Maintenance strategy selection and its impact in maintenance function

Purpose In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies. Design/methodology/approach A systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed. Findings For the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation. Practical implications From the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al ., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications. Social implications This study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section. Originality/value From the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.

Maintenance strategy selection (MSS) usually has multiple, contradicting objectives. These include reducing cost, increasing availability, reducing unplanned downtime and meeting both safety and environmental requirements. This paper presents the use of analytic hierarchy process (AHP) in solving a MSS problem for a natural gas processing plant. The plant is currently adopting timely based preventive maintenance strategy for all equipment. Six different pieces of equipment were studied to evaluate five different maintenance strategies against multiple criteria such as cost, damage and applicability. Results showed that significant changes should be made to the current maintenance strategy, which would better utilize the resources of the plant, reduce the total maintenance cost, and increase the equipment availability.

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.

<p>Infrastructures have to meet certain requirements during their service life in order to fulfil particular economical and societal needs. These requirements can be categorized into four groups: proper functioning, economy, society & culture and environment. However, until today investigations on the selection of maintenance strategies are primarily cost-oriented. Maintenance strategies de- veloped on the basis of such investigations do not necessarily lead to an optimal solution with re- gards to requirements other than economic efficiency. This paper deals with the necessary frame- work for an appropriate selection of maintenance strategies for infrastructures. A simulation proce- dure is presented which allows for the development and examination of maintenance strategies with regard to different maintenance objectives. The procedure uses Monte Carlo simulation technique and takes into account data in relation to the structural properties, possible deterioration mechanisms, the effect of maintenance measures and the costs.</p>

It is a constant objective for manufacturing companies to improve the efficiency and effectiveness of their maintenance processes. Avoiding unexpected breakdowns that result in high costs and production losses is a major concern. Not only the selection of appropriate maintenance strategies, but also the use of appropriate methods and tools to support decision making in this area is essential to achieve this goal. This article presents the possibility of using machine learning methods to develop predictive models to support decision making in maintenance management. For this purpose, three different machine learning methods have been considered: Neural Networks (NN), the k-Nearest Neighbours algorithm (KNN) and Support Vector Machines (SVM). These models were constructed using variables identified through statistical analysis as having a significant impact on the effectiveness of maintenance processes, specifically the availability of machinery in the production process. The developed models were subjected to a qualitative evaluation, which led to the identification of the SVM model as the most suitable to support decision making in the planning and execution of maintenance processes.

Development of a novel methodology for root cause analysis and selection of maintenance strategy for a thermal power plant: A data exploration approach

Resilience efficiency importance measure for the selection of a component maintenance strategy to improve system performance recovery

Hospitals outsource several activities of the service support in order to focus on the core healthcare production as maintenance service. Recently, faced to the sophistication and the costs of medical equipment that continue to escalate, governments have implemented new reforms to control costs and improve the efficiency and the quality. Hospitals become interested in minimizing the total operational cost, by optimizing healthcare production planning and their support activities. Reorganizing the medical equipment maintenance service becomes a priority for the hospital managers to reduce the cost and the dependency on external parties while ensuring that the medical devices are safe, accurate, and operating at the required level of performance. In this article, we propose an efficient procedure to take the appropriate decisions for medical equipment maintenance such as the selection of maintenance strategy, the insourcing/outsourcing, and the selection of contracts’ type and content. A practical application of this procedure in the Tunisian context is considered. Nevertheless, our procedure is general and can be tailored to hospitals in both developed and developing countries.

\nThe road-maintenance strategies vary from one country to another, and even between the road management authorities within a one single country. It is not surprising, as there is not best maintenance method, nor a universal set of strategies that can be applied equally everywhere. Generally, there are strategies acceptable in certain conditions, but the factors influencing the selection process are diverse and very complex. This paper will present a review of the various measures that can be applied on the roads, which is certainly the basic condition of selection of an appropriate maintenance strategy.\n

The present paper articulates the applicability of genetic algorithms (GAs) as an optimization tool capable of supporting decision-makers (DMs) to make the right decisions throughout the selection of an optimal pavement maintenance strategy and to predict future pavement condition. GAs efficiently take advantage of historical information to locate search points with improved performance. In this regard, pavement condition index (PCI) for the in-service pavement of the selected case study (Expressway No.1 (R4/A) in Iraq) is estimated based on ASTM D6433-11 and using MicroPAVER 6.5.2 software. Moreover, the related field measurements of the in-service pavement distresses are carried out and classified. To predict the optimal maintenance strategy for the pavement segments within the selected pavement portion case study, a GA optimization technique is implemented as an application of stochastic approach using EVOLVER 6.3.1 software to evaluate the pavement performance based on PCI. For the required validation process of the predicted PCI results obtained by the GA technique, predicted PCI results obtained by experts' opinions based on the design questionnaire were estimated and applied. The statistical validation analyses showed that the predicted PCI values obtained via EVOLVER 6 Genetic Algorithm software seem to be close to those obtained via the analyses of the experts' questionnaires. Based on the research outcomes, it is concluded that one can recognize using the presented procedure throughout the implementation of stochastic approach in the form of GA to predict the optimal pavement maintenance strategy for in-service pavement.

