Attribute Control Charts Research Articles

Design of a new attribute chart based on inspection run length

The conventional np chart detects shifts in fraction nonconforming of the process depending on the total number of nonconforming units, which is obtained until all of the units within a sample are completely inspected. To improve the effectiveness of inspection capacity, this paper presents a new attribute control chart, namely the Inspection Run Length (IRL) control chart, which detects shifts in fraction nonconforming of the process depending on a novel inspection strategy. This strategy employs information about the number of conforming and nonconforming units and inspection run length during the inspection process to curtail the number of units that need to be inspected. The optimal design parameters of the proposed control chart are derived based on optimising average run length (ARL) properties. In addition, the performance of the proposed IRL chart is evaluated and compared with the np chart and CRL-type charts. Comparison studies show that the proposed chart achieves a significant reduction in inspection time and cost and an improvement in performance (a lower out-of-control ARL) while holding the false alarm rate ( in-control ARL) at a specified level. Finally, two industrial examples are given to illustrate how to simply apply the proposed chart to the manufacturing process.

Monitoring bivariate zero-inflated Poisson processes: an alternative to copula-based bivariate attribute control chart

Occurrences of two (or more) types of defects are not uncommon in high quality (or zero-inflated) processes. Bivariate (or multivariate) attribute control charts are required to be developed for monitoring such processes. Fatahi et al. (2012) introduced the idea of applying copula function to derive joint distribution of two correlated zero-inflated Poisson distributions, which can be used to develop bivariate attribute control chart (BACC) for monitoring bivariate zero-inflated Poisson (BZIP) processes. However, the copula-based model often fails to represent bivariate zero-inflated data, which we come across in real-life situations. Li et al. (1999) model for BZIP distribution is quite flexible. Thus, it can overcome the limitations of Fatahi et al. (2012) model. In this paper, BACC is developed based on Li et al. (1999) model. The performances of the proposed BACC, in terms of in-control and out-of-control average run lengths, are evaluated extensively using simulations. The results show that the proposed BACC are well capable of detecting shifts in the parameters of a BZIP process. Further, two case studies on BZIP process data obtained by past researchers reveal that the BACC developed based on Li et al. (1999) model is more effective than the BACC developed based on copula-based model.

A study of attribute control charts for censored lifetime data

In many recent articles authors have advocated the use of attribute charts based on the number of censored values for monitoring censored lifetime data. In this article we compare the average run-length performance of one-sided and two-sided Shewhart charts based on the conditional expected value (CEV) with the corresponding attribute charts. The overall findings indicate that using an attribute chart to monitor the mean of a Weibull lifetime probability distribution with right censoring does not perform as well as the CEV-based chart.

Time Truncated Attribute Median Control Charts for Logistic-Exponential Process Distribution

Abstract In this article, time truncated attribute control chart is proposed for logistic-exponential distribution based on four acceptance sampling plans. The performance of the proposed control chart is evaluated in terms of average run length. Furthermore, the efficiency of the proposed attribute control chart is compared for all given acceptance sampling plans, viz., single sampling plan, repetitive sampling plan, multiple dependent state sampling plan and multiple dependent state repetitive sampling plan. Also, the application of the proposed control chart is illustrated by using real life example.

Optimal design of time-between-event control charts with parameter estimation

The tr chart is a Shewhart-type control chart used to monitor the time between events, especially in high-quality processes. It has been shown to be more efficient than classical attribute control charts based on count data. In practical applications, the in-control process parameters are often unknown and need to be estimated from a Phase I reference sample. When the available Phase I data are small and the chart parameters have to be estimated, a popular approach is to adjust the control chart limits from a conditional perspective to avoid frequent false alarms using the exceedance probability criterion. However, this approach ignores the practitioner-to-practitioner (p-to-p) variation caused by the random Phase I reference samples, which results in getting different control limits and chart performance for each practitioner Large p-to-p variation makes practitioners to have a limited confidence on using their own estimated charts. Hence, in this article, we propose to optimize the tr chart via an exact method so that it has a minimum p-to-p variation. Comparisons between the optimal and conventionally adjusted charts are made in both the in- and out-of-control cases. The most important results are that the optimal chart has a far smaller p-to-p variation and its unconditional average run length values are closer to the desired ones compared to the conventional approach regardless of the in- or out-of-control cases. Finally, two real examples are presented to illustrate the implementation of the proposed chart.

