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Precise and logical plant maintenance evaluation, which enables enterprise to distinguish its most suitable maintenance pattern for application, is the base of improvement in plant maintenance. In this paper a new method based on data envelopment analysis (DEA) is proposed to evaluate the relative efficiency of plant maintenance. In this method, a systematic functional model is built to analyze the input/output of plant maintenance, which is processed as a service system, and DEA is applied as a calculate model for plant maintenance evaluation. Practical implications. The aim of this work is to provide management with a method to evaluate and quantify the relative efficiency of plant maintenance among a group of units. The maintenance pattern, scheme and experience of the relatively effective unit are guidance to others in improving themselves in plant maintenance and the quantified evaluation result points out the detailed maintenance limitation of relatively less efficient units so that the management can make corresponding improvement scheme.

The productivity and output levels of construction plant and equipment depends in part upon a plant operator’s maintenance proficiency; such that a higher degree of proficiency helps ensure that machinery is maintained in good operational order. In the absence of maintenance proficiency, the potential for machine breakdown (and hence lower productivity) is greater. Using data gathered from plant and equipment experts within the UK, plant operators’ maintenance proficiency are modelled using a radial basis function (RBF) artificial neural network (ANN). Results indicate that the developed ANN model was able to classify proficiency at 89 per cent accuracy using 10 significant variables. These variables were: working nightshifts, new mechanical innovations, extreme weather conditions, planning skills, operator finger dexterity, years experience with a plant item, working with managers with less knowledge of plant/equipment, operator training by apprenticeship, working under pressure of time and duration of training period. It is proffered that these variables may be used as a basis for categorizing plant operators in terms of maintenance proficiency and, that their potential for influencing operator training programmes needs to be considered.

The level of deterioration of educational facilities, such as leaking classroom roofs, inadequate good seats, obsolete offices, broken shutters and doors, outdated school buildings, power plants and office equipment like ICT media, laboratories, offices and workshops, despite the involvement of Alumni association in the maintenance of educational facilities for the effective actualization of school objectives was a worrisome nightmare in Kogi state. This, therefore, prompted the researchers to explore the Alumni’s participation in plant maintenance for effective implementation of the universal basic education (UBE) programme in public junior secondary schools in Kogi state. The purpose of this study was guided by the research question which stated that to what extent does the Alumni Association participate in plant maintenance for effective implementation of the UBE programme in public junior secondary schools in Kogi state?

A quantitative research approach using a descriptive survey was adopted for the study. The sample was drawn using a proportionate stratified sampling technique comprising 387 participants which consisted of 191 (49%) urban junior secondary school principals and 196 (51%) rural principals in selected UBE junior secondary schools. Alumni’s Participation in Plant Maintenance Questionnaire was used as an instrument for data collection. The data were analysed with the aid of mean and standard deviation for the research question and z-test statistics at 0.05 level of significance and the value of z-crit. of 1.96 was used to determine the rejection or otherwise of the hypotheses.

The descriptive analysis revealed that the average mean set of 2.64 pointed to the fact that the respondents averagely agreed that there was a high extent to which Alumni participate in plant maintenance in urban than rural areas for effective implementation of the UBE programme in public junior secondary schools in Kogi state. This indicates that Alumni contribute to the management of UBE schools in Kogi state, especially in the areas of funding, infrastructural facilities, discipline, politics and quality control. This finding also shows that the contributions of the alumni to educational institutions are still unclear if they have made contributions to education in some areas and none in other areas making their relevance to plant maintenance unclear.

In terms of practical implications, the study has contributed to knowledge in that it is the first of this form of a study carried out in Kogi state, and as such the findings of the research will make contributions to the physique of information on plant maintenance for the profitable implementation of the UBE programme in Kogi state. Besides, the degree of plant preservation for the implementation of the UBE programme among applicable stakeholders in Kogi state is nevertheless at a low extent.

Researchers have conducted studies that show how non-state Alumni members contribute to the administration of education across different states. Some of these studies revealed that Alumni members have assisted schools in the provision of teaching and learning materials at the senior secondary or tertiary education level. There are no sufficient studies to show how these Alumni members have contributed to the implementation of the free education programme, especially in public junior secondary schools in Kogi state and this is the gap this study intends to fill.

