Application Of Control Charts Research Articles

Transforming healthcare performance monitoring - A cutting-edge approach with generalized additive profiles: GAMs for healthcare quality monitoring.

Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring.

Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19.

Control charts help epidemiologists and healthcare professionals monitor disease incidence and prevalence in real time, preventing outbreaks and health emergencies. However, there remains a notable gap in the comprehensive exploration and application of these techniques, particularly in the context of monitoring and managing disease outbreaks. This study analyses and categorizes worldwide control chart applications from 2000 to 2023 in outbreak monitoring in over 20 countries, focusing on corona-virus (COVID-19), and chooses optimal control charts for monitoring US COVID-19 death waves from February 2020 to December 2023. The systematic literature review analyzes available 35 articles, categorizing data by year, variable, country, study type, and chart design. A selected optimal chart is applied to monitor COVID-19 death patterns and waves in the USA. Control chart adoption in epidemiology monitoring increased during the COVID-19 pandemic, with annual patterns showing a rise in 2021 to 2023 (18%, 36%, 41%). Important variables from 2000 to 2019 include influenza counts, Salmonella cases, and infection rates, while COVID-19 studies focus more on cases, infection rates, symptoms, and deaths. Among 22 countries, the USA (29%) is the top applier of control charts. The monitoring of USA COVID-19 deaths reveals 8 waves with varying severity > > > > > > > . The associated with the JN.1 variant, highlights ongoing challenges. This study emphasizes the significance of control charts in outbreak monitoring for early disease diagnosis and intervention. Control charts help healthcare workers manage epidemics using data-driven methods, improving public health. COVID-19 mortality analysis emphasizes their importance, encouraging worldwide use.

INDIVIDUAL CONTROL CHARTS FOR NON-NORMAL DATA

Individual control charts are a suitable option to consider when the processes being studied change slowly. If obtaining standard samples for Shewhart control charts is expensive or in the case of studying statistical characteristics of the technological process, they are used. Control charts are used to control technological processes in the case of a normal distribution of the possible values of the controlled parameter. Sometimes a quasi-normal distribution of the studied parameter is allowed. In practice, it is often necessary to deal with technological processes in which samples are not distributed according to a normal distribution. In such cases, using individual control charts can lead to type I and II errors. This is because the control limits of the charts are calculated for the mean and standard deviation of the normal distribution. Control charts of individual values are most sensitive to the deviation of the distribution from the normal one since individual values are used to construct the limits, to which the central limit theorem does not apply, as in the case of control charts of mean values. Therefore, it is extremely important to develop methods of applying the individual control charts for random samples, the general population of which is not normally distributed. The study examines the application of individual control charts for a random process that is non-normally distributed. This is achieved by using the normalization method to transform samples of the studied parameter into samples that are distributed normally or quasi-normally. The normalization is performed using the Box-Cox method with further construction of control charts based on the transformed samples. The mathematical apparatus of the individual control chart application for samples with non-normal distribution is described. Numerical modeling of the application of individual control charts for the chi-square distributed samples, which have a significant distribution asymmetry for small values of the shape factor, and for the samples obtained by normalizing the chi-square one, was performed. According to the research findings, the utilization of individual control charts for samples exhibiting a significantly asymmetric distribution results in a higher likelihood of type I errors being detected. However, when the method of sample normalization is employed, this issue is almost entirely eliminated. Modeling process disruption, by changing a certain individual value, also demonstrated the emergence of false signals of process disruption.

ARIMA MODEL VERIFICATION WITH OUTLIER FACTORS USING CONTROL CHART

Control charts are often used in quality control processes, especially in the industrial sector because of their significant benefits in increasing industrial production. However, control charts can also be used throughout the field of time series modeling to evaluate measures of accuracy represented by a particular time series model. The application of control charts in this research meets the criteria for evaluating accuracy. However, it is not certain that the time series model will have a high level of accuracy. There are various factors that can influence this phenomenon, one of which is the potential for outliers. Therefore, it is very important to perform time series modeling by adding an outlier factor. The residuals of the time series model obtained are used to create a control chart for model verification. The aim of this research is to evaluate the validity of time series models by looking at the influence of outlier characteristics to improve their accuracy. This research studies the accuracy of a time series model built using Gross Domestic Product (GDP) data in Indonesia. There are two different models, namely the ARIMA model without outlier factors and the ARIMA model with outlier factors which are used for research purposes. Both models were performed using the same data set. The results of this study indicate that the ARIMA model with outlier factors has better accuracy than the ARIMA model without outlier factors. This conclusion can be drawn based on the observation that the residual value is within the predetermined control limits, thus indicating that the process is in a state of statistical control.