Utilizing Repetitive Sampling in the Construction of a Control Chart for Lindley Distribution with Time Truncation

The monitoring of the process relies on the average product lifetime, which adheres to the Lindley distribution and employs a truncated life test-based attribute control chart. Control limits for these charts are determined through a repetitive sampling procedure. Two inner and outer control limits are established based on the desired average run length (ARL). Tables are generated for varying ARL values by adjusting shift values while keeping the shape parameter of the Lindley distribution constant. The effectiveness of the control charts is assessed through application to real-time data, and the performance of the proposed chart is evaluated using simulation data. A comparative analysis is conducted with single sampling scheme based on ARL values, and the results are presented and discussed.

Robust Control Chart Application in Semiconductor Manufacturing Process

Statistical Process Control (SPC) charts are frequently used in the semiconductor manufacturing environment to monitor process quality and detect special-cause variations, hence, to take corrective actions when necessary. The important aspects of control charts to consider on production floors are identifying the primary objective of implementing control charts, the type of data to monitor and the most appropriate control limits to establish. When the quality data is a type of attribute data like the proportion of defectives from a production lot, a p-chart approach is most suitable. In p-chart applications, although the assumption of normally distributed process data is not mandatory, the widespread practice is to assume the normal distribution of process data when establishing the control limits. Yet again, the reality of industrial settings is that process data are most likely influenced by outliers, resulting in highly skewed distributions. This paper addresses these issues by proposing robust SPC charting techniques to detect special-cause variations in the semiconductor manufacturing processes. Here, we present a case study of a semiconductor company in Malaysia, Dominant Opto Technologies Sdn. Bhd. to propose three robust statistical approaches for monitoring the proportion of defectives in production lots. We apply M-estimates, median, and interquartile range to calculate the upper control limits (UCL) and found that robust estimators are more effective in detecting early process deterioration and capturing the out-of-control (OOC) conditions better than traditional control charts. By proposing robust methods, this study enlightens the practical aspects of process quality improvement for real-life manufacturing setups. Because a high OOC rate may impact manufacturing productivity, we recommend the decision-makers choose the types of control charts based on the implications of each robust approach toward quality and productivity. The significance of this study includes providing insights into setting up the appropriate attribute control charts for detecting defective proportions for professionals and SPC researchers working in these areas.

Analysis of Employee Work Quality Using Statistical Process Control and Compliance with Standard Operating Procedures (Study in the Merchandise Inspection Department of the Foodhall Senayan City)

The objective of this research is to evaluate the effectiveness of employee performance in implementing Standard Operating Procedures (SOP) within the Merchandise Inspection/Receiving Department at The Foodhall Senayan City. This research is of a qualitative descriptive nature, with a focus on Statistical Process Control (SPC) as a measurement instrument. It employs the Implementation of Seven Key Elements in SOP, covering efficiency, consistency, error minimization, problem-solving, and defensive limitations. Five individuals participated in this research: Branch Manager, Receiving Supervisor, and three Receiving Staff members. Data collection techniques were based on direct observation, interviews, and documents used as references for the receiving staff's work procedures. In this study, the author used Statistical Process Control (SPC) on Attribute Control Charts, specifically using P-charts as a measurement tool for Quality Control checks on goods. The Implementation of Seven Key Elements in SOP was used to deepen the investigation into problem-solving that could not be explained using numerical data. Based on the analysis results using Statistical Process Control (SPC) for the current period, the average reversal rate is 0.02, indicating proximity to the Lower Control Limit (LCL) of 0 or zero defects. Meanwhile, regarding the usage of the Implementation of Seven Key Elements in SOP in the Merchandise Inspection/Receiving Department at The Foodhall Senayan City, it is evident that in the implementation of these seven key elements, there are still some shortcomings. These deficiencies include error minimization, process mapping, employee protection, and defensive limitations.

Peta Kendali p Berdasarkan Metode Peningkatan Transformasi Akar Kuadrat

When the proportion of nonconformities is small, the effectiveness of the control chart performance becomes inadequate because it has a skewness that causes asymmetryc. Therefore, the Improved Square Root Transformation (ISRT) method is used to construct the attribute control chart to increase the accuracy of the chart control limit which is called the ISRT- control chart. In this study, the effectiveness of the ISRT- control chart perfomance is compared with the control chart after being applied to the data on the number of defects in the newspaper production process at PT. Radar Sulteng Membangun. The results showed that the production process at PT. Radar Sulteng Membangun was not in a statistically controlled and the ARL value obtained on the ISRT- control chart is much smaller than the ARL value for the control chart, so that the ISRT- chart is more effective and sensitive to detecting changes in the production process which produces in a small proportion of nonconformities.