The purpose of this paper to develop a novel strategic approach to handle corrective maintenance procedure in the event of a breakdown/disruption of service. A proposal to minimize the recovery time and the breakdown cost in the system in construction plant is presented.

The past plant breakdown records of a construction organization are considered for the analysis. From the previous breakdown records, a high level metric using Pareto analysis and the cause effect analysis is used to identify the main breakdown main codes (BMC) and the subsequent breakdown sub codes (BSC). Prioritized BMC and BSCs are used to formulate dedicated breakdown maintenance teams, which act swiftly in the event of the breakdown with the modified methods.

The study was conducted, on four different types of heavy lifting/earth moving/material handling system equipment, which are used to load/unload/haul and transport construction materials. Failure due to tyre puncture and allied problems contribute to maximum failure. A strategy plan to minimize this type of failure is proposed. With the identification of the most contributing BMCs and BSCs, it is further proposed to develop an “overall breakdown maintenance management”.

The collected data pertains to the construction plant located in a particular region, namely the Middle East, and hence the proposed solution is dedicated/relatively applicable to similar plant from the same region. A more robust model can be suggested considering the work environment in the other regions.

The proposed methodology is highly adaptable by similar industries operating in the Middle East region.

Construction plant and equipment contribute to the success of construction organizations, by providing enhanced output, reduced manpower requirement, ease of work and timely completion of the project. Delays in completion of projects generally have both social and economical impact on the contractors and the buyers. The proposed model will bring down the lead‐time of the project and enable the contractors to crash down their project completion time.

Numerous studies on preventive maintenance models and procedures are available for a system and in particular to construction plant maintenance in the literature. This model attempts to handle the issues of unpredictable breakdowns in the construction plant to minimise the breakdown time. The proposed model is a novel approach which enables a quick recovery of the construction plant, attributed from the breakdown parameters derived from the previous history of the work records/environment.

Hiring, selecting or assessing plant operatives' proficiency in the UK construction industry is an increasingly difficult task. A number of plant operator certification schemes are available to practitioners and each scheme trains to a myriad of bespoke standards. Consequently, the decision to employ a candidate often rests upon the employer's intuition and judgement and creates an unnecessary dilemma. To address this aforementioned problem, findings of research work that modelled plant operators' maintenance proficiency is presented. A UK nationwide survey was conducted to elicit plant professional opinion on what ‘training and educational’ (T&E) attributes constitute ‘good’ operator proficiency. The data was then arranged into three categories of operator maintenance proficiency: good, average and poor Multivariate Discriminant Analysis (MDA) was used on 75 percent of a simulated data set. The model utilised five T&E attributes, namely: duration of training provided, operator holder of alternative training card (not Certificate of Training Achievement (CTA) or Scottish/National Vocational Qualifications (S/NVQ)), operator's oral communication skills, operator's planning skills and operator's mechanical knowledge. Performance analysis revealed that model classification accuracy was 89.10 percent. The remaining 25 percent hold out sample was then modelled for validation purposes using the derived MDA model. Accuracy of the sub‐sample model was high at 77.60 percent whilst a paired sample T‐tests for the 75 percent and 25 percent sample data established that there was no significant statistical difference between actual and predicted classifications. Future work is proposed that aims to model other factors that influence operator maintenance proficiency; namely, work situational, motivational management and personal factors.

The market‐oriented competitive environment in electric utilities has forced many power plants to become more conscious of the role of maintenance management in enhancing their equipment performances and consequently improving the quality of their services. Good equipment maintenance practices can improve the reliability of the power system; maintenance has become the prominent management issue for electric utilities. In recent years, power plants have started using benchmarking to identify the best practices for enhancing their maintenance works. In this paper, a case on benchmarking for maintenance management in a large‐scale power plant is analyzed. Benchmarking is used to search for optimum methods for maintenance management practices in order to improve the overall effectiveness of the operations and maintenance of the plant. By adopting the best practices appropriately, benchmarking could help plants to become more cost‐effective in maintenance. However, for plants looking for breakthrough improvement in maintenance, on top of benchmarking, other means, i.e. intelligent decision support system (IDSS) for maintenance, are required as well.

