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10.2: Development of Quality as a Competitive Advantage

  • Page ID
    4937
  • Learning Objectives

    1. Describe the historical events and forces that led up to today’s emphasis on quality as a competitive requirement.
    2. Describe quality awards in Japan and the United States.
    3. Describe quality programs and standards such as TQM, Six Sigma, and ISO 9000.
    4. Describe and calculate the cost of quality.

    Quality management is an approach to work that has become increasingly important as global cooperation and competition have increased. A review of the history of quality management explains why it is so important to companies and why clients often require projects to document their processes to satisfy quality standards.

    Statistical Control Before World War II

    Prior to the late 1700s, products such as firearms and clocks were made as individual works where the parts were adjusted to each other so they could work together. If a part broke, a new one had to be made by hand to fit. In 1790 in France, Honoré Blanc demonstrated that he could make musket parts so nearly identical that a musket could be assembled from bins of parts chosen at random (Alder, 1997). The practice of making parts to a high level of accuracy in their dimensions and finishes made the parts interchangeable. The use of interchangeable parts became the founding principle of assembly line manufacturing to produce all manner of goods from sewing machines to automobiles. The manufacturers of firearms and weapons were often the leaders in improving quality because reliable and safe operation of weapons and their rapid repair is a matter of life and death.

    Statistical Control in the United States During World War II

    During World War II, factories were converted from manufacturing consumer goods to weapons. War plants had to make large numbers of parts as fast as possible while doing it safely for the workers and for the service members who used them. Important improvements in quality control (QC)—the management of production standards through statistical interpretation of random product measurements, which emphasizes consistency and accuracy—were made during this period. A key figure in the history of quality management who was an important person in the war effort was Walter Shewhart at Bell Telephone Laboratories. Shewhart recognized that real processes seldom behaved like theoretical random distributions and tended to change with time. He separated causes of variation into two categories: chance cause and assignable cause. Chance causes could be ignored if they did not cause too much variation, and trying to eliminate them often made the problem worse, but assignable causes could be fixed. To help distinguish between variations caused by random events and trends that indicated assignable causes, Shewhart introduced the control chart, which is also known as a type of run chart because data are collected while the process is running. A control chart has time on the bottom axis and a plot of sample measurements. The mean, upper control limit, lower control limit, and warning lines that are two sigma from the mean are indicated by horizontal lines.

    Control Chart Shows Production Variation of Gasoline

    The refinery quality control manager takes samples each day of the 87 octane gasoline for twenty days and charts the data on a control chart, as shown below.

     

    Figure 10.6 Control Chart Displaying Variations Due to Chance Causes

    Control Chart Displaying Variations Due to Change Causes

     

    She recognizes that the highest and lowest measurements are not part of a trend and are probably due to chance causes. However, the control chart from the next twenty days, as shown below, indicates an upward trend that might be due to an assignable cause. She alerts the process manager to let him know that there is a problem that needs to be fixed before the product exceeds the upper control limit. This might indicate the need to initiate a project to fix the problem.

     

    Figure 10.7 Control Chart Displaying Variations That Might Be Due to an Assignable Cause

    Control Chart Displaying Variations That Might Be Due to an Assignable Cause