Nonparametric control charts for bivariate high-quality processes

For attribute data with (very) low rates of defectives, attractive control charts can be based on the maximum of subsequent groups of r failure times, for some suitable r≥1, like r=5. Such charts combine good performance with often highly needed robustness, as they allow a nonparametric adaptation already for Phase I samples of ordinary size. In the present paper we address the problem of extending this approach to the situation where two characteristics have to be monitored simultaneously. Generalization to the multivariate case is straightforward

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Integrating Multiobjective Optimization With The Six Sigma Methodology For Online Process Control

Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense

Gravitational Search Algorithm: A Novel Optimization for Economic Design in Discontinuous Model

Control charts are generally utilized to monitor and maintain the statistical control of a process. Designing a control chart means selection of three parameters such as sample size n, sampling interval h and width of control limits k. To maintain a control chart we have to incur various types of cost such as prevention costs, appraisal costs, internal failure costs, external failure costs and total cost. In economic design the objective is to minimize the total cost associated with control chart. Thus, the economic design is one type of unconstrained optimization problem. Economic designs of X-bar control chart for two types of manufacturing process models namely continuous and discontinuous is provided in the literature. In this project, gravitational search optimization has been utilized for the economic design of X-bar chart for discontinuous process. The results were observed to be comparable to that reported by the literature

Operational Decision-Making in Healthcare Using Control Charts

The primary objective of this thesis was to design a framework supplemented with guidelines for the healthcare managers to select an appropriate type of control chart for operational decision-making. A systematic literature review was conducted to gauge the extent to which control charts were being used in a healthcare setting for clinical decision making and operational decision-making purposes. The findings showed that the application of control charts was almost equal for the clinical decision-making sector and the operational decision-making sector. On further analysis, the ability of control charts to function as a standalone tool was affirmed by the vast majority of studies where it was deployed as a primary tool for quality improvement purposes. The framework contains some prerequisites with regards to data collection and construction of control charts. Also, the metrics involved are clearly identified: Quality, Financial, Volume and Utilization; and subsequently defined. The guidelines were created keeping the metric and possible scenario/s that can be associated with it into consideration. These guidelines would save the healthcare managers their time and significantly reduce the chances of selecting an inappropriate type of control chart. Potential operational areas for the usage of control charts are also discussed in the thesis. In order to demonstrate the way in which the prescribed framework can be implemented in a real-life hospital environment, a regional hospital was chosen and the yearly rate of Surgical Site Infections (SSI) for colon surgery was monitored using an appropriate control chart which was selected by following the guidelines outlined in the framework

Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts