scholarly journals PENERAPAN DIAGRAM KENDALI MAXIMUM MULTIVARIATE CUMULATIVE SUM (MAX-MCUSUM) PADA PENGENDALIAN KUALITAS PRODUK KACANG (Studi Kasus: Produk Kacang Garing di PT XY)

2022 ◽  
Vol 10 (4) ◽  
pp. 573-582
Author(s):  
Sintia Rizki Aprilianti ◽  
Tatik Widiharih ◽  
Sudarno Sudarno
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Quality Standards ◽  
Quality Characteristic ◽  
Statistical Quality Control ◽  
Industry Data ◽  
Machine Material ◽  
Small Shift ◽  
Pareto Chart ◽  
Main Factor

Now, Statistical quality control be a particular concern to large companies.PT XY is one of the largest nut company in Indonesia that has implemented the quality standards of a product. Max-MCUSUM control chart becomes a tool that is graphically used to monitor and evaluate whether the process is under control or nut. Based on Cheng and Thaga (2005), Max-MCUSUM control chart takes precedence over detecting small shift based on average and variability in industry data. The quality characteristic of Kacang Garing will be variables, namely broken nut skin, bean seed 1, and foam nut skin. Max-MCUSUM control chart is controlled with the control limit (h) from ARL (Average Run Length) simulation of 370 is 429,69. ARL is an average of samples that need to be decribed before it goes out of control. The research continued with multivariate capability process with MCp worth 0,905 and MCpk worth 1,355. Those value indicates that Kacang Garing has met the quality specification stipulated by PT XY. Broken nut skin becomes the most dominant cause based on pareto chart and carried out analysis by using fishbone chart so that is known the main factor causing broken nut skin are machine, material, and method. 

scholarly journals A Fuzzy Bivariate Poisson Control Chart

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 573 ◽  
Author(s):  
Wibawati ◽  
Muhammad Mashuri ◽  
Purhadi ◽  
Irhamah
Keyword(s):  
Parameter Estimation ◽  
Control Chart ◽  
Simulation Study ◽  
Average Run Length ◽  
Triangular Fuzzy Number ◽  
Quality Characteristic ◽  
Graphical Form ◽  
Monitoring Process ◽  
Fuzzy Parameter ◽  
Work First

In the present paper, we develop a fuzzy bivariate Poisson (FBP) control chart based on a fuzzy c chart. The FBP chart is used to monitor the sum of the nonconformities of each quality characteristic. There are two contributions of this work. First, we propose a new fuzzy parameter estimation to create a triangular fuzzy number (TFN). Second, our control chart is flexible, because we involve the α c u t to measure the level of tightness of inspection. Furthermore, the statistic of FBP is being able to visualise the monitoring process in a graphical form. In addition, the simulation study indicates that the performance of our proposed chart, based on average run length (ARL), is more sensitive than the performance of a conventional bivariate Poisson (BP) chart. Moreover, an illustration example shows that the FBP chart has relatively more sensitive performance compared to the conventional BP chart.

scholarly journals Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Muhammad Saleem ◽  
Rehan Ahmad Khan Sherwani ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Quality Characteristics ◽  
Statistical Quality Control ◽  
Nonnormal Distributions ◽  
Run Lengths ◽  
Chart Design ◽  
Dependent State

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

scholarly journals Precision Analysis of Poisson Control Chart Based on Sample Size

2020 ◽  
Vol 16 (3) ◽  
pp. 325
Author(s):  
Elsa Resa Sari
Keyword(s):  
Quality Control ◽  
Sample Size ◽  
Control Chart ◽  
Average Run Length ◽  
Statistical Quality Control ◽  
Sample Sizes ◽  
Precision Analysis ◽  
Run Length ◽  
Statistical Quality ◽  
Result Show

One technique used in performing statistical quality control is by poisson control chart. Poisson control chart used in data that have the same mean and varians for monitoring the number of defects in the study. In some cases, the different sample sizes influence the control chart performance. The control chart performance can be measured using average run length (ARL). The smaller ARL’s value, the better type of control chart. In this study, we used different sample sizes  that is  and mean . The result show the best performance of control chart is when  and m = 200, because its has a smaller ARL’s value.                            

scholarly journals X bar control chart for non-normal symmetric distributions

2016 ◽  
Vol 13 (2) ◽  
Author(s):  
Kristina Veljkovic
Keyword(s):  
Control Chart ◽  
Goodness Of Fit ◽  
Theoretical Distribution ◽  
Quality Characteristic ◽  
Statistical Quality Control ◽  
Symmetric Distributions ◽  
Sample Mean ◽  
Uniform Distributions ◽  
Pearson Type ◽  
Control Limits

