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High reliability, maintainability, safety and supportability are expected from today’s modern systems. In the recent years supportability has been widely accepted as a major factor in logistics discipline. The main purpose of this paper is to demonstrate how advanced mathematical models can be used to analyse the effect of supportability on systems availability. The paper discusses supportability aspects and its effect on operational availability of complex systems using advanced mathematical models like Markov, semi‐Markov and non‐Markov models. The powerful mathematical models discussed in the paper would help reliability engineers and practitioners to predict the logistic support requirements to achieve specified operational availability.

Proposes optimization models for spare provisioning. In the first model, considers a series system with m components where each component can have a maximum of (n‐1) spares. The objective function is to maximize the availability of the system satisfying a constraint on space required for the spares. In the second model, considers a series‐parallel system where each component of the system can have a maximum of (n‐1) spares. The optimization models developed in the paper can be solved using general purpose software such as SOLVER of EXCEL. Also presents an efficient branch and bound algorithm which can be used to solve the optimization problem.

Mathematical models for reliability prediction under multiple condition parameters are discussed. The models are developed under the assumption that the different condition parameters can be independent or dependent. Presents a model based on the continuous time Markov chain where the rate of degradation of the system depends on the state of the system and the fact that different parameters that cause system degradation can be dependent. The models are illustrated using an example.

Prediction of the duration of the downtime caused by maintenance, especially in the cases where the system considered consists of several repairable items, presents a challenge for maintenance managers, because of possible revenue losses during these intervals of time. Responds to this challenge through the new methodology for the fast, accurate prediction of maintainability measures related to the group replacement maintenance policy. It is applicable to group maintenance tasks in which individual replacement tasks are performed: simultaneously, sequentially, and combined. The method presented could be successfully used at the planning stage of the operations/production process when the information available is based on previous experience only, as well as at the stage when the process is performed. The applicability and usefulness of the methodology proposed is demonstrated through an illustrative numerical example.

Considers the new role of chief mechanic at Boeing – a role introduced for the 777 model. Looks at factors such as design build teams made up of members from areas such as engineering and customer support and who stayed involved throughout the design process. Considers aspects such as maintainability impact on availability and the education of design engineers. Provides information on built‐in‐test‐equipment and the aircraft information management system. Notes that the role of chief mechanic is now a permanent position at Boeing and suggests that the chief mechanic’s involvement throughout the design and development of the 777 was key to the success of the project.

The purpose of this paper is to develop a science‐based formulation of quality of maintenance processes.

The paper takes the form of scientific analysis of the faulty maintenance tasks to determine the pattern of phenomena and to understand the nature of causing mechanisms.

Faulty maintenance tasks are found to exist in statistical analysis of the tens of thousands of maintenance tasks in defence, aerospace, transportation (including Formula 1 Grand Prix racing), communication and other industries, which has led to the formulation of the probabilistic axiom of the quality of maintenance.

This axiom has a profound impact on the properties of any maintainable system, such as reliability, availability, safety, cost, effectiveness and many others, on the one hand, and associated processes such as manufacturing, operation, logistics support, on the other.

The paper presents the formulation of the second axiom of Mirce Mechanics, which is related to the quality of the maintenance processes and a new classification of quality‐based maintenance tasks.

Accurate selection of the quality of maintenance resources, such as spares, tools, equipment, personnel, technical data, facilities and so forth, has a strong influence on mission success, and the cost of ownership. Among maintenance resources, spare part requirements are the most difficult to predict. Items within any system are very different from the point of view of reliability, cost, weight, volume, etc. Hence, the selection of spares for a planned mission must take into account all these features. Presents the generic mission success‐driven spares model developed for complex systems. The mathematical analysis used in the model enables the user to make the most suitable selection of the spares package for the planned mission. The illustrative examples presented clearly demonstrate the applicability and usefulness of the model introduced.

Effective building maintenance management is vital for reducing the impact of building defects and costly building maintenance work. Such practice significantly contributes to the public sector in terms of cost reduction, improved effectiveness and efficiency in maintenance works, increasing safety and well-being of the occupants, expanding the life of building stocks and expanding the value of investment for the government building assets. The maintenance practice in Malaysia is still emerging in comparison to many developed nations. The purpose of this study is to determine the current scenario of the building maintenance approach at the design stage in public buildings in Malaysia.

An extensive questionnaire survey was held, involving 312 key players (38 per cent out of 820 predetermined participants) in Malaysia.

The analysis showed that building maintenance in public building in Malaysia still uses the reactive approach. Despite the Malaysian Government’s adoption of a proactive maintenance approach, unfortunately, there are some misunderstandings among the key players regarding the approach. The research results demonstrate that the key players had a lack of understanding on the concept of the proactive maintenance management approach and that they were either ignorant or had insufficient awareness of proactive maintenance approach.

The contribution of this study is useful for the government to adopt a more proactive building maintenance policy at the design stage, to give awareness on proactive building maintenance to the key players in their construction project and as a guide to the key players to adopt a maintenance plan at the design stage of work.

Spare parts needs are dependent on the characteristics of the product in question, e.g. its reliability and maintainability, and the characteristics of the environment in which the product is going to be used (e.g. the temperature, humidity, and the user/operator's skills and capabilities), which constitute covariates. The covariates have a significant influence on the system reliability characteristics and consequently on the number of required spare parts. The main objective of this research study is to evaluate the associated risks (i.e. risk of shortage of spare parts) in estimation of the required number of spare parts due to not considering the characteristics of system operating environment.

An event tree is a graphical logic model that identifies and quantifies possible outcomes following an initiating event (non‐considering system operating environment in this case) in spare parts planning. In the present research a risk analysis is performed through a new and non‐standard event tree analysis. It used an event tree analysis in which the states of organization and managerial decisions took place in risk analysis.

In the present study a modified form of event tree analysis was introduced and implemented. In the new version the undesired states were used instead of barriers in combination with events and consequents changes as a safety function in event tree analysis. The output of the event tree analysis shows that there is a considerable operational risk due to losses (production and economical) associated with the non‐consideration of the machine working environment.

In the estimation of the accurate amount of support and spare parts needed for any industrial system/machine, it is strongly recommended to take the product operating environment into account. This can be proved by the event tree risk analysis method used in a modified and non‐standard form in the present research. The results of risk analysis can help managers in making accurate decisions for product support and spare part needs in the future.

Modified event tree analysis is a new approach suggested for visualizing the risk associated with non‐considering of system operating environment in required support/spare parts estimation. Visualization of risk in graphics can facilitate correct decision making in spare parts planning.

Degradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.

A progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.

A comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.

The proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.