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

Mukherjee and Chakraborti 1 proposed a single distribution-free (nonparametric) Shewhart-type chart based on the Lepage 2 statistic for simultaneously monitoring both the location and the scale parameters of a continuous distribution when both of these parameters are unknown. In the present work, we consider a single distribution-free CUSUM chart, based on the Lepage 2 statistic, referred to as the CUSUM-Lepage (denoted by CL) chart. The proposed chart is distribution-free (nonparametric) and therefore, the in control (denoted IC) properties of the chart remain invariant and known for all continuous distributions. Control limits are tabulated for implementation of the proposed chart in practice. The IC and out of control (denoted OOC) performance properties of the chart are investigated through simulation studies in terms of the average, the standard deviation, the median and some percentiles of the run length distribution. Detailed comparison with a competing Shewhart-type chart is presented. Several existing CUSUM charts are also considered in the performance comparison. The proposed CL chart is found to perform very well in the location-scale models. We also examine the effect of the choice of the reference value (k) of CUSUM chart on the performance of the CL chart. The proposed chart is illustrated with a real data set. Summary and conclusions are presented.

The use of the 14-d earthworm toxicity test to aid in the evaluation of the ecological impact of contaminated soils is becoming increasingly widespread. However, the method is in need of further standardization. As part of this continuing process, the choice of reference toxicants was evaluated. Reference toxicants were rated in relation to the following criteria: (a) reproducibility, (b) low human health hazard, (c) feasibility of measurement, and (d) chemical stability. Potassium chloride (KCl) and ammonium chloride (NH,CI) were evaluated as possible alternatives to the one currently in common use, 2-chloroacetamide. Potassium chloride rated the best for the combination of the four criteria, followed by NH4C1 and 2-chloroacetamide. Coefficients of variation (C.V.s) from control charts of six definitive tests were used to measure reproducibility. The best reproducibility (lowest C.V.) was shown by KC1, followed by NH4C1 and 2-chloroacetamide. Toxicants ranked KCl < NH4C1 << 2-chloroacetamide in terms of health hazard; and KC1 = NH,Cl> 2-chloroacetamide in terms of measurement feasibility. Both 2-chloroacetamide and NH,Cl changed in concentration during testing. Evidence is also presented that 2-chloroacetamide degrades rapidly during testing, and that, as dead worms decay, ammonification may be adding another toxicant, ammonia, to tests.

In the context of quality assurance strategies, statistical process control techniques and conformance testing are necessary to perform a correct quality auditing of process outcomes. However, data collection is based on measurements and every measurement is intrinsically affected by uncertainty. Even if adopted instruments are in a condition of metrological confirmation, random and systematic measurement errors can not be completely eliminated. Moreover, the consequence of wrong measurement-based decisions can seriously decrease company profits because of larger repairing and shipping costs, as well as for the loss of reputation due to customers' dissatisfaction. This paper deals with a theoretical analysis aimed at estimating the growth in decisional risks due to both random and systematic errors. Also, it provides some useful guidelines about how to choose the Test Uncertainty Ratio (TUR) of industry-rated measurement instruments in order to bound the risk of making wrong decisions below a preset maximum value.

In some quality control applications, quality of a product or process can be characterized by a relationship between two or more variables that is typically referred to as profile. Moreover, in some situations, there are several correlated quality characteristics, which can be modeled as a set of linear functions of one explanatory variable. We refer to this as multivariate simple linear profiles structure. In this paper, we propose the use of three control chart schemes for Phase II monitoring of multivariate simple linear profiles. The statistical performance of the proposed methods is evaluated in term of average run length criterion and reveals that the control chart schemes are effective in detecting shifts in the process parameters. In addition, the applicability of the proposed methods is illustrated using a real case of calibration application.

