Application Of Statistical Process Control Research Articles

Use of statistical process control in quality improvement projects in abdominal surgery: a PRISMA systematic review

BackgroundThe use of quality improvement methodology has increased in recent years due to a perceived benefit in effectively reducing morbidity, mortality and length of stay. Statistical process control (SPC) is...

The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

The widespread implementation of computer technology has led to an increased use of Machine Vision Systems (MVS) for quality control in advanced manufacturing industries. The application of Statistical Process Control (SPC) techniques, especially control charts, is one of the most important and effective ways for fault detection in this field. Several statistical control charts have been extended to monitor images in the framework of MVS where the major aim is to improve the on-line detection ability, which is equivalent to avoid production of nonconforming products. In various industrial and non-industrial applications, control charts have been shown to be better able to detect anomalies in the underlying process(es) by integrating machine and ensemble learning techniques. However, in the field of image monitoring, approaches based on intelligent techniques have rarely been proposed. To bridge this gap and to improve the detection ability of control charts in monitoring image-based quality characteristics, this paper presents a novel control chart that combines machine learning, ensemble learning and image partitioning. In order to reach the maximum performance in Phase II SPC applications, specific designing and parameter tuning procedures are provided. The superiority of the proposed Partitioning Ensemble Control Chart (PECC) is verified by comparative analysis including conventional monitoring schemes by means of extensive simulations, in which different fault sizes and fault locations are applied to the images. Finally, a real MVS for quality control of O-rings is discussed to illustrate the practical application of the PECC.

Application of Statistical Process Control for the Assessment of Supplier Performance and Process Capability in an Automotive Manufacturing Company

One of the most important attributes to manufacturing is product quality. It relates to the historical rejection rate during a period of time of the products delivered to the customer. Rejection is due to deviations from specifications in the raw material supplied by suppliers, process design, and the quality control of the manufacturing process. The non-conforming parts can be detected during incoming inspection or during work in process. With regards to this, the standardization and maintenance of process equipment are of prime importance in ensuring the quality of a process and the products that are derived. Cost is another important attribute of the product as it affects the bottom-line. In this regard the competence of the supplier of raw material to design, develop and launch products within specifications becomes imperative in a manufacturing process. In the current competitive environment, it is crucial to assess suppliers as good quality raw material are important in the development stage, as this can have an adverse effect on the customers response. Supplier flexibility involves the response time when engineering changes are needed during the development stage. As a result, research and development activities are used to initiate and measure the ability of the supplier to provide support during the process. It is an important attribute as most products, after launching, demand continuous improvement to remain competitive. Procurement of materials and equipment is considered the first step in supply chain management of many companies. It is also broadly known that the performance of suppliers directly influences the company’s efficiency and competitiveness. Supplier performance evaluation is a crucial process to identify strengths and weaknesses of suppliers which can help the company to manage them. There are various supplier performance evaluation methods. In this study process quality of the existing system of producing steel shafts for an automotive industry was first evaluated and based on this study the machine was recommended for upgrading by engineering design. This was due to the process capability analysis of the machine, which did not meet the stipulated standard of 1.3 for the machine’s capability index. Although, the raw material supplied by the suppliers were within the measured confidence interval, the summary reports did not meet the required standards. In order to develop a performance evaluation system of the suppliers of raw material to the company, four main-criteria, may be developed, namely: quality, delivery, service, and flexibility. Statistical analytical studies performed during the process on five selected suppliers of raw material showed that the most important criteria of the process metrics was machine performance, followed by quality of suppliers in terms of the supply of raw material. Based on this study, the relationship between the quality of the supplier, the performance of the machine and the specification to the consumer may be assessed.

New methods for phase II monitoring of multivariate simple linear profiles

In some statistical process control applications, the quality of a process or product is characterized by a function that relates a response variable to one or more explanatory variables, referred to as a “profile”. In some cases, multivariate simple linear profiles are required for effective quality modeling. In these profiles, there is a set of correlated response variables, regressed on an explanatory variable. There have been only a limited number of studies on multivariate simple linear profiles monitoring. In this paper, three new methods are proposed based on multivariate homogeneously weighted moving average (MHWMA) control chart to improve monitoring of multivariate simple linear profiles. The performance of the proposed methods is evaluated by simulated ARL metric. A comprehensive comparison is also conducted between the performance of the proposed methods and the existing ones. The results indicated that the proposed methods functioned very well under the various conditions considered. In addition, the practical application of the proposed control charts is also demonstrated using two real case studies.

