One of the best examples of the use of statistical quality control in clerical operations is found in Aldens' Mail Order House in Chicago. Statistical quality control was begun at Aldens' early in 1945 by the installation of sample inspection and the control chart in one of the order-picking departments (Mercer, 2003). Within two months, the error ratio in this department fell from 3% to less than 1% while efficiency increased from 82% to 107% (Walton, 2012). Since then, use of the system has been extended throughout the organization (23 departments by June, 1947) with such outstanding success that it has gained the complete support of top management. Over a two-year period, statistical quality control brought about a reduction in errors of 25.4% as indicated by customer adjustments (Box & Colleagues, 2009).
With few exceptions, statistical quality control in the federal government has been confined to engineering or construction activities (e.g., Army and Navy ordnance). The Bureau of the Census and the National Office of Vital Statistics of the Federal Security Agency have experimented with dif Quality circles (QC) are organizational interventions that seek to increase an organization's productivity and the quality of its products through direct employee participation (Walton, 2012). The underlying assumption is that such participation will result in useful suggestions for improving work methods and quality control, and for increasing employee commitment to implement these changes (Sensenbrenner, 2005). A quality circle is composed of a small group of employees, doing similar work, who volunteer to meet periodically to discuss production, quality, and related problems, to investigate causes, recommend solutions, and take corrective actions to the extent of their authority (Sherr & Teeter, 2010).
Normally, a company-wide steering committee of both union and management representatives decides where in the organization quality circles should be introduced and what types of problems are appropriate for the quality circles to work on. Once initiated, a quality circle (consisting of about ten employees from a work unit and their immediate supervisor) holds a weekly one-hour meeting to discuss ways of improving productivity and related issues (Walton, 2000). To aid their effectiveness, the group and its leader are trained in group dynamics, problem solving, data analysis, quality control, and the presentation of information and recommendations to management. Circle leaders usually receive about three days of training prior to the circle's first meeting (Sensenbrenner, 2005). Circle members receive their training during the first eight to ten circle meetings. These meetings are held on company time and at company expense, and the decision to implement any of the group's suggestions remains ultimately with management. External facilitators, who have received about five days of training in the use of quality circle techniques and are usually company employees, guide and assist the quality circle during the meetings (Walters, 2007).
Within the federal sector, the Navy was the first to implement a quality circle program in 1979 in its Norfolk Naval Shipyard. By 1980 the shipyard claimed to have achieved a four-to-one cost-benefit ratio. The Navy has since expanded its QC program to a number of its bases and shipyards. In addition, a variety of other federal agencies (including the Air Force, the Veteran's Administration, and the Public Health Service) have all begun to experiment with their own quality circle programs. Interest in the QC process among federal agencies appears to be rapidly growing (Mercer, 2003).
The Bureau of the Census, for example, in processing 1940 census figures for housing and population used statistical sampling in the verification of card punching and maintained quality control charts on each individual puncher (Walton, 2000). Great care was taken when setting up the sampling system to develop criteria for selecting the punchers whose work should be sample verified. Length of experience, average error rate, and fluctuations in error rate were determined to be the controlling factors (Sensenbrenner, 2005). Over 90% of the qualified punchers stayed within the upper control limit plotted on their respective charts. Investigation of the reasons for errors in the case of those who exceeded the permitted limit revealed such assignable causes as (1) schedules poorly filled out by the enumerator, (2) a puncher who had returned to work too soon after a siege of measles, and (3) sickness in the family of a puncher. With this knowledge as to the causes of errors, management was able to take intelligent action to remedy situations. This statistical quality control system was estimated to have saved $263,000 in direct labor costs; indirect savings were estimated to have paid for the cost of the system. In addition, speedier service in the preparation of the final statistical tables was obtained (Walters, 2007).
This example of statistical quality control in the federal Bureau of the Census is one of the rare instances in which that technique has been used in government clerical operations (Sherr & Teeter, 2010). Yet there are a great number of similar kinds of operations performed by federal, state, and local agencies where the technique appears to be applicable. The test of applicability is whether like articles are turned out in quantity. Apparent possibilities include large-scale repetitive operations such as warehousing, purchasing, tabulating, mailing, billing, filing, publications distribution, reproduction operations, processing of personnel actions, and processing of various types of claims (Walton, 2012). Any government department handling a large volume of work -- a city water department or assessor's office, a state highway or welfare department, or almost any large bureau or agency -- offers fertile ground for the application of this technique (Box & Colleagues, 2009).
The major obstacle to the spread of statistical quality control seems to have been the failure of government management people to promote it. The government has made commendable progress in adapting the technique to its research, engineering, and scientific activities. It remains for government management people to carry on in the vast areas of government which have been relatively untouched by the engineer, scientist, or statistician (Sherr & Teeter, 2010).
The increasing recognition of statistical quality control as an effective management tool is testified to by a growing body of literature on the subject, the introduction of courses dealing with it at a number of universities, and the establishment in 1946 of the American Society for Quality Control (Box & Colleagues, 2009). Its widespread use in government will depend in part upon further experimentation with the technique as applied to mass paper work activities; primarily it will depend, however, upon the development of an awareness of its usefulness on the part of people dealing with broad management problems whether from the line or staff point-of-view. Statistical quality control of itself cannot put quality into a product; its function is to inform management effectively and economically of the degree to which quality goals are being met and whether assignable causes for variation are at work.
Box, George E.P. And Colleagues (2009). "Quality in the Community: One City's Experience.' American Society for Quality Control Annual Quality Congress, Toronto.
Matthews, Jay and Peter Katel (2012). "The Cost of Quality." NEWSWEEK (September 7):48-49.
Mercer, James L. (2003). Strategic Planning for Public Managers. Westport, CT: Greenwood Publishing Group.
Perez, Antonio and Jim Ziaja (2008). "Promoting a Total Quality Initiative in the Community." QUALITY OBSERVER (April): 1, 12-13, 22. Peters, Thomas J. (1991). "Excellence in Government? I'm All for it! Maybe.'…