The use of the 14-d earthworm toxicity test to aid in the evaluation of the ecological impact of contaminated soils is becoming increasingly widespread. However, the method is in need of further standardization. As part of this continuing process, the choice of reference toxicants was evaluated. Reference toxicants were rated in relation to the following criteria: (a) reproducibility, (b) low human health hazard, (c) feasibility of measurement, and (d) chemical stability. Potassium chloride (KCl) and ammonium chloride (NH,CI) were evaluated as possible alternatives to the one currently in common use, 2-chloroacetamide. Potassium chloride rated the best for the combination of the four criteria, followed by NH4C1 and 2-chloroacetamide. Coefficients of variation (C.V.s) from control charts of six definitive tests were used to measure reproducibility. The best reproducibility (lowest C.V.) was shown by KC1, followed by NH4C1 and 2-chloroacetamide. Toxicants ranked KCl < NH4C1 << 2-chloroacetamide in terms of health hazard; and KC1 = NH,Cl> 2-chloroacetamide in terms of measurement feasibility. Both 2-chloroacetamide and NH,Cl changed in concentration during testing. Evidence is also presented that 2-chloroacetamide degrades rapidly during testing, and that, as dead worms decay, ammonification may be adding another toxicant, ammonia, to tests.

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.

Inventory replenishment rules have been recognized as a major cause of the bullwhip effect and inventory instability in multi-echelon supply chains. There is a trade-off between bullwhip effect and inventory stability where mitigating the bullwhip effect through order smoothing might increase inventory instability. Therefore, there is a substantial need for inventory control policies that can cope with supply chains dynamics. This paper attempts to formulate an inventory control system based on a statistical control chart approach to handle the trade-off between order variability amplification and inventory stability. The proposed replenishment system, namely IR-SPC, incorporates individual control charts to control both the inventory position and the placed orders. A simulation study has been conducted to evaluate and compare the IR-SPC with a generalized order-up-to that has order smoothing mechanism. The comparisons showed that the IR-SPC outperforms both the smoothing order-up-to policy and the Min-Max inventory policy in terms of bullwhip effect and inventory performances.

Shewhart control charts are constructed by using the Hunter¸ab color scale parameters to assure maintenance of the color of raisins and dried figs during storage in modified atmosphere packages, vacuum packages, or nylon bags. These control charts may be used to maintain the quality within acceptable limits and make it possible to readjust storage conditions if the acceptable limits should be violated.

Monitoring algorithms, such as the Shewhart and Cusum control charts, are often used for monitoring purposes in the chemical industry or within an environmental context. The statistical properties of these algorithms are known to be highly responsive to measurement errors. Recent studies have underlined the important role played by the twocomponent measurement error model in chemical and environmental monitoring. In the present work, we study the effects of the twocomponent error model on the performance of the X and S Shewhart control charts. Results reveal that gauge imprecision may seriously alter the statistical properties of the control charts. We propose how to reduce the effects of measurement errors, and illustrate how to take errors into account in the design of monitoring algorithms.

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 statistical quality control, usually the mean and variance of a manufacturing process are monitored jointly by two statistical control charts, e.g., a X chart and a R chart. Because of the efficiency of double sampling (DS) X charts in detecting shifts in process mean and DS s charts in process standard deviation it seems reasonable to investigate the joint DS X and s charts for statistical quality control. In this paper, a joint DS X and s chart scheme is proposed. The statistical design of the joint DS X and s charts is defined and formulated as an optimization problem and solved using a genetic algorithm. The performance of the joint DS X and s charts is also investigated. The results of the investigation indicate that the joint DS X and s charts offer a better statistical efficiency in terms of average run length (ARL) than combined EWMA and CUSUM schemes, omnibus EWMA scheme over certain shift ranges when all schemes are optimized to detect certain shifts. In comparison with the joint standard, two-stage samplings and variable sampling size X and R charts, the joint DS charts offer a better statistical efficiency for all ranges of the shifts.

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 ...

Nonparametric procedures are presented for the problem of detecting changes in the process median (or mean), or changes in the process variability when samples are taken at regular time intervals. The proposed procedures are based on sign-test statistics computed for each sample, and are used in Shewhart and cumulative sum control charts. When the process is in control the run length distributions for the proposed nonparametric control charts do not depend on the distribution of the observations. An additional advantage of the non-parametric control charts is that the variance of the process does not need to be established in order to set up a control chart for the mean. Comparisons with the corresponding parametric control charts are presented. It is also shown that curtailed sampling plans can considerably reduce the expected number of observations used in the Shewhart control schemes based on the sign statistic.

Apart from their uses in the context of statistical process control, control charts are also used as an exploratory tool in the context of exploratory data analysis. These two application situations are not similar and, as a consequence, control charting methodology should be adjusted to the exploratory context. In this article we make an inventory of the requirements for control charts that are used for exploratory analysis and we propose a procedure that meets these requirements. Robustness against assignable causes of variation appears to be important. The proposed methodology is illustrated from two real-life examples.

