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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.

Research suggests that personal motivation is a critical internal driving force that, if harnessed, can significantly improve an operator's productivity rate when working mobile plant and machinery. The purpose of this paper is to investigate the impact of personal motivation upon plant operator productivity (and examine those variables that stimulate personal motivational forces).

To achieve this, an artificial neural network (ANN) was developed. The ANN's topology comprised a multilayer perceptron, with one hidden layer and three output classifications of “good”, “average”, and “poor” (productivity performance). During development of this model, a non‐linear dynamic mapping function and metric were used to improve its classification accuracy. The model initially utilised 32 independent (input) variables identified from the literature such as: pay bonuses; relationships between work colleagues; promotion prospects; and job satisfaction.

Subsequent analyses condensed these variables down to the five most significant (i.e. best motivational classifiers of operative productivity), these being: (v₂) receipt of payment for overtime, (v₁₁) job promotion potential, (v₂₂) a safe working environment, (v₂₄) variety of work activities, and (v₃₁) availability of flexible work patterns. Model accuracy when employing these most significant predictors was high at 87.67 per cent. By testing on a hold out sample of original data, the developed model was validated as being reliable and robust.

The main conclusion of the work is that operators' personal motivation can best be encouraged by paying attention to “personal satisfiers” and “security” aspects, with particular emphasis being given to work flexibility and variety, a safe work environment, and appropriate operator remuneration. By delivering and exploiting these variables, employers can improve plant productivity rates and, as a consequence, company profitability.

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.

To evaluate the Cusum model as a means of rapid trend detection and dynamic response classifier in a low signal/noise environment.

The method used was the design and implementation of an IT system for automatic on‐line monitoring of press operator performance.

Operators engaged following an in‐house skills enhancement programme display a wide range of performance profiles.

Identifies opportunities for further management investigation of working practices identified as leading to regression plus operators obviously learning to “work smarter, not harder”.

A range of novel performance templates based on the Cusum model have been derived and tested on 18 press operators.

The Cusum model is already well established in the literature. This case study shows that the model can be readily exploited via automatic operator performance monitoring and subsequent classification.

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.

IN recent issues we have had contributionsion the future of Work Study as seen by Council members of the Institute of Industrial Technicians, the Society of Industrial Engineers and the Work Study Society.

Excavator productivity calculations embrace myriad variables, which in turn, can be modelled in several ways. A key productivity variable is operator competence (O _c ) because this can impact on so many of the other variables. Earlier research has studied excavator productivity, but little has attempted to simultaneously model productivity variables in relation to O _c . The purpose of this paper is to address the void in extant literature.

A numeric, theoretical analysis is undertaken using the Caterpillar® hydraulic excavator productivity model to estimate excavator production, given: first, variance in modifying factors based on derived maximum and minimum values; and second, variance resulting from linear calculations based on excavator operator competence.

Excavator productivity resulting from incremental variance of modifying factors in isolation is shown to be linear except, in the case of bucket payload. Simultaneous application of modifying variables results in a greater, curvilinear productivity trend; while it is demonstrated that quantification of key modifying factors can to a significant extent be related to operator competence.

Findings add to productivity literature generally and to that of plant and equipment more specifically. Results will help productivity estimation of excavation in a practical sense while informing subsequent design of an empirical academic research of this problem.

Originality relates principally to determining modifying factor ranges and their analysis of simultaneous effect on each other, especially, as influenced on assumptions of operator competence.

Drawing on the theoretical concept of organisational fit, this paper questions the relevance of employees' participation in the link between continuous improvement (CI) and operational performance. The literature has long emphasised that to be successful, CI implementation needs to rely on employees' involvement as soon as its inception. This paper argues that this approach is not generalisable.

Based on a database of 330 firms across 15 countries, regression analyses were used to hypothesise that the fit between CI and employee participation is positively associated with operational performance, and that the fit between CI and centralisation of authority is negatively associated with operational performance. The authors also ran a robustness check with polynomial regression analyses and the response surface methodology.

CI–employee participation fit is positively associated with operational performance, suggesting that there is less need for employees to be involved when a firm has scarcely developed CI. Employee participation becomes gradually more relevant as CI progresses. Moreover, the results demonstrate that the CI–centralisation of authority fit is negatively associated with operational performance, suggesting that a top-down management approach with centralised authority is preferable when CI is low, whereas a bottom-up management approach is helpful when a firm has extensively developed CI.

This research draws on the concept of organisational fit to explore the relationships between internal practices in the operations management literature. The authors suggest that managers should dynamically balance the practices of employee participation and centralisation of authority as CI improves. This study highlights that CI has different evolutionary levels that require different managerial approaches and practices.

In recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement.

The input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models.

The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students.

Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students.

The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.