Predictive quality assurance for linear accelerator target failure using statistical process control

The performance of a linear accelerator (Linac) depends on the integrity of its x-ray target. The sudden failure of its target not only breaks down the Linac but also could contribute significant disruptions to patient care. This work is to develop a predicative quality assurance (QA) method using Statistical Process Control (SPC) and AutoRegressive Integrated Moving Average (ARIMA) modeling to identify the risk of target failure before it occurs. In the past years, we observed two incidents of target failure among our Linacs. Retrospectively, we collected past daily QA data (from both open fields and enhanced dynamic wedge (EDW) measurements) and analyzed its historical trend using methods of SPC and ARIMA. SPC is a technique that monitors process performance based on statistical analysis. ARIMA is a time-series forecasting algorithm that can be used to estimate future values based on its past pattern. Both have been evaluated for predictive QA in radiotherapy. Application of SPC on open beam QA data would not yield an early warning signal to the pending target failures. However, when the same SPC methodology applies to EDW measurements, the control limits were breached a couple of weeks before the target failed. EDW mechanism introduces nonuniform magnification factors over its wedge-directed beam profiles and is responsible for the sensitivity of its profile to changing beam properties induced by a degrading target. Further extension of the warning period may be possible by using ARIMA modeling. Predicative QA for EDW daily data using SPC and ARIMA methods may provide an early QA warning to incoming Linac target failure.

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.

Combination of Sequential Sampling Technique with GLR Control Charts for Monitoring Linear Profiles Based on the Random Explanatory Variables

Control charts play a beneficial role in the manufacturing process by reduction of non-compatible products and improving the final quality. In line with these aims, several adaptive methods in which samples can be taken with variable sampling rates and intervals have been proposed in the area of statistical process control (SPC). In some SPC applications, it is important to monitor a relationship between the response and independent variables—this is called profile monitoring. This article proposes adaptive generalized likelihood ratio (GLR) control charts based on variable sampling interval (VSI) and sequential sampling (SS) techniques for monitoring simple linear profiles. Because in some real-life problems, it may be possible that the user cannot control the values of explanatory variables; thus, in this paper, we focus on such a scenario. The performance of the proposed method is compared under three different situations, i.e., the fixed sampling rate (FSR), VSI, and SS, based on average time to signal (ATS) criteria for phase II analysis. Since the SS approach uses a novel sampling procedure based on the statistic magnitude, it has a superior performance over other competing charts. Several simulation studies indicate the superiority as the SS approach yields lower ATS values when there are single-step changes in the intercept, slope, standard deviation of the error term, and explanatory variables. In addition, some other related sensitivity analysis indicates that other aspects of the proposed methods, such as computational time, comparison with other control charts, and consideration of fixed explanatory variables. Furthermore, the results are supported by a real-life illustrative example from the adhesive manufacturing industry.

Assessing the robustness of cement-hydrogel-based binders as 3D printing materials

The printing performance of two cement-hydrogel pastes at 0.25 and an 0.3 gel-to-cement mass ratios was investigated for a gel containing 4 mass percent hydroxy ethyl methyl cellulose (HEMC). The effect of varying printing process parameters, print layer height and print speed, on the dimensional accuracy of 3D printed objects was used to assess the printing robustness of the two cement-hydrogel formulations. The performance of the printing process was assessed using deviations in the dimensions and analyzed statistically using capability and performance indices, control charts, and process capability histograms, from which optimal processing conditions were identified. This work is a novel application of statistical process control (SPC) to a laboratory-scale cement-based printing process. It was found that the 0.25 gel-to-cement mass ratio paste performed better offering more resistance to shape distortion. While both pastes exhibited smooth extrusion, the lower 0.25 gel-to-cement ratio paste was more rigid. The goal was to have zero deviation in the three dimensions of the printed cylinders, height, outer diameter and wall thickness. While both paste formulations produced a negative deviation with respect to cylinder height, a similar magnitude of positive deviation was observed in the cylinder thickness. However, the two pastes performed very differently with respect to external diameter and the optimum printing conditions differ for both pastes. The lowest deviation in cylinder height was realized for the 0.25 ratio paste at conditions that differ from those producing optimal outcomes for the 0.3 ratio paste. Conversely, the lowest deviation in the cylinder thickness and external diameter was realized with the 0.3 ratio paste. Not surprising, different print metrics require different optimal process parameters, indicating that global optima must be assessed as a compromise of select or target print qualities.

