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.
|Title of host publication||Proceedings of the Design Society|
|Publisher||Cambridge University Press|
|Pages||2701 - 2710|
|Publication status||Published - 2021|
|Event||23rd International Conference on Engineering Design (ICED21) - Chalmers University of Technology, Gothenburg, Sweden|
Duration: 16 Aug 2021 → 20 Aug 2021
|Conference||23rd International Conference on Engineering Design (ICED21)|
|Location||Chalmers University of Technology|
|Period||16/08/2021 → 20/08/2021|
Bibliographical noteThis 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.
- Knowledge management
- Large-scale engineering systems
- Big data