scholarly journals Vector Autoregressive-Based Maximum MCUSUM Control Chart for Monitoring the Quality of White Crystal Sugar

2021 ◽  
Vol 2123 (1) ◽  
pp. 012034
Author(s):  
H Khusna ◽  
M Mashuri ◽  
Wibawati
Keyword(s):  
Control Chart ◽  
Sulphur Dioxide ◽  
Control Charts ◽  
Var Model ◽  
White Crystal ◽  
Resampling Methods ◽  
Vector Autoregressive ◽  
Alarm Signals ◽  
Daily Solution

Abstract The white crystal sugar which is widely consumed sugar has two critical to qualities, namely the index of solution colour and the level of sulphur dioxide. These quality characteristics have small mean and variability shifts, as well as autocorrelation pattern. This research aims to propose residual-based Maximum Multivariate Cumulative Sum (Max-MCUSUM) control chart, one of the single control charts to monitor small shifts of mean and variability simultaneously, for monitoring the quality of white crystal sugar. The vector autoregressive (VAR) model is utilized to model the daily solution colour index and the daily sulphur dioxide level, then the residuals are monitored using Max-MCUSUM chart. The VAR-based Max-MCUSUM chart employs bootstrap, one of the nonparametric resampling methods, to estimate the control limit. The results of white crystal sugar quality control show that the processes in the last week of August 2020 need to be improved. Monitoring the white crystal sugar data using conventional control chart leads to many false alarm signals. Furthermore, the proposed control chart is more sensitive than the residual-based MEWMA and residual-based Hotelling’s T 2 charts in case of monitoring the quality of white crystal sugar.

Improving Phase I Monitoring of Dirichlet Regression Profiles

2018 ◽  
Vol 25 (06) ◽  
pp. 1850030 ◽  
Author(s):  
Hourieh Foroutan ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Control Charts ◽  
Compositional Data ◽  
Ratio Test ◽  
Statistical Process ◽  
Explanatory Variables ◽  
Monitoring Procedure ◽  
Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

Improving Benchmarking Student Learning Outcomes Using Repetitive Sampling Control Chart

2016 ◽  
Vol 13 (10) ◽  
pp. 7036-7039
Author(s):  
Nawal G Alghamdi ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Assessment Tool ◽  
Educational Programs ◽  
Management Program ◽  
Pass Rate ◽  
Shewhart Control Charts ◽  
Process Understanding ◽  
Shewhart Control

In recent years research on the application of Shewhart control charts in evaluating the performance of educational programs have gain sufficient grounds. These control charts aid in process understanding and identify changes that indicate either improvement or deterioration in quality of the program. Current research proposes control charts using repetitive sampling on the data taken from Weber State University’s construction management program, which uses the Associate Constructor Level 1 exam as an assessment tool. A code was developed to run the proposed control charts. Both the traditional and proposed charts were plotted using R software. The results indicate that the proposed control charts are comparatively more efficient than the traditional control charts in assessment of educational programs and minimizing false positives. At the end comparison of the benchmark—pass rate and traditional control chart with the proposed control chart has also been elucidated so that the proposed control charts may be readily employed in evaluating any educational program by academic counsellors.

scholarly journals A Control Chart Based on Moving Average Model Functioned for Poisson Distribution

2020 ◽  
Vol 3 (10) ◽  
Author(s):  
Hira Arooj ◽  
◽  
Khawar Iqbal Malik ◽  
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Current Control ◽  
Average Model ◽  
Time Intervals ◽  
Manufacture Process ◽  
Moving Average Model

A control chart used with MA control chart to track the number of faulty products or faults suggested. When the characteristics of quality of interest obey a Poisson distribution. A specified number of objects are observed at various time intervals in order to observe the number of non-conformities. By measuring ARLs under different sample sizes and parameters by considering ARLs in power, the output of the proposed chart is evaluated. It should be noted The proposed control chart seems to be morereliable than the traditional current control charts in detecting small adjustments in the manufacture process.

scholarly journals DIAGRAM KONTROL MULTIVARIAT np DAN DIAGRAM KONTROL JARAK CHI-SQUARE DALAM PENGENDALIAN KUALITAS PRODUK KAIN DENIM (Studi Kasus di PT Apac Inti Corpora)