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.

New modification of three parameter Sujatha distribution and its SPC applications

In this paper, a new probability model called as area biased three parameter Sujatha distribution has been studied. Various structural properties of this new distribution have been discussed and its model parameters are estimated using the maximum likelihood estimation. a new distribution is examined and analysed with real life data set to discuss its significance. Finally, the techniques of a New Modification of Three Parameter have been used to the Sujatha distribution to application of Statistical Process Control to check the performance of the production Process. The main objective of this paper is to introduce a control chart using area biased three parameter Sujatha distribution in order to study the production system and monitor the same.

A bivariate exponentially weighted moving average control chart based on exceedance statistics

Nonparametric control charts are more practical tools for statistical process control (SPC), as they are robust in situations in which the underlying distribution is unknown. Comprehensibility and simplicity of exceedance statistics provide great convenience to analysts in multivariate SPC applications. By using the exceedance statistics, analysts save time and avoid complex calculations. Therefore, in this study, a bivariate nonparametric exponentially weighted moving average (BEWMA-EX) control chart is proposed based on the exceedance statistics to detect the shifts in the location parameter. The performance of the proposed BEWMA-EX chart is compared with the multivariate sign EWMA (MSEWMA) control chart under some well-known bivariate distributions, such as bivariate normal, t, and gamma distributions. The BEWMA-EX chart outperforms the MSEWMA control chart in terms of run-length properties. To highlight the importance of the stated study, the BEWMA-EX chart is implemented on industrial engineering datasets related to coal power plant and aluminum electrolytic capacitor manufacturing processes. The findings are promising and support the simulated results.

Production Cost Calculation Approach Based on Data Collected During Manufacturing Process

Abstract The aim of the article is to present a proposal and discuss the production cost calculation procedures based on data collected during manufacturing process, according to standard SPC control chart evaluation. It is applied as a tool to support the process of continuous improvement of the manufacturing process and improve profitability by reducing the cost of production. Research methodology - the study uses the results of a literature review on the issue of cost analysis and their modeling. Key elements are the main cost components, but also those that are considered less important and may be overall decisive. Application of Statistical Process Control - as a method of data collection for cost modeling to improve overall cost structure is proposed. Originality / value - the considerations presented in the study may be the basis for determining the key components of the cost of production. The proposed simulation model allows for the determination of the main quality cost factors. This, in turn, should allow to define the main directions of searching for the optimization of the product cost in order to achieve the expected level of cost and quality.

Use of Statistical Process Control Methods for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacteria Outbreaks: A Single-Center Pilot Study.

Nontuberculous mycobacteria (NTM) are emerging pathogens increasingly implicated in healthcare facility-associated (HCFA) infections and outbreaks. We analyzed the performance of statistical process control (SPC) methods in detecting HCFA NTM outbreaks. We retrospectively analyzed 3 NTM outbreaks that occurred from 2013 to 2016 at a tertiary care hospital. The outbreaks consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition, cardiac surgery-associated extrapulmonary MABC infection, and a bronchoscopy-associated pseudo-outbreak of Mycobacterium avium complex (MAC). We analyzed monthly case rates of unique patients who had positive respiratory cultures for MABC, non-respiratory cultures for MABC, and bronchoalveolar lavage cultures for MAC, respectively. For each outbreak, we used these rates to construct a pilot moving average (MA) SPC chart with a rolling baseline window. We also explored the performance of numerous alternative control charts, including exponentially weighted MA, Shewhart, and cumulative sum charts. The pilot MA chart detected each outbreak within 2 months of outbreak onset, preceding actual outbreak detection by an average of 6 months. Over a combined 117 months of pre-outbreak and post-outbreak surveillance, no false-positive SPC signals occurred (specificity, 100%). Prospective use of this chart for NTM surveillance could have prevented an estimated 108 cases of NTM. Six high-performing alternative charts detected all outbreaks during the month of onset, with specificities ranging from 85.7% to 94.9%. SPC methods have potential to substantially improve HCFA NTM surveillance, promoting early outbreak detection and prevention of NTM infections. Additional study is needed to determine the best application of SPC for prospective HCFA NTM surveillance in other settings.

