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The purpose of this paper is to study the evolution of a system stationary availability and determine the optimal preventive maintenance period, which maximises it in a context where preventive and corrective maintenance actions are imperfect and have non‐negligible durations.

The quasi‐renewal process approach and a (p, q) rule are respectively used to model corrective and preventive maintenance. Considering the durations of the preventive and corrective maintenance actions as well as their respective efficiency extents, a mathematical model and a numerical algorithm are developed in order to compute the system stationary availability.

It has been proven that for any given situation regarding the system, the repair and preventive maintenance efficiency extents, and the downtime durations for preventive and corrective maintenance, there is necessarily a finite optimal period T* of preventive maintenance which maximises the system stationary availability. A sufficient condition for the uniqueness of the optimal solution has also been derived. Numerical examples illustrated how preventive and corrective maintenance efficiency extents affect simultaneously the system optimal availability.

The study considers a general industrial framework where preventive and corrective maintenance actions are imperfect. In fact, neither the best‐qualified technicians nor the most suitable tools or spare parts are found to carry out maintenance actions. In such a context for a large variety of technical systems, when implementing preventive maintenance policies one should take into account the efficiency extents of maintenance actions as well as their durations in order to evaluate and optimise the system availability. The paper provides maintenance managers with a decision model allowing not only the computation and optimisation of system availability, but also the investigation of how preventive and corrective maintenance efficiency extents affect simultaneously the system optimal availability. The proposed model also allows one to find to what extent corrective actions ineffectiveness should be tolerated without having an important availability loss.

The paper proposes a modified formulation of the quasi‐renewal process taking into account the non‐negligible duration of corrective maintenance actions and periodic preventive maintenance. A new numerical algorithm is also developed in this context to compute the quasi‐renewal function that it is impossible to find in closed form. This allowed the computation and optimisation of system stationary availability.

One of the main problems, when choosing between preventive or corrective maintenance for the production and maintenance departments in the eight firms studied, is to attempt to establish the state of a particular production system or individual production line. In order to carry out a cost analysis it is imperative to be able to measure how preventive maintenance will both reduce the deterioration of the state of the object and improve the state of the object at the point in time that the maintenance is carried out. This can be explained by the fact that the departments lack methods for measuring and estimating the effects. The model presented in this paper determines whether to schedule preventive maintenance and the model trades off the capital costs of preventive maintenance and the sum of corrective maintenance and down‐time costs based on the production line’s state.

This research developed and examined a mathematical model for concurrent corrective and preventive maintenance policy of a system of series configuration.

A mathematical model was developed to maximize availability, and maximal net revenues, and minimal cost. Different probability distributions for time to failure and time to repair were considered. The model was then implemented on a real case study, which was studied under corrective maintenance policy and concurrent corrective and preventive policy.

A comparison between results at current policy (90 days) and optimal period of corrective and preventive policy was conducted. It was found that availability, profit was increased from 94.4% and $20.091 – 96.5% and $24.803, respectively. Further, the cost was reduced from $1104.8 to $797.22.

The proposed optimization model can be adopted in planning maintenance activities for a single machine as well as for a system of series configuration machines under various probability distributions.

The proposed model can significantly enhance performance of the production as well as maintenance systems. In addition, the developed model may support maintenance engineering in effective management of maintenance resources and the performance of its activities.

This research considers a mathematical model with multi-objective functions and distinct probability distributions for time-to-failure for a system of series machines. Moreover, appropriate approximation solution was deployed to find integral of some functions. Finally, it provides maintenance planning for a single machine or a series of machines.

This paper presents the results of a case/simulation study that evaluated a number of potential maintenance policies for a flexible manufacturing system (FMS). Empirical data were used to structure the operation of the FMS, and to simulate its failures and repairs on the shop floor. Five maintenance policies – corrective, 30‐day preventive, 90‐day preventive, on‐failure opportunistic, and 30‐day opportunistic – were compared on four performance criteria: equipment utilization, machine downtime, through‐put, and average flow time. The “30‐day opportunistic” policy performed best overall, although the “corrective” policy was a close second, outperformed only in the area of equipment utilization. The “on‐failure opportunistic” policy performed poorly on every measure of system performance.

The purpose of this paper is to emphasize that the choice of the most appropriate maintenance model and policies is the best way to reduce significantly the maintenance costs as well as to optimize the useful Key Performance Indicators – failure rates, reliability, mean time between failures, mean time to repair, and equipment availabilities.

