Research Paper: Quality Control Pressures to Improve Management

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Quality Control

Pressures to improve management in government have long been couched in terms of efficiency and economy. As a result, considerable progress has been made in the direction of increasing production and reducing costs. Comparatively little use has been made, however, of effective methods for controlling the equally important element of the quality of work (Walton, 2000).

Reduced costs and increased production are illusory gains if they are achieved at the expense of serious deterioration in quality. In any activity it is imperative to determine standards of quality as well as standards of quantity and cost. Although the relative importance of these three factors may vary in different situations, each of them must be considered in every case (Walters, 2007). The purpose of this article is to stress the importance of defining the degree of quality wanted in government operations and to point out that once these quality goals are set, management can use the relatively new technique of statistical Quality Control to see that these goals are met (Perez & Ziaja, 2008).

Quality cannot be controlled until a decision is reached upon the desired quality standards or goals. In most cases it is possible to design a procedure to attain almost any degree of accuracy. But the higher the degree of accuracy desired, the greater will be the cost. It is possible to approach perfection, but only at the expense of excessive checks to correct the errors, which inevitably appear in any process. The taxpaper will get the most for his dollar if quality goals are realistic enough so that expenditures to prevent errors are not greater than the costs resulting from the errors (Matthews & Peter, 2012).

Once quality goals have been set and operations stabilized, statistical quality control enters the picture (Walton, 2012). Statistical quality control is simply a method for determining the extent to which quality goals are being met without examining every item produced, and for telling management whether or not the errors or variations which occur are exceeding normal expectations. It was introduced into large-scale manufacture in the United States in the 1930's (Walters, 2007). During the war the technique spread rapidly in British and American war factories and resulted in tremendous savings. The Western Electric Company, for example, cut its rejects on some items up to 50% and saved millions of dollars in overhead. In another case, armor-plate rejection percentages were reduced from 33 to 3 (Matthews & Peter, 2012).

In industry, especially when dealing with manufactured items, quality goals are usually stated in terms of such characteristics as dimensions, weight, or durability; in terms of fraction defective (e.g., the ratio of broken panes of glass to the total number inspected); or in terms of defects per unit (e.g., the number of imperfections in a bolt of cloth, or the number of missing parts in an assembled item) (Perez & Ziaja, 2008). These types of goals are not appropriate, however, to the clerical operations so frequently found in government operations. In most government agencies, whether at the local, state, or federal level, quality goals can be set more effectively in terms of number of errors made (Walters, 2007).

In general, the quality of a given product can be determined in three ways: (1) by analyzing the complaints of those people who use or are affected by the product, (2) by surveying the opinions and attitudes of people familiar with the product, or (3) by some form of inspection, review, or test of the product itself (Walton, 2000). Although industry has made significant strides in the analysis of customer complaints and in surveying customer opinion, government has done little exploring in these areas (Matthews & Peter, 2012). Government seems to have concentrated on the third method, with the result that many a citizen's complaint of government delays and red tape can be traced to excessive inspections, checks, and reviews. Despite this emphasis, government offices have made relatively little use of the most modern version of inspection -- statistical quality control. This recently developed technique appears to offer the greatest possibilities for effectively and economically insuring that quality goals are met (Perez & Ziaja, 2008.

Statistical quality control employs two statistical techniques: the control chart and statistical sampling. Both of these techniques are based on the laws of probability (Box & Colleagues, 2009).

The Control Chart

The control chart has been developed in various forms. Essentially, however, it is a device for plotting data (such as dimensions, errors, weights, or similar pertinent figures) so as immediately to reveal the frequency and extent of variation from standards or goals. Control limits based upon the established tolerance limits for the data being dealt with are placed upon the chart. Variations that fall within the control limits may be considered as due to chance or unknown causes. These causes bring about what may be called the natural variability of a process (Perez & Ziaja, 2008. Variations that fall outside the control limits are danger signals and indicate that there is a definite, assignable cause at work helping to bring about the variations. The control chart tells the manager at a glance whether his process is in control (i.e., within the control limits); thus he need not dissipate his energies tracking down random variations, but can begin to act the moment an assignable cause appears (Matthews & Peter, 2012).

The control chart has been likened to a highway whose control limits are the shoulders on one side and the center line on the other. No car driving along the highway can maintain a perfectly straight path. Unevenness in the road, play in the steering wheel, gusts of wind, and a host of other factors cause slight variations in the path of the car. It would hardly be worthwhile to investigate the causes of these small irregularities. However, the moment the car swerves outside one of the limits, an assignable cause can be assumed to exist and an investigation should be begun. The cause may turn out to be a defect in the steering mechanism, a sleepy driver, a "one- armed" driver, or some similar specific correctable factor (Sherr & Teeter, 2010).

The primary value of control charts is that they tell the manager when assignable causes for variations are at work. They contribute an additional advantage, however, in that they publicize production results; thus they furnish a convenient way of stimulating competition, either among groups doing similar work, or within the same group by permitting comparison of present and past records (Mercer, 2003).

Statistical Sampling

The second technique involved in statistical quality control is statistical sampling. Statistical sampling attempts to insure a true picture of the whole by use of a random sample (i.e., one in which every item has an equal chance of being inspected) which is at the same time thorough (i.e., all variations in the sample are discovered) and regular (i.e., recurring consistently rather than at long and irregular intervals). Statisticians have worked out tables so that once the quality goal is determined (e.g., 1% errors allowed) and the percentage of errors made by the inspectors is known, the size of sample to be used to insure the quality goal can be determined. In certain cases the system of sampling permits the use of a larger sample if the variations in the sample taken exceed a specified amount. This method is frequently used when testing the relative acceptability of purchased items (Matthews & Peter, 2012).

Sample testing or inspection has two primary advantages. In the first place, it saves time and money. The size of the sample can be calculated so as to assure the desired degree of quality (Walton, 2000). To the extent that the sample size represents less than 100% review, there is a saving in inspection time and cost. In the second place, sample testing often results in improving the quality of work. A worker who knows that only a portion of his work is to be reviewed feels an increased sense of responsibility and exercises greater care. Experience has shown that the work of the inspector also will be more reliable when he concentrates his attention upon only a selected portion of the items (Perez & Ziaja, 2008).

Quality Control in Office Operations

The statistical quality control system described above is usually said to have originated with Walter a. Shewhart of the American Telephone Company in the early 1920's (Walters, 2007). During the last war, the great need for speedy production, the enormous increase in actual production, and the shortage of qualified inspectors made it imperative that effective methods of quality control be adopted. As a result, the use of statistical quality control spread rapidly in both the United States and Great Britain in ordnance factories (including Army and Navy ordnance plants) and in industries producing such items as electrical equipment, steel, automobiles, and photographic equipment (Walton, 2000).

The application of statistical quality control to clerical operations (i.e., mass paper work activities) as opposed to manufacturing is of more recent vintage.… [END OF PREVIEW]

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