Enhancing Business Performance Using Statistical Quality Control Techniques

Statistical process control (SPC) charts play a very important role in the determination of process capability. It is important that SPC be achieved via the use of appropriate control charts before process capability indices are calculated, so that the indices reflect the long‐term capability of a process to produce product within specification. A significant portion of the theoretical research dedicated to process capability indices has focused on those index estimators that utilize a pooled estimator of process variance, but data collected in subsamples from an in‐control SPC chart, most likely an X − R or X − s chart, is preferred in practice for index estimation to insure process stability. This article is focused on the significant work published to date on process capability indices estimated using subsamples, including confidence bounds and hypothesis testing procedures for the primary indices C p , C pk , and C pm .

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.

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

Part 1 Statistical process control: development of SPC what SPC is and is not online SPC methods off-line process control SPC methodolgy other factors affecting the success of SPC. Part 2 Some basic distributions: attribute data countable data geometric distribution the Normal distribution distributions derived from the Normal distribution application of results testing for normality the Normal approximation to the binomial distribution Normal approximation to the Poisson distribution. Part 3 Process variation: reasons for process variation types of process variation some models for process variation sampling error and measurement error. Part 5 Basic Shewhart control charts for continuous variables: control charts for average level charts for control of (within group) process spread the average run length (ARL) special problems some theoretical results for Shewhart charts charts for control of process spread. Part 6 Extensions to Shewhart charts for one-at-a-time data: one-at-a-time sampling estimation of sigma for one-at-a-time data details of further control charts for control of process average level control of process spread choice of charting method practical use of Shewhart and moving average charts properties of EWMA and MA charts. Part 7 Cumulative sum techniques for continuous variables: CuSum charts - for control of average level, for control of process spread nomogram for CuSums. Part 8 Further theoretical results on control charts for continuous variables: the effect of departures from assumption on moments of x and s(2) Shewhart charts - Markov chain approach cumulative Sum charts charts for control of process spread. Part 9 The design of control charts for specification limits: single specification limits - chart for means double specification limits - high capability processes, an alternative approach. Part 10 Control of discrete data processes: Shewhart charts - for countable data (c and u), for attribute data (np and p) CuSum charts for countable data, for attribute data comparison of Shewhart and CuSum schemes. Part 11 Sampling inspection: classification of inspection plans some properties of sampling plans methods of using sampling plans for attributes. Part 12 Inspection by variables: single specification limit - sigma known, sigma unknown estimation of fraction non-conforming, single specification limit double specification limit - sigma known, sigma unknown multivariate sampling plans. Part 13 Standard sampling systems: statement of method for inspection by attributes inspection by variables International Standards for process and quality control. Part 14 Adaptive sampling plans: CSP-1 and the AOQL criterion theory of CSP-1 the AEDL criterion. (Part contents).

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.

–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.

Both process monitoring and fault isolation are important and challenging tasks for quality control and improvement in high-dimensional processes. Under a practical assumption that not all variables would shift simultaneously, this paper proposes a variable-selection-based multivariate statistical process control (SPC) procedure for process monitoring and fault diagnosis. A forward-selection algorithm is first utilized to screen out potential out-of-control variables; a multivariate control chart is then set up to monitor suspicious variables. Therefore, detection of faulty conditions and isolation of faulty variables can be achieved in one step. Both simulation studies and a real example have shown the effectiveness of the proposed procedure.

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.

Control charts and other related techniques for statistical process monitoring are in widespread use. The last 20 years have seen increasing emphasis on statistical process control as a practical approach to reducing variability in industrial applications. Simultaneously, there has been renewed interest in the subject by the academic and research communities. This has resulted in many new techniques and procedures, as well as better understanding of the performance and the limitations of well-established ones. This panel discussion is a technical discussion of control charts and other related methods for process monitoring. The panel consists of a group of researchers and practitioners who are involved in the development, evaluation, and application of these techniques. Topics considered by the panel include multivariate quality control, autocorrelated data, enhancements to the basic Shewhart control charts, economic design and related issues, and the relationship of statistical process monitoring and direct process adjustment through engineering control. The panel considers practical aspects of the application of these techniques as well as future trends in research and development.

