Kinds of Control (OM)

From the point of view of control methods, we can apply statistical control concepts by sampling the output of a process to keep that process in a state of statistical control(process control) or by sampling a lot of incoming materials to see whether it is acceptable (acceptance sampling).

In process control, we monitor the actual ongoing process that makes the units. This allows us to make adjustments and corrections as soon they are needed so that bad units are never produced in any quantity. This procedure is a direct application of the statistical control chart, and, as with acceptance sampling parallel procedures are available for those situations in which sampling is done by attributes and for those in which measurements are made of variables that measure quality characteristics. Figure bellows summaries the classifications of statistical control models. These control procedures should be used by suppliers to control their processes prior to shipment. The acceptance sampling procedures are designed to ensure the quality of incoming materials.

Acceptance sampling lets us control the level of outgoing quality from an inspection point to ensure that, on the average no more than some specified percentage of defective items will pass. This procedure assumes that the parts or products have already been produced. We wish to set up procedures and decision rules to ensure that outgoing quality will be as specified or better. In the simplest case of acceptance sampling, we draw a random sample of size n from the total N and decide, on the basis of the sample, whether or not to accept the entire lot. If the sample signals a decision to reject the lot, the lot may be subjected to 100 percent inspection during which all bad parts are sorted out, or it may be returned to the original supplier. Parallel acceptance sampling procedures can be used to classify parts as simply good or bad (sampling by attributes) or to make some kind of actual measurement that indicates how good or bad a part is (sampling by variables).

The methods of statistical quality control referred to below are of considerable importance in quality assurance.

Classification of Statistical Control Models

Statistical control models

Process control

Based on attributes that permit classification as good or bad

Based on measurement of variables

Based on attributes that permit classification as good or bad

Based on measurement of variables

Controlling the Quality of Services

The previous material dealing with industrial quality control has clear objectives about what to measure and control sophisticated methodology for accomplishing these ends. In nonprofit organizations, however, the objectives and outputs seem less well defined and the control methodology seems relatively crude.

The profit motive provides a focus for all kinds of managerial controls, including quality. By contrast, non-profit organizations exist to render service, and their success is judged in those terms. Measuring the quality of the services is difficult in part because the attributes of quality are somewhat more diffuse. Is quality health care measured by the death rate, the length of a hospital stay, or the treatment process used for a specific disease? Is the quality of police protection measured by the excesses? Note the following anomaly: if the size of the police force is increased, crime rates have been observed to increase because more of the crimes committed are acted on and reported. Is the quality of fire service measured by reaction time, the annual extent of fire damage, or some other factor? In partial answer to these questions, we must note that the quality characteristics of most of these kinds of services are multi-dimensional and are often controversial and reducing quality measurement to something comparable to specific dimensions or chemical composition may be impossible. —