Application of Statistical Process Control to Monitor Underground Coal Mine Fires Based on CO Emissions

ABSTRACT Toxic and threatening gases are the most hazardous components of mine fires. Carbon monoxide is one of these lethal gases. It can be used to monitor the progression of fires in underground coal mines. The purpose of this study is to monitor mine fires using statistical process control. Using quality control charts, the lower and upper limits of the carbon monoxide measurement values were evaluated in the first stage. The variations in carbon monoxide readings caused by a mine fire were then analyzed using regression control charts. Positive slope changes indicated that the fire was spreading, while negative slope changes indicated that the fire had been extinguished and stable slope changes indicated that the fire was under control. By analyzing the time-dependent change of carbon monoxide in underground mines using this method, an effective methodology for controlling coal fires was established.

SPC-based model for evaluation of training processes in industrial context

Purpose: This article aims to present successful practices in the management of training processes based on virtual reality and augmented reality, namely a strategy for evaluating the process with the principle of continuous improvement in mind, and monitoring its performance in terms of productivity and waste levels. It is proposed to apply Statistical Process Control (SPC) tools to develop control charts for monitoring individual events (i-charts).Design/methodology/approach: The methodology is based on a case study developed in an industrial project and is guided by a literature review on Work-Based Learning (WBL) and SPC.Findings: The developed work shows that SPC tools are suitable for supporting decision making in situations where the data to be analyzed is generated by human-computer interactions, e.g., involving students and virtual learning environments.Originality/value: The innovative aspect presented in the article lies in the evaluation of the effectiveness of pedagogical resources arranged in simulation environments based on virtual and augmented reality. The accumulated knowledge about the application of SPC in service areas, and others that demand data analysis, reinforces the hypothesis of the suitability of its application in the case presented. This is an original application of SPC, normally used in business processes quality control, but which in this case is applied in an innovative way to the evaluation of industrial training processes, with the same spirit for which it was designed, i.e. to provide the means to manage the quality of a process.

Inappropriate Testing of Streptococcal Pharyngitis in Children Aged Below 3 Years: Application of Statistical Process Control.

Group A strep tests in patients aged below 3 years are not recommended unless the patient has appropriate symptoms and a positive contact or signs of complications. The purpose of this quality improvement project was to increase the percentage of appropriately ordered strep tests among providers. Data were collected retrospectively and prospectively from 1163 patient visits. Providers were exposed to educational interventions, an electronic medical record order change, and provider feedback. Proportional control charts characterized the providers' behaviors and determined significant improvement among testing. The result was an increase in appropriate tests (13.7% to 37.8%), and the control charts showed sustainable results over time. This project demonstrates the efficacy of these methods to encourage antibiotic stewardship among providers. Furthermore, the interventions used here can be applied to other areas with low-value diagnostic testing. Future studies should investigate whether parental anxiety and educational programs influence testing and evaluate the efficacy of certain strategies.

Monitoring Length of Stay of Acute Myocardial Infarction Patients: A Times Series Analysis Using Statistical Process Control.

