Control Charts and Process Capability

Statistical process control is an industry-based methodology for measuring and controlling quality during the manufacturing process. The case study presented in this paper deals with the determination of process capability ratio (PCR), process capability index (Cpk) and level of correlation for a 'drive shaft run outs' of the manufacturing industry. Data have been taken from the manufacturing process and the -X and R control charts have been plotted to find whether the processes are with-in or out of statistical control. Process capability analysis has been done to find the effectiveness of the processes. It is found that the 36.02% of drive shaft run outs is beyond the control, which is really a higher rejection level and major concern for the industry. The samples of 'drive shaft run outs' are taken from a manufacturing industry located in India and estimated the level of correlation among the observations. The level of correlation among the observations of the 'drive shaft run outs' has been computed and matched with the suggested optimal schemes of Shewhart -X chart for sample size (n) of four. The presented case study shows the level of correlation of 0.50.

–Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many other applications. Conventional SPC charts are designed for cases when process observations are independent at different observation times. In practice, however, serial data correlation almost always exists in sequential data. It has been well demonstrated in the literature that control charts designed for independent data are unstable for monitoring serially correlated data. Thus, it is important to develop control charts specifically for monitoring serially correlated data. To this end, there is some existing discussion in the SPC literature. Most existing methods are based on parametric time series modeling and residual monitoring, where the data are often assumed to be normally distributed. In applications, however, the assumed parametric time series model with a given order and the normality assumption are often invalid, resulting in unstable process monitoring. Although there is some nice discussion on robust design of such residual monitoring control charts, the suggested designs can only handle certain special cases well. In this article, we try to make another effort by proposing a novel control chart that makes use of the restarting mechanism of a CUSUM chart and the related spring length concept. Our proposed chart uses observations within the spring length of the current time point and ignores all history data that are beyond the spring length. It does not require any parametric time series model and/or a parametric process distribution. It only requires the assumption that process observation at a given time point is associated with nearby observations and independent of observations that are far away in observation times, which should be reasonable for many applications. Numerical studies show that it performs well in different cases.

BackgroundWe recently showed that the empirical use of a combination of 2 moving average (MA) statistical process control (SPC) charts was highly sensitive and specific for detecting potentially important increases in surgical site infection (SSI) rates. We performed this follow-up study to examine the performance of these same SPC charts when applied to known SSI outbreaks.MethodsWe retrospectively applied 2 MA SPC charts to all 30 SSI outbreaks investigated from 2007 to 2015 in a network of over 50 community hospitals. These outbreaks were detected via routine SSI surveillance activities that occurred in the network. We reviewed prior outbreak investigation documentation to determine the estimated time of outbreak onset and time of traditional surveillance outbreak detection. The first SPC chart utilized procedure-specific, composite SSI data from the hospital network for its baseline; the baseline for the second chart was calculated from SSI data from the outbreak hospital undergoing analysis. Both charts used rolling baseline windows but varied in baseline window size, rolling baseline lag, and MA window size. SPC chart outbreak detection occurred when either chart had a data point above the upper control limit of 1 standard deviation. Time of SPC detection was compared with both time of outbreak onset and time of traditional surveillance detection.ResultsWith the dual chart approach, SPC detected all 30 outbreaks, including detection of 25 outbreaks (83%) prior to their estimated onset (Figure 1). SPC detection occurred a median of 16 months (interquartile range, 12–21 months) prior to the date of traditional outbreak detection, which never occurred prior to outbreak onset. Both individual SPC charts exhibited at least 90% sensitivity in outbreak detection, but the dual chart approach showed superior sensitivity and speed of detection (Figure 2).ConclusionA strategy that employed optimized, dual MA SPC charts retrospectively detected all SSI outbreaks that occurred over 9 years in a network of community hospitals. SPC outbreak detection occurred earlier than traditional surveillance detection. These optimized SPC charts merit prospective study to evaluate their ability to promote early detection of SSI clusters in real-world scenarios.DisclosuresAll Authors: No reported Disclosures.

Statistical Process Control (SPC) chart is a graphical tool that helps to identify a possible process shift. Though the SPC chart is, in theory, a monitoring tool that reveals only the result of a statistical hypothesis testing, in practice the chart's signaling is often used for making process adjustment. In this paper we develop a robust estimator of the process mean for processes monitored by SPC charts. This mean estimate can serve as an important reference for investigating assignable causes and taking appropriate corrective actions. When the control chart is implemented as a control device, the mean estimate can also provide a more accurate assessment of the amount of adjustment to be made to the process. We also demonstrate in this paper that when the process shift is relatively small, the proposed mean estimate outperforms the mean estimate usually used for the CUSUM scheme.

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.

Shewhart X and R control charts and process capability indices, proven to be effective tools in statistical process control are widely used under the assumption that the measurement system is free from errors. However, measurement variability is unavoidable and may be evaluated by the measurement system discrimination ratio (DR). This paper investigates the effects of measurement system variability evaluated by DR on the process capability indices Cp and C pm , on the expected non conforming units of product per million (ppm), on the expected mean value of the Taguchi loss function (E (Loss)) and on the Shewhart charts properties. It is shown that when measurement system variability is neglected, an overestimation of ppm and underestimation of E (Loss) are induced. Moreover, significant effects of the measurement variability on the control chart properties were made in evidence. Therefore, control charts limits calculation methods based on process real state were developed. An example is provided in order to compare the proposed limits with those traditionally calculated for Shewhart X , R charts.