A nonparametric adaptive EWMA control chart for monitoring mixed continuous and categorical data using self-starting strategy

In the context of big data-driven smart manufacturing, data is often characterized by high dimensionality, numerous variables, and complex associations. As a result, mixed continuous and categorical data are increasingly common. In the case of data that consist of a combination of continuous and categorical variables with unknown distributions, the application of traditional parametric control charts for monitoring purposes becomes challenging. Therefore, a novel nonparametric adaptive exponentially weighted moving average (EWMA) control chart using a self-starting strategy is proposed in this paper. First, the process of converting continuous data into categorical data is conducted using the data categorization method. The log-linear modeling is subsequently employed to investigate the relationships between various variables. Next, a nonparametric adaptive EWMA statistic is constructed to monitor mixed continuous and categorical data, and the self-starting strategy is employed to gradually expand the size of the in-control (IC) dataset and to update the parameters in real time. Then, a numerical simulation is conducted to compare the IC and out-of-control (OC) performance of the proposed chart with other control charts. According to the simulation result, it can be concluded that the proposed chart offers a more effective means of detecting shifts in the production process. Finally, a concrete illustration of an authentic dataset regarding red wine quality is provided to further clarify the effectiveness of the proposed control chart in monitoring mixed continuous and categorical data.

USE OF CONTROL CHARTS OF CUMULATIVE AMOUNTS FOR THE METHOD OF EXTENDING THE SERVICE LIFE OF THE IONIZING RADIATION SOURCE ACCORDING TO CALIBRATION RESULTS

The article forms and substantiates the application of control charts for the method of extending the service life of the source of ionizing radiation (SIR). An algorithm for its aim was developed. The main types of con- trol charts and the features of their application were considered. A methodology for constructing a cusum map for extending the service life of SIRs based on the results of their calibrations, as well as a methodology for checking the results of SIR calibration for statistical controllability, were developed. Extrapolation methods were considered, and the method of least squares was chosen as the most optimal for this technique.

USE OF CONTROL GRAPHICS TO IMPROVE THE ANTHRACITE CALCINATION PROCESS IN AN ELECTRIC FURNACE

In the present work, the study of the application of control charts was carried out in the improvement of an anthracite calcination process in an electric oven for the manufacture of electrode paste (Soderberg). As a support for the control charts, the Standardization tools, Cause and Effect Diagram and the Six Sigma methodology were used to identify problems and proposals of improvement for it. Additionally, the anthracite calcination process was also presented, which when done correctly, with material within the proposed resistivity range, guarantees the final quality of the product produced at the pulp mill. The results of this work show that with the use of the Control Graph together with the Six Sigma DMAIC Methodology and the Cause and Effect Diagram, it was possible to reduce the amount of material outside the range of value specification from 70% to 14%, an 56% reduction, which represents an average decrease of 179.2 tons of material out of specification per month, generating a drop in the amount of material to be reprocessed, which translates into savings for the company and the ability to supply material for the manufacture of electrode paste.

Remedial approaches to decrease the effect of measurement errors on polynomial profile monitoring

In the application of quality control charts, most of the researches in profile monitoring are based on accurate measurements. Measurement errors, however, often exist in many manufacturing and service environment. In this paper, first, we investigate the effect of neglecting measurement errors on the performance of four common control charts for Phase II monitoring of polynomial profiles. Simulation studies in terms of average run length (ARL) metric show that neglecting the measurement errors adversely affects the capability of all mentioned control charts. Then, we reconstruct the control charts based on two consistent estimators and two remedial approaches including multiple measurement and increasing sample size are utilized to decrease the effect of measurement errors. The results show that the remedial approaches compensate for the mentioned effect to a certain extent.

