Statistical Control Tools Research Articles

Various Control Charts to Monitor the Quality of Resistant Cement Production in the Kabaisa Cement Factory for the Year 2023

Control charts are an effective tool for statistical control of production quality processes by drawing samples from the production randomly and at specific time periods. In this research, different control charts were used for the purpose of monitoring the quality of resistant cement production in the Kubaisa Cement Factory, which is located in Al - Anbar Governorate. The weights of bags of cement (resistant cement) (which are measured in kilograms) were monitored using a new packing device, and after monitoring production for a period of (50) days in the months of November and December for a sample size of (5) units per day. The data was recorded on a regular and continuous period, , and the quality of production was examined by applying deferent statistical control charts . these charts are the arithmetic mean chart, the range chart, the standard deviation chart, the moving average chart, the Geometric Moving Average(GMA) chart . and Individual-Moving Range chart .The charts were applied using the statistical program (Minitab), and the results showed that the most of the charts were in control, with some notes.

Services Administration In Education. Utilizing Control Charts In Educational Management: An Approach For Assessing The Mathematics Scores In Panhellenic Examinations

Background: The choices offered to students and their parents complicate the process of finding an optimal solution that meets their needs, as competition increases1 . The management of each educational organization is responsible for providing services information and curricula offered by the tutorials, as well as for designing innovative, cost-effective, and efficacious solutions2 . A learning organization with a student-oriented marketing approach is considered by many researchers as an effective mix to mixture that meets the needs of customers, students, and parents3,4. An organization with the above-mentioned features which apprehends its position, predicts the behavior of its customers, meets their needs and preferences, and enables marketing to be much more targeted and successful. As a result, it provides highvalue services, which are its comparative advantage in the market5 . The evaluation and intervention of education service providers and curriculum designers ensures the correction of errors and the adaptation to student and societal needs in all educational system processes. One of the most reliable statistical control tools of quality services and processes is the mean and standard deviation control charts6 . Both respond to the requirements of in educational processes as they are described by the normal distribution. Materials and Methods In this work, the statistical tools have been utilized to analyze the mathematics scores in the economic and scientific studies orientation of the Panhellenic exams from 2003 to 20227 . The objective of this analysis is to investigate the application of control charts as statistical methods and identify the factors responsible for any out-of-control processes. Conclusion: The study findings revealed that the modification of the curricula (all chapters of Algebra topics were removed, and only mathematical analysis topics remained) negatively affected the mean students' scores from 2015 to 2017, with a simultaneous reduction of variance. Distance education which was mandated due to the pandemic crisis did not negatively affect the mean values of the mathematics scores and increased the variance. A decrease in the number of the Panhellenic exams candidates is expected due to the demographic problem of Greece8,9. Moreover, extensive learning gaps for students who were in high school and elementary school during distance education are evident10. Finally, the service administration authorities in educational organizations and those who are responsible for the development of educational strategies should monitor student performance with the use of appropriate tools. This will help them to adapt their policies, educational tools, and curricula to the current conditions if it would be necessary

Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy

The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system’s forecast data are under control for the future.

ИСПОЛЬЗОВАНИЕ ВОЗМОЖНОСТЕЙ ПО "MEASURELINK" ДЛЯ ЦЕЛЕЙ АВТОМАТИЗИРОВАННОГО СТАТИСТИЧЕСКОГО КОНТРОЛЯ КАЧЕСТВА ПО РЕЗУЛЬТАТАМ ИЗМЕРЕНИЙ, ПОЛУЧЕННЫХ КОНТРОЛЬНО-ИЗМЕРИТЕЛЬНЫМИ МАШИНАМИ

The article provides an overview of the main statistical control tools used in machine-building enterprises. In order to automate the process of processing control results and making management decisions, the use of the capabilities of coordinate measuring machines and their embedded software is considered. The paper highlights the capabilities of the «MeasureLink» software, which allows processing and storing statistical information for a batch of parts.

DSC Phase Transition Profiles Analyzed by Control Charts to Determine Markers for the Authenticity and Deterioration of Flaxseed Oil during Storage.

An approach of implementing X-bar and R control charts as a statistical control tool to monitor the changes in the melting profile of fresh and stored flaxseed oils by differential scanning calorimetry (DSC) was used. Phase transition melting profiles were collected after 0, 2, 4, and 6 months of storing flaxseed oils, originating from five different cultivars. Four peaks at around -36, -30, -25, and -12 °C were identified using the deconvolution analysis procedure, which enabled the data to be collected at peak temperature (T), peak height (h), the peak area (A), and the percentages of the area (P A), as well as the ratio calculated from these parameters. Control charts obtained for the second peak of the melting profile showed a significant decrease of peak height (h2) from 0.50 to 0.39 W/g and the percentage of the area (P A2) from 50 to 38%, within the storage time (p ≤ 0.05); thus, they were considered to be indicators of oil deterioration. Strong negative correlations of the unstable parameters of DSC with chemical indicators of the oils' oxidative stability (PV, p-AV, TOTOX) were found. For DSC parameters, related to the first peak (h1, A1) and the third peak (h3, A3), changes were statistically not significant within storage (p > 0.05); thus, they can be used as markers of flaxseed oil authenticity. The study demonstrated that X-bar and R control charts could effectively monitor changes in the specific peaks and calculated ratios from the DSC melting profile of fresh and stored flaxseed oils, serving as reliable indicators of oil deterioration.