In statistical quality control, X bar control chart is extensively used to monitor a change in the process mean. In this paper, X bar control chart for non-normal symmetric distributions is proposed. For chosen Student, Laplace, logistic and uniform distributions of quality characteristic, we calculated theoretical distribution of standardized sample mean and fitted Pearson type II or type VII distributions. Width of control limits and power of the X bar control chart were established, giving evidence of the goodness of fit of the corresponding Pearson distribution to the theoretical distribution of standardized sample mean. For implementation of X bar control chart in practice, numerical example of construction of a proposed chart is given.

scholarly journals Nonparametric Double EWMA Control Chart for Process Monitoring

2016 ◽  
Vol 39 (2) ◽  
pp. 167 ◽  
Author(s):  
Muhammad Riaza ◽  
Saddam Akber Abbasib
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Average Run Length ◽  
Location Parameter ◽  
Quality Characteristic ◽  
Quadratic Loss ◽  
Run Length ◽  
Ewma Control Chart ◽  
Monitoring Process ◽  
Nonparametric Techniques

<p>In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical sit- uations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed scheme is compared with its recent existing counterparts, namely the nonparametric EWMA and the nonparametric CUSUM schemes. The performance mea- sures used are the average run length (ARL), standard deviation of the run length (SDRL) and extra quadratic loss (EQL). We observed that the pro- posed chart outperforms the said existing schemes to detect shifts in the process mean level. We also provide an illustrative example for practical considerations.</p>

scholarly journals Stability assessment and process capability analysis in a food pasta company

2021 ◽  
pp. 231-251
Author(s):  
Roxana González Álvarez ◽  
Aníbal Barrera García ◽  
Ana Beatriz Guerra Morffi ◽  
Juan Felipe Medina Mendieta
Keyword(s):  
Control Charts ◽  
Process Capability ◽  
Quality Characteristic ◽  
Statistical Quality Control ◽  
Quality Level ◽  
Statistical Control ◽  
Pareto Chart ◽  
Process Capability Analysis ◽  
Reduction Techniques ◽  
Capability Analysis

Statistical quality control is a set of tools and techniques that allows to verify, monitor and control the variability of processes to improve product quality and business competitiveness. The objective of this study was to evaluate the pasta production process of a company that belongs to the food industry sector in terms of stability and compliance of quality specifications. The Six Sigma improvement methodology was used, which focuses on identifying and eliminating the causes of variation in the processes. Data collection was accomplished by the use of different techniques, such as: interviews, brainstorming, review of documents, teamwork and direct observation. In addition, process documentation techniques and classical quality tools including Pareto chart, control charts, process capability analysis, histogram, Ishikawa diagram and experimental design were used. Multivariate data reduction techniques were also applied. The results showed for the quality characteristic Humidity that the process is out of statistical control and it is uncapable to meet the required specifications, for which the causes were investigated and improvement actions were proposed, achieving an increase in the sigma quality level.

scholarly journals PENERAPAN METODE PENGENDALIAN KUALITAS MEWMA BERDASARKAN ARL DENGAN PENDEKATAN RANTAI MARKOV (Studi Kasus: Batik Semarang 16, Meteseh)

2021 ◽  
Vol 10 (1) ◽  
pp. 125-135
Author(s):  
Enggartya Andini ◽  
Sudarno Sudarno ◽  
Rita Rahmawati
Keyword(s):  
Markov Chain ◽  
Production Process ◽  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Control Limit ◽  
Quality Characteristic ◽  
Markov Chain Approach ◽  
Mewma Control Chart ◽  
Weighted Moving Average