2018 ◽  
Vol 7 (4) ◽  
pp. 385-396
Author(s):  
Dwi Harti Pujiana ◽  
Mustafid Mustafid ◽  
Di Asih I Maruddani
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Control Charts ◽  
Process Capability ◽  
Chi Square ◽  
Multivariate Control Charts ◽  
Distance Control ◽  
Denim Fabric ◽  
Multivariate Control

Denim fabric sort number 78032 is one type of fabric in the last 4 years almost every month produced by PT Apac Inti Corpora. In the continuity of denim fabric production process, there are data defects (non-conformity) that causes the quality of denim fabric decreases. To maintain the consistency of the quality of products produced in accordance with the specified specifications, it is necessary to control the quality of the production process that has been running for this. Multivariate control charts attributes used are multivariate control charts np using the number of samples and the proportion of disability data with correlation between variables while the chi-square distance control charts use squared distances with uncorrelated data between variables. The results showed that in the multivariate control chart np there were 2 out-of-control observations in the phase II data using control limits from phase I data already controlled by the value of BKA of 636321.4. While in the chi-square distance control chart showed all observations are in in-control condition with BKA value of 0.06536. Controlled production process obtained multivariate process capability value  for multivariate control np diagram of 0.625142 <1 which means the process is not capable, while the value of process capability in the chi-square distance control chart is 1.1329> 1 which means the process is capable. Keywords: denim fabric, multivariate np control chart, chi-square distance control chart, multivariate process capability

scholarly journals Control Chart in the Service Industry: A Case Study in a University Health Clinic

2020 ◽  
Vol 1 (4) ◽  
pp. 436-441
Author(s):  
Johan K. Runtuk
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Processing Time ◽  
Service Industry ◽  
Services Quality ◽  
Service Level ◽  
Health Clinic ◽  
Capability Analysis

This study analyzes the service quality of a University health clinic in Bekasi, WestJava, Indonesia. A control chart and capability analysis will be employed to analyzethe services' quality, especially registration processing time and the drug defect permonth. This study uses the CUSUM and EWMA control charts for detecting thesmall shifts in the processing time. The findings revealed that the current processesare in control and capable of meeting the current service level. There is only onepoint outside the upper limit. But it can be ignored/deleted since the assignablecause causes that event. This study gives direction for the Clinic to improve theservice quality.

scholarly journals Application of Laney control chart in assessment of microbiological quality of oral pharmaceutical filterable products

2017 ◽  
Vol 52 (3) ◽  
pp. 239-246
Author(s):  
ME Eissa
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Dosage Form ◽  
Microbiological Quality ◽  
Drug Market ◽  
Good Manufacturing Practice ◽  
Dispersion Pattern ◽  
Distribution Fitting ◽  
Pharmaceutical Dosage Form

Evaluation of microbiological quality of pharmaceutical product is an important criterion for safe release of the dosage form to the drug market. Monitoring of the stability of monitored characteristic attribute is crucial to judge the degree of compliance of the manufacturing processes to reproducible procedures and good manufacturing practice (GMP). Two non-sterile liquid oral products were monitored during ten months of study for their microbiological stability characteristics using control charts. Since total viable count (TVC) results did not follow any distribution type, the application of Laney U? chart was found to be the best approach in such instances using relevant statistical software packages. The results of TVC for both products failed to follow the closest distribution in distribution fitting test at ? = 0.05. Interestingly, both products demonstrated several spots of out-of-control (OOC) states, although none of their batches showed out-of-specification (OOS) results. These OOC conditions require further investigations in order to provide corrective/preventive actions for improvement of bioburden stability of the manufactured pharmaceuticals. Accordingly, Laney attribute control chart may be regarded as very handy tool to evaluate microbiological characteristic of pharmaceutical dosage form, when results show significant over-dispersion or under-dispersion pattern in data distribution.Bangladesh J. Sci. Ind. Res. 52(3), 239-246, 2017

Use of the Control Chart in Clinical Chemistry

1959 ◽  
Vol 5 (4) ◽  
pp. 309-319 ◽  
Author(s):  
Richard J Henry
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Clinical Chemistry ◽  
The State ◽  
Chemical Laboratory ◽  
Clinical Chemical ◽  
Laboratory Results ◽  
Continuous Index

Abstract The routine use of control charts in the clinical chemical laboratory is well worth while. It cannot help but play a major role in raising the overall quality of laboratory results, and it provides a continuous index of the state of control of the test.

