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The paper presents a methodology to utilise available information from condition monitoring systems. Before a new approach to determine optimal time for overhaul/replacement is introduced, a brief review of existing mathematical models for this purpose is presented. Unlike the usual approach of looking at failure rates and life time distributions, the focus is put on extracting information from models of continuous‐time deterioration processes. Defining a finite number of condition levels of the system, the continuous‐time deterioration process is described by a condition transition probability matrix. All input data are modelled as a function of time or system status. We have also the flexibility to include cyclic variation as for example changes in production demand.

A replacement model over two cycles, with decision variables based on age at replacement of the current fleet and size of the new fleet is considered. In order to consider both age at replacement and fleet size, the concept of penalty cost for unmet demand is introduced and modelled using results from birth‐and‐death processes. Optimal values for decision variables may be found through minimization of the total discounted cost per unit time or the equivalent rent value. The role of the penalty cost and its influence on decision variables is emphasized. A numerical solution is proposed and illustrated using data on a particular fleet of medical equipment.

The purpose of this research is to explore and define the digital maturity of events using the Industry 4.0 model (I4.0) to create a definition for Events 4.0 (E4.0) and to place various relevant technologies on a scale of digital maturity.

In a mixed methods approach, we carried out a qualitative social media analysis and a quantitative survey of tourism and events academics. These surveys and the thorough literature review that preceded them allowed us to map the digital technologies used in events to levels of a digital maturity model.

We found that engagement with technology at events and delegate knowledge satisfactorily coexists for and across a number of different experiential levels. However, relative to I4.0, event research and the events industry appear to be digitally immature. At the top of the digital maturity scale, E4.0 might be defined as an event that is digitally managed; frequently upgrades its digital technology; fully integrates its communication systems; and optimizes digital operations and communication for event delivery, marketing, and customer experience. We expect E4.0 to drive further engagement with digital technologies and develop further research.

This study has responded to calls from the academic literature to provide a greater understanding of the digital maturity of events and how events engage with digital technology. Furthermore, the research is the first to introduce the concept of E4.0 into the academic literature. This work also provides insights for events practitioners which include the better understanding of the digital maturity of events and the widespread use of digital technology in event delivery.

The purpose of this paper is to model a block-based inspection policy for a multi-component system with stochastic dependence. Some components may develop a hidden failure, an occurrence of which neither stops the system nor accelerates the other components’ deterioration. On the other hand, other components may experience three states: healthy, defective and revealed failures. Any revealed failure of each component not only stops the system but also generates a shock to all the other ones, which increases their occurrence rate of hidden, defect and revealed failures.

A block-based inspection policy is considered to take advantage of economic dependence as follows. In addition to the periodic inspections, the system is also inspected at revealed failures’ moments of each component to detect and fix both defects and hidden failures on all the other components. To calculate the expected total cost, the recursive equations for the required expected values is first mathematically derived. Then, due to computational complexity, an efficient Monte Carlo simulation algorithm is designed to calculate the expected values.

The proposed approach is illustrated through a numerical example, and the optimal periodic inspection interval over a finite time horizon is obtained via minimization of the expected total cost. Finally, the correctness of the results is validated by conducting sensitivity analysis.

Planning an appropriate inspection policy over a finite time horizon becomes more complicated when considering a multi-component system because different units may experience different failure modes with stochastic dependence.

The purpose of this paper is to propose a condition-based opportunistic maintenance policy considering economic dependence for a series–parallel hybrid system with a K-out-of-N redundant structure, where a single component in series is denoted as subsystem1, and K-out-of-N redundant structure is denoted as subsystem2.

Based on the theory of Residual Useful Life (RUL), inspection points are determined, and then different maintenance actions are adopted in the purpose of minimizing the cost rate. Both perfect and imperfect maintenance actions are carried out for subsystem1. More significantly, regarding economic dependence, condition-based opportunistic maintenance is designed for the series–parallel hybrid system: preemptive maintenance for subsystem1, and both preemptive and postponed maintenance for subsystem2.

The sensitivity analysis indicates that the proposed policy outperforms two classical maintenance policies, incurring the lowest total cost rate under the context of both heterogeneous and quasi-homogeneous K-out-of-N subsystems.

This model can be applied in series–parallel systems with redundant structures that are widely used in power transmission systems in electric power plants, manufacturing systems in textile factories and sewerage systems. Considering inconvenience and high cost incurred in the inspection of hybrid systems, this model helps production managers better maintain these systems.

