A model for predicting plant maintenance costs

David J. Edwards; Gary D. Holt; Frank C. Harris

doi:10.1080/014461900370960

A model for predicting plant maintenance costs

David J. Edwards, Gary D. Holt, Frank C. Harris

University of Wolverhampton

Research output: Contribution to journal › Article › peer-review

22 Citations (Scopus)

Abstract

A model is presented that predicts the total cost of plant maintenance (i.e. direct cost of maintenance plus indirect cost of lost production) and is derived studying a random sample of tracked hydraulic excavators. Analysis is based on the machine history file data of 33 plant items, modelled using multiple regression (MR) analysis. Validation of the model was determined via the combination of an observed high R² at 0.94 and various statistical tests which confirmed the prerequisites of a rigorous MR analysis. Machine weight, type of industry and company attitude towards predictive maintenance were found to be the best predictor variables of total plant maintenance cost. The paper also discusses reasons underlying the inclusion of predictor variables in the final model, and concludes with clear directions for future research in this field.

Original language	English
Pages (from-to)	65-75
Number of pages	11
Journal	Construction Management and Economics
Volume	18
Issue number	1
DOIs	https://doi.org/10.1080/014461900370960
Publication status	Published - 2000
Externally published	Yes

Keywords

Construction plant
Plant downtime
Plant maintenance cost
Tracked hydraulic excavators

ASJC Scopus subject areas

Management Information Systems
Building and Construction
Industrial and Manufacturing Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1080/014461900370960

Cite this

@article{077df0e543fa458fb735e50f35926342,

title = "A model for predicting plant maintenance costs",

abstract = "A model is presented that predicts the total cost of plant maintenance (i.e. direct cost of maintenance plus indirect cost of lost production) and is derived studying a random sample of tracked hydraulic excavators. Analysis is based on the machine history file data of 33 plant items, modelled using multiple regression (MR) analysis. Validation of the model was determined via the combination of an observed high R2 at 0.94 and various statistical tests which confirmed the prerequisites of a rigorous MR analysis. Machine weight, type of industry and company attitude towards predictive maintenance were found to be the best predictor variables of total plant maintenance cost. The paper also discusses reasons underlying the inclusion of predictor variables in the final model, and concludes with clear directions for future research in this field.",

keywords = "Construction plant, Plant downtime, Plant maintenance cost, Tracked hydraulic excavators",

author = "Edwards, {David J.} and Holt, {Gary D.} and Harris, {Frank C.}",

year = "2000",

doi = "10.1080/014461900370960",

language = "English",

volume = "18",

pages = "65--75",

journal = "Construction Management and Economics",

issn = "0144-6193",

publisher = "Taylor and Francis Ltd.",

number = "1",

}

TY - JOUR

T1 - A model for predicting plant maintenance costs

AU - Edwards, David J.

AU - Holt, Gary D.

AU - Harris, Frank C.

PY - 2000

Y1 - 2000

N2 - A model is presented that predicts the total cost of plant maintenance (i.e. direct cost of maintenance plus indirect cost of lost production) and is derived studying a random sample of tracked hydraulic excavators. Analysis is based on the machine history file data of 33 plant items, modelled using multiple regression (MR) analysis. Validation of the model was determined via the combination of an observed high R2 at 0.94 and various statistical tests which confirmed the prerequisites of a rigorous MR analysis. Machine weight, type of industry and company attitude towards predictive maintenance were found to be the best predictor variables of total plant maintenance cost. The paper also discusses reasons underlying the inclusion of predictor variables in the final model, and concludes with clear directions for future research in this field.

AB - A model is presented that predicts the total cost of plant maintenance (i.e. direct cost of maintenance plus indirect cost of lost production) and is derived studying a random sample of tracked hydraulic excavators. Analysis is based on the machine history file data of 33 plant items, modelled using multiple regression (MR) analysis. Validation of the model was determined via the combination of an observed high R2 at 0.94 and various statistical tests which confirmed the prerequisites of a rigorous MR analysis. Machine weight, type of industry and company attitude towards predictive maintenance were found to be the best predictor variables of total plant maintenance cost. The paper also discusses reasons underlying the inclusion of predictor variables in the final model, and concludes with clear directions for future research in this field.

KW - Construction plant

KW - Plant downtime

KW - Plant maintenance cost

KW - Tracked hydraulic excavators

UR - http://www.scopus.com/inward/record.url?scp=0002100471&partnerID=8YFLogxK

U2 - 10.1080/014461900370960

DO - 10.1080/014461900370960

M3 - Article

AN - SCOPUS:0002100471

SN - 0144-6193

VL - 18

SP - 65

EP - 75

JO - Construction Management and Economics

JF - Construction Management and Economics

IS - 1

ER -

A model for predicting plant maintenance costs

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this