Given that length of stay (LOS) of acute myocardial infarction (AMI) patients has a significant impact on the utilization of hospital resources and the health status of communities, this study focused on how best to monitor LOS of AMI patients admitted to U.S. hospitals by employing statistical process control (SPC). Data were abstracted from the Healthcare Cost and Utilization Project Nationwide Readmissions Database between 2010 and 2016. A total of 1,491 patients were examined in the study. Patients who were admitted to nonfederal government (public) hospitals in metropolitan areas of at least 1 million residents with the primary diagnosis of AMI were abstracted. They were excluded if they developed AMI secondary to an interventional procedure or surgery, died during their index hospitalization, and were admitted and discharged on the same day. Patients were also excluded if they were discharged to short-term hospitals, nursing facilities, intermediate care facilities, home healthcare, or against medical advice. Individual moving range (I-MR) charts were used to monitor LOS of individual AMI patients in each subgroup from 2010 to 2016. The results showed I-MR charts could be used to indicate statistically out-of-control signals on LOS. Specifically, I-MR charts showed that LOS decreased between 2010 and 2016. LOS appeared to be longer at teaching hospitals compared to nonteaching hospitals and varied by gender. Female patients appeared to stay longer than male patients in the hospitals. The application of SPC and control charts can facilitate improved decision-making in healthcare organizations. This study shows the value of integrating control charts in administrative and medical decision-making processes. It may also help healthcare providers and managers achieve higher quality and lower cost of care.

A practical application of statistical process control to evaluate the performance rate of academic programmes: implications and suggestions

PurposeThis study aims to properly and objectively assess the students’ study progress in bachelor programmes by applying statistical process control (SPC). Specifically, the authors focused their analysis on the variation in performance rates in business studies courses taught at a Spanish University.Design/methodology/approachA qualitative methodology was used, using an action-based case study developed in a public university. Previous research and theoretical issues related to quality indicators of the training programmes were discussed, followed by the application of SPC to assess these outputs.FindingsThe evaluation of the performance rate of the courses that comprised the training programs through the SPC revealed significant differences with respect to the evaluations obtained through traditional evaluation procedures. Similarly, the results show differences in the control parameters (central line and control interval), depending on the adopted approach (by programmes, by academic year and by department).Research limitations/implicationsThis study has inherent limitations linked to both the methodology and selection of data sources.Practical implicationsThe SPC approach provides a framework to properly and objectively assess the quality indicators involved in quality assurance processes in higher education.Originality/valueThis paper contributes to the discourse on the importance of a robust and effective assessment of quality indicators of the academic curriculum in the higher education context through the application of quality control tools such as SPC.

Application of Statistical Process Control for Structural Health Monitoring of a High-Speed Railway Track System

To make full use of the massive monitoring data accumulated in China high-speed railways, an improved statistical process control (SPC) framework was introduced to analyze the discontinuous monitoring data of the track system on a high-speed railway elevated station. Multilinear regression models and time series difference equation (TSDE) models were first developed to separate common-cause variations in the monitoring data. Then, individual control charts, moving range control charts, and exponentially weighted moving average control charts were constructed to detect special-cause variations. Results showed that the variations of girder displacement and track slab–girder relative displacements mainly resulted from temperature effects and linear trends related with material damages. Moreover, visible serial dependence was found in the regression model residuals, which could be effectively captured by the TSDE model. Numerous outliers were detected at the measuring points of rail–track slab displacement 15 and track slab–girder relative displacement 17 by more than three control charts, implying higher sensitivity to special causes. With respect to the special causes triggering the anomalous responses of local and overall track systems, sixteen and twenty-eight significant special events were detected, respectively.

Capacity Analysis of Cable Insulation Crosslinking Production Process Based on IoT Big Data

In the cable insulation crosslinking production process, the process quality is determined by multiple processes and multiple related process variables, and there are complex autocorrelation and cross-correlation among these process variables. In this paper, different process variables are studied. By implementing statistical process control on a univariate, single value control chart (I) and moving range control chart () are drawn, and process capability CP is analyzed to find variables with weak process capability for improvement. At the same time, based on the background of the continuous development of the Internet of Things and big data technology, this paper evaluates the complex autocorrelation and cross-correlation between the overall process variables, adopts the multivariate statistical process control technology, and combines the multivariate HOTELLING T2 control chart and MEWMA control chart to reflect the statistical characteristics of multiple variables, thus avoiding misjudgment or omission of process quality monitoring model. By studying the setting of cable production process parameters and quality standards, combined with the application of complete multivariate statistical process control (MSPC) around the evaluation results, process, and technical requirements, this research can provide effective guidance for the cable insulation crosslinking production process