<h3>Background</h3> Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal...

Results of a multicentre randomised controlled trial of statistical process control charts and structured diagnostic tools to reduce ward-acquired meticillin-resistant Staphylococcus aureus: the CHART Project

The presence of autocorrelation in time series data significantly affects the performance of statistical process control charts. For process data collected in real time, consecutive observations result in serial dependence that violates the assumption of sample independence. This paper focuses on the improvement of statistical process control (SPC) charts of individual observations when they are designed to monitor stationary processes in the presence of data autocorrelation. Three widely used SPC tools, Shewhart chart, Hotelling's T2 chart, and PCA‐based SPC charts, are chosen to demonstrate the proposed approach. There has been a modified version of SPC chart shown to be effective in accommodating the autocorrelation behavior, relying on a model‐based estimation of chart parameters. Process model estimation, however, is a challenging task, especially for complex systems. In this paper, a model‐free monitoring approach is proposed for SPC charts of individual observations, aimed at reducing the effect of autocorrelation on chart performance. It relies on a skipping strategy that creates sample bins with little or no autocorrelation. A single skipping chart and a combined skipping chart (CSC) are established, and a parameter sensitivity study is performed through simulations. In the case studies, the advantages of the CSC are noted as being a model‐free approach and having a performance consistent with that of the benchmark modified chart.

Purpose – In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues. Design/methodology/approach – In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages. Findings – The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings. Research limitations/implications – For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN. Practical implications – The application case is related to a piston company in Konya’s industry area. Originality/value – In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.

Optimization design of the charts for monitoring process capability

In this study, fuzzy mean and range control charts were used to follow the feeding material and concentrate production line of Eti Aluminum Co. Fuzzy control charts were collected from the factory over a period of time and compared to Shewhart control charts used by the factory. The results showed that fuzzy control charts detected errors in the production process more accurately than Shewhart control charts. This method has increased quality and efficiency. Process capability indices (PCIs) provide numerical measures as to whether a process has confirmed the defined capability prerequisite. These were used to measure the process's ability to decide how well it meets specification limits (SLs). PCIs have been implemented by companies to evaluate quality and efficiency performance. Fuzzy process capability analysis using X-R control charts gave more accurate results.

Web quality studies commonly focus on predefined attributes to assess online business activity. However, these studies often do not pay enough attention to the concept of continuous improvement, which is an important consideration in maintaining accurate evaluations of web quality over time. This study aims to demonstrate the possible benefits of statistical process control (SPC) charts in assessing web quality. After illustrating the use of X-bar and R control charts as web quality assessment tools, a procedure for tracing the causes of quality problems is introduced.

6579 Background: Collecting electronic patient reported outcomes (ePROs) reduces the burden of cancer and prevents acute care hospitalizations. However, broad implementation in the routine care setting remains challenging, partly because the optimal implementation strategies – actions that are most likely to encourage adoption and use – are not known. Since 2019, the SIMPRO Consortium has deployed an ePRO-based symptom management program (eSyM) across 6 community-based cancer centers. Methods: We used statistical process control (SPC) charts to identify variation due to special versus common causes (random variation) for one key performance indicator (KPI) – the weekly ePROs response rate. All 6 SIMPRO sites contributed data for this analysis: 3 for chemotherapy patients and 3 for surgery patients. Sites had to have at least 18 months of KPI data; information from the first 4 months after eSyM rollout were excluded to account for program ramp-up. The KPI was calculated as the number of patients who submitted an ePRO questionnaire divided by the number eligible to submit a questionnaire each week. The upper and lower confidence limits were set at +/- 2 sigma to allow for 95% of observations to fall within the limits. Three rules were applied to identify special cause variation: 1) astronomical points (outside sigma limits), 2) shifts (8 or more points above or below a center line), and 3) trends (6 or more points consecutively ascending or descending). Implementation strategies that mapped to special cause variations were selected as those most likely to impact the KPI. Results: Across the 6 sites that deployed eSyM, the mean KPI performance ranged from 19.7%-35.7%. SPC charts detected multiple special cause variations in the KPI at all 6 sites, though the patterns varied by site. All sites demonstrated positive and negative astronomical variations as well as positive and negative shifts. Three sites demonstrated negative trends; no sites demonstrated positive trends. Direct patient outreach was the implementation strategy most frequently associated with special cause variation, followed by changes in promoting the ePRO program to clinical staff. Positive changes were associated with strategy initiation; negative changes were associated with strategy discontinuation. Conclusions: Applying SPC charts to a KPI from the eSyM deployment highlighted the effect of personal communication with patients and providers, suggesting that these implementation strategies could offer value to other sites deploying eSyM. The absence of positive trends early in eSyM program deployment underscores the need to identify better implementation strategies and allocate more resources to foster program adoption and sustainability. Applying SPCs to other KPIs, such as symptom severity and healthcare utilization, could improve eSyM deployment and impact.

Statistical quality control for the evaluation of the uniformity of microsprinkler irrigation with photovoltaic solar energy