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The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.

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.

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

General aviation aircraft has a wide range of applications, and effective cost management is one of the hot spots in the research of general aviation manufacturers. The purpose of this paper is to build a flexible engineering method to predict maintenance cost of general aviation aircraft.

To establish a reasonable general aviation aircraft maintenance cost prediction model, it is necessary to analyze the influencing factors and extract the main components of maintenance cost. The maintenance cost is divided by engineering method, and the estimation model of each component cost is established. Then, the general aviation aircraft maintenance cost model is obtained. The results show that the relative error of this method is between 13% and 20%, which has a good estimation accuracy and can be effectively used to estimate the maintenance cost of general aviation aircraft.

The maintenance cost plays an important role in the life cycle cost of general aviation aircraft. Accurate cost prediction method is of great significance to the optimal design of general aviation aircraft. However, there are few prediction models suitable for maintenance cost, the proposed approach is meaningful and quite desirable.

To some extent, this method overcomes the shortage of the work on maintenance cost prediction for general aviation aircraft. The model established in this paper has certain generality, which can provide some reference for general aviation aircraft design and operation enterprises.

The Maintenance Management Framework for Asset Management (MMFAM) is a recently modeled framework to ensure the alignment of maintenance management with physical asset management based on the ISO 55000 series for asset management. In this context, the purpose of this paper is to discuss the applicability of the MMFAM considering the operational context of a hydroelectric power plant.

The paper adopted the case study method for the discussion of the applicability of the MMFAM to a real operational context. A hydroelectric power plant was chosen as the scope of the case study due to its relevance since the electricity sector is an example of an asset-intensive industry in which asset management performance is fundamental. To gain a detailed understanding of the organization, data were collected through direct requests to the plant, informal meetings with technical collaborators, a technical visit to the hydroelectric plant and on-site data collection. Then, the MMFAM processes were demonstrated based on this information and the results supported the discussion of the MMFAM applicability.

The case study provided a deeper understanding of the processes included in the MMFAM. In addition, the results suggested the applicability of the framework to other organizations besides the hydroelectric sector due to its generic approach and the possibility of choosing appropriate tools to support and implement the MMFAM processes.

The case study is expected to contribute to the practical understanding of the MMFAM processes within an operational context and assist maintenance professionals and researchers in their implementation in other organizations.

Although the literature provides different maintenance management frameworks, their practical discussion based on a real operational context is still a gap. Accordingly, this paper discusses the MMFAM under a case study method to expand its understanding beyond theory and contribute to practical comprehension in depth.

The purpose of this paper is to investigate the relationship between total productive maintenance (TPM) practices and operational performance (OP) in soft drinks manufacturing industry, Ethiopia.

In this study acceptability and implementation of five TPM practices (i.e., dependent factors: autonomous maintenance (AUT); safety, health and environment (SHE); education and training (EDT); focused improvement; and planned maintenance (PLM)) in soft drinks manufacturing industry have been elaborated to ascertain the benefits accrued as a result of successful TPM practices (i.e., independent variables) on OP (i.e., dependent variables). A self-administered survey seven-point Likert scale questionnaire was used for primary data collection. By using simple random sampling technique a total of 100 useable responses resulted in a 66.66 per cent response rate. Descriptive (mean, standard deviation) and inferential statistics (factor analysis, correlation, simple and multiple regression analysis) analysis were performed using Statistical Package for Social Sciences (SPSS) software (version-28) to identify the relationship and effect of TPM practices on OP. Five hypotheses were developed and tested.

Results show that four of the TPM practices were positively and significantly correlated with OP. Aggregate TPM shows positive and significant correlation with OP. Four hypotheses results revealed that the AUT; SHE; EDT and PLM practices have positive and significant relationship with OP and significantly improve OP. The results also show that the TPM practices have positive and significant relationship with OP and significantly improve cost effectiveness, product quality, on-time delivery and volume flexibility.

