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.
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 product. Lower quality costs the organization not only in terms of customer goodwill and loyalty, but also in terms of costs for scrap and rework. It has been estimated that in some companies, scrap and rework costs run as high as 20 to 30 percent of sales; an unacceptable cost in a time of high competition.
Total Quality Management
In reaction to increased competition at home and abroad, companies have come to realize that to be successful in the global marketplace, they need to have a constant eye on the quality of their products and services. There are just too many competitors eagerly waiting to gain a larger market share for a company to be able to rest on its laurels and not be concerned with continuing quality. The 1980s brought with them an increased concern with quality and saw the development of the concept "total quality management," a management strategy that attempts to continually increase the quality of goods and services as well as customer satisfaction through raising awareness of quality concerns across the organization. The rallying cries for US businesses became: "Quality is everyone's job" and "do it right the first time."
One of the key points to the total quality management approach is kaizen, a Japanese concept of continuously searching for incremental improvement. This is considered by some observers to be the most important difference between US and Japanese businesses. The achievement of kaizen is accomplished through the integration of research and development efforts with production facilities and getting the underlying business processes "right." The key to doing this is through the application of statistical processes and tools in a search for better processes and improved quality. It is a truism that if productivity and quality are to improve, a change in current processes is needed. Statistics provides a rational basis on which to make these changes and addresses the questions of what data need to be collected and how they should be analyzed in order to give quality control engineers and managers the information they need to continuously improve the quality of goods and services.
Descriptive Statistics Tools
Descriptive statistics used in quality control include histograms, Pareto diagrams, scatter plots, and graphs.
- Histograms are a type of vertical bar chart that graphs frequencies of objects within various classes on the y axis against the classes on the x axis. Frequencies are graphed as a series of rectangles.
- A Pareto diagram is a vertical bar chart that graphs the number and types of defects for a product or service against the order of magnitude (from greatest to least). These charts are used to display the most common types of defects in ranked order of occurrence. Pareto charts are often shown with cumulative percentage line graphs to show to more easily show the total percentage of errors accounted for by various defects.
- Another type of graph commonly used in quality control is the scatter plot. This type of diagram graphically depicts two-variable numerical data so that the relationship between the variables can be examined. For example, one might want to know the relationship between number of defects observed in a given month and the cost of the loss of quality to the company. The two values (number and cost) could be graphed on a two-dimensional graph so that one could better understand the relationship.
Quality control engineering is most frequently concerned with the quality of goods produced on a production line. With today's high technology equipment and emphasis on automation, it would be tempting to assume that production lines would repeatedly produce quality products without adjustment. Unfortunately, this assumption flies in the face of the laws of physics and of probability. No matter how automated a process or how advanced the technology used to control quality, errors -- in the form of defects and waste -- continue to creep in. Sometimes these errors are due to "noise," random variability that occurs naturally. For example, the amount or quality of ore produced from a mine varies naturally from day to day. Changes in quality or quantity can affect the inputs into the production line (e.g., lower quality ore may result in greater breakage of the widgets which were produced using it). Other errors, however, are due to problems with the process, equipment, materials, or humans working the line. It is the task of the quality control engineer to examine the process for ways that it can be continually improved in order to increase the quality of the product.
Measuring Statistical Control
One of the ways that quality control engineers deal with this situation is through the use of quality control charts. This is a simple graphing procedure that helps quality control engineers and managers monitor processes and determine whether or not they are in control. Quality control charts are based on two statistical ideas. First, random noise occurs naturally in any process. Second, within a random process there is a certain amount of regularity. This means that only five percent of the time (i.e., one occurrence in 20) will a variable differ from its mean by more than two standard deviations. Within these parameters, a process is said to be within statistical control...
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