Statistical process control for software

Introduction of statistical process control in the setting of a small blood centre was tested, both on the regular red blood cell production and specifically to test if a difference was seen in the quality of the platelets produced, when a change was made from a relatively large inexperienced occasional component manufacturing staff to an experienced regular manufacturing staff. Production of blood products is a semi-automated process in which the manual steps may be difficult to control. This study was performed in an ongoing effort to improve the control and optimize the quality of the blood components produced and gives an example of how to meet EU legislative requirements in a small-scale production centre. Data included quality control measurements in 363 units of red blood cells, 79 units of platelets produced by an occasional staff with 11 technologists and 79 units of platelets produced by an experienced staff with four technologists. We applied statistical process control to examine if time series of quality control values were in statistical control. Leucocyte count in red blood cells was out of statistical control. Platelet concentration and volume of the platelets produced by the occasional staff were out of control, which was not the case with the experienced staff. Introduction of control charts to a small blood centre has elucidated the difficulties in controlling the blood production and shown the advantage of using experienced regular component manufacturing staff.

Questions about the validity of measurements made in an E/sup 3/ laboratory using software to control the instrumentation are a continuing concern for EMC engineers. In previous work, it has been shown that the use of statistical process control (SPC) methods to gage and track the variability of system measurements can provide confidence in the repeatability of the system hardware setup, and in the data it produces. Once the hardware system stability has been demonstrated, it is possible to also utilize SPC historical information to assist in confirming the validity of the data gathered using the system's control software.

The use of statistical process control (SPC) procedures such as Shewhart charts has been one of the pillars of the quality revolution. The Shewhart charts display process data under test and provide statistical decision making tools to assess the process quality (Shewhart 1931). These simple charting methods have proven to be powerful tools for monitoring the ‘health’ of a process, for detecting special causes and events, and showing if the process in under control. In quality monitoring (Box and Luceno 1997) applications of process industry we must often use uncertain measurements and SPC is a good tool for handling them.

This paper reports on the findings of a questionnaire survey into the use of statistical process control (SPC) in a sample of United Kingdom automotive suppliers. All the 76 respondents had attended the Ford‐approved three‐day course on SPC held at the University of Manchester Institute of Science and Technology (UMIST). The survey investigated the following broad issues of SPC: motivation, implementation, application, suppliers and customers, and benefits. A number of findings surprised the authors; these included the number of organizations who had not appointed a SPC facilitator, organizations experiencing difficulties in deciding what type of control charts to use, control charts being ‘filled‐in’ by people not directly responsible for control of a process, charts being displayed away from work areas and the frankness of some suppliers in declaring that the vast majority of their manufacturing processes are not in a state of statistical control. If there is an overall message arising from the findings it is that the introduction and application of SPC should not be overcomplicated.

To investigate and illustrate the use of statistical process control (SPC) for prospective monitoring and detection of regional changes in prescription opioid abuse rates. A demonstration project was conducted by applying several types of SPC charts to Louisiana TANF (Temporary Assistance for Needy Families) applicant self-reported opioid illicit use data collected during 21 months. These charts were constructed and examined for signals of abuse rate changes both at the aggregate statewide level and for each state region (parish) separately. SPC identified increases in opioid illicit use in 2 parishes and subsequent decreases in both regions. These fluctuations were not as apparent when the data were examined using traditional bar graphs and summary tables. The remaining parishes exhibited stable abuse rates with no statistically significant changes over time. These results illustrate proof of concept for monitoring prescription drug abuse regionally via statistical control charts and the potential of this approach for real-time prospective surveillance for abuse rate changes.

The aim of this research is to expand a framework that integrates two important concepts: statistical process control (SPC) and engineering process control (EPC). Most of the literature researches on integrated SPC/EPC systems are focused into continuous process mainly with algorithmic SPC. The integrated SPC/EPC systems in batch process control have not received the same degree of attention. This paper is a first of its kind in integrated SPC/EPC systems that applied in batch process. The proposed SPC/EPC integration is performed continually in active SPC for the batch making advance and in run-to-run (RTR) control action between batches. As a validation step, the proposed approach is applied to an industrial batch alkyd polymerisation reactor. Through this case study application, process engineers at the company are now able to use a valuable decision making tool when the production process is affected by certain disruptions, with obvious consequences on product quality, productivity and competitiveness.

Use of statistical process control to detect important changes in failure rates is well-ingrained across industries, including manufacturing, services, and healthcare. Although significant research has focused on optimizing signal detection, less is known about the optimal manner by which to respond to signals once detected. To address this important practical question, we develop a response optimization framework assuming process changes of low through high severity can occur, each of which is probabilistically detected and classified correctly or incorrectly. Based on perceived severity, a practitioner responds in one of several manners varying in cost, effectiveness, and timeliness, which in turn produces different costs until the process is reset to statistical control. Experimental results from an application to hospital-acquired Clostridium difficile infections indicate the optimal response can differ significantly based on organization size, baseline rate, and infection cost but less so on classification accuracy. Hospitals with 80 to 600 beds can save between 30% and 60% of their expected total cost per out-of-control event by using optimized versus non-optimized response policies. Simulation analysis across a range of settings indicates that optimal solutions also result in significantly lower total cost variances. The average annual number of deaths prevented ranges between one and six per out-of-control event. This approach also can be used in other applications to identify context-specific optimal response policies generalizable to any type of surveillance or chart method.

