### Abstract

Original language | English |
---|---|

Pages (from-to) | 176-185 |

Number of pages | 10 |

Journal | International Journal of Quality and Reliability Management |

Volume | 14 |

Issue number | 2 |

DOIs | |

Publication status | Published - 1 Jan 1997 |

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### Keywords

- Process efficiency
- Quality
- Reliability
- Systems

### Cite this

*International Journal of Quality and Reliability Management*,

*14*(2), 176-185. https://doi.org/10.1108/02656719710165437

}

*International Journal of Quality and Reliability Management*, vol. 14, no. 2, pp. 176-185. https://doi.org/10.1108/02656719710165437

**A regression model for matching parallel systems.** / NIBLER, Roger.

Research output: Journal Publications › Journal Article (refereed)

TY - JOUR

T1 - A regression model for matching parallel systems

AU - NIBLER, Roger

PY - 1997/1/1

Y1 - 1997/1/1

N2 - 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.

AB - 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.

KW - Process efficiency

KW - Quality

KW - Reliability

KW - Systems

UR - http://commons.ln.edu.hk/sw_master/6947

U2 - 10.1108/02656719710165437

DO - 10.1108/02656719710165437

M3 - Journal Article (refereed)

VL - 14

SP - 176

EP - 185

JO - International Journal of Quality and Reliability Management

JF - International Journal of Quality and Reliability Management

SN - 0265-671X

IS - 2

ER -