Application Of Statistical Quality Control Research Articles

PENERAPAN METODE STATISTICAL QUALITY CONTROL DAN FAULT TREE ANALYSIS PADA PROSES STERILISASI DI CV. PASIFIC HARVEST BANYUWANGI

The purpose of this research is to find out the application of Statistical Quality Control and Fault Tree Analysis methods to control the types of defects that occur during the sterilization process, find out the causes of their occurrence, and provide suggestions for improvement. The research methods used were research preparation (literature study), field observations, problem solving, interviews, data collection, data processing, and data analysis. Application of the Statistical Quality Control method to control the types of defects that occur by using the seven tools. While the application of the Fault Tree Analysis method is by making a fault tree and determining the minimum cut set then analyzing it quantitatively. The results of the fishbone diagram analysis found that there are 3 factors that cause defects in the sterilization process, namely machines, materials, and humans. Proposed improvements to reduce the level of defects that often occur, namely carrying out daily inspection and maintenance of the machine, the machine must be calibrated, carrying out more thorough checks on cans sent from suppliers, disciplining employees by holding briefings once a week, conducting training on how to handle them properly and correct every 3 months or 6 months to employees, the seamer operator monitors the time during the draining process to the seamer process, checks the seamer machine before using the production process so as not to hinder the can closing process, and performs temperature sensors on a monthly basisThe purpose of this research is to find out the application of Statistical Quality Control and Fault Tree Analysis methods to control the types of defects that occur during the sterilization process, find out the causes of their occurrence, and provide suggestions for improvement. The research methods used were research preparation (literature study), field observations, problem solving, interviews, data collection, data processing, and data analysis. Application of the Statistical Quality Control method to control the types of defects that occur by using the seven tools. While the application of the Fault Tree Analysis method is by making a fault tree and determining the minimum cut set then analyzing it quantitatively. The results of the fishbone diagram analysis found that there are 3 factors that cause defects in the sterilization process, namely machines, materials, and humans. Proposed improvements to reduce the level of defects that often occur, namely carrying out daily inspection and maintenance of the machine, the machine must be calibrated, carrying out more thorough checks on cans sent from suppliers, disciplining employees by holding briefings once a week, conducting training on how to handle them properly and correct every 3 months or 6 months to employees, the seamer operator monitors the time during the draining process to the seamer process, checks the seamer machine before using the production process so as not to hinder the can closing process, and performs temperature sensors on a monthly basis

The Quality Control Analysis Using Statistical Quality Control (SQC) Methods in Reducing Defective Muslim Clothing Products on CV. Mawaheejra

Introduction/Main Objectives: This study determines the quality control of CV. Mawaheejra, determine the factors causing product defects and the application of Statistical Quality Control (SQC) methods. Background Problems: CV. Mawaheejra has carried out control but the quality control is not optimal because of poor quality and errors often occur in the production process which causes product defects that not comply with the quality standards. Research Methods: This study uses a quantitative method. It carried data collection techniques through observation, interviews, documentation, and literature studies. Data analysis using Statistical Quality Control (SQC) methods of flowcharts, check sheets, Pareto charts, control charts, and fishbone diagrams. Finding/Results: Based on the results of research with check sheet aids found four types of defects. In the Pareto chart found the highest number of product disability percentage of 38.47%. From the control chart result, it found deviations because of the U control chart there are three points that exceed UCL and two points that exceed LCL. From the fishbone diagram, there are five factors that are most prominently caused by human and machine factors. Conclusion: Based on the results of the analysis, the company must conduct supervision of employees and machine maintenance to reduce product defects.

