Systematic failure analysis generally enhances the ability of engineering decision-makers to obtain a holistic view of the causal relationships that often exist within the systems they manage. Such analyses are made more difficult by uncertainties and organisational complexities associated with critical and inevitable industrial maintenance activities such as major overhauls, outages, shutdowns, and turnarounds (MoOSTs). This is perhaps due to the ratio of tasks-to-duration typically permitted. While core themes of MoOSTs including planning, contracts, costing, execution, etc., have been the focus of most research activities, it is worth noting that the ability to successfully transfer and retain MoOSTs knowledge is still under-investigated. Effectively implementing a case study-based approach for data collection, the current study explores the harmonisation of various risk assessments (i.e., fault tree analysis and reliability block diagrams) and multicriteria decision analysis (MCDA) tools to investigate perceived barriers to MoOSTs knowledge management and experience transfer. The case study selected for this study is a dual process line all-integrated cement manufacturing plant (the largest of such process configuration in its region). The justification for this choice of industry was driven by the volume and frequency of MoOSTs executed each year (typically 4–1 per process line), thereby providing a good opportunity to interact with industrial experts with immense experience in the management/execution of MoOSTs within their industry. A multilayered methodology was adopted for information gathering, whereby baseline knowledge from an earlier conducted systematic review of MoOSTs practices/approaches provided fundamental theoretical trends, which was then complemented by field-based data (from face-to-face interviews, focus group sessions, questionnaires, and secondary information from company MoOSTs documentation). During the analysis, fault tree analysis (FTA) and reliability block diagrams (RBDs) were simultaneously used to generate the causal relationships and criticality that exist between identified barriers, while the MCDA (in this case analytical hierarchy process) was used to identify and prioritise barriers to MoOSTs knowledge management and experience transfer, based on sensitivity analysis and consistency of approach. The primary aim of this study is to logically conceptualise core barriers/limiters to knowledge in temporary industrial project environments such as MoOSTs, as well as enhance the ability of decision-makers to prioritise learning efforts. The results obtained from analysis of data identify three major main criteria (barriers) and 23 subcriteria ranked according to level of importance as indicated from expert opinions.

Maintenance experts involved in managing major maintenance activities such as; Major overhauls, outages, shutdowns and turnarounds (MoOSTs) are constantly faced with uncertainties during the planning and/or execution phases, which often stretches beyond the organisation’s standard operating procedures and require the intervention of staff expertise. This underpins a need to complement and sustain existing efforts in managing uncertainties in MoOSTs through the transformation of knowledgeable actions generated from experts’ tacit-based knowledge. However, a vital approach to achieve such transformation is by prioritising maintenance activities during MoOSTs. Two methods for prioritising maintenance activities were adopted in this study; one involved a traditional qualitative method for task criticality assessment. The other, a quantitative method, utilised a Fuzzy inference system, mapping membership functions of two crisp inputs and output accompanied by If-Then rules specifically developed for this study. Prior information from a 5-year quantitative dataset was obtained from a case study with appreciable frequency for performing MoOSTs; in this case, a Rotary Kiln system (RKS) was utilised in demonstrating practical applicability. The selection of the two methods was informed by their perceived suitability to adequately analyse the available dataset. Results and analysis of the two methods indicated that the obtained Fuzzy criticality numbers were more sensitive and capable of examining the degree of changes to membership functions. However, the usefulness of the traditional qualitative method as a complementary approach lies in its ability to provide a baseline for informing expert opinions, which are critical in developing specific If-Then rules for the Fuzzy inference system.

Major overhauls, outages, shutdowns and turnarounds (MoOSTs) are significant maintenance interventions needed on a periodic basis to optimise the performance of physical industrial assets (PIAs). However, uncertainties in the forms of emergent and discovery work which sometimes cause delays and cost overruns are quite common partly because, MoOSTs are characterised by inherent challenges such as, but not limited to, short execution spans, volatility in ever-evolving schedules, task complexities as well as huge offline production and/or operation costs etc. Furthermore, in the literature, other complex elements which further constrains decision-makers in MoOSTs from satisfactorily achieving predetermined objectives have been identified, one of which is the lack of a formalised approach for capturing tacit knowledge from experienced practitioners. Consequently, because MoOSTs is an applied discipline, significant human endeavours are required in the planning and management, which makes it pertinent to examine and obtain the perspectives of experienced MoOSTs practitioners. Therefore, the aims of this study are two folds; firstly, to examine the extent of alignment between findings from literature as it relates to the challenges encountered during MoOSTs, as well as probe their underlying causes in practice. Secondly, to show how relevant the findings from this study would be in providing a baseline for establishing a proposal for capturing MoOSTs knowledge and the transfer of experience. The research approach adopted; thematic synthesis of themes which emerged from knowledge management challenges in MoOSTs identified via an earlier systematic literature review (SLR); and then, the identified challenges were validated through conducting interviews with practitioners. Demography analysis as well as specific MoOSTs related questions were administered via questionnaires, which were then analysed using frequency analysis method. Additionally, semi-structured interviews were conducted to investigate the perceptions of practitioners on pertinent MoOSTs issues. Both questionnaires and interview questions were formulated by findings obtained from the SLR, so as to examine whether the knowledge management challenges identified in the literature exist in practice, and if they do to what extent. In total, the selected responses of 49 practitioners, with origin across five industries were examined to determine the extent of alignment between literature and the practice-based perspectives. Based on the results, nine challenges were identified as critical themes, six of which were associated with managing knowledge. The study identified not only known constraints from literature but also their underlying causes based on the perspective of practitioners involved in multiple MoOSTs, which is crucial for developing sustainable mitigation. A unique contribution of this research is the mapping of demographic information such as industry, country, job class, years of experience, MoOSTs organization size, frequency for performing MoOSTs, etc., to responses obtained from participants, which has not been shown in literature prior to now. The importance of such rigorous efforts in the research design, is crucial for enabling the adoption of holistic approaches to eliminating the underlying causes of challenges encountered in MoOSTs, based on first hand reporting of people involved. In addition, the relevance of such first-hand analyses of responses obtained from this study; serve as baseline for the introduction of the proposal to adequately manage knowledge management issues in this discipline.

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Systematic failure analysis enhances the ability of decision makers to implement strategies that are beneficial to systems they manage. However, in industrial maintenance activities such as, Major overhauls, outages, shutdowns and turnarounds (MoOSTs) there is scarcity of knowledge and experience, limiting the effectiveness of such failure analysis. Transformation of knowledgeable actions generated from experts’ tacit based knowledge from performing MoOSTs is encouraged. A key step to achieve such transformation is by prioritizing maintenance efforts by critically assessing relevant maintenance attributes. Criticality analysis of tasks is considered as an effective approach for prioritizing MoOSTs activities. This paper combines a traditional approach for analysing attributes of frequency and consequence factor values ranked by experts using a mathematical relationship to determine critical activities as well as a fuzzy logic system to develop a fuzzy inference system (FIS) for generating fuzzy criticality numbers of MoOSTs activities. In this regard, the traditional method qualitative criticality matrix, and boundary settings by experts provide baseline information for the FIS, to establish If-Then rules and map membership functions of two crisp inputs and output. Practical applicability is demonstrated using a Raw Mill System (RMS) from a cement manufacturing plant. The comparison of results from the two methods shows slight variations in criticality numbers, howbeit a consistent ability to capture critical MoOSTs activities. Moreover, the validity of results obtained by the fuzzy logic system is enhanced and more superior because it can demonstrate sensitivity.