A regression model for matching parallel systems

Contends that parallel production sub‐systems may be cost effective and, in some cases, they are the only technically feasible way to improve output quality. Operating in either a standby‐redundancy or an ongoing capacity, parallel production sub‐systems are likely to be most cost effective when placed in upstream operations, where the leverage effect on output quality is considerably higher. Although typical research in parallel systems assumes a known distribution to estimate the output reliability of the parallel configuration, explains how this study used a simulated production environment to develop a regression model for assigning parallel system components by monitoring their actual past performance, and was therefore distribution free. Applying variables monitored during a previous production run in which quality is measured in a binary manner (i.e. either as acceptable or unacceptable), the model was used to determine optimal pairs of parallel subsystems. Claims that this matching model was about 2.5 times more accurate than Markov analysis in predicting the output quality of a given pair of parallel systems. The inclusion of an additional variable in the regression resulted in the model explaining about 75 per cent of the output variability of the parallel configurations and thus could potentially predict quality in lieu of direct inspection.