As suggested by Box and Kramer [1] and others, it is possible to reduce both the special cause and common cause variations by applying Statistical Process Control (SPC) methods to monitor the output of an Automatic Process Control (APC) controlled process. In this paper, we develop an economic model for SPC monitoring of APC controlled processes. We also develop an economic loss-based criterion, the Average Quality Cost (AQC), to evaluate the performance of SPC charting methods. The AQC and the traditional average run length of three common SPC charts are investigated and compared.

Statistical process control (SPC) and automatic process control (APC) are two important methods that have been used for improving product quality and process productivity. SPC is used for process monitoring, while APC is used for process adjustment. SPC reduces process variability by detecting and eliminating special causes of process variation, while APC reduces variability by adjusting the process to keep the product variability on target. Both the methods were initially thought to be in conflict with each other, but in recent years, many researchers have shown their interest in integrating SPC and APC techniques to reduce total variability of the process. They have found that the techniques used in these two methods are complementary rather than contradictory. A considerable amount of work has appeared in the literature about methods that combine SPC and APC techniques for the same process. In this paper, we will cover preliminaries on SPC and APC and outline the major techniques applied by both. Literature survey related to the issues facing their integration will be outlined. We will also summarise different strategies followed to achieve their integration. Possible future directions of research will be highlighted and our concluding remarks will be indicated.

This study proposes novel framework to enhance statistical process control (SPC) of water quality by addressing the pervasive issue of autocorrelation in time-series data. We investigate the characteristics of pH, turbidity, and KMnO₄ in Surabaya city's water, revealing significant autocorrelation that compromises statistical independence assumption crucial for reliable SPC. To overcome this, Generative Adversarial Network (GAN) model was developed to generate decorrelated residual time-series. The efficacy of GAN model in reducing autocorrelation was quantitatively validated, achieving Mean Squared Error (MSE) of 0.0054, Root Mean Squared Error (RMSE) of 0.0738, and Mean Absolute Error (MAE) of 0.0556. Subsequently, these GAN-derived residuals were integrated into Multivariate Exponentially Weighted Moving Average (MEWMA) control chart for process monitoring. Phase I analysis detected 33 out-of-control signals; after identifying and removing outliers, process was brought under statistical control with no further out-of-control signals detected. However, subsequent Phase II online monitoring detected eight statistically significant out-of-control signals, indicating a potential loss of process stability over time. Our findings underscore the significant utility of GAN-based residual analysis as a robust strategy for mitigating autocorrelation effects in environmental water quality data. This approach leads to improved process monitoring and enables early anomaly detection, crucial for proactive water quality management.

Functional data characterize the quality or reliability performance of many manufacturing processes. As can be seen in the literature, such data are informative in process monitoring and control for nanomachining, for ultra-thin semiconductor fabrication, and for antenna, steel-stamping, or chemical manufacturing processes. Many functional data in manufacturing applications show complicated transient patterns such as peaks representing important process characteristics. Wavelet transforms are popular in the computing and engineering fields for handling these types of complicated functional data. This article develops a wavelet-based statistical process control (SPC) procedure for detecting ‘out-of-control’ events that signal process abnormalities. Simulation-based evaluations of average run length indicate that our new procedure performs better than extensions from well-known methods in the literature. More importantly, unlike recent SPC research on linear profile data for monitoring global changes of data patterns, our methods focus on local changes in data segments. In contrast to most of the SPC procedures developed for detecting a known type of process change, our idea of updating the selected parameters adaptively can handle many types of process changes whether known or unknown. Finally, due to the data-reduction efficiency of wavelet thresholding, our procedure can deal effectively with large data sets.

Implementation of statistical process control (SPC) in an automated environment requires a number of issues to be addressed. Changes in sample data distribution and statistical properties such as independence will affect the use and interpretation of traditional SPC procedures; changed monitoring and adjustment techniques will influence the subsequent decision-making; at the same time, automation could facilitate implementation of SPC with other control techniques. In this paper, the potential of combining SPC with engineering process control methods is discussed. Comparative analysis between traditional SPC methods and SPC combined with feedback control is presented. Actual and simulated data are used to illustrate the procedure which, because of its self-tuning ability, could greatly reduce system readjustment while most of the advantages of traditional SPC are preserved at the same time.

The relationship between the library and its patrons is damaged and the library's reputation suffers when returned items are not checked in. An informal survey reveals librarians' concern for this problem and their efforts to combat it, although few libraries collect objective measurements of errors or the effects of improvement efforts. Brigham Young University's Harold B. Lee Library is engaged in a quality improvement effort that includes the use of statistical process control and has resulted in an 86% reduction in errors over a two-year period. BYU's experience demonstrates the importance of objective measures in ensuring service quality.

Improved quality of continuous flow manufacturing through the combination of statistical process control and conventional computer process control

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in Indonesia’s economy, including in the garment industry. The subject of this study is MSME X, located in Cimahi City, which specializes in the production of knitted clothing. This research aims to identify production problems, analyze the causes of defects, and design improvement strategies using the Six Sigma method with the DMAIC (Define, Measure, Analyze, Improve, Control) approach. In the Define stage, the main problems are identified through the Critical to Quality (CTQ) approach, which reveals three major types of defects: button defects (39%), stitching defects (32%), and fabric or pattern defects (29%). Next, the Measure stage is conducted to assess process performance using quality indicators. The calculation results show a Defects per Million Opportunities (DPMO) value of 10,968.95 and a Sigma Level of 3.79, indicating room for improvement. The Analyze stage utilizes Pareto and Fishbone diagrams to identify root causes. The Improve stage applies Failure Mode and Effect Analysis (FMEA) to determine improvement priorities. The Control stage involves the use of Statistical Process Control (SPC), regular inspections, and an incentive system. This approach is expected to reduce defect rates and sustainably improve production efficiency.