Setting process control chart limits for rounded-off measurements

Measurements can often be imprecise and subjected to rounding-off. Typically, this rounding-off is ignored and assumed to have little to no effect. However, when the measuring scale step is not negligible, it may affect statistical control tools such as X‾-chart. Designing statistical process controls without considering the effects of rounding leads to high exposure to false negative results. This study illustrates the effects of rounding on the X-chart and shows that the result may further deteriorate due to asymmetry (incompatibility of the process and the measuring device parameters). A new simple method to design control limits is proposed, based on maintaining the original characteristics of the chart as devised by Shewhart.

Multicriteria assessment of optimal forecasting models in decision support systems to ensure the navigation safety

When developing the Decision Support System (DSS), the operability of the internal motion and maneuvering control systems of the vessel is characterized by a large number of parameters, which should be monitored in order to achieve the expected results. The task is to develop an appropriate methodology for automatic monitoring of these systems, which, using a minimum set of sensors, makes it possible to predict the state of the vessel and change the dangerous state to the safe one. To implement the method of automatic monitoring, a set of statistical control tools is selected depending on the alleged violations and the level of correlation of parameters. The uncorrelated parameters are monitored by instruments based on the Shewhart map [1], the correlated parameters are monitored on the basis of Hotelling statistics [2]. This approach makes it possible to diagnose the pre-emergency and emergency states of ship control systems in on-line mode. The method used for multi-level integrated monitoring of the technical state of control systems in on-line mode can improve the reliability of of identification of the technical state of vessel subsystems and expandthe scope of application of monitoring and diagnostics tools. The data obtained can become the basis for the development of rational decisions in the DSS at the level of control subsystems for the vessel transfer from the dangerous to safe state.

A Gaussian process approach for monitoring autocorrelated batch production processes

AbstractIn statistical process monitoring, statistical control tools are used to identify deviations from normal operating conditions. In many industrial processes, such as batch production processes, multiple process variables must be monitored as they play a key role in the quality of the final product. Several monitoring tools are found in the literature that deal with the case of multiple process variables, but a few of them deal with the case of autocorrelated data. Existing tools that are used to monitor autocorrelated process variables have two main drawbacks. First, they are run offline. That is, monitoring is performed at the end of the production cycle and hence no corrective actions can be made. Second, these tools utilize dimensionality reduction techniques to solve for computational complexity issues. A Gaussian Process–based modeling approach is proposed in this work to monitor batch production processes online. The proposed monitoring approach takes into consideration of both the correlation between process variables and the within variable autocorrelations. A simulated and two real data sets were used to evaluate the performance of the proposed modeling approach. Model performance metrics showed that the proposed modeling approach has similar performance to the optimal case of Shewhart type control charts. Process status classification accuracy of about 92–98% were achieved for the cases considered.

ОПТИМИЗАЦИЯ ПАРАМЕТРОВ АЛГОРИТМА ОБОБЩЕННОЙ ДИСПЕРСИИ ПРИ СТАТИСТИЧЕСКОМ КОНТРОЛЕ ПРОЦЕССА

When monitoring a real production process using statistical methods, the question of early detection of violations arises. In most cases, several indicators are monitored simultaneously in the production process, and a change in the values of some indicators leads to a change in others. If there is a dependence of indicators for their monitoring, multivariate statistical control tools are used, in particular generalized variance chart. By varying the parameters of the chart, its efficiency can be significantly increased, this allows minimizing the time the process is in an unstable state.Applying the approach of A. Duncan, which he developed for Shewhart charts, a formula for the expectation of the duration of an unstable state of a process was obtained and a Python program was developed to minimize it. To test the set optimization problem, the calculation of the data of two process indicators is given and the optimal parameters of the generalized variance chart are obtained, at which the duration of the process in an unstable state is minimal.

Performance of statistical control tools in identifying the defect on plunger- A review

In this paper, we carried out a study to upsurge the brilliance of the plungers used in the braking system. The main focus has been taken on various defects that occur in the plungers during manufacturing. In this work, we analyzed the types of defects that transpire in the plunger. The foremost issue is acknowledged using tools like Pareto graphic representation, root and effect diagram, Failure Mode and Effect Analysis, Trend Analysis and Control chart. We identified more frequently occurring defects and major causes and effects of these defects in plungers. Based on the result of the analysis, interpretation measures are taken to overcome the defects of the plungers that will help to improve the overall efficiency of the brake system in vehicles.

