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The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

This paper aims to exemplify the use of project management tools in the scheduling of aircraft maintenance activities. This process is known as maintenance, repair and overhaul and it has gained importance within the aeronautical sector due to its expected growth in the coming years; however, it also faces increasing competitiveness in its market. This fact gives rise to the need of acting in maintenance management and seeking lower costs while maintaining the quality of the service provided. The purpose of this paper is to propose the structuring of a procedure that aims to reduce the total maintenance time (downtime) and guarantee the delivery of the aircraft on time.

The paper, through a case study at a Brazilian aircraft maintenance center, used critical path method and critical chain project management, the latter being derived from the theory of constraints, with the purpose of analyzing resources systematically and synchronizing the activities in the precedence network.

As a result, it is shown that downtime can be reduced from 11 to 5 days and improvements are proposed to achieve greater market competitiveness.

This paper demonstrates the competitive advantage that resulted from the application of project management tools in the aircraft maintenance planning and execution.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

This paper is based on research addressing quality of construction schedules. The paper aims to structure a Schedule Health Assessment method and present it as a means to carry out the evaluation of construction schedules.

The development of the Schedule Health assessment method can be characterised as constructive research. The structuring of the method is based on analysis of factors forming the overall quality of construction schedules. The method has been tested in a proof of concept study. This comprised a case study in which four master schedules developed by junior production managers were evaluated using the Schedule Health assessment method.

It is possible to construct a method for the quality evaluation of construction schedules.

The completed testing is still rather limited since it is based merely on experiences of junior production managers with a single case.

The Schedule Health assessment method can in a useful manner make the quality evaluation of construction schedules easy to approach and effective process.

This research has produced a novel method for the quality evaluation of construction schedules.

A key factor adversely affecting contractor social networking performance is the improper handling and information management of contractor’s services delivery on websites. Contractor social networking is particularly problematic on industrialised building system (IBS) infrastructure maintenance projects where contractor’s certified quality product and firms are not matched with maintenance specialisation services. The paper aims to discuss this issue.

This paper reports on the early stages of research which is developing a new information and communications technology (ICT)-based approach to managing contractor social networking on IBS infrastructure maintenance schemes. As a precursor to this work, the paper reviews current contractor social networking websites practices on IBS infrastructure maintenance projects and explores the ICT tools and techniques currently being employed on such projects.

The findings reveal the need for more sophisticated contractor social networking websites solutions which accord with the needs of IBS infrastructure maintenance schemes.

The paper concludes by presenting a research framework for developing such a system in the future.

Inadequate strategic planning and maintenance budget may undermine the maintenance of the Higher Education Institution Building (HEIB). Studies have shown that a customised maintenance concept such as Soft System Methodology (SSM) can improve public building maintenance operations. There is a paucity of studies regarding public HEIB maintenance in Nigeria via an SSM approach. Therefore, the research investigated the state of public HEIB and developed a framework to improve public HEIB maintenance practices in Nigeria.

The research adopted SSM to understand Nigeria’s public HEIB maintenance practices. The SSM permitted a substitute approach to improve public HEIB maintenance practices via a developed framework. Data were collated via virtual interviews with experts, and findings were presented in line with the SSM seven steps.

Findings show that besides the shoddy state of public HEIB maintenance, there is no public digitalised HEIB framework to improve maintenance practices across Nigeria’s higher education institutions. The study developed a digitalised framework with the support of Computerised Maintenance Management System from the findings. It would reposition the public HEIB and stir up various agencies/departments/units managing maintenance for better service delivery via integrated delivery, practical, methodological and managerial aspects.

The research investigated Nigeria’s public HEIB maintenance practices via SSM to identify the required document and propose a feasible framework to improve Nigeria’s HEIB maintenance practices. Besides the developed conceptual framework, Nigeria’s HEIB maintenance practitioners and higher institution chief executives can use the recommended framework as guidelines to improve HEIB maintenance practices.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.