Resilience-Based Restoration Model for Optimizing Corrosion Repair Strategies in Tunnel Lining

The thickness of pavement layers is an important parameter used in Pavement Management Systems (PMS). Thickness data are used for pavement condition assessment, performance predictions, selection of maintenance strategies and rehabilitation treatments, basic quality assessment, and as input to overlay thickness design. Pavement thickness is usually determined from direct testing such core samples, nondestructive testing such as radar, or historical records such as pavements network database. This paper proposes the use of Bayesian Influence Diagrams as a tool in providing a probabilistic model for thickness determination procedure in flexible pavements. The Bayesian Influence Diagram Model is presented as a framework for addressing uncertainties involved in capturing quantitative and qualitative information in the asphalt layer thickness determination procedures. The model is also used to perform value of information analysis in the determination of pavement layer thickness. The Influence Diagram representation facilitates the assessment of coherent prior distributions and makes it easier for knowledge engineers and other decision makers to express and understand more general kinds of dependency and independency assumptions.

Purpose This study aims to assess and enhance the production efficiency and reliability of fertilizer manufacturing plants through the application of Markovian analysis. By integrating stochastic modeling with performance management theory, the research provides a predictive performance measurement framework that links reliability metrics to key business outcomes. The research develops a probabilistic framework to model system state transitions, evaluate system availability and optimize maintenance strategies to reduce downtime and improve overall plant performance. Design/methodology/approach A discrete-time Markov chain model is constructed to represent the operational dynamics of critical units within a fertilizer plant. Historical operational and maintenance data are analyzed to develop transition probability matrices that capture the likelihood of state transitions between operational, idle and failure conditions. Key performance metrics such as steady-state probabilities, mean first passage times and recurrence times are computed to assess long-term system behavior. Statistical hypothesis testing – including paired t-tests, Wilcoxon signed-rank tests and chi-square tests – is employed to validate improvements in reliability and efficiency following the implementation of Markov-based maintenance strategies. Regression analysis is also conducted to examine the relationships between operational parameters (e.g. downtime and failure frequency) and production output. Findings The analysis reveals that the production units remain in the operational (RUN) state approximately 61.01% of the time, compared to 53% prior to optimization. Markovian-based maintenance strategies significantly reduced average weekly downtime from 42 h to 29 h (p < 0.001). Weekly production output increased from an average of 1,250 tons to 1,375 tons (p < 0.001). A chi-square test confirmed statistically significant changes in system state transitions (p < 0.001), favoring increased operational continuity. Confidence intervals constructed for key reliability parameters further strengthened the robustness of the findings. Practical implications This study provides a data-driven methodology for improving maintenance planning and production reliability in fertilizer plants. By modeling system behavior through Markovian analysis and applying statistical validation techniques, maintenance managers can develop predictive strategies that reduce unplanned downtime and enhance production efficiency. The methodology is adaptable to other continuous process industries where uptime and reliability are operational priorities and can be integrated into existing performance management systems to support data-driven decision-making and strategic alignment of maintenance activities with productivity goals. Originality/value This study offers a novel academic contribution by applying discrete-time Markov chain modeling to fertilizer manufacturing using empirical operational data. It advances performance management research by integrating stochastic modeling with statistical validation to quantify production efficiency and system reliability. The linkage between probabilistic reliability metrics (e.g. steady-state probabilities and mean first passage times) and business key performance indicators (e.g. downtime and weekly output) provides a new data-driven framework for industrial performance evaluation. This work bridges theoretical modeling with applied maintenance strategies, offering a transferable methodology relevant to researchers and practitioners seeking to optimize reliability and productivity in continuous-process industries.

A mathematical programming model to select maintenance strategies in railway networks

Abstract. One of the most important problems in production management and operations is maintenance & repair. Setting up a proper maintenance & repair program prevents either unexpected failures and production disorders or time loss and expensive costs. On the other hand, this program increases useful life of machinery and keeps moderate level of productivity. The goal of this research is identification of suitable criterion for selection of an optimum maintenance & repair strategy, with their weighted importance, by Fuzzy-ANP method in textile industry. Thus, a model was designed for selection of proper criteria for maintenance & repair. After identification of proper criteria, the optimum maintenance & repair strategy in textile industry was selected by Expert Analysis method by a questionnaire. Then ANP technique was used to determine weights of indices. In this step, views of experts for pair comparisons of indices and their weights were extracted from the questionnaires. Finally, the results show that preventive repair strategy has the highest score of 0.43703, situation-based strategy with 0.242812, and predictive strategy with 0.16236. The score of maintenance & repair based on reliability is 0.157798, which is the lowest score.