The EWMA chart for the standard deviation is a useful tool for monitoring the variability of a process quality characteristic. The performance of this chart is usually evaluated under the assumption of known parameters. However, in practice, process parameters are estimated from an in-control Phase I data set. A modified EWMA control chart is proposed for monitoring the standard deviation when the parameters are estimated. The Run Length properties of this chart are studied and its performance is evaluated by comparing it with the same chart but with process parameters assumed known.

A Ajustes a planes estratégicos H Operación funcional A Ajustes a planes operativos estratégicos V V E l ió d p Revisión trimestral cumplimiento de directrices Evaluación de: Cumplimiento directrices operativas EL TABLERO DE CONTROL O CUADRO DE MANDO INTEGRAL DE MANDO INTEGRAL Es una herramienta de control empresarial que p q permite establecer y monitorear los objetivos de una empresa y los de sus diferentes áreas o unidades.

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 ...

In statistical process control (SPC) methodology, quantitative standard control charts are often based on the assumption that the observations are normally distributed. In practice, normality can fail and consequently the determination of assignable causes may result in error. After pointing out the limitations of hypothesis testing methodology commonly used for discriminating between Gaussian and non-Gaussian populations, a very flexible family of statistical distributions is presented in this paper and proposed to be introduced in SPC methodology: the generalized lambda distributions (GLD). It is shown that the control limits usually considered in SPC are accurately predicted when modelling usual statistical laws by means of these distributions. Besides, simulation results reveal that an acceptable accuracy is obtained even for a rather reduced number of initial observations (approximately a hundred). Finally, a specific user-friendly software have been used to process, using the SPC Western Electric rules, experimental data originating from an industrial production line. This example and the fact that it enables us to avoid choosing an a priori statistical law emphasize the relevance of using the GLD in SPC.

This work presents a comparative study of the performance of the cumulative sum (CuSum), as well as the exponentially weighted moving average (EWMA) control charts. The objective of this research is to verify when CuSum and EWMA control charts do the best control region, in order to detect small changes in the process average. Starting from the data of a productive process, several series were simulated. CuSum and EWMA control charts were used to determine the average run length (ARL) to detect a condition out of control. ARL found by each chart which was then, compared. It was observed that the CuSum control chart practically did not sign points out of control for the levels of variation between^1.0 standard deviation. For these variation levels the EWMA control chart was more efficient than CuSum. Among the parameters EWMA control chart the ones with constant l ¼ 0:10 and 0.05, with the respective control limits L ¼ 2:814 and 2.625, were the ones that detected larger number of altered positions. q

The process of quality assurance should demonstrate that the method and the analytical instrument provide accurate and precise results, or whether deterioration occurs. For this, the quality procedure should include tests which provide information on the characteristic performance of the method. According to the recent literature, useful procedures contributing to the overall quality of analytical results are illustrated in relation with liquid chromatography. Parameters examined are carry-over, column statement, accuracy and precision. The performance of the whole system from the extraction to measurement of analytes of interest must be monitored with control charts. Data representing the routine performance of chromatographic equipment systems and method allow predicting long-term uncertainties and confidence intervals.

HACCP (Hazard Analysis Critical Control Point) is a preventive system concerned with food safety. This system has become a prerequisite for t ransactions involving food products.The purpose was to set up specific HA CCP plan for Sudanese biscuit processing plant in an existing b iscuit processing plant in Wad Medani, central Sudan. A specific generic HACCP model was developed to improvesafety and quality of biscuit produced in this plant. This was based on actual conditions in the biscuit manufacturing plant, the seven principles of HACCP and several existing generic models of HACCP using qualitative approach. Six-member HA CCP team was constituted and HACCP chart, verification procedures and record-keeping were init iated. Four CCPs were identified in the p roduction of biscuit in the manufacturing plant. The most important identified CCPs were raw material receipt and storage, blending, packaging and product storage. However, the HACCP plan in this study has not been implemented in the biscuit making process. Based on the findings of this study, the authors recommend implementation of HA CCP system in all food facilit ies.

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.