Optimized two-stage time-truncated control chart for Weibull distribution

In this article, an attribute control chart is proposed when the lifetime of a product follows a Weibull distribution in two-stage sampling, which is based on the number of failures from a truncated life test. The coefficients of the proposed double sampling attribute control chart and the test duration are determined so that the average run length when the process is in control is close to the target value. An overview is reported on how double sampling np control charts work. Tables reporting the out-of-control average run lengths are given for various shift parameters. A case study is given to illustrate the proposed control chart for industrial use. A comparison of two-stage and single-stage sampling of failure of products is discussed.

Time truncated attribute control chart for the generalized Rayleigh distributed quality characteristics and beyond

Purpose First is to develop the time truncated median control chart for the Rayleigh distribution (RD) and generalized RD (GRD), respectively. Second is to evaluate the performance of the proposed attribute control chart which depends on the average run length (ARL) and third is to include real life examples for application purpose of the proposed attribute control chart.Design/methodology/approach (1) Select a random sample of size n from each subgroup from the production process and put them on a test for specified time t, where t = ? × µe. Then, count the numbers of failed items in each subgroup up to time t. (2) Step 2: Using np chart, define D = np, the number of failures, which also a random variable follows the Binomial distribution. It is better to use D = np chart rather than p chart because the authors are using number of failure rather than proportion of failure p. When the process is in control, then the parameters of the binomial distribution are n and p0, respectively. (3) Step 3: The process is said to be in control if LCL = D = UCL; otherwise, the process is said to be out of control. Hence, LCL and UCL for the proposed control chart.Findings From the findings, it is concluded that the GRD has smaller ARL values than the RD for specified values of parameters, which indicate that GRD performing well for out of control signal as compared to the RD.Research limitations/implications This developed control chart is applicable when real life situation coincide with RD and GRD.Social implications Researcher can directly use presented study and save consumers from accepting bad lot and also encourage producers to make good quality products so that society can take benefit from their products.Originality/value This article dealt with time truncated attribute median control chart for non-normal distributions, namely, the RD and GRD, respectively. The structure of the proposed control chart is developed based on median lifetime of the RD and GRD, respectively.

Control charts using half-normal and half-exponential power distributions using repetitive sampling

This manuscript presents the development of an attribute control chart (ACC) designed to monitor the number of defective items in manufacturing processes. The charts are specifically tailored using time-truncated life test (TTLT) for two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD) under a repetitive sampling scheme (RSS). To assess the effectiveness of the proposed control charts, both in-control (IC) and out-of-control (OOC) scenarios are considered by deriving the average run length (ARL). Various factors, including sample sizes, control coefficients, and truncated constants for shifted phases, are taken into account to evaluate the performance of the charts in terms of ARL. The behavior of ARLs is analyzed in the shifted process by introducing shifts in its parameters. The superiority of the HEPD-based chart is highlighted by comparing it with both the HND-based ACC and the ACC based on the Exponential distribution (ED) under TTLT using RSS. The results showcase the superior performance of the proposed HEPD-based chart, indicated by smaller ARL values. Additionally, the benefits of another proposed ACC using HND are compared with the ED-based ACC under RSS, further confirming the effectiveness of the HND-based approach through smaller ARLs Finally, the proposed control charts are evaluated through simulation testing and real-life implementation, emphasizing their practical applicability in real-world manufacturing settings.

Empirical Bayes Prediction for an Attribute Control Chart in Quality Monitoring

Empirical Bayes Prediction for an Attribute Control Chart in Quality Monitoring

Attribute Control Chart based on SkSP-R Scheme under Assumption of Half Normal Distribution

Attribute Control Chart based on SkSP-R Scheme under Assumption of Half Normal Distribution

A Combined Runs Rules Scheme for Monitoring General Inflated Poisson Processes

In this work, a control chart with multiple runs rules is proposed and studied in the case of monitoring inflated processes. Usually, Shewhart-type control charts for attributes do not have a lower control limit, especially when the in-control process mean level is very low, such as in the case of processes with a low number of defects per inspected unit. Therefore, it is not possible to detect a decrease in the process mean level. A common solution to this problem is to apply a runs rule on the lower side of the chart. Motivated by this approach, we suggest a Shewhart-type chart, supplemented with two runs rules; one is used for detecting decreases in process mean level, and the other is used for improving the chart’s sensitivity in the detection of small and moderate increasing shifts in the process mean level. Using the Markov chain method, we examine the performance of various schemes in terms of the average run length and the expected average run length. Two illustrative examples for the use of the proposed schemes in practice are also discussed. The numerical results show that the considered schemes can detect efficiently various shifts in process parameters in either direction.

