Quality control and social processes: a case for acceptance sampling

Abstract

The “at the source” emphasis of total quality management (TQM) has reduced the reliance on post-production statistical quality control approaches such as acceptance sampling. In cases where it is appropriate more proactive approaches such as statistical process control have improved productivity in manufacturing environments. For social processes where the inputs are ill-defined and the outputs are difficult to measure, traditional quality control approaches have rarely been applied. Addresses the problem of monitoring use of illegal drugs, a critical social problem. Because the inputs, the use of drugs, are not easy to document and the process which results in an individual’s decision to use drugs is too complex to model, one must rely on the detection of drugs as a measurement of drug abuse. The behaviour of interest is the detection of illegal drug use through urine testing. The technique for monitoring this behaviour in a population of interest is single-attribute, Bayesian acceptance sampling. Applies a partial drug-testing methodology based on single-attribute acceptance sampling to a population of probationers in Madison County, Illinois, USA. The approach offers probation offices with a lower cost approach to monitoring drug use among populations of known drug users. The use of acceptance sampling allows Madison County to reduce the total cost of testing by reducing the total amount of testing that must be done to monitor use of drugs among their probation populations.