Systematic failure analysis enhances the ability of decision makers to implement strategies that are beneficial to the systems they manage. However, there are situations where the effectiveness of such failure analysis are limited due to uncertainties and organisational complexities, a major challenge that is inevitable for industrial maintenance projects. Major overhauls, outages, shutdowns and turnarounds (MoOSTs) represent a distinct type of such industrial maintenance projects which are performed overtime on engineering physical assets prone to degradation and eventual failures. They usually involve a wide range of maintenance activities such as those ones performed during normal operations but, on a larger scale, which makes them critical and capital extensive. &nbsp;Over the years, many research proposals has been adopted to eliminate project uncertainties, but the impact of these research proposals in improving the predicted outcomes of MoOSTs can be argued based on the large amount of data suggesting that many projects do not meet their predetermined outcomes.&nbsp; 
There is a need to complement existing efforts in managing uncertainties in MoOSTs through transformation of knowledgeable actions generated from experts’ tacit based knowledge. The criticality of such endeavours can be argued based on systematic analysis of knowledge trends showing that effective identification and transfer of learning and experience are limited within MoOSTs in comparison with other industrial operations elements. A key step to achieve such transformation is by prioritising maintenance efforts by critically assessing relevant maintenance attributes. Evidence deduced from this study, indicate that a holistic assessment to identify and rank maintenance activities that poses the highest levels of risk during MoOSTs execution phase can provide greater understanding of the level of knowledge and experience that would be required during planning in order to improve project outcomes. 
This paper adopts a methodology that evaluates two methods for multi-attribute criticality assessment and ranking of MoOSTs activities. The first method utilises a traditional approach for analysing attributes of frequency and consequences factor&nbsp;values ranked by experts, and computed into a mathematical relationship to determine task criticality as well as to generate a qualitative criticality matrix. The second method, utilises a quantitative approach by means of a fuzzy inference system developed for this study and was selected because of its ability to overcome challenges associated with imprecise judgements. The fuzzy inference system involves mapping of membership functions of two crisp inputs and output which is accompanied by the use of IF-THEN rules based on decision-makers’ preferences. Practical applicability was demonstrated using a case study with appreciable frequency for performing MoOSTs.&nbsp; 
The comparison of results from the two methods shows slight but consistent variations between criticality levels of fuzzy criticality numbers and traditional criticality numbers. The study proposes this multi-attribute decision support framework for prioritising major maintenance activities to reduce ambiguity in experts’ evaluations which can increase risk and safety issues during MoOSTs. An inverse proportionality logic adopted here for tasks frequency ensures capture of rarely performed MoOSTs tasks, a vital approach to eliminating uncertainties. The combination of attributes to assess MoOSTs activities provides insights to development of a framework for codifying important knowledge elements that can improve uncertainties in task-execution.&nbsp;

A Multi-Attribute Knowledge Criticality Framework for Ranking Major Maintenance Activities: A Case Study of Cement Raw Mill Plant

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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