Optimizing and evaluating performance quality control of the production process of disposable essentials using approach vague goal programming

ABSTRACTTo have effective production planning, it is necessary to control the quality of processes. This paper aims at improving the performance of the disposable essentials process using statistical quality control and goal programming in a vague environment. Therefore, in this study, the conditions are examined in a vague environment that is a distance-based environment. The disposable essentials process in Kach Company was studied. Statistical control tools were used to characterize the existing process for four-factor responses including the average of disposable glasses’ weights, heights, crater diameters, and volumes. Goal programming was then utilized to find the combination of optimal factors setting in a vague environment; also, the fuzzy regression model is used to predict the responses of the four described factors. Optimization results show that the process capability index values for disposable glasses were improved.

Innovative Business Development. Statistical Control of Processes in Six Sigma

Introduction. The success and competitiveness of any enterprise are facilitated by the introduction of innovative approaches and continuous improvement of technological processes. In addition, it is not enough simply to modernize the equipment. A comprehensive approach to the optimization of technology, the organization of an effective management system and the building of a corporate culture is needed. Effective implementation of changes is often hampered by an aggressive external environment that can nullify any innovations and improvements. The Six Sigma methodology offers tools that timely identify the negative effects of external and internal factors and help to eliminate their negative impact. Methods. The study is based on statistical and econometric methods. The graphs of the control are used to estimate the deviation from the statistically stable state. Calculations were made using a software package for the processing of statistical data Minitab. Results. Six Sigma has tools for statistical control that allow determining the causes that divert the process from the state of equilibrium. To do this, it is necessary to monitor key indicators recorded on the control charts. The result of the use of control charts are signals indicating the appearance of negative factors. In this article, practical case studies show how statistical stability can be used to improve the stability and efficiency of processes by making timely and informed management decisions. Discussion. The concept of Six Sigma has been successfully used for many years by leading world companies. Possessing effective tools to reduce variability and improve quality, it is becoming more and more in demand in our country. The application of the Six Sigma approaches in process optimization in Russian enterprises helps to improve the competitiveness and quality of domestic products, which is especially relevant in the context of the import substitution policy.

INFLUENCE OF THE PLOTS FORMAT ON THE PERFORMANCE INDEX OF THE SOYBEAN COMBINE HARVESTER

ABSTRACT: The intensive mechanization during the harvest operation and success of this operation depends on several factors, one of which is the length and format of the plots. The aim of this study was to evaluate the operating performance of a soybean harvester in different geometric plots shape, using as quality indicators performance parameters by means of statistical control tools of process. The experiment was conducted in March 2014 in the city of Uberaba - MG. Operational capacity was evaluated in the mechanical harvesting of soybeans in three plots: irregular, rectangular and trapezoidal. Performance evaluations of soybean harvester were made from the following parameters: displacement speed, engine rotation, cylinder rotation and concave opening. The grain water contents affect the quality indicators of the soybean harvester performance in all plots shapes. The harvester performance is considered able to maintain between the specification limits and close to the target, to speed quality indicators the irregular and the trapezoidal plots and for cylinder rotation the trapezoidal plot.

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The increasing of global rubber consumption is an opportunity as well as a challenge for Indonesian rubber produsers to increase the quantity and the quality of production. Faced the competition between countries, the quality of rubber products should be enhanced adapted to consumer demand. This study aims to determine whether the quality of the rubber produced in PTPN IX (Persero) Garden Batujamus/Kerjoarum still within the control or not. This study uses time series data, in the form of rubber production data during March 2012 - February 201, that were analyzed descriptively using Control p Chart Analysis. Control p Chart describe the proportion of production damage that can be tolerated as a tool for statistical Control p Chart Application as Quality Control Tools for Rubber Production in PTPN IX Batujamus/Kerjoarum process control. This study shows that the quality of the rubber produced by PTPN IX (Persero) Garden Batujamus/ Kerjoarum is out of the control. The Control p Chart proves that there are still many points that are outside the production control line. The production domination of the third RSS type (RSS 3) cause this problem, so that the profit and the efficiency of the company can be increased if the RSS 3 product can be controlled and be changed with the production of RSS 1.

