Standardising Maintenance Jobs to Improve Grouping Decision Making

Abstract

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.

Modularizing Maintenance for Improved Production Impact Clarification

“…The data describing maintenance actions can be many varied when multiple languages, variable industry terms, and a free text format are used. The free text format can be difficult to compare in large quantities, rendering the data difficult to use (Agergaard et al, 2021). The process dimension pertains to aspects such as maintenance management processes (Ben-Daya et al, 2009;Deighton, 2016;Sigsgaard et al, 2020), decision making (Ruschel et al, 2017), information governance (Chilamkurti et al, 2014;Hodkiewicz and Ho, 2016), and human resources (Dansk Standard, 2008;Gulati, 2012).…”

Improving periodic maintenance performance: a grouping and heuristic approach

“…The concept of component grouping introduced in this step is inspired by product platform theory, which is based on the principle that utilizing grouping methodologies to standardize product families will eventually help solve the issue of product development and management complexity (Harlou, 2006). Such grouping ideology has recently been utilized to standardize maintenance jobs under high structural complexity and limited data availability (Agergaard et al, 2021). For equipment in relation to preventive maintenance, grouping criteria have been proposed based on shared intervals (Shafiee and Finkelstein, 2015), criticality (Li et al, 2018), equipment function, maintenance action (Soleymani et al, 2020), reliability and cost (Martinod et al, 2018).…”

Systematic maintenance action modularization for improved initiative prioritization

“…Maintenance actions come from operational data in the form of maintenance instructions. Maintenance action information typically exists in a free text format attached to a unique maintenance job and location (Agergaard et al, 2021;. A maintenance job is in this paper considered the collection of operational information required to perform a maintenance objective.…”

“…Increasing size and complexity of production facilities is putting a strain on informed decision making in maintenance. The difficulties arise from a lack of overview when hundreds of thousands of pieces of equipment need to be maintained (Agergaard et al, 2021; K. V. Sigsgaard et al, 2021). Such large facilities introduce larger amounts of variation in equipment types and conditions, making the maintenance actions required further varied.…”

“…Such large facilities introduce larger amounts of variation in equipment types and conditions, making the maintenance actions required further varied. This variation makes it difficult to know what maintenance actions were actually performed during a maintenance job, decreasing the knowledge-basis for decision making (Agergaard et al, 2021(Agergaard et al, , 2022. In product family management, modularization approaches have been applied to handle similar issues of large amounts of variation that makes it difficult to gain an overview that can allow standardization and improvement.…”