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STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

Published online by Cambridge University Press:  27 July 2021

Julie Krogh Agergaard ,
Kristoffer Vandrup Sigsgaard ,
Niels Henrik Mortensen ,
Jingrui Ge ,
Kasper Barslund Hansen  and
Waqas Khalid
Julie Krogh Agergaard*
Affiliation:
Technical University of Denmark
Kristoffer Vandrup Sigsgaard
Affiliation:
Technical University of Denmark
Niels Henrik Mortensen
Affiliation:
Technical University of Denmark
Jingrui Ge
Affiliation:
Technical University of Denmark
Kasper Barslund Hansen
Affiliation:
Technical University of Denmark
Waqas Khalid
Affiliation:
Technical University of Denmark
*
Agergaard, Julie Krogh, Technical University of Denmark, Mechanical Engineering, Denmark, [email protected]

Abstract

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Maintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.

This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.

Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Keywords

ModularizationMaintenanceKnowledge managementLarge-scale engineering systemsBig data
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2701 - 2710
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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