Continuous Quality Improvement by Statistical Process Control Implementation in Cocoa Agroindustry

Downstream sector development for agricultural commodities will encourage agroindustry, increase added value and farmers' income. The development of small-medium cocoa agroindustry is one of the downstream sector development strategies for cocoa. Product quality is one of the problems affecting the performance of cocoa-based agroindustry. Chocolate products face obstacles to meet the quality. Besides, consumers want organoleptic tests related to aroma and flavor as an additional requirement. Statistical Quality Control (SQC) is a method that could maintain production quality at a minimum cost level and achieve efficiency. SQC can see the defect in the production and trend of increasing or decreasing to make improvements. This study aims to analyse the quality of cocoa products with the application of SQC as a continuous improvement strategy. The research method uses a case study. The research phase includes observations, interviews, documentation, measurements, and literature studies. Implementing SQC used check sheets, histograms, Pareto diagrams, and cause-and-effect diagrams related to the product of chocolate by sensory evaluation. Control is carried out in the conching and tempering. Based on a simple SQC, the small-medium cocoa agroindustry can obtain alternative production improvements to increase the final product quality.

Application of Statistical Quality Control in Monitoring the Production, Packaging and Marketing Process of Sachet Water

Statistical process control is a technique employed to enhance the quality and productivity of processes and the distribution or marketing of its products. Sachet water is a product that has become popular and is being used as a replacement for lack of potable water. It is an alternative that is readily available, affordable but with questions about its purity, production and marketing processes. The objective of this study is to apply statistical control charts in monitoring the production, packaging and distribution or marketing processes of sachet water in Nigeria. This paper employed statistical quality control approach to monitor process stability in a Table Water manufacturing company. Quality control tools such as p-chart, u-chart, X-bar and R charts as well as process capability chart were use to observed field data obtained from the sachet water manufacturing company on important processes of sachet water production and marketing for 30 working days. This was done to check if the processes were in control or out of control and to verify the capability of the marketing process of the product meeting preset specifications. With this, the statistical control charts suitable for the processes were constructed using package “qcc” in R software version 3.6.1. The results from p-chart and u-chart showed that the production and packaging process of the product is not in control and hence the need for further investigations and corrective measures to prevent variability in the process and thus allowing improvement in the quality of the product. Also, the results from X-bar and R charts showed that the marking process was in statistical process control in respects of the product sales recorded by the four independent marketers, with no assignable cause of variation. It also revealed that, the product marketing process has low capability of successfully attending the preset specification limits in respect of the product sales and hence generating low profit for the company.

STATISTICAL QUALITY CONTROL IN A SUPERMARKET CHAIN OF RIO GRANDE DO NORTE, BRAZIL

Purpose: This article aims to analyze the level of losses due to damage in a supermarket chain to understand the reason for these occurrences.
 Methodology/Approach: From data collected in May 2018, a survey of the most critical departments and items with the highest percentage of losses was performed and statistical control was applied from the p graph. A case study in the capital of Rio Grande do Norte.
 Findings: It was found that 84.6% of losses were concentrated in the meat, dairy, beverages, eggs and grocery departments. It was also found that the fraction non-conforming of losses is out of statistical control, which led to the interruption of monitoring.
 Research Limitation/implication: The data provided by the company, as the reason for each breakdown of the items was not informed. Thus, without this information, quality control was interrupted.
 Originality/Value of paper: This work lies in the gap of the Brazilian literature on the application of statistical quality control in losses by damaged products in a supermarket chain, since there are numerous studies that analyzed losses in supermarkets in Brazil. None of them performed damage monitoring, using the control chart p.

PENERAPAN STATISTICAL QUALITY CONTROL (SQC) PADA PENGENDALIAN MUTU MINYAK TELON (STUDI KASUS DI PT.X)