A self-starting non-restarting CUSUM chart for monitoring Poisson count data with time-varying sample sizes

Many studies have focused on monitoring the incidence rate of Poisson count data with time-varying sample sizes, with control charts being widely used. However, almost all of the existing reports mainly focus on solving the problem of time-varying sample sizes. Rare attention is paid to the real-life problems where the data cannot restart. Besides, these studies aim at Phase II with the in-control parameters known, which is unrealistic in real applications. As far as we know, there is no research on the non-restarting and unknown in-control parameters control charts, but it is essential and critical for extending applications of control charts. To address these limitations, this study offered two self-starting non-restarting cumulative sum (CUSUM) control charts based on the modified test to monitor the incidence rate of non-restarting Poisson data with time-varying sample sizes. The methods can be applied to other control charts and take into account both Phase I and Phase II without assumptions of time-varying sample sizes. Moreover, the monitor can be carried out with small Phase I samples. Simulation results show that the second control chart proposed in this study (SSNR-CUSUM-P2) is optimal compared to the common non-restarting control chart (E) and the first control chart proposed in this study (SSNR-CUSUM-P1). The SSNR-CUSUM-P2 chart has the highest similarity to the control chart with known parameters (T) in both post-outbreak false alarm rate and outbreak time point set. Finally, a dataset on German measles patients and a dataset on US employment are used to illustrate the application of the methods.

PE038 - Application of Cumulative SUM control charts (CUSUM) in the learning curve of robotic radical prostatectomy

PE038 - Application of Cumulative SUM control charts (CUSUM) in the learning curve of robotic radical prostatectomy

Application of condition-based maintenance for electrical generators based on statistical control charts

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality.Moreover, heat monitoring surpassed vibration and noise in failure detection, where temperature control charts successfully identified failure. The overall results support the significant role of statistical charts in decision-making regarding condition-based maintenance for electrical equipment like generators.•Application of statistical control charts for condition-based maintenance of electrical generators.•Detecting dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts.•Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process

Statistical parametric and non-parametric control charts for monitoring residential water consumption

The adoption of strategies for monitoring water consumption is essential for water resources management, contributing to the promotion of the sustainability in the water sector. Statistical process control (SPC) charts, which are widely used in the industrial sector, are statistical methods developed to improve the quality of products and processes. The application of this method has reached other areas over the last decades and has recently been identified as an option for environmental monitoring. In this context, the application of SPC charts emerges as an option for water consumption monitoring, whether in a building or an urban scale. Thus, this article aims to analyze the application of statistical process control charts in the monitoring of water consumption of two housing compounds in Joinville, southern Brazil. The methodological procedures include the use of the Shewhart and the EWMA control charts in addition to the non-parametric alternative, the EWMA-SN, assessing the effectiveness of these techniques in detecting water leaks in residential apartment buildings. The data sets, obtained through a telemetry metering system from the water utility, represent a period of 243 days. The results show that control charts are a powerful tool in identifying changes in water consumption patterns, with the EWMA chart flagging the leaks sooner.

The Optimal Control Chart Selection for Monitoring COVID-19 Phases: A Case Study of Daily Deaths in the USA.