Dynamic production monitoring in pig herds III. Modeling and monitoring mortality rate at herd level

Management and monitoring systems may enable the farmers to enhance production results and reduce labor time. The aim of this paper is to develop a dynamic monitoring system for mortality rates of sows and piglets. For this purpose a model for mortality rates is implemented using a Dynamic Generalized Linear Model. Variance components are pre-estimated using an Expectation-Maximization algorithm applied on a dataset containing data from 15 herds, each of them including observations over a period ranging from three to nine years. Data are registrations of events for insemination, farrowing (including stillborn and live born), number of weaned piglets and death of sows. The model provides reliable forecasting on weekly basis. Detection of impaired mortality rate is performed by statistical control tools that give warnings when the mortality (rate) shows sudden or gradual changes. For each herd, mortality rate profile, analysis of model components over time and detection of alarms are computed for two categories, namely sows and piglets.

Dynamic production monitoring in pig herds II. Modeling and monitoring farrowing rate at herd level

Good management in animal production systems is becoming of paramount importance. The aim of this paper was to develop a dynamic monitoring system for farrowing rate. A farrowing rate model was implemented using a dynamic generalized linear model (DGLM). Variance components were pre-estimated using an expectation-maximization (EM) algorithm applied on a dataset containing data from 15 herds, each of them including insemination and farrowing observations over a period ranging from 150 to 800weeks. The model included a set of parameters describing the parity-specific farrowing rate and the re-insemination effect. It also provided reliable forecasting on weekly basis. Statistical control tools were used to give warnings in case of impaired farrowing rate. For each herd, farrowing rate profile, analysis of model components over time and detection of alarms were computed. The model provided a good overview of the development of the parity specific farrowing rate over time and the control charts were able to detect impaired results. Suggestions for future improvements include addition of parity-specific control charts, calibration of the charts for use in practice and inclusion of a sow effect in the farrowing model.

Dynamic production monitoring in pig herds I: Modeling and monitoring litter size at herd and sow level

Monitoring animal production results in real time is a challenge. Existing management information systems (MIS) in pig production are typically based on static statements of selected key figures. The objective of this paper is to develop a dynamic monitoring system for litter size at herd and sow level, with weekly updates. For this purpose, a modified litter size model, based on an existing model found in the literature, is implemented using dynamic linear models (DLMs). The variance components are pre-estimated from the individual herd database using a maximum-likelihood technique in combination with an Expectation–Maximization (EM) algorithm applied on a larger dataset with observations from 15 herds. The model includes a set of parameters describing the parity-specific mean litter sizes (herd level), a time trend describing the genetic progress (herd level), and the individual sow effects (sow level). It provides reliable forecasting with known precision, on a weekly basis, for future production. Individual sow values, useful for the culling strategy, are also computed. In a second step, statistical control tools are applied. Shewhart Control Charts and V-masks are used to give warnings in case of impaired litter size results. The model is applied on data from 15 herds, each of them including a period ranging from 150 to 800 weeks. For each herd, the litter size profile, the litter size over time, the sow individual effect and sow economic value, are computed. Perspectives for further development of the model can take into account indices including conception rate, service rate, mortality rate etc. Such a model can be used as a basis for developing a new, dynamic, management tool.

Developing and implementing statistical process control tools in a Jordanian company

Statistical Quality Control (SQC) is a branch of Total Quality Management (TQM) that defines a quality philosophy and set the guiding principles that represent the foundation for a continuously improving organisation. Statistical Process Control (SPC) and Acceptance Sampling are the two major parts of SQC. SPC is an effective tool that aims to get and keep processes under control and ensure that the product is manufactured as designed and intended. SPC is comprised of seven tools: Pareto diagram, cause and effect diagram, check sheets, process flow diagram, scatter diagram, histogram and control charts. A case study has been carried out to monitor real life data in a Jordanian manufacturing company that specialises in producing fertilisers. Pareto diagram, histograms and control charts for variables were implemented to investigate the major causes of non-conformities and possible remedies were proposed. The full strength of the seven statistical control tools was implemented and other companies are encouraged to follow suite and implement them simultaneously.

Reconstruction of the surface topography of randomly textured silicon

AbstractWe describe an algorithm that reconstructs a surface model from stereoscopic scanning electron micrographs of randomly textured silicon surfaces. From the reconstructed model we extract size distributions of the {111}‐faceted upright pyramids. The algorithm is thus a useful tool for statistical control of the texturing process. Copyright © 2002 John Wiley & Sons, Ltd.

Gráfico de controle de regressão aplicado na monitoração de processos

Esta pesquisa tem por objetivo empregar o gráfico de controle de regressão, como ferramenta de controle estatístico, para monitorar processos produtivos, onde uma variável de estado, que seja de interesse, possa ser expressa como função de uma variável de controle. Existem vários estudos sobre o controle de variáveis em processos produtivos, mas, na maioria das vezes, são em relação ao controle de cada variável, separadamente, não podendo ser utilizados para um estudo comparativo. Esta pesquisa, portanto, apresenta uma técnica eficiente no controle simultâneo de variáveis correlacionadas.