Maintenance Resources Optimization Using Pareto Analysis

Managing resource allocation for optimum effectiveness at various levels of maintenance activities is always a challenging task. Optimizing maintenance resources enables an organization to set priorities towards achieving certain goals which are availability and reliability of the equipment for operational excellence. The purpose of this analysis is to determine the optimum resources allocation proportions among the failure modes and to identify the failure modes that have the greatest cumulative effect on the equipment's downtime. This paper presents a methodology using the Pareto analysis in conjunction with failure mode effect and criticality Analysis in maintenance resources optimization. The approach is based on ensuring all failure mode criticality number are considered to obtain the significant failures mode that you should focus on as a priority. The analysis shows that failure mode; FM5, FM 3, FM 2, FM 12, FM 7 and FM 13 are confirmation to the Pareto principle, identifying that most of the downtime of the Instrumentation Air Compressors originated from these failure modes.

Authors

Institutions

Publication Details

Journal
International Journal of Manufacturing Materials and Mechanical Engineering
Published
2024-08-29
DOI
https://doi.org/10.4018/ijmmme.353392
Citations
2
Primary Topic
Reliability and Maintenance Optimization
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Maintenance Resources Optimization Using Pareto Analysis

Stella N. Arinze, Solomon H. Ebenuwa, Emenike Raymond, Augustine Onyenwe Nwajana et al.
2 citations
International Journal of Manufacturing Materials and Mechanical Engineering
Reliability and Maintenance Optimization
0.73
article

Maintenance Resources Optimization Using Pareto Analysis

Stella N. Arinze, Solomon H. Ebenuwa, Emenike Raymond, Augustine Onyenwe Nwajana, Gregory Omozuhiomwen Egbe
article en
2 citations

Abstract

Managing resource allocation for optimum effectiveness at various levels of maintenance activities is always a challenging task. Optimizing maintenance resources enables an organization to set priorities towards achieving certain goals which are availability and reliability of the equipment for operational excellence. The purpose of this analysis is to determine the optimum resources allocation proportions among the failure modes and to identify the failure modes that have the greatest cumulative effect on the equipment's downtime. This paper presents a methodology using the Pareto analysis in conjunction with failure mode effect and criticality Analysis in maintenance resources optimization. The approach is based on ensuring all failure mode criticality number are considered to obtain the significant failures mode that you should focus on as a priority. The analysis shows that failure mode; FM5, FM 3, FM 2, FM 12, FM 7 and FM 13 are confirmation to the Pareto principle, identifying that most of the downtime of the Instrumentation Air Compressors originated from these failure modes.

International Journal of Manufacturing Materials and Mechanical EngineeringVol. 13(1)
Enugu State University of Science and Technology (NG), University of Greenwich (GB), University of Benin (NG)
Openalex Percentile: Top 30%
Reliability and Maintenance Optimization
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Maintenance Resources Optimization Using Pareto Analysis — Stella N. Arinze, Solomon H. Ebenuwa, et al. · International Journal of Manufacturing Materials and Mechanical Engineering (2024) | TGRS Research Map | TGRS