Optimization design of the charts for monitoring process capability

The quality of footwear products such as shoes is a major factor that influences the success of a company in producing footwear products. Shoe companies usually have various quality control methods to maintain the production process. One of the most widely used methods in the industry is the statistical quality control method. PT ABC is a company that produces sports shoes located in Bandung. ABC Company is experiencing problems in terms of production, namely the number of defective products produced is still high. This study aims to apply statistical quality control to control the number of defects that appear in the production process. The application process is focused on the problems that arise in the screen printing division where there are many defects that appear during the production process. The steps taken in this study include data preparation and initial analysis using check sheets and Pareto diagrams, data adequacy tests, making p control charts, and analyzing results. Based on the control chart that has been made on the initial defect proportion and the proportion of defective data after improvements have been made to the screen printing process, it can be concluded that there is a decrease in the average value of the defect proportion of 0.02. This figure is quite significant. In addition, there was a drastic decrease in the LCL (lower control limit) value. This significant decrease is a positive indication that the improvements made to the screen printing process have succeeded in reducing product defects significantly. Keywords: Control Chart, Quality Control, Shoes Defect, Statistical Quality Control

In manufacturing companies, production activities are a very important activity. If the production activities produce a lot of defective products, it will cause losses in the form of additional time and raw material costs for repairing these defective products, so that quality control is needed so that the company can produce products that comply with predetermined quality standards.
 This final project research was conducted at PT. XYZ Majalengka where this company is engaged in the service industry, production activities at PT. XYZ Majalengka is carried out based on a make to order response demand strategy, namely the company carries out production activities when there are orders from consumers. This research focuses on the production process of the pirn winder machine which functions to wind yarn raw materials according to the desired weight and according to the available TFO capacity (700 gr, 714 gr, 1000gr or 1500gr).
 However, regardless of production quality standards on the pirn winder machine, it does not rule out the possibility that defects will always occur. Therefore it is necessary to have quality control in the production process of this pirn winder machine. Its function is clear to reduce the number of defects and improve the quality of production produced.
 Statistical Process Control (SPC) is a statistical analysis used for quality control, starting from monitoring, analyzing, predicting, controlling and improving the production process and product quality through control charts. Statistical quality control using SPC (Statistical Process Control) has seven statistical tools that can be used as tools to control quality, namely, check sheets, histograms, P control charts, pareto diagrams, cause and effect diagrams, scatter diagrams, and diagrams. process.

The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification of process irregularities across various fields. This research proposes and implements a novel SQC methodology in agricultural areas. A control chart is one of the SQC tools that facilitates real-time monitoring of multiple activities, including agricultural yield, industrial yield, and hospital outcomes. Advanced control charts with symmetrical data are being subjected to the new SQC method, which is suitable for this purpose. This research aims to develop a novel hybrid exponentially weighted moving average control chart for detecting the coefficient of variation (CV) using a repetitive sampling method called the HEWMARS-CV control chart. It is an effective tool for monitoring the mean and variance of a process simultaneously. The HEWMARS-CV control chart used the repetitive sampling scheme to generate two pairs of control limits to enhance the performance of the control chart. The proposed control chart is compared with the classical HEWMA and Shewhart control charts regarding the average run length (ARL) when the data has a normal distribution. The Monte Carlo simulation method is utilized to approximate the ARL values of the proposed control charts to determine their performance. The proposed control chart detects small shifts in CV values more effectively than the existing control chart. An illustrative application related to monitor the wheat yield at Rothamsted Experimental Station in Great Britain is also incorporated to demonstrate the efficiency of the proposed control chart. The efficiency of the proposed HEWMARS-CV control chart on the real data shows that the proposed control chart can detect a shift in the CV of the process, and it is superior to the existing control chart in terms of the average run length.

Data mining model-based control charts for multivariate and autocorrelated processes

Non-normal processes are common in practice. In this paper, we propose a novel approach to defining bootstrap process capability index (PCI) control charts to monitor the performance of in-control skew normal processes. We use a bootstrap method to calculate phase I control limits of the corresponding PCI control charts. The β-risk curves of the associated PCI control charts will be used to assess the performance of the PCI control charts. We use Monte-Carlo simulation to evaluate the performance of the proposed PCI control charts. A numerical example to illustrate the implementation of the proposed control charts.