<p><em>Telon oil is</em><em> one of </em><em> </em><em>the </em><em>traditional medicine in the form of </em><em> </em><em>liquid preparations that serves to provide a sense of warmth to the wearer. PT</em><em>.X</em><em> is one of the companies that produce</em><em> </em><em>telon</em><em> oil</em><em>.</em><em> To maintain</em><em> the quality of telon oil from PT.X</em><em> product</em><em>, required overall quality control that is starting from the quality control of raw materials, quality control process to the quality control of the final product. The purpose of this research is to know the application of Statistical Quality Control (SQC) in controlling the quality of telon oil in PT X. </em><em>F</em><em>inal product</em><em> quality</em><em> become one of the measurement of success of a process, so it needs a good quality control. SQC method used in this research is Pareto Diagram and Cause and Effect Diagram. Pareto diagram is a bar graph </em><em>that </em><em>show the problem based on the order of the number of occurrences of the most number of problems until the least happened. A causal diagram is often called a fishbone diagram, a tool for identifying potential causes of an effect or problem. The result of applying the method indicates that 80% defect is caused by unsuitable volume and on the incompatibility of Expired Date (ED) code. The damage is caused by several factors namely the method, labor, and machine while the most potential factor is the volume conformity to reduce the number of defect products.</em></p>

Dripper testing: Application of statistical quality control for measurement system analysis

ABSTRACT Laboratory tests for technical evaluation or irrigation material testing involve the measurement of many variables, as well as monitoring and control of test conditions. This study, carried out in 2016, aimed at using statistical quality control techniques to evaluate results of dripper tests. Exponentially weighted moving average control charts were elaborated, besides capability indices for the measurement of the test pressure and water temperature; and study on repeatability and reproducibility (Gage RR) of flow measurement system using 10 replicates, in three work shifts (morning, afternoon and evening), with 25 emitters. Both the test pressure and water temperature remained stable, with “excellent” performance for the pressure adjustment process by integrative-derivative proportional controller. The variability between emitters was the component with highest contribution to the total variance of the flow measurements, with 96.77% of the total variance due to the variability between parts. The measurement system was classified as “acceptable” or “approved” by the Gage RR study; and non-random causes of significant variability were not identified in the routine of tests.

Prognostics and Health Management for Rechargeable Batteries

After numerous successful applications of statistical quality control and reliability methods in manufacturing industries in the past several decades, the quality paradigm has shifted from manufacturing application to field operation in many industries. This shift was triggered by continuous concerns in product reliability, system safety, and failure prevention, and made possible by latest advancement in data collection technologies and development of powerful modelling algorithms. This general field of research provides tremendous opportunities for interdisciplinary research and has been classified as prognostics and system health management (PHM). In this talk we will share our experience of PHM research in the application of rechargeable batteries through methods in data mining, diagnostics and prognostics, anomaly detection and monitoring.

Monitoring and change-point estimation for spline-modeled non-linear profiles in phase II

In some applications of statistical quality control, quality of a process or a product is best characterized by a functional relationship between a response variable and one or more explanatory variables. This relationship is referred to as a profile. In certain cases, the quality of a process or a product is better described by a non-linear profile which does not follow a specific parametric model. In these circumstances, nonparametric approaches with greater flexibility in modeling the complicated profiles are adopted. In this research, the spline smoothing method is used to model a complicated non-linear profile and the Hotelling T2 control chart based on the spline coefficients is used to monitor the process. After receiving an out-of-control signal, a maximum likelihood estimator is employed for change point estimation. The simulation studies, which include both global and local shifts, provide appropriate evaluation of the performance of the proposed estimation and monitoring procedure. The results indicate that the proposed method detects large global shifts while it is very sensitive in detecting local shifts.