Epidemiologists frequently adopt statistical process control tools, like control charts, to detect changes in the incidence or prevalence of a specific disease in real time, thereby protecting against outbreaks and emergent health concerns. Control charts have proven essential in instantly identifying fluctuations in infection rates, spotting emerging patterns, and enabling timely reaction measures in the context of COVID-19 monitoring. This study aims to review and select an optimal control chart in epidemiology to monitor variations in COVID-19 deaths and understand pandemic mortality patterns. An essential aspect of the present study is selecting an appropriate monitoring technique for distinct deaths in the USA in 7 phases, including pre-growth, growth, and post-growth phases. Stage-1 evaluated control chart applications in epidemiology departments of 12 countries between 2000 and 2022. The study assessed various control charts and identified the optimal one based on maximum shift detection using sample data. This study considered at Shewhart (Javid Shabbir2, ) control charts and exponentially weighted moving average (EWMA) control chart with smoothing parameters were all investigated in this study. In Stage-2, we applied the EWMA control chart for monitoring because of its outstanding shift detection capabilities and compatibility with the present data. Daily deaths have been monitored from March 2020 to February 2023. Control charts in epidemiology show growing use, with the United States leading at 42% applications among top countries. During the application on COVID-19 deaths, the EWMA chart accurately depicted mortality dynamics from March 2020 to February 2022, indicating six distinct stages of death. The third and fifth waves were extremely catastrophic, resulting in a considerable loss of life. Significantly, a persistent sixth wave appeared from March 2022 to February 2023. The EWMA map effectively determined the peaks associated with each wave by thoroughly examining the time and amount of deaths, providing vital insights into the pandemic's progression. The severity of each wave was measured by the average number of deaths. The United States entered a seventh phase (6th wave) from March 2022 to February 2023, marked by fewer deaths. While reassuring, it remains crucial to maintain vaccinations and pandemic control measures. Control charts enable early detection of daily COVID-19 deaths, providing a systematic strategy for government and medical staff. Incorporating the EWMA chart for monitoring immunizations, cases, and deaths is recommended.

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring.

The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.

Real Time Statistical Process Control for Autocorrelated Serial Data: A Simulation Approach

Computer measurement systems play an important role on process automation and Industry 4.0 implementation strategies. They can be easily integrated on modern production systems, enabling real time test and control of multiple product and process characteristics that need to be monitored. If for one side the big data provided by these systems is an important asset for production analytics and optimization, on the other hand, the high frequency data sampling, commonly used in these systems, can lead to autocorrelated data violating, this way, statistical independence requirements for statistical process control implementation. In this paper we present a simulation model, using digital recursive filters, to properly handle and deal with these issues. The model demonstrates how to eliminate the autocorrelation from data time series, creating and ensuring the conditions for statistical process control application through the application of real time control charts. A performance comparison between Shewhart of Residuals and Exponentially Weighted Moving Average (EWMA) of Individual Observations control charts is made for autocorrelated data time series with the presence of different mean shift amplitude perturbations.

APPLICATION OF STATISTICAL PROCESS CONTROL CHARTS: A CASE STUDY IN DAIRY INDUSTRY

Quality is a major part for entire industrial ecosystem when it comes to achieving, maintaining, and, improving the quality of products and services taking consideration of customer needs. Special causes responsible for controllable variations can be detected with the help of quality control charts-one of the quality management tools. Process can be improved by removing detected causes of variation bringing process under control. Statistical Process Control (SPC) Charts are quality control tools based on statistical principles which help to achieve quality of products by detecting special cause(s) of variation in the process involved. This paper incorporates a case study on application of Statistical Process Control (SPC) Charts tested at Sujal Dairy Pvt Ltd, Kathmandu, Nepal. Corresponding mean charts (Xbar charts) and range charts (R-Charts) represent data regarding quality variation for weight of milk pouch and SNF (Solid-Non-Fats) for both packaging machine 1 and machine 2. Results show that SNF variation for both machine and weight variation for machine 2 have been found to be under control whereas weight variation for machine 1 has been found to be out of control. Possible causes of variation have been grouped with the help of fishbone diagram and then compared with standard operating procedures for machine which led to detection of causes of variation. Finally, process has been better improved after removal of detected causes of variation bringing process under control.

Monitoring of Linear Profiles Using Linear Mixed Model in the Presence of Measurement Errors

In the application of control charts, most of the research in profile monitoring is based on accurate measurements. Measurement errors, however, often exist in many manufacturing and service environments. In this paper, we apply linear mixed models in the presence of measurement errors in fixed effects. We discuss three modified multivariate charts, namely Hotelling’s T2, multivariate exponential weighted moving average (MEWMA) control chart, and multivariate cumulative sum (MCUSUM) control chart. Performance comparisons are made in terms of the average run length (ARL) and average extra quadratic loss (AEQL). Finally, a real data example on healthcare expenditures is used to illustrate the implementation of the proposed monitoring schemes.

