Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time

Antonio M. Cruz, Cameron Barr, Elsa Puñales-Pozo

Resultado de la investigación: Contribución a RevistaArtículo

1 Cita (Scopus)

Resumen

Objectives This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. Materials and methods The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Results Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CErt, 0.415 positive coefficient), stock service response time (Stockrt, 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stockrt, CErt and priority, in that order. Clustering techniques revealed the main causes of high TAT values. Conclusions This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.
Idioma originalEnglish (US)
Páginas (desde-hasta)808-817
Número de páginas10
PublicaciónRevista de Salud Publica
DOI
EstadoPublished - ene 1 2008

Huella dactilar

Linear Models
Maintenance
Biomedical Engineering
Cluster Analysis
Reaction Time
Research

Citar esto

Cruz, Antonio M. ; Barr, Cameron ; Puñales-Pozo, Elsa. / Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time. En: Revista de Salud Publica. 2008 ; pp. 808-817.
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Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time. / Cruz, Antonio M.; Barr, Cameron; Puñales-Pozo, Elsa.

En: Revista de Salud Publica, 01.01.2008, p. 808-817.

Resultado de la investigación: Contribución a RevistaArtículo

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AU - Barr, Cameron

AU - Puñales-Pozo, Elsa

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N2 - Objectives This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. Materials and methods The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Results Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CErt, 0.415 positive coefficient), stock service response time (Stockrt, 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stockrt, CErt and priority, in that order. Clustering techniques revealed the main causes of high TAT values. Conclusions This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

AB - Objectives This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. Materials and methods The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Results Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CErt, 0.415 positive coefficient), stock service response time (Stockrt, 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stockrt, CErt and priority, in that order. Clustering techniques revealed the main causes of high TAT values. Conclusions This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

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