Control charts for monitoring the mean of AR(1) data

New product development is one of the most powerful but difficult activities in business. It is also a very important factor affecting final product quality. There are many techniques available for new product development. Experimental design is now regarded as one of the most significant techniques. In this article, we will discuss how to use the technique of experimental design in developing a new product - an extrusion press. In order to provide a better understanding of this specific process, a brief description of the extrusion press is presented. To ensure the successful development of the extrusion press, customer requirements and expectations were obtained by detailed market research. The critical and non-critical factors affecting the performance of the extrusion press were identified in preliminary experiments. Through conducting single factorial experiments, the critical factorial levels were determined. The relationships between the performance indexes of the extrusion press and the four critical factors were determined on the basis of multi-factorial experiments. The mathematical models for the performance of the extrusion press were established according to a central composite rotatable design. The best combination of the four critical factors and the optimum performance indexes were determined by optimum design. The results were verified by conducting a confirmatory experiment. Finally, a number of conclusions became evident.

Process Control, the Bull Whip Effect and the Supply Chain

The purpose is to introduce the demand for statistical quality control practice in the supply chain environment. We show both the need and application of these measures, especially the need for multivariate quality concepts to reduce the costs of operating supply chains, to control the flow throughout the supply chain and in the dynamic behavior of supply chains to utilize concepts associated with multivariate methods and auto correlated variables. We note that the quality output is as important as the “bull whip” efficiency in the supply chain

The Quality Movement in the Supply Chain Environment

The purpose is to introduce the demand for the quality movement practice in the supply chain environment. We show both the need and application of these measures, especially the need for multivariate quality concepts to reduce the costs of operating supply chains, to control the flow throughout the supply chain. The purpose is to reduce costs in the supply chain system and improve the probability of meeting the “due time.

The Quality Movement in Hospital Care

The purpose is to introduce the demand for the quality movement practice in hospital care. We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment

Data Analytics and Managing Health and Medical Care

The purpose is to introduce the demand for the quality movement practice in problems associated with public health diagnostic testing and other health related problems We examine problems involving 1 Multivariate control charts which simultaneously monitor correlated variables 2 we explain why the scale on multivariate control charts is unrelated to the scale of the individual Variables control charts and 3 discover that out of control signals in multivariate charts do not reveal which variable or combination of variables causes the signal and application of quality monitoring New methods provide methods for MPC charts focus on the average run length as the decision factor We indicate that other decision criteria in multivariate control charts are availableand these methods can be useful in evaluating the design and implementation of multivariate charts in special circumstance

Analyzing Data Utilized in Process Control and Continuous Improvement

Process control under the guidelines of reducing the costs of operations, production and inspection and produce high quality service and operations is examine to see if data from various parent populations can change the results and interpretations. With the purpose of achieving a minimal amount or error generation from the examination of control charts in univariate SPC, we compare data taken from three parent populations, i.e., Normal, Poisson and Exponential. Data and graphical analysis of Statistics permits one to visualize problems associated with where data comes from and whether it is satisfactory to use. We focus of univariate application but discuss multivariate application. This research will enable us to evaluate the conditions brought on by serial correlation and time series characteristics of models. KeywordsProcess control; Operations; Production costs; Dat

A Binary Control Chart to Detect Small Jumps

The classic N p chart gives a signal if the number of successes in a sequence of inde- pendent binary variables exceeds a control limit. Motivated by engineering applications in industrial image processing and, to some extent, financial statistics, we study a simple modification of this chart, which uses only the most recent observations. Our aim is to construct a control chart for detecting a shift of an unknown size, allowing for an unknown distribution of the error terms. Simulation studies indicate that the proposed chart is su- perior in terms of out-of-control average run length, when one is interest in the detection of very small shifts. We provide a (functional) central limit theorem under a change-point model with local alternatives which explains that unexpected and interesting behavior. Since real observations are often not independent, the question arises whether these re- sults still hold true for the dependent case. Indeed, our asymptotic results work under the fairly general condition that the observations form a martingale difference array. This enlarges the applicability of our results considerably, firstly, to a large class time series models, and, secondly, to locally dependent image data, as we demonstrate by an example

Determinants of Credit Risk in Indian State-owned Banks: An Empirical Investigation

The determinants of credit risk of banks in emerging economies have received limited attention in the literature. Using advanced panel data techniques, the paper seeks to examine the factors affecting problem loans of Indian state-owned banks for the period 1994-2005, taking into account both macroeconomic factors as well as microeconomic variables. The findings reveal that at the macro level, GDP growth and at the bank level, real loan growth, operating expenses and bank size play an important role in influencing problem loans. The study performs certain robustness tests of the results and discusses several policy implications of the analysis.credit risk; banking; state-owned banks; India

Highly comparative feature-based time-series classification

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation

Framework for development of data analysis protocols for ground water quality monitoring, A

Also issued as thesis (Ph. D.)--Colorado State University, 1992.June 1993.Includes bibliographical references (pages 75-85)