The benefits gained by TPM practices in selected soft drinks manufacturing industry have been highlighted, that could be genuine source of motivation to other companies to go in for TPM program. This research contributes to the literature by examining the contingency of various TPM enabling factors in the context of the Ethiopian soft drinks manufacturing sector, and it, therefore, provides direction to increase the success rate of TPM implementation. Study offers academics and practitioners a better understanding of the relationship and effect of the TPM practices on the OPs. Thus, practitioners will be able to make better and more effective decisions about the implementation of TPM practices for better OP results.

The relationship between the five factors TPM practices and OP has not yet been studied or reported in the case of soft drink manufacturing industry. The questionnaire manner and items developed, factor considered in this study, sampling method, deeply statistical data analysis techniques used, soft drink manufacturing industry, developing country like Ethiopia make this study unique and revealed the gap identification in this area. The study has contributed to the TPM literature with a better understanding of the five TPM practices and their association with a soft drink manufacturing industry OP that will provide valuable knowledge to top-management of manufacturing companies, to refine their current TPM practices and subsequently improve OP.

This paper aims to optimize the maintenance scheduling of emergency rescue wagons in railway companies using a genetic algorithm (GA). It offers an integrated model for simultaneously solving maintenance planning, preventive maintenance, prognostic information and resource planning from which optimal levels of the system performance in terms of cost and repair time could be determined.

This study initially evaluates the previous types of research in presenting the optimal model of the rescue train wagon for maintenance and repair planning and lists the identified criteria based on experts' opinions using fuzzy analytic hierarchy process (FAHP) and stepwise weight assessment ratio analysis (SWARA) techniques. Then, the final weight of the desired criteria is calculated. Later, the final decision matrix is evaluated by the experts. The final normal decision matrix is formed to select the optimal maintenance and repair planning plan based on the GA. Finally, two strategies including joint optimization strategy of preventive maintenance planning are compared with the independent preventive maintenance planning strategy.

According to the primary results, three primary parameters, including technology, human damage caused by negligence and the average failure rate, should be considered for launching the GA model. Based on the results of the second part; by comparing the preventive maintenance planning strategy independently, the joint optimization strategy reduces the total cost of production up to 8.25%. Comparison results show that the total cost of joint optimization production is less than independent preventive maintenance and repair planning. Moreover, the value of total process time in joint optimization strategy was reduced by 1.2% compared to independent preventive maintenance (PM) planning (from 137.90 to 136.20 h).

The novelty of this paper lies on the application of the GAs to develop an optimized PM scheduling to localize the maintenance planning for maximizing productivity, avoid train accidents, reduce costs and increase efficiency and capacity.

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.

School infrastructure is one of critical factors that significantly contribute to the educational outcomes, and therefore, maintaining the high quality of school infrastructure becomes of critical importance. Due to the ageing of school assets over time in combination with budget constraint and rapid growth of student enrolment, many public schools are currently struggling to maintain the required standard for long term. However, to date, the goal of providing the best maintenance practices to public schools has not been achieved.

The present study focuses on studying the balance between the asset and maintenance management strategies and the funding model through conducting state-of-the-art literature review and qualitative analysis in the context of public schools in Australia and other developed countries around the world. Review of journal articles, different government reports and other available resources were used to collect and analyse the data in this study.

As part of this review, significant under investment in maintenance and asset renewals were identified as main challenges in asset management in public school facilities. Although different maintenance strategies were used in school infrastructure, adequate funding, adequate robust asset management plans (AMPs) and the involvement of private sectors have been identified as the key factors that govern the success in school infrastructure maintenance. It also shows that funding of approximately 2–3% of asset replacement value (ARV) on school infrastructure is required to maintain school facilities for long-term. Further, the procurement methods such as public private partnership including private finance initiatives (PFIs) have shown great improvements in maintenance process in school infrastructure.

The study provides a review of different AMPs and funding models in school infrastructure and their efficiencies and shortcoming in detail. Different states and countries use different maintenance models, and challenges associated with each model were also discussed. Further this study also provides some conclusive evidence for better maintenance performance for school buildings.

This paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.

A literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.

Each maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.

The literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.

The novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.