An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Average Run Length (ARL) with the Markov Chain approach. The markov chain is the chance of today's event is only influenced by yesterday's incident, in this case the chance of the incident in question is the incident in getting a sampel of data on the production process of batik cloth to get a product that is in accordance with the company standards. ARL is the average number of sample points drawn before a point indicates an uncontrollable state. In this study, 60 sample data were used which consisted of three quality characteristics, namely the length of the cloth, the width of the cloth, and the time of the fabric for the production of written batik in Batik Semarang 16 Meteseh. Based on the results and discussion that has been done, the MEWMA controller chart uses the λ weighting which is determined using trial and error. MEWMA control chart can not be said to be stable and controlled with λ = 0.6, after calculating, the value is obtained Upper Control Limit (BKA) of 11.3864 and Lower Control Limit (BKB) of 0. It is known that from 60 data samples there is a Tj2 value that comes out from the upper control limit (BKA) where the amount of 15.70871, which indicates the production process is not controlled statistically. Improvements to the MEWMA controller chart can be done based on the ARL with the Markov Chain approach. In this final project, the ARL value used is 200, the magnitude of the process shift is 1.7 and the r value is 0.28, where the value of r is a constant obtained on the r parameter graph. The optimal MEWMA control chart based on ARL with the Markov Chain approach can be said to be stable and controlled if there is no Tj2 value that is outside the upper control limit (BKA). The results of the MEWMA control chart based on the ARL with the Markov Chain approach show that the process is not statistically capable because the MCpm value is 0.516797 and the MCpmk value is 0.437807, the value indicates a process capability index value of less than 1. Keywords: Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability process.

Nonparametric Control Charts Design and Analysis for Small Lot Production Based on the Moving Average

2014 ◽  
Vol 988 ◽  
pp. 461-466
Author(s):  
Yu Hao Deng ◽  
Hai Ping Zhu ◽  
Guo Jun Zhang ◽  
Hui Yin ◽  
Fan Mao Liu
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Small Samples ◽  
Lot Size ◽  
Small Shift ◽  
Average Sampling ◽  
Run Lengths ◽  
Production Lot Size

This paper designed a moving average sampling method for small samples, further designed moving average (MA) control chart and moving average cumulative sum (MACS) control chart respectively, and calculated the in-control and out-of-control average run length for both charts. The charts are robust, which can monitor the process state effectively without knowing the distribution. Through analyzing the control chart costs and quality loss that is related to the production lot size, the control chart parameters are reasonably optimized. By comparing the average run lengths and some numerical examples, the paper finds that MACS chart has a good performance on detecting small shift within the small samples under the nonparametric condition.

scholarly journals Monitoring The Level Of Students GPA's Over Time

2010 ◽  
Vol 3 (6) ◽  
pp. 43-50
Author(s):  
Saad T. Bakir ◽  
Bob McNeal
Keyword(s):  
False Alarm ◽  
Normal Distribution ◽  
False Alarm Rate ◽  
Control Chart ◽  
Average Run Length ◽  
Statistical Quality Control ◽  
Grade Point ◽  
Run Length ◽  
College Of Business ◽  
Over Time

A nonparametric (or distribution-free) statistical quality control chart is used to monitor the cumulative grade point averages (GPAs) of students over time. The chart is designed to detect any statistically significant positive or negative shifts in student GPAs from a desired target level. This nonparametric control chart is based on the signed-ranks of the GPAs of the sampled students. The exact false alarm rate and the in-control average run length of the proposed chart can be computed exactly and are independent of the underlying probability distribution of GPAs. The traditional Shewhart X-bar control chart for monitoring the mean of a process is based on the assumption that data follows a normal distribution. However, student GPAs may differ significantly from the normal distribution. As a result, using a traditional control chart to monitor the GPAs of students may lead to incorrectly specifying the control limits and the average run length and/or the false alarm rate of the chart. A test study was conducted at the College of Business Administration at Alabama State University. The study monitored the median cumulative GPAs of management majors during the period Spring 2005 through Spring 2009. The study revealed that the GPAs of students were stable at a median level of 2.6 over the period of the study.

An Enhanced Performance to Monitor Process Mean with Modified Exponentially Weighted Moving Average – Sign Control Chart

2021 ◽  
Author(s):  
Rattikarn Taboran ◽  
Saowanit Sukparungsee
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Performance Comparison ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Small Shift ◽  
Weighted Moving Average ◽  
Exponentially Weighted

The purpose of this research is to enhance performance for detecting a change in process mean by combining modified exponentially weighted moving average and sign control charts. This is nonparametric control chart which effective alternatives to the parametric control chart so called MEWMA-Sign. The nonparametric control chart can serve when process observations is deviated from normal distribution assumption. Generally, the performance of control charts are widely measured by average run length (ARL) divided into two cases; in control ARL (ARL0) and out of control ARL (ARL1). In this paper, the performance comparison is investigated when processes are non-normal distributions. The performance of the MEWMA-Sign is compared EWMA-Sign control chart by considering from a minimum value of ARL1. The numerical results found that the MEWMASign performs better than EWMA-Sign in order to detect a very small shift of mean process. Additionally, the real application of the MEWMA-Sign and EWMA-Sign are presented.

Sign in / Sign up

Export Citation Format

Share Document