Using the attribute control chart to monitor a chemical manufacturing process

The chemical and pharmaceutical industries have quality as a prerequisite for the smooth running of their processes. Quality monitoring in this type of industrial activity is carried out using attribute control charts, among others. The aim of this article is to show a case study carried out in a company in the south of the state of Rio de Janeiro which used this type of control chart, more specifically the NP control chart, to monitor an antibiotic manufacturing process. The results showed the need for some adjustments and management actions will be taken to improve the process.

Monitoring the Quality of PeduliLindungi Application based on Customer Reviews on Google Play Using Hybrid Naïve Bayes -Laney p' Attribute Control Chart

Indonesia is battling the COVID-19 pandemic. One of the government's strategies to break the virus's transmission chain is to track digital contacts in Indonesia using the PeduliLindungi application. The Google Play comment section is where users can express their opinions about the app. User opinions discovered on Google Play can be used to perform sentiment analysis and quality evaluation. The Naïve Bayes classification can be used to identify how user opinions contain positive, neutral, or negative sentiments in user reviews of the PeduliLindungi app on Google Play. The p and Laney p' charts can be used for quality evaluation. Laney p' control chart is an attribute chart used to monitor the proportion of defects with large and varied sample sizes. The data used in this study is from April 1, 2020, to March 31, 2022. According to the sentiment analysis results of user reviews of the PeduliLindungi app on Google Play, there are more negative reviews than positive classes. The classification accuracy has an Area Under Curve (AUC) value of 89.05%. This result shows that the test data has good classification. The monitoring results using p and Laney p' charts based on ratings and user reviews of the PeduliLindungi app show that the processes are still not statistically controlled. These findings indicate that the app developer still needs to make improvements.

A New Sine-Based Distributional Method with Symmetrical and Asymmetrical Natures: Control Chart with Industrial Implication

Control charts are widely used in quality control and industrial sectors. Because of their important role, researchers are focusing on the development of new control charts. According to our study, there is no significant amount of published work on control charts using trigonometrically generated distribution methods. In this paper, we contribute to this interesting research gap by developing a new control chart using a sine-based distributional method. The proposed distributional method (or family of probability distributions) may be called a new modified sine-G family of distributions. Based on the new modified sine-G method, a novel modification of the Weibull distribution, namely, a new modified sine-Weibull distribution, is introduced. The new modified sine-Weibull distribution is flexible enough to capture symmetrical and asymmetrical behaviors of its density function. An industrial application is considered to show the importance and implacability of the proposed distribution in quality control. Based on the proposed model, an attribute control chart is developed under a truncated life test. The control chart limits (ARLs) are also computed for the proposed model. The ARLs of the proposed control chart are compared with the attribute control chart of the Weibull distribution. The results show that the developed chart is more efficient than the existing attribute control chart for the Weibull distribution.

A combined adaptive double sampling and variable sampling interval control chart for monitoring three-level products

Three-level control charts have been developed for monitoring processes where the quality of products is characterized by classifying the product characteristic into three discrete levels. In this article, a combined double sampling and variable sampling interval (DSVSI) attribute control chart is developed for monitoring three-level products. This research is motivated by recent studies done on combining the DSVSI scheme with traditional attribute charts and their results recommend the application of the DSVSI attribute charts. The proposed DSVSI three-level chart is compared with the existing standard, adaptive, and EWMA three-level charts. The results approve the superiority of the proposed scheme and recommend it as an efficient three-level control chart in application. An example is also provided to illustrate the application of the proposed chart.

A new statistical methodology using the sine function: Control chart with an application to survival times data.

Statistical methodologies have a wider range of practical applications in every applied sector including education, reliability, management, hydrology, and healthcare sciences. Among the mentioned sectors, the implementation of statistical models in health sectors is very crucial. In the recent era, researchers have shown a deep interest in using the trigonometric function to develop new statistical methodologies. In this article, we propose a new statistical methodology using the trigonometric function, namely, a new trigonometric sine-G family of distribution. A subcase (special member) of the new trigonometric sine-G method called a new trigonometric sine-Weibull distribution is studied. The estimators of the new trigonometric sine-Weibull distribution are derived. A simulation study of the new trigonometric sine-Weibull distribution is also provided. The applicability of the new trigonometric sine-Weibull distribution is shown by considering a data set taken from the biomedical sector. Furthermore, we introduce an attribute control chart for the lifetime of an entity that follows the new trigonometric sine-Weibull distribution in terms of the number of failure items before a fixed time period is investigated. The performance of the suggested chart is investigated using the average run length. A comparative study and real example are given for the proposed control chart. Based on our study of the existing literature, we did not find any published work on the development of a control chart using new probability distributions that are developed based on the trigonometric function. This surprising gap is a key and interesting motivation of this research.