More, Sagar, Tuladhar, Rabin, Grainger, Daniel, and Milne, William (2024) Maintenance decision-making and its relevance in engineering asset management. Maintenance, Reliability and Condition Monitoring, 4 (1). pp. 1-17.

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Abstract

Engineering asset management (EAM) has received a lot of attention in the last few decades. Despite this, industries struggle to identify the best strategies for maintaining assets. The decision-making around selecting a relevant maintenance strategy generally considers factors like risk, performance and cost. Risk management is, usually, largely subjective and industries consequently make investments in a subjective manner, making the allocation of budget unstructured and arbitrary. Generally, industries focus only on either overt risks or basic performance of assets, thus creating uncertainties in the decision-making process. Recently, however, maintenance decision-making has evolved from a subjective assessment, chiefly dependent on expert opinions, to utilizing live-data-sensor technology. The attitude towards component failures and how to address them has changed drastically with the evolution of maintenance strategies. Additionally, the emergence and use of several tools and models have assisted the drafting and implementation of effective maintenance strategies. These advancements, however, have only considered discrete parameters while modelling, instead of using an integrated approach. One of the primary factors which can address this shortfall and make the decision-making process more robust is the economic element. To enable an effective decision-making process, it is imperative to consider quantifiable determinants and include economic parameters while drafting maintenance policies. This paper reviews maintenance decision-making strategies in EAM and also highlights its relevance through an economic lens.

Item ID:	90214
Item Type:	Article (Research - C1)
ISSN:	2669-2961
Copyright Information:	Copyright © 2024 Sagar More, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date Deposited:	21 May 2026 00:15
FoR Codes:	40 ENGINEERING > 4010 Engineering practice and education > 401099 Engineering practice and education not elsewhere classified @ 100%
SEO Codes:	16 EDUCATION AND TRAINING > 1699 Other education and training > 169999 Other education and training not elsewhere classified @ 100%
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