A Sequence of Laboratories for Beginning Statics Classes

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.

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.

Increasing customer demands and the need to stay competitive create challenges in high-volume manufacturing companies competing in global markets. Increasing pressures to produce high-quality products with fewer resources can cause degradation in procedural compliance, particularly when compliance conflicts with the ability to meet production schedules. Reaction to statistical process control (SPC) out-of-control conditions is one such procedure. SPC procedures often require that processes he shut down until the cause of an out-of-control condition is identified and removed. Poor integration of SPC procedures into existing business systems makes compliance with this type of procedure stressful for many levels of the organization. Using focus group data, sample surveys, and focused interviewing, a model is derived that describes the SPC system changes that can occur. The interrelationships between these organizational functions, their respective knowledge levels, and what motivates them is critical to understanding why attempts to integrate SPC into business systems can fail. This article proposes a sociotechnical model describing the de-evolution of SPC in organizations where careful consideration is not applied to the integration of SPC into existing business systems. Advice to practitioners on the avoidance of SPC de-evolution is provided.

Six sigma process improvement in the emergency department: A pilot study using statistical process control methods to analyze ecg times in the emergency department

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.

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.

It has been widely reported that floating gate irradiation using a charged beam can shift device parameters with the scaling of the devices [1-4]. For high voltage (HV) devices, the effects of electron beam (EB) induced damage, however, have not been reported. This paper describes how charge damage during EB exposure should also be considered for high voltage (HV) devices when scanning electron microscope (SEM) is employed for probe guidance. In this study, the effects of EB cathode potential on CMOS transistor threshold voltage and off-state current are investigated using HV, middle voltage (MV), and low voltage (LV) devices. The experimental results show that, to avoid damage, the acceleration voltage of EB should be lower.

Moving towards process-based radiotherapy quality assurance using statistical process control

Characterization of wheat-based biscuit cooking process by statistical process control techniques

Introduction Statistical Process Control(SPC) entails developing ability to achieve neverending improvement of quality. Control here means more than meeting this year's budget. It means what Juran[l] refers to as the habit of improvement, or what Deming[2] calls philosophy of continuing improvement. The various techniques of SPC provide means of achieving improvement on a continuing basis. Benefits of improved quality include increased productivity, better competitive position, and increased employee involvement. Since measured quality of a product is always subject to a certain amount of variability due to chance, statistical signals make it possible to separate variation due to common (chance) causes from variation outside of this system. The reasons for these assignable, or special, causes of variation outside of normal pattern can be discovered and corrected. The emphasis is shifted from dealing with problems after they occur, to detection and prevention. As there are two types of variation, there are two ways of improving system or process. One way is to reduce variation owing to common causes and other is to reduce variation due to special causes. The statistics do not detect special cause variation, they allow a trained operator to do this. If not apprehended and corrected, this type of variation may recur. The other way of improving process is to reduce variation owing to common causes. This requires action on process itself, and this is management's responsibility. A process is operating in statistical control when only source of variation is due to common causes. Process capability is ability of a process to meet customer expectations (specifications). The basis for continuing improvement is to bring process into statistical control by detecting and eliminating special causes of variation, so that performance is predictable.

The objective of this article is to apply Statistical Process Control (SPC) to guarantee the safety in the operation of a sterilization chamber with ethylene oxide gas in thermo-sensitive material. One of the most used in Mexico; but at the same time, one of the most unfavorable methods due to the risks it presents to exposed workers. Environmental and personal measurements were carried out to control the exposure limits. Once the data was collected, SPC tools such as variable control charts (X bar-R chart) were applied to verify that the process was under statistical control. The results of the environmental and personal measurements of the sterilization process carried out by the manufacturer of surgical and healing material showed the process is within the permitted exposure limits. Recommendations are extended in relation to the use of personal protection equipment and working environment conditions, to fulfill with the requirements of national regulatory frameworks.

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.

A statistical weld process monitoring system is described. Using data collected while welding, the welding statistical process control (SPC) tool provides weld process quality control by implementing techniques of data trending analysis, tolerance analysis, and sequential analysis. The SPC system computes the mean, standard deviation, and range of each of the parameters sampled by the data collection system. Changes in the mean, standard deviation, and range are displayed using control (or trend) charts. The control chart displays a function of a parameter with respect to the ordering of the weld records (for a single weld) or weld number (for multiple welds). The SPC tool also permits plotting tolerance charts of the mean, standard deviation, and range for each of the sampled parameters. The tolerance chart is plotted versus the record number (or weld number) and consists of a vertical line for each record (or weld number) showing the minimum and maximum value of that parameter for that record (or weld number). The upper control limit (UCL), lower control limit (LCL), and nominal value may also be displayed on the tolerance chart printout. The SPC also performs sequential analysis, which allows the user to examine the process as it goes along, which in turn may permit the user to locate a possible change in the process before it goes out of control. Work directed toward developing an expert interpreter of the voluminous statistical output generated by the SPC is also described.

Software process monitoring a complex activity. Recently many authors have been suggesting the use of Statistical Process Control (SPC) for software process monitoring while others have pointed out potential pitfalls in using this approach. SPC often used as is without the appropriate customizations or extensions to software, given the peculiarities and differences of software processes compared to manufacturing ones. As so the starting point to understand how and whether SPC can be used in software to understand its contribution in monitoring processes. This work puts together experience collected by the authors in using SPC in industrial contexts, points out the main issues concerning software process monitoring and highlights how the technique addresses them. The main contribution of the paper to formalize and put a set of guidelines together in a disciplined process for guiding practitioners in correctly using SPC during process monitoring.

A Study of Process Variability of the Injection Molding of Plastics Parts Using Statistical Process Control (SPC)Process variability in the manufacturing of products is a serious concern because if leftunchecked could lead to product wastes such as a scraps, low productivity, and poor qualityproducts. To prevent these unwanted effects from happening, statistical process control (SPC), astatistical tool, is used to monitor and control process variability. SPC assumes thatmanufactured products have measureable attributes such as mass, dimensions of the products,mechanical properties, and visual appearance to name a few. These attributes are affected bynatural and assignable causes. Natural causes are inherent to the process and may includevariables such as ambient temperature, machine vibration, and relative humidity; these variablesare often very difficult to control. Unlike natural causes, assignable causes are controllable andmay include items such as bad or worn-out machine components that should be replaced. Bymonitoring a process, an assignable cause is detected when process variability exceeds theexpected range caused by natural causes. The primary advantage of SPC is that it detects afaulty process, which if corrected, prevents the manufacturing of defective products unliketraditional quality control practice that identifies defective products after they have beenproduced. The traditional method of quality control leads to a costly manufacturing process.In a manufacturing engineering technology program, SPC was used to monitor and control theinjection molding of plastics parts. Students monitored several injection molding processvariables using SPC x-bar and range control charts while producing 300 plastics parts. The massof the products was used as an attribute representing parts quality. After analyzing the processdata, students were able to determine if the process was stable, that is, in control. Results of thisstudy will be presented at the annual conference. Also to be shared with the audience is thestudents' learning outcomes that emanates from integrating SPC with the injection moldingprocess.