Statistical Management of Food Production Processes

2020 ◽  
pp. 30-35
Author(s):  
N.A. Jurk ◽  
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Food Production ◽  
Time Interval ◽  
Bakery Products ◽  
Shewhart Control ◽  
Manufactured Products ◽  
Mnemonic Technique ◽  
The Stability

The article presents scientific research in the field of statistical controllability of the food production process using the example of bakery products for a certain time interval using statistical methods of quality management. During quality control of finished products, defects in bakery products were identified, while the initial data were recorded in the developed form of a checklist for registering defects. It has been established that the most common defect is packaging leakage. For the subsequent statistical assessment of the stability of the production process and further analysis of the causes of the identified defect, a Shewhart control chart (p-card by an alternative feature) was used, which allows you to control the quality of manufactured products by the number of defects detected. Analyzing the control chart, it was concluded that studied process is conditionally stable, and the emerging defects are random. At the last stage of the research, the Ishikawa causal diagram was used, developed using the 6M mnemonic technique, in order to identify the most significant causes that affect the occurrence of the considered defect in bakery products. A more detailed study will allow the enterprise to produce food products that meet the established requirements.

MORTALITY FORECASTING WITH A SPATIALLY PENALIZED SMOOTHED VAR MODEL

2020 ◽  
pp. 1-29
Author(s):  
Le Chang ◽  
Yanlin Shi
Keyword(s):  
Population Data ◽  
Coefficient Matrix ◽  
Gradient Algorithm ◽  
Var Model ◽  
Vector Autoregressive ◽  
Mortality Forecasting ◽  
Dependency Structure ◽  
Svar Model ◽  
The Uk ◽  
Sparsity Structure

Abstract This paper investigates a high-dimensional vector-autoregressive (VAR) model in mortality modeling and forecasting. We propose an extension of the sparse VAR (SVAR) model fitted on the log-mortality improvements, which we name “spatially penalized smoothed VAR” (SSVAR). By adaptively penalizing the coefficients based on the distances between ages, SSVAR not only allows a flexible data-driven sparsity structure of the coefficient matrix but simultaneously ensures interpretable coefficients including cohort effects. Moreover, by incorporating the smoothness penalties, divergence in forecast mortality rates of neighboring ages is largely reduced, compared with the existing SVAR model. A novel estimation approach that uses the accelerated proximal gradient algorithm is proposed to solve SSVAR efficiently. Similarly, we propose estimating the precision matrix of the residuals using a spatially penalized graphical Lasso to further study the dependency structure of the residuals. Using the UK and France population data, we demonstrate that the SSVAR model consistently outperforms the famous Lee–Carter, Hyndman–Ullah, and two VAR-type models in forecasting accuracy. Finally, we discuss the extension of the SSVAR model to multi-population mortality forecasting with an illustrative example that demonstrates its superiority in forecasting over existing approaches.

scholarly journals On Performance of Two-Parameter Gompertz-Based X¯ Control Charts

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Johnson A. Adewara ◽  
Kayode S. Adekeye ◽  
Olubisi L. Aako
Keyword(s):  
Control Chart ◽  
Coverage Probability ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Correction Method ◽  
Gompertz Distribution ◽  
Real Life Data ◽  
Control Limits ◽  
Two Parameter

In this paper, two methods of control chart were proposed to monitor the process based on the two-parameter Gompertz distribution. The proposed methods are the Gompertz Shewhart approach and Gompertz skewness correction method. A simulation study was conducted to compare the performance of the proposed chart with that of the skewness correction approach for various sample sizes. Furthermore, real-life data on thickness of paint on refrigerators which are nonnormal data that have attributes of a Gompertz distribution were used to illustrate the proposed control chart. The coverage probability (CP), control limit interval (CLI), and average run length (ARL) were used to measure the performance of the two methods. It was found that the Gompertz exact method where the control limits are calculated through the percentiles of the underline distribution has the highest coverage probability, while the Gompertz Shewhart approach and Gompertz skewness correction method have the least CLI and ARL. Hence, the two-parameter Gompertz-based methods would detect out-of-control faster for Gompertz-based X¯ charts.

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