The RNVP-based process monitoring with transforming non-normal data to multivariate normal data

In the modern industry, process monitoring with control charts is essential to improve processes by decreasing defects. Control charts, which are based on grounded statistical theory, have been used as an online process monitoring method to detect out-of-control. However, because control charts assume that the process data are normally distributed (a.k.a. the assumption of normality), they do not perform as well as failing to detect out-of-control when assumptions are not held. In practice, most process data violate the assumption of normality, the application of control charts in real manufacturing sites is limited. Therefore, to address this limitation, this study proposes a real-valued non-volume preserving (RNVP)-based control chart. The proposed method first transforms the process data to follow a multivariate normal distribution, and then monitors the transformed data using a control chart. As a result of conducting numerical experiments to evaluate the effectiveness of the proposed method, it was found that the performance of the proposed method was superior to that of the existing control charts in terms of Type II error rate and average run length.

Monitoring dairy heifer growth through control charts

The efficiency of replacement programmes in dairy farms depends largely on the heifers’ growth rate. We provide a case study of control chart application to monitor the weight of dairy replacement heifers. A research dairy farm in Córdoba province, Argentina, provided the monthly samples of BW measurements of 2, 9, and 14.5-month-old heifers born between 2017 and 2019. The data were used to build control charts for the mean and for SD, with moving range control limits in order to consider varying sample sizes. In each age group, control charts for the mean showed at least one out-of-control sample, whereas control charts for SD showed one sample out of control for 9-month-old heifers. Each sample outside the control limits implies that a potentially identifiable cause has occurred. Therefore, the producer could identify the event causing the deviation and make the necessary changes according to the heifer’s age. Control charts provide the producer and consultant with graphical information and quick alerts to support the decision-making process of the replacement programme. These tools are useful at the farm level to monitor heifer weights and support management decisions.

STABILITY ANALYSIS OF THE TECHNOLOGICAL PROCESS OF MECHANICAL WOOD PROCESSING USING CONTROL CHARTS

This paper presents a quality control tool, that is, a quality control method, which in this case concernsthe mechanical processing of wood, but in general, it can be implemented in any form of production (mechanicalengineering, traffic, food technology...). The goal of the research is to show the way of applying the control chartmethod, that is, to determine the stability of the technological process of mechanical wood processing in order to beable to define appropriate corrective measures in order to improve that part of the production process. The controlchart that was created in this paper is a graph consisting of a horizontal (abscissa) and a vertical axis (ordinate). Theorder of observations is plotted on the abscissa, and the corresponding values on the ordinate. Apart from theprecise creation of control charts, their correct interpretation is also of great importance. It is easiest to interpretcontrol charts where the process is "outside statistical control", that is, the process where the values of individualmeasurements are outside the control limits. This means that some special cause of variation is present in the processand an adjustment needs to be made. However, placing all points within the control limits, that is, obtaining acontrol map on which the process is "within statistical control", does not necessarily mean that such a process isacceptable by statisticians. On the basis of the results, that is, on the basis of the graph that has the shape of a brokenline, we observe whether the control limits have been exceeded, and we draw certain conclusions about the stabilityof the process. If the graph is between the control limits, we can conclude that our process is stable and undercontrol, that is, that our quality variation is within normal limits. If there are any jumps, ie. if some value in someobservation is out of bounds (above the upper control limit or below the lower control limit), then we conclude thatour process is not stable, not under control and that our quality variation is not within normal limits. In that case,certain corrective measures were proposed, which should improve the technological process of mechanical woodprocessing and bring it under control.Control charts, as an engineering-mathematical tool, are a very suitable tool for achieving the goals of statisticalcontrol. Optimal application of control charts ensures constant monitoring of the process, direction towards thedesired flow and undertaking of possible corrective measures. Also, their application ensures the fulfillment of thedesigned or required quality characteristics, that is, the fulfillment of the requirements of a certain standard. Withtheir simplicity of application and reliability, they provide users with convenience in work and relevant processindicators, and provide process controllers with valid data about the process flow. As such, they represent a tool thatkeeps the entire process within the given limits.