Estimation of the Optimal Statistical Quality Control Sampling Time Intervals Using a Residual Risk Measure

BackgroundAn open problem in clinical chemistry is the estimation of the optimal sampling time intervals for the application of statistical quality control (QC) procedures that are based on the measurement of control materials. This is a probabilistic risk assessment problem that requires reliability analysis of the analytical system, and the estimation of the risk caused by the measurement error.Methodology/Principal FindingsAssuming that the states of the analytical system are the reliability state, the maintenance state, the critical-failure modes and their combinations, we can define risk functions based on the mean time of the states, their measurement error and the medically acceptable measurement error. Consequently, a residual risk measure rr can be defined for each sampling time interval. The rr depends on the state probability vectors of the analytical system, the state transition probability matrices before and after each application of the QC procedure and the state mean time matrices. As optimal sampling time intervals can be defined those minimizing a QC related cost measure while the rr is acceptable. I developed an algorithm that estimates the rr for any QC sampling time interval of a QC procedure applied to analytical systems with an arbitrary number of critical-failure modes, assuming any failure time and measurement error probability density function for each mode. Furthermore, given the acceptable rr, it can estimate the optimal QC sampling time intervals.Conclusions/SignificanceIt is possible to rationally estimate the optimal QC sampling time intervals of an analytical system to sustain an acceptable residual risk with the minimum QC related cost. For the optimization the reliability analysis of the analytical system and the risk analysis of the measurement error are needed.

Quality management and Juran's legacy

AbstractQuality management provides the framework for the industrial application of statistical quality control, design of experiments, quality improvement and reliability methods. It is therefore helpful for quality engineers and statisticians to be familiar with basic quality management principles. In this article we discuss Dr Joseph M. Juran's important contributions to modern quality management concepts, principles and models. Many people have contributed to modern quality management. However, through his extensive writings covering more than six decades, Juran has managed to distill and synthesize the subject. He has provided us with a coherent framework and terminology and anticipated many of the principles that subsequently became incorporated under the Six Sigma umbrella. We briefly outline Juran's framework and discuss a number of key contributions he has made to the subject of managing for quality. Copyright © 2007 John Wiley & Sons, Ltd.

Application of statistical quality control for limestone grade: a case study

The statistical quality control is not much applied to the minerals industry but is considerably promising. The present study was carried out in Maratha Cement Works, GACL, India for this purpose. Based on the available data, the present study focuses on the characteristics of the deposit and provides an understanding whether the statistical quality control can be applied in the grade quality and if so, what effect it can make.

Laboratory quality control: using patient data to assess analytical performance.

Quality control plays a vital role helping to ensure the reliability of laboratory test results. The application of statistical quality control has been a component of laboratory medicine for approximately 50 years. Many of the control rules based on the early applications of statistical quality control have remained essentially unchanged since their initial introduction. Optimization of quality control rules can vary depending on the application for which a test is to be used. This review explores the various applications of laboratory quality control procedures and their role in identifying laboratory error. The ubiquitous use of computers in today's laboratories has enabled the development of more sophisticated means of assessing laboratory quality. The use of the Six Sigma technique and its adoption by the laboratory community is one example. Other examples include the use of patient-derived quality control procedures as a means of assessing laboratory performance. Early examples of these types of applications include use of Bull's algorithm, anion gap measurements, and delta checking. More recent applications include the correlation of laboratory test results, the average of normals procedure, and the Bhattacharya method.

Statistical Process Analysis of Medical Incidents

Personnel engaged in the medical field have implemented continual improvement by team activities in an effort to construct a system that reduces the risks involved in medical care. Knowledge in total quality management (TQM), especially statistical quality control (SQC) developed for industry, seems to be applicable to medical care. This paper describes the application of SQC to continual improvement in medical care.

Minimal Detectable Biases of GPS observations for a weighted ionosphere

The theory and application of statistical quality control is well established in precise positioning, navigation and geodesy. Quality control is made up of several contributing factors, one of which is internal reliability. Internal reliability describes the ability to find biases in observational data and is represented by the Minimal Detectable Bias (MDB). The MDB provides a diagnostic tool to infer the strength with which positioning models can be validated. In this contribution closed-form expressions will be given for the MDBs of GPS code and carrier observations for three different baseline models: the geometry-free model and two variants of the geometry-based model. These expressions apply toany numberofcarrier frequencies. The expressions take into account the presence of ionospheric disturbances by weighting these effects. As such, they are applicable to baselines of any length.