The purpose of this paper is to propose a methodology to evaluate the annual maintenance budget (AMB) for a plant system as a percentage of its asset replacement value (ARV).

Variables influencing the budget for the plant system are identified and modeled in terms of a plant maintenance Budget digraph. The nodes in the digraph represent the function of budget influencing variables and edges represent the degree of influence among these. The plant maintenance budget function is derived from the equivalent matrix of the plant maintenance budget digraph and is used to evaluate the AMB for the plant system as a percentage of its ARV. The presented model is illustrated with an example.

The proposed methodology enables the maintenance managers and practicing engineers to evaluate the AMB for maintenance tasks based on plant specific variables that can vary from one plant to another, without resorting to general rules-of-thumb method of budgeting or to expert judgment.

The methodology suggested does not claim to cover all aspects of budgeting and is confined to direct maintenance costs.

A fair estimation of AMB for a plant system gives an appropriate direction to maintenance actions and ensures that the assets will be kept in healthy and reliable state. This is desirable because it will ensure that maintenance resources are appropriately benchmarked and the resources are utilized judiciously. This ensures maintenance effectiveness.

The paper presents a structural approach to budgeting for maintenance tasks. This approach is valuable for plant managers, with ease in implementation.

Maintenance expenditures represent more than net profits of several manufacturing companies. Maintenance costs are easy to reduce in a year simply by deferring maintenance activities. Examines maintenance expenditures of US chemical companies from 1975 to 1991. Uses a nonlinear model based on seemingly unrelated regression analysis to identify various factors that influence the expenditures. Uses the ratios of maintenance spending to cost of property, plant and equipment, and buildings (gross plant), cost of property, plant and equipment, and buildings minus accumulated depreciation (net plant) and cost of goods sold as dependent variables. Shows that the age of property, plant and equipment, and buildings, company size, and return on assets have significant influence on the maintenance expenditures. The leverage ratios adversely affect the ratios of maintenance costs to gross plant and net plant.

The chemical plant (CP) maintenance industry has been under increasing pressure by process designers to demonstrate its evaluation and information management of model checking (MC) on the durability’s performance and design of plant control instrument. This main problem has been termed as imperfect maintenance actions (IMAs) level. Although IMAs have been explored in interdisciplinary maintenance environments, less is known about what imperfect maintenance problems currently exist and what their causes are, such as the recent explosion in the Beirut city (4 August 2020, about 181 fatalities). The aim of this paper is to identify how CP maintenance environments could integrate MC within their processes.

To achieve this aim, a comprehensive literature review of the existing conceptualisation of MC practices is reviewed and the main features of information and communication technology tools and techniques currently being employed on such IMA projects are carried out and synthesised into a conceptual framework for integrating MC in the automation system process.

The literature reveals that various CP designers conceptualise MC in different ways. MC is commonly shaped by long-term compliance to fulfil the requirement for maintaining a comfortable durability risk on imperfect maintenance schemes of CP projects. Also, there is a lack of common approaches for integrating the delivery process of MC. The conceptual framework demonstrates the importance of early integration of MC in the design phase to identify alternative methods to cogenerate, monitor and optimise MC.

Thus far, this study advances the knowledge about how CP maintenance environments can ensure MC delivery. This paper highlights the need for further research to integrate MC in CP maintenance environments. A future study could validate the framework across the design phase with different CP project designers.

Construction plant maintenance practice and its plant operators are inextricably linked. This is because, unlike plant operating within the manufacturing sector, construction plant is largely dependent upon operator skill and competence to maintain the item in a safe, fully operational condition. Research has previously successfully modelled machine breakdown, but revealed that the operator’s impact upon machine breakdown rates can be considerable. A conceptual model methodology with which to assess the maintenance proficiency of individual plant operators is presented. Specifically, an artificial intelligent classification model is proposed as a means of classifying plant operator maintenance proficiency into one of three bandings. These are good, average and poor. The results of such work will form the basis of new prescriptive guidelines, for incorporation into the new certificate of training achievement (CTA) scheme, available to inexperienced construction plant operators. The paper concludes with an indication of the palpable benefits of such research, to plant owners and the construction industry at large.