Academic journal article Human Factors

The Design of a Visual Display for the Presentation of Statistical Quality Control Information to Operators on the Plant Floor

Academic journal article Human Factors

The Design of a Visual Display for the Presentation of Statistical Quality Control Information to Operators on the Plant Floor

Article excerpt

We applied human-centered design methodologies to enhance the presentation of product quality information to operators on a manufacturing plant floor. First, an initial visual display concept that integrated a pictorial representation of a product with standard graphical and tabular information about the product's quality was refined through iterative design and testing. A preliminary study was then conducted to determine the specific features of such a display (termed a pictorial control chart) from among eight candidate detail designs. Finally, a formal study was conducted to compare the performance of operators using this refined pictorial control chart design with their performance using a conventional control chart. Operators completed a quality control task in significantly less time using the pictorial control chart. There were no significant differences in the number of errors committed with the two charts. Subjective measures showed a significant preference for the pictorial control chart. Actual or potential applications of this research include the development of quality control tools that are useful to and usable by operators on the manufacturing plant floor.


Quality has been defined as "fitness for use" (Juran & Gryna, 1980). To make a product fit for use in a world of complex products, markets, and competition, quality control must focus on preventing defects rather than on responding to them. X-bar and R control charts (also called variable control charts) are tools used in industry to prevent defects. They allow for the control of one variable per piece, per chart and are used when data are continuous (Charbonneau & Webster, 1978). Continuous data include measures of a dimension, a weight, an output, a hardness, and a tensile strength.

A typical X-bar and R control chart is shown in Figure 1; it is a graphical display of a quality characteristic (the outer diameter of a bearing) that has been computed from a sample of three bearings versus the sample number or time. X-bar represents the average outer diameter of the bearings for each sample, and R, the range of the sample, is the difference between the largest and smallest observations in the sample. The X-bar and R control charts contain center lines that represent the average value and average range, respectively, of the quality characteristic when the manufacturing process is in statistical control.

Two other horizontal lines, called the upper control limit (UCL) and the lower control limit (LCL), are shown in each chart. These control limits are determined such that if the process is in control, nearly all of the sample points will fall within them. A point that is outside the control limits is interpreted as a signal or evidence that the process is out of control. This case requires investigation and corrective action to find and eliminate the cause of this behavior (Montgomery, 1991).

Although the term control chart has been universally accepted and used, the chart does not actually control anything. It simply provides a basis for action and is effective only if those responsible for making decisions act on the information that the chart reveals (Charbonneau & Webster, 1978). As decision-making responsibility shifts to those closest to the production of the product, these workers on the plant floor must construct, analyze, and act on the information provided by the control chart. However, a number of companies eager to implement statistical quality control (SQC) on the plant floor have been frustrated by the fact that apparent deficiencies in reading, writing, and arithmetic skills among a significant proportion of their employees inhibit their workers' ability to be trained (Lewis & Kales, 1991; Mast, 1988). Statistical methods must be presented in a simple, straightforward, nonthreatening manner and must be demonstrated to be a practical and helpful tool in the workplace. …

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