Applications of statistical quality control to cardiac surgery

Although originally developed for use in manufacturing statistical quality control techniques may be applicable to other frequently performed, standardized processes. We employed statistical quality control charts (X- s, p, and u) to analyze perioperative morbidity and mortality and length of stay in 1,131 nonemergent, isolated, primary coronary bypass operations conducted within a 17-quarter time period. The incidence of the most common adverse outcomes, including death, myocardial infarction, stroke, and atrial fibrillation, appeared to follow the laws of statistical fluctuation and were in statistical control. Postoperative bleeding, leg-wound infection, and the summation of total and major complications were out of statistical control in the early quarters of the study period but showed progressive improvement, as did postoperative length of stay. The incidence of morbidity and mortality after primary, isolated, nonemergent coronary bypass operations may be described by standard models of statistical fluctuation. Statistical quality control may be a valuable method to analyze the variability of these adverse postoperative events over time, with the ultimate goal of reducing that variability and producing better outcomes.

Decision Support and Statistical Quality Control

Examines some principles of the design and implementation of decision support systems in the context of statistical quality control. These principles are a function both of the level within the organization at which the decision is made and of the type of decision that is made. The principles are well known in the field of decision support systems and have proved effective. Argues that these principles are of great value to researchers, managers and quality specialists in the field of statistical quality control as well. The argument is supported by a number of actual examples in the application of statistical quality control where knowledge of the principles proved of value in improving quality.

Statistical Quality Control in Retail Banking

The importance of quality and statistical quality control as strategic tools, well established in the manufacturing community, has recently been extended to the service sector. The application of statistical quality control techniques within the banking industry is discussed. Recommendations for establishing a quality control programme within a typical banking service environment are provided.

ESSENTIAL PRINCIPLES FOR DECISIONMAKING IN THE APPLICATION OF STATISTICAL QUALITY CONTROL

[This abstract is based on the author's abstract.] The goals and justifications of major applications of statistical quality control (SQC) are provided in nonmathematical terms for the benefit of quality practitioners and those they communicate ..

RELIABILITY ENGINEERING

Journal of the American Society for Naval EngineersVolume 73, Issue 2 p. 251-256 RELIABILITY ENGINEERING BESSE B. DAY, BESSE B. DAYSearch for more papers by this author BESSE B. DAY, BESSE B. DAYSearch for more papers by this author First published: May 1961 https://doi.org/10.1111/j.1559-3584.1961.tb03296.x THE AUTHOR: earned her B.S. in Mathematics at Missouri State College, Warrenburg; and her M.A. in Mathematics and Statistics at the University of Michigan. Worked thereafter principally in the application of statistical techniques in research, development and quality control of production in the fields of engineering and forestry. 1927-29 Victor Talking Machine Company; 1930-43 V. S. Forest Service; 1943-47 the Johns Hopkins University Applied Physics Laboratory, on the development and production of the radio proximity fuse; 1947–1954 head of Statistician's Office, U. S. Naval Engineering Experiment Station, Annapolis, Maryland; presently Consulting Statistician to the Chief of the Bureau of Ships, with additional duties as Head, Statistical Engineering Section, Research and Development; Fellow American Statistical Association and American Society for Quality Control; member of the International Statistical Institute; Past Chairman of the Section on Physical and Engineering Sciences of American Statistical Association; past member of Executive Committee of ASQC; published and given papers on applications of Statistics to engineering problems. 1954 Am. Stat. Assoc. Program Chairman; 1957 Brumbaugh Award winner for paper in Industrial Quality Control judged to have made the greatest contribution to the development of industrial applications of statistical quality control; member Washington Academy of Science; July 1, 1960 the Association of Senior Engineers of Bureau of Ships. In 1959 was married to C. E. Mauss but retains original name for professional purposes. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume73, Issue2May 1961Pages 251-256 RelatedInformation