‏‫‬‭‬‭Principles of Wood Science and Technology , ‏‫‬‭‬‭Principles of Wood Science and Technology , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Principles of Wood Science and Technology

Contributors. Preface to the Fifth Edition. How to Use the Handbook. How to Think About Quality. The Quality Planning Process. The Quality Control Process. The Quality Improvement Process. Process Management. Quality and Income. Quality and Costs. Measurement, Information, and Decision-Making. Computer Applications to Quality Systems. The ISO 9000 Family of International Standards. Benchmarking. Strategic Deployment. Total Quality Management. Human Resources and Quality. Training for Quality. Project Management and Product Development. Market Research and Marketing. Quality in Research and Development. Software Development. Operations. Inspection and Test. Job Shop Industries. Customer Service. Administrative and Support Operations. Process Industries. Quality in a High Tech Industry. Automotive Industry. Travel and Hospitality Industries. Government Services. Health Care Services. Financial Services Industries. Second-Generation Data Quality Systems. Quality and Society. Quality and the National Culture. Quality in Developing Countries. Quality in Western Europe. Quality in Central and Eastern Europe. Quality in the United States. Quality in Japan. Quality in the People's Republic of China. Quality in Latin America. Basic Statistical Methods. Statistical Process Control. Acceptance Sampling. Design and Analysis of Experiments. Reliability Concepts and Data Analysis. Appendix I-Glossary of Symbols and Abbreviations. Appendix II-Tables and Charts. Appendix III-Selected Quality Standards, Specifications, and Related Documents. Appendix IV-Quality Systems Terminology. Appendix V-Quality Improvement Tools. Name Index. Subject Index.

/pdf/juran-s-quality-handbook-380luj5glr.pdf

Juran's quality handbook

In this paper, we study the effects of an imperfect production process on the optimal production cycle time. The system is assumed to deteriorate during the production process and produce some proportion of defective items. The optimal production cycle is derived, and is shown to be shorter than that of the classical Economic Manufacturing Quantity model. The analysis is extended to the case where the defective rate is a function of the set-up cost, for which the set-up cost level and the production cycle time are jointly optimized. Finally, we also consider the case where the deterioration process is dynamic in its nature, i.e., the proportion of defective items is not constant. Both linear, exponential, and multi-state deteriorating processes are studied. Numerical examples are provided to illustrate the derivation of the optimal production cycle time in these situations.

Economic Production Cycles with Imperfect Production Processes

This paper reviews the literature on quantitatively-oriented approaches for determining lot sizes when production or procurement yields are random. We discuss issues related to the modeling of costs, yield uncertainty, and performance in the context of systems with random yields. We provide a review of the existing literature, concentrating on descriptions of the types of problems that have been solved and important structural results. We identify a variety of shortcomings of the literature in addressing problems encountered in practice, and suggest directions for future research.

/pdf/lot-sizing-with-random-yields-a-review-35sglvd8vy.pdf

Lot Sizing with Random Yields: A Review

Improvement of health care requires making changes in processes of care and service delivery. Although process performance is measured to determine if these changes are having the desired beneficial effects, this analysis is complicated by the existence of natural variation—that is, repeated measurements naturally yield different values and, even if nothing was done, a subsequent measurement might seem to indicate a better or worse performance. Traditional statistical analysis methods account for natural variation but require aggregation of measurements over time, which can delay decision making. Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data, often yielding insights into the data more quickly and in a way more understandable to lay decision makers. SPC and its primary tool—the control chart—provide researchers and practitioners with a method of better understanding and communicating data from healthcare improvement efforts. This paper provides an overview of SPC and several practical examples of the healthcare applications of control charts.

https://qualitysafety.bmj.com/content/qhc/12/6/458.full.pdf

Statistical process control as a tool for research and healthcare improvement

This title is a substantial revision of one of the leading textbooks designed for the statistical quality control course taught in departments of industrial engineering, operations research and statistics . While maintaining its already successful writing style and pedagogy, this title has also incorporated key organizational changes in order to reflect recent trends in the field. The text features large quantity of examples and student problems and a strong introduction to the proper use and misuse of control charts. In this edition several chapters were streamlined, and consolidations and profitability were brought forward in the text. There is new material on experimental design, a reduced emphasis on acceptance sampling, and enhanced attention to the managerial and organizational aspects of quality control. Free SPC expert software is packaged with the text for use as a statistical and graphical tool. Text plus 3.5 diskette.

Statistical Quality Control

This is a book about a particular type of decision making. It explains the principles and techniques needed for making decisions about the acquisition and retirement of capital goods by industry and government. Normally, such decisions should be made on grounds of long-run economy. Because engineers make many such decisions and make recommendations for many others, the body of principles and techniques relating to them has been called "engineering economy." The same concepts and methods that are helpful in guiding decisions about investments in capital goods are useful in certain kinds of decisions between alternative types of financing. Applications to these other areas of decision making are also discussed in this book. It can serve as a working manual for engineers, management personnel, government officials, and others whose duties require them to make decisions about investments in capital goods. The underlying philosophy regarding comparisons of alternatives is the same as in previous editions. As before, continued emphasis throughout the book is placed on the following two important points: (1) It is prospective differences between alternatives that are relevant in their comparison. (2) The fundamental question regarding a proposed investment in capital goods is whether the investment is likely to be recovered plus a return commensurate with the risk and with the return obtainable from other opportunities for the use of limited resources. The purpose of calculations that involve the time value of money should be to answer this question.

Eugene L. Grant

Papers

Statistical Quality Control

Principles of engineering economy

Statistical Quality Control.

Statistical Quality Control (5th ed.).

Statistical Quality Control.