAK Sejarah artikel: Penelitian ini bertujuan untuk menemukenali jenis cacat yang ada pada proses pembuatan botol air 600 ml di PT TIB Jember, kemudian berupaya mengatasinya. Alat bantu yang diterapkan adalah peta kendali p Statistical Process Control (SPC). Peta kendali bulan September 2019 menunjukkan bahwa beberapa data produksi melebihi batas kendali atas (BKA/UCL). Pemilahan jenis cacat dikategorikan menjadi tiga: botol memutih, bentuk tidak sesuai, dan pecah. Dari diagram Pareto terungkap bahwa cacat berdasarkan rangking adalah 58.57% botol memutih, 26.36% bentuk tidak sesuai dan botol pecah sebesar 15,07%. Dengan bantuan fishbone diagram ditemukan beberapa penyebab cacat. Pertama, suhu ruangan yang tidak konsisten yang mengakibatkan preform (bahan dasar botol) tidak matang dengan sempurna sehingga pada saat perentangan pada mold terjadi botol memutih pada bagian pinggul atau bawah botol. Kedua, pemanas (heater) yang seringkali mati menyebabkan bentuk tidak sempurna. Matinya heater ini disebabkan oleh kebocoron selang dari pendingin heater yang retak getas karena usia. Beberapa saran diberikan untuk memperbaiki mutu. Salah satu saran sudah dilaksanakan yaitu penggantian selang-selang tersebut. Pemeriksaan ulang terhadap kualitas produksi dilakukan, peta kendali p bulan Oktober 2019 menunjukkan bahwa tidak ada lagi data yang keluar dari batas kendali. Terjadi penurunan tingkat cacat dari 0,74% ke 0,55 % (0,19%). ABSTRACT Keywords: This research aim is to findout type of defects in prodution process of 600 ml bottle water at PT TIB Jember, then some attemps to resolve the problem. The p-chart of Statistical Process Control was employed. The control chart in September 2019 shown that some data beyond the ultimate control limit (UCL). The defects were categorized into three: whitening, deviate form, and broken. The Pareto chart revealed that the aforemention defects were 59.7%, 26.36% and 15.07% respectively. By using fishbone diagram some root cause of the defects were found. First, the unstable workpalce temperature which disturb the preform melting process prior to blowing, therefore causing whitening the bottle. Second, the heater which is often turned off while processing that caused deviate form. The frequently turned off the heater was caused by cracked-damage along the hoses of overdue. Some suggestions were given, one of them was replacing the hoses by the new ones. The new control chart on October 2019 shown that all data within the limits following the hoses replacement. It also reduced the rate of defect by 0.19% from 0.74% to 0.55%. defect; quality control; SPC © 2020 Penulis. Dipublikasikan oleh Baristand Industri Padang. Akses terbuka dibawah lisensi CC BY-NC-SA