In maintenance literature, much attention has been received in repairing strategies on hybrid systems with economic dependence considering preemptive maintenance. Limited work has considered postponed maintenance. However, this paper uses both condition-based preemptive and postponed maintenance on the issue of economic dependence bringing opportunities for grouping maintenance activities for a series–parallel hybrid system.

The purpose of this paper is to represent replacement policies (rules) in the form of a matrix. Visualization of replacement rules is useful for maintenance records. Matrix representation is more effective than the verbal description usually provided, as it allows better understanding of the specifics of the different replacement rules without careful research of their mathematical models.

This approach employs mathematical models to investigate the simple conditions (requirements) for replacement of system component with illustrative examples. When comparing the different replacement rules a cost structure is applied to takes into account the nature and technology of disassembly assembly actions for the repair unit.

Representation of replacement rules in the matrix form is useful when describing planned replacement models, opportunity replacement models, group replacement models and others, as well as computer modeling of the renewal process. Forming simple conditions for the replacement of system components ensures the total average repair cost is minimized. These conditions can be applied in the early stages of creating a maintenance program for the machine.

Replacement matrices can be specified in a technical manual for maintenance of machines to achieve reliable operation and to reduce repair costs. Replacement matrices can be put into practical use for maintenance records and may be included in the maintenance procedures library of CMMSs. Developed in the paper, the replacement matrix, the conditions for replacement of system components and the cost structure will help engineers to make decisions at the time of repair for assembly units.

Proposed in the paper is a new approach to the visualization of the replacement rules and cost structure which simplifies the analysis of options for repair actions. The proposed technique contributes to the record of maintenance actions and the decision making process for replacement.

The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs).

The problem is a four‐parameter optimization with two parameters being k‐dimensional. The optimal solution is obtained by using a four‐stage approach where at each stage a one‐parameter optimization is solved.

Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF.

There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment.

Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

The purpose of this paper is to review the literature on maintenance optimization models and associated case studies. For these optimization models critical observations are made.

The paper systematically classifies the published literature using different techniques, and also identifies the possible gaps.

The paper outlines important techniques used in various maintenance optimization models including the analytical hierarchy process, the Bayesian approach, the Galbraith information processing model and genetic algorithms. There is an emerging trend towards uses of simulation for maintenance optimization which has changed the maintenance view.

A limited literature is available on the classification of maintenance optimization models and on its associated case studies. The paper classifies the literature on maintenance optimization models on different optimization techniques and based on emerging trends it outlines the directions for future research in the area of maintenance optimization.

The paper provides many references and case studies on maintenance optimization models and techniques. It gives useful references for maintenance management professionals and researchers working on maintenance optimization.

The purpose of this paper is to investigate age replacement policies for two-component parallel system with stochastic dependence. The stochastic dependence considered, is modeled by a one-sided domino effect. The failure of component 1 at instant t may induce the failure of component 2 at instant t+τ with probability p _1→2. The time delay τ is a random variable with known probability density function h _{p 1→2} (.). The system is considered in a failed state when both components are failed. The proposed replacement policies suggest to replace the system upon failure or at age T whichever occurs first.

In the first policy, costs and durations associated with maintenance activities are supposed to be constant. In the second replacement policy, the preventive replacement cost depends on the system’s state and age. The expected cost per unit of time over an infinite span is derived and numerical examples are presented.

In this paper and especially in the second policy, the authors find that the authors can get a more economical policy if the authors consider that the preventive replacement cost is not constant but depends on T.

In this paper, the authors take into account of the stochastic dependence between system components. This dependence affects the global reliability of the system and replacement’s periodicity. It can be used to measure the performance of the system et introduced into design phase of the system.

Due to a lack of data, many maintenance optimisation models have to be initialised on the basis of expert judgment. Rather than eliciting the parameters of a continuous lifetime distribution, experts give more reliable answers when assessing a discrete lifetime distribution. If the prior uncertainty in the probabilities of failure per unit time is expressed in terms of a Dirichlet distribution, Bayes estimates can be obtained of three cost‐based criteria to compare maintenance decisions over unbounded time‐horizons: first, the expected average costs per unit time; second, the expected discounted costs over an unbounded horizon; and third, the expected equivalent average costs per unit time. Illustrates the maintenance model by determining optimal age replacement and lifecycle costing policies, which optimally balance both the failure cost against the preventive repair cost, and the initial cost against the future cost.