In order to implement the Asset Effectiveness Optimization AEO as well as the Overall Equipment Effectiveness OEE, improving productivity in a complex food‐products plant, the paper presents a theoretical and experimental study related to the maintenance costs directly associated with the equipment used in production tasks.

The developed tool is an efficient method of calculating the maintenance costs and allows one by means of computational simulation to define the most advisable maintenance policy. On the other hand, the proposed relationships are universal and could be used as an economic evaluation indicator for other industries and equipment.

Further research should include the application of the proposed methodology to the similar equipment of other food‐products plants as well as to other different equipment in order to create benchmarking procedures. This generator of technical information is the most appropriate method of optimizing maintenance key performance indicators.

As is well known, equipment availability must be as close to 100 per cent as possible, in order to avoid non‐planned breakdowns with the consequent production losses. Then it is important to adopt the most advisable maintenance policies and practices, the proposed methodology being an efficient tool for evaluating the maintenance performance and, in addition, for optimizing procedures.

The proposed methodology represents an efficient way to evaluate the maintenance performance as well as to choose better maintenance policies and practices in order to reduce costs and increase maintenance key performance indicators.

This study aims to identify the relationship between different maintenance practices and productive efficiency in continuous process productive plants as well as the moderating effect of good training practices.

The empirical data were drawn from a database containing 609 observations of 29 productive units. Scales were validated using the Q-sort method. The panel data technique was used as the analysis methodology, with the inclusion of fixed effects for each productive plant.

Maintenance practices can effectively contribute to increasing the overall equipment effectiveness (OEE) of firms. Application of predictive maintenance practices should be considered as the primary training tool.

This study used a secondary database, limiting the research design and data manipulation.

The article provides practitioners with an analysis of maintenance practices by category (predictive, preventive and corrective), and the impact of each practice on the OEE of continuous process productive plants. Moreover, it explores the importance of training for extracting more results from maintenance practices.

Companies are investing in new technologies, but it is also essential to invest in training people. There is a demand for Industry 4.0 through the introduction of upskilling and reskilling programs.

This study used practice-based view (PBV) theory to explain how maintenance practices help firms achieve greater OEE. Furthermore, it introduced training practice as a moderating variable in the relationship between maintenance practices and OEE.

Points out that increasing pressure to prolong the useful life of a building without compromising the objectives of maintenance has led to an increasing interest in methods of integrated maintenance management. As an alternative to budget‐driven maintenance strategies, develops a new approach to selecting an appropriate maintenance strategy which relies on determining the consequences of failure of every item in the building, and determining a suitable strategy for each one. Analyses the relative advantages and disadvantages of corrective, preventive and condition‐based strategies. Advocates a novel, systematic approach to the management of building maintenance. Suggests this method will help maintenance engineers and managers to reduce the cost of maintenance while preserving the safety, health and satisfaction of the user.

The purpose of this paper is to propose a new formulation of the overall equipment effectiveness (OEE) relying on the process approach (PA) in maintenance. This new indicator will be designated by OEE-TCQ.

Methodology followed in this paper is based on using two major parts. The first part deals with the use of time-driven activity-based costing (TDABC) and program evaluation and review technique (PERT) methods to formulate the temporal and financial components of the OEE-TCQ. The second part concerns the quality component formulation of OEE-TCQ by using the analytical hierarchy process (AHP) method and quality’s improvement (Six Sigma).

It is a new formulation of the OEE, based on the PA applied to operational maintenance processes, and also is interesting for improvement maintenance.

The new OEE-TCQ indicator can be used to support maintenance managers to have a three-dimensional analysis based on time, financial and quality criteria, also to improve time, cost and quality of each activity of maintenance operational processes.

This paper is one of few papers in maintenance proposing a new formulation of the OEE by using the combination of several methods especially TDABC, PERT, AHP and Six Sigma. It proposes in part a new quality evaluation approach for each activity of maintenance operational processes. Also, it suggests a new formulation for defects by millions of opportunities.

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

The purpose of this paper is to present a methodology for forecasting the availability off-highway trucks used in a fleet for large transport operations of ore and sterile rock from an open pit mine.

This methodology enables the estimation of the number of hours of preventive and corrective maintenance required, which are used to predict truck availability. The authors used historical data for the maintenance strategy based on the hours of operation.

These data are statistically analyzed to obtain the key quantities and statistical models required to project availability and to develop equipment replacement plans.

A methodology for forecasting availability to assets in open pit mining industry was implemented.