Statistical Quality Control: A Modern Introduction -6/E.

This book gives you a sound understanding of the principles of statistical quality control (SQC) and shows how to apply them in a variety of situations for quality control and improvement. With this text, you’ll learn how to apply state-of-the-art techniques for statistical process monitoring and control, designing experiments for process characterization and optimization, conduct process robustness studies, and implement quality management techniques. You’re appreciate the significant updates in this book including: • In-depth attention to DMAIC, the problem-solving strategy of Six Sigma, that will give you an excellent framework to use in conducting quality improvement projects • New examples that illustrate applications of statistical quality improvement techniques in non-manufacturing settings. Many examples and exercises are based on real data • New development in the area of measurement systems analysis • New features of Minitab V15 incorporated into the text • Numerous new examples, exercises, problems, and techniques to enhance your absorption of the materia

Product Quality Control Analysis with Statistical Process Control (SPC) Method in Weaving Section (Case Study PT.I)

Abstract - PT.I is a company that produces synthetic rattan furniture on an export scale. The various products at PT.I are an attraction for consumers, such as classic and modern weaving models. However, the number of defects produced is higher than the tolerance limit company. Based on data from the weaving section, it shows that there are 5 types of defects that are not in accordance with the standards and quality that have been determined by the company. The purpose of this study is to identify the types of defects that often occur in PT. I and identify the main factors causing product defects using the SPC method. The seven tools consist of a check sheet, histogram, stratification, scatter diagram, p control chart, Pareto diagram, and fishbone diagram. The results showed that the number of defects in October and November exceeded the limit set by the company. The highest level of disability occurred in October. The higher the number of production, the higher the number of product defects. Based on the results of the p control chart, it can be seen that the product is outside the control limits that it should have. The process is in a state of uncontrollability or is still experiencing deviations. To suppress or reduce the number of product defects that occur in production, 3 types of dominant defects can be applied, namely the woven model (254 units), loose woven (122 units), and nail-looking woven (119 units). Factors causing defects in production are derived from human factors/workers, methods, materials/raw materials and work environment

QUALITY CONTROL ANALYSIS USING STATISTICAL PROCESS CONTROL METHOD TO REDUCE THE LEVEL OF DEFECTIVE PRODUCTS AT PT. GAYA PANTES SEMESTAMA

Quality is the features and characteristics of product or service that have the ability to fulfill the consumers’ needs today and in the future. Quality control is a technique and planned activity. The aim of this activity is to achieve, maintain, and increase the quality of product or service to equal the standard and fulfill consumer satisfaction. This research aims to know the description of the production process, examines the factors that lead to the failure of the product, find out the actions that should be done by the company to reduce the level of defects on their production. PT. Gaya Pantes Semestama engaged in the fabric industry. This research used the descriptive and analysis method that used is Statistical Process Control in the form check sheet, Pareto diagram, fishbone, and P-Chart. From the result of the research, the main factors causes the defects are human, machine, raw material, and work environment

ON THE BOOTSTRAP METHOD OF ESTIMATION OF RESPONSE SURFACE FUNCTION

Nowadays, in many fields of science it is necessary to carry out miscellaneous analyses using classical statistical methods, which usually have correct assumptions. These assumptions in the research realities cannot always be met, which makes it impossible to carry out analyses and leads to incorrect conclusions and recommendations.The study of the production process largely consists in the use of tools of statistical quality control which are based on classical statistical methods. These methods result in some improvements in technological and economic results of the manufacturing process. One of the tools of statistical quality control is the design of experiments, whose important element is the estimation of response surface function.The aim of this paper is to present the bootstrap method of estimation of response surface function and its use for empirical data

Statistical quality control applied to a telemetry system acceptance prodedure Technical report no. 13

Statistical quality control applied to telemetry system acceptance procedur