statistical quality control
RESEARCH STARTERS
ACADEMIC TOPIC OVERVIEWS
Statistical Quality Control
Statistics > Statistical Quality Control
Table of Contents
Abstract
Overview
Total Quality Management
Descriptive Statistics & Quality Control
Types of Quality Control Charts
Applications
The Six Sigma Process
Goal of Six Sigma
Terms & Concepts
Bibliography
Suggested Reading
Abstract
To be successful in today 's global marketplace, companies need to have a constant eye on the quality of their products and services. Quality control utilizes tools from both descriptive statistics and inferential statistics in the continuing pursuit of quality. Quality control charts are a family of simple graphing procedures that help quality control engineers and managers monitor processes and determine whether or not they are in control. In addition, inferential statistics can help the quality control engineer make deductions from the data that can be applied to refining business processes so that they better meet quality goals. Statistical approaches to quality control, however, are not without problems and continuing development of new tools is needed. An example of an approach to statistical quality control is Six Sigma which attempts to keep processes within specification 99.9999997 percent of the time.
Overview
The globalization of many businesses has brought with it a concomitant concern about quality. Foreign countries with lower wage structures can often produce goods more cheaply than can be done in the United States. Although stories of the recall of foreign-made goods often reach the front page, the quiet march of superior products made in other countries does not. Japan, for example, had a reputation for shoddy workmanship before the
Second World War. They have recovered, however, and are now known for the excellence of their automobiles and electronics.
For many applications, it is no longer possible to say that "made in the USA" implies a better
ACADEMIC TOPIC OVERVIEWS
Statistical Quality Control
Statistics > Statistical Quality Control
Table of Contents
Abstract
Overview
Total Quality Management
Descriptive Statistics & Quality Control
Types of Quality Control Charts
Applications
The Six Sigma Process
Goal of Six Sigma
Terms & Concepts
Bibliography
Suggested Reading
Abstract
To be successful in today 's global marketplace, companies need to have a constant eye on the quality of their products and services. Quality control utilizes tools from both descriptive statistics and inferential statistics in the continuing pursuit of quality. Quality control charts are a family of simple graphing procedures that help quality control engineers and managers monitor processes and determine whether or not they are in control. In addition, inferential statistics can help the quality control engineer make deductions from the data that can be applied to refining business processes so that they better meet quality goals. Statistical approaches to quality control, however, are not without problems and continuing development of new tools is needed. An example of an approach to statistical quality control is Six Sigma which attempts to keep processes within specification 99.9999997 percent of the time.
Overview
The globalization of many businesses has brought with it a concomitant concern about quality. Foreign countries with lower wage structures can often produce goods more cheaply than can be done in the United States. Although stories of the recall of foreign-made goods often reach the front page, the quiet march of superior products made in other countries does not. Japan, for example, had a reputation for shoddy workmanship before the
Second World War. They have recovered, however, and are now known for the excellence of their automobiles and electronics.
For many applications, it is no longer possible to say that "made in the USA" implies a better
Bibliography: Black, K. (2006). Business statistics for contemporary decision making (4th ed.). New York: John Wiley & Sons. Gutiérrez, L., Bustinza, O. F., & Molina, V. (2012). Six sigma, absorptive capacity and organisational learning orientation 50(3), 661-675. Retrieved November 27, 2013 from EBSCO Online Database Business Source Premier John, P. W. (1990). Statistical methods in engineering and quality assurance Kumar, M., Antony, J., & Tiwari, M. K. (2011). Six Sigma implementation framework for SMEs - a roadmap to manage and sustain the change. International Journal of Production Research, 49(18), 5449-5467 November 27, 2013 from EBSCO Online Database Business Source Premier Routledge. Retrieved August 21, 2007, from EBSCO Online Database Business Source Complete Sans, W. (2013). A critical review of statistical methods used in quality control 97-142. Retrieved November 27, 2013 from EBSCO Online Database Business Source Premier Beckford, J. (2002). Quality. London: Routledge. Caulcutt, R. (2001). Why is Six Sigma so successful? Journal of Applied Statistics, 28(3/4), 301-306 21, 2007, from EBSCO Online Database Business Source Complete (2001). SPC as a tool for creating a successful business measurement framework 28(3/4), 325-334. Retrieved August 21, 2007, from EBSCO Online Database Business Source Complete CONKLIN, J. D. (2013). Roundabout estimation. Quality Progress, 4(5), 54-56 operation. Quality Engineering, 17(2), 279-289. Retrieved August 21, 2007, from EBSCO Online Database Business Yang, M., Wu, Z., Lee, K., & Khoo, M. C. (2012). The X control chart for monitoring process shifts in mean and