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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.

A literature review is presented in the subject of construction plant and equipment management (CPeM) to: delineate the subject; consider its development over recent years; and identify principal themes within it. The paper aims to close the gap in knowledge, by using these objectives as a mechanism to observe how research themes relate to primary CPeM functions, and to suggest future research direction.

A thematic review of CPeM academic literature (in the main, refereed journal papers published in English‐speaking countries over the last decade) is undertaken; the nature of identified themes is discussed, for instance, regarding why they might have evolved as they have; and based on the foregone, themes for future research in the field are proffered.

CPeM is found well established within the broader subject of construction management. Eight principal themes are identified, namely plant maintenance; downtime and productivity; optimisation; robotics and automation; health and safety; operators and competence; machine control; and “miscellaneous”.

It is proffered that based on informational/technological advancements coupled with growing environmental/financial pressures, future CPeM research will strive to facilitate even greater plant reliability and safer modes of working. It is suggested that “optimum production methods” and “minimal resource consumption” will become inherent theme goals.

This is the first time that CPeM research has been consolidated and reviewed for publication in this manner.

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.

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 purpose of this paper is to review current literature analyzing human factors in maintenance, and areas in need of further research are suggested.

The review applies a novel framework for systematically categorizing human factors in maintenance into three major categories: human error/reliability calculation, workplace design/macro-ergonomics and human resource management. The framework further incorporates two well-known human factor frameworks, i.e., the Swiss Cheese model and the ergonomic domains framework.

Human factors in maintenance is a pressing problem. The framework yields important insights regarding the influence of human factors in maintenance decision making. By incorporating various approaches, a robust framework for analyzing human factors in maintenance is derived.

The framework assists decision makers and maintenance practitioners to evaluate the influence of human factors from different perspectives, e.g. human error, macro-ergonomics, work planning and human performance. Moreover, the review addresses an important subject in maintenance decision making more so in view of few human error reviews in maintenance literature.

The purpose of this paper is to analyze the rationale for choice of suppliers and the influence these decisions have on the firm’s capabilities.

The authors examine the choice of in-house operations vs buying maintenance in the Swedish mining industry through a qualitative case study approach.

The findings reveal a strong tendency to outsource maintenance.

This in turn has a strong influence on the firm’s capabilities and long-term competitive advantage and sustainability.

Based on the empirical findings, the authors comment on the strength and weaknesses of the different outsourcing and attempt to find practical solutions that assist the firm in creating competitive advantage.

The unique contribution of this study is that it extends prior firm capabilities studies by investigating the impact of capability loss specifically in complex, intricate maintenance processes in a dynamic industry.

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.

Following increases in national demands on healthcare facilities and services, healthcare facilities management (FM) has gradually matured to become an established research and development topic. This paper reviews the state of the art in the main domains related to healthcare FM and defines the central themes in the development of a healthcare FM model. FM, maintenance management and performance management are reviewed in a wider context, and the main domains of healthcare FM are discussed. The five salient topics included in healthcare FM are maintenance management, performance management, risk management, supply services management, and development. These five core domains are interrelated, and can be integrated using information and communications technology, which provides the desired environment required for the challenging decision making and development prevalent in healthcare FM.

Healthcare facility managers work in complex and dynamic environments where critical decisions are constantly made. Providing them with enhanced decision support systems would result in a positive impact on the productivity and success of the projects they undertake, as well as the sustainability of critical healthcare infrastructure. The purpose of this paper is to propose a conceptual ambient intelligent environment for enhancing the decision-making process of the facility managers. This low-cost data-rich environment would use building information modeling (BIM) and mobile augmented reality (MAR) as technological bases for the natural human-computer interfaces and aerial drones as technological tools.

This paper presents a scenario for the integration of augmented reality (AR) and building information modeling (BIM) to build an ambient intelligent (AmI) environment for facility managers where mobile, natural, user interfaces would provide the users with required data to facilitate their critical decision-making process. The technological requirements for having such an intelligent environment are also discussed.

The proposed BIM-MAR-based approach is capable of enhancing maintenance related practices for facility managers who are mobile to integrate with their facilities' intelligent environment. This approach is also capable of providing a collaborative environment in which different stakeholders, across geographically distributed areas, could work together to solve facility management tasks.

In this paper ambient intelligence will be considered for the first time in the area of healthcare facility management practices to provide facility managers with an intelligent BIM-based environment to access facility information and consequently enhance their decision-making process.

The study consists of a literature study and a case study. The need for a method via which to handle instruction complexity was identified in both studies. The proposed method was developed based on methods from the literature and experience from the case company.

The purpose of the study presented in this paper is to investigate how linking different maintenance domains in a modular maintenance instruction architecture can help reduce the complexity of maintenance instructions.

The proposed method combines knowledge from the operational and physical domains to reduce the number of instruction task variants. In a case study, the number of instruction task modules was reduced from 224 to 20, covering 83% of the maintenance performed on emergency shutdown valves.

The study showed that the other methods proposed within the body of maintenance literature mainly focus on the development of modular instructions, without the reduction of complexity and non-value-adding variation observed in the product architecture literature.