HACCP (Hazard Analysis Critical Control Point) is a preventive system concerned with food safety. This system has become a prerequisite for t ransactions involving food products.The purpose was to set up specific HA CCP plan for Sudanese biscuit processing plant in an existing b iscuit processing plant in Wad Medani, central Sudan. A specific generic HACCP model was developed to improvesafety and quality of biscuit produced in this plant. This was based on actual conditions in the biscuit manufacturing plant, the seven principles of HACCP and several existing generic models of HACCP using qualitative approach. Six-member HA CCP team was constituted and HACCP chart, verification procedures and record-keeping were init iated. Four CCPs were identified in the p roduction of biscuit in the manufacturing plant. The most important identified CCPs were raw material receipt and storage, blending, packaging and product storage. However, the HACCP plan in this study has not been implemented in the biscuit making process. Based on the findings of this study, the authors recommend implementation of HA CCP system in all food facilit ies.

In this experimental study some statistical calculations have been made to eliminate quality problems such as undesirable tolerance limits and out of circularity of spherodial cast iron parts during machining. X-R control charts have been constructed on the data obtained from this manufacturing to discover and correct assignable causes, so that the machine capability (Cp) and the process capability (Cpk) can be determined.

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

a b s t r a c t CUSUM control chart has been widely used for monitoring the process variance. It is usually used assuming that the nominal process variance is known. However, several researchers have shown that the ability of control charts to signal when a process is out of control is seriously affected unless process parameters are estimated from a large incontrol Phase I data set. In this paper we derive the run length properties of a CUSUM chart for monitoring dispersion with estimated process variance and we evaluate the performance of this chart by comparing it with the same chart but with assumed known process parameters.

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

The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control is largely used in industries for monitoring the process parameters. It is a standard method for visualizing and controlling processes on the basis of measurements of randomly selected samples. The decisions about what needs to be improved, the possible methods to improve it, and the steps to take after getting results from the charts are all made by humans and based on wisdom and experience. The statistical process control described in this paper gives the details about the SPC, its advantages and limitation, applications and information regarding the control charts.

In this paper, we develop a process control approach to detect linear trends in the process mean. A statistic based on the deviation between the target mean and the expected mean of the process is used in the development of the new approach. The statistic is shown to have a Chi-square distribution. The approach is described and its performance is compared with Cumulative Sum (CUSUM), Exponentially Weighted Moving Average (EWMA), and Shewhart charts in detecting linear trends in the process mean. The results indicate that proposed approach is effective in detecting small to large trends. We also investigate the run length properties of the proposed approach under linear trends and compare its values with simulation results. Finally, we analyze the performance of the proposed approach in detecting the time when a drift occurs in the process and compare it with CUSUM and EWMA estimators. The results show that the proposed approach is more effective in detecting drift time for moderate and large trends.

The Hotelling's χ 2 control chart is one of the most widely used multivariate charting procedures for monitoring the vector of means of several quality characteristics. As a Shewhart-type control chart, it incorporates information pertaining to most recently inspected sample and subsequently it is relatively insensitive in quickly detecting small magnitude shifts in the process mean vector. A popular solution suggested to overcome this handicap was the use of runs and scans rules as criteria to declare a process out-of-control. During the last years, the examination of Hotelling's χ 2 control charts supplemented with various runs rules has attracted continuous research interest. In the present article we study the performance of the Hotelling's χ 2 control chart supplemented with a r-out-of-m runs rule. The new control chart demonstrates an improved performance over other competitive runs rules based control charts.

Three analytical methods for determination of uranium in environmental samples by a fluorescence technique have been validated and compared in accordance with the Eurachem Guide on method validation. The first method depends on uranium separation from iron using the mercury anode technique; in the other two methods uranium is separated from iron on an anion exchange column by use of either a solution of hydrochloric acid containing ascorbic acid and hydrazine hydrate or a dilute sulfuric acid solution. Detection limits, repeatability, reproducibility, and recovery coefficient were the main validation characteristics. The results showed that better statistical values can be achieved by using the third method. Control charts for in-house control samples and international intercomparison samples have also shown that the third method is more statistically stable with time. In addition, uncertainties of measurement were estimated and compared for the three methods. It was found that the Eurachem Guide and comparison of quality statistical validation data can be good tools for selection of the appropriate method for an application.

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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&#x27;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.