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Due to its key role in the successful delivery of construction projects, construction productivity is one of the most researched topics in construction domain. While the majority of previous research is focused on the productivity of labor-intensive activities, there is a lack of research on the productivity of equipment-intensive activities. The purpose of this paper is to address this research gap by developing a comprehensive list of factors influencing the productivity of equipment-intensive activities and determining the most influential factors through interview surveys.

A list of 201 factors influencing the productivity of equipment-intensive activities was developed through the review of 287 articles, selected from the ten top-ranked construction journals, by searching for construction productivity in the articles’ titles, abstracts or keywords. Next, the most influential factors were determined by conducting interview surveys with 35 construction experts. To ensure that the interviewees were aware of the research objectives and the distinction between labor- and equipment-intensive activities, an information session was held prior to conducting the surveys, and the surveys were conducted in interview format to allow for clarification and discussion throughout the process.

Project management respondents identified foreman-, safety- and crew-related factors as the categories with the most influence on productivity; tradespeople respondents identified foreman-, equipment- and crew-related factors as the most influential categories. In total, 14 factors were identified, for which there was a significant difference between the perspectives of project management and tradespeople regarding the factors’ influence on productivity.

This paper provides a comprehensive list of factors influencing the productivity of equipment-intensive activities. It identifies the most influential factors through an interview survey of 35 construction experts, who are familiar with the challenges of equipment-intensive activities based on their experience with such activities in the industrial construction sector of Alberta, Canada. Additionally, the differences between the factors that influence the productivity of labor- and equipment-intensive activities are discussed by comparing the findings of this paper with previous research focused on labor intensive activities.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

The main objective of this research is to identify the most important human resource management (HRM) practices, which have the potential to enhance labour productivity using fuzzy synthetic evaluation approach.

The study used a mixed-methods research design in which qualitative data were collected and analysed during Phase I and quantitative data were analysed during Phase II. Nineteen experts who have experience in building construction projects were involved in interviews conducted in Phase I. During Phase II, quantitative data were collected from contractors that were involved in the delivery of building projects using questionnaires and the data were analysed using FSE technique.

Clear delegation of responsibility, stability of organisational structure and crew composition are found to be the three most important HRM practices that can enhance productivity in building construction projects. The findings of the study showed that the overall importance index computed using the FSE model is 3.65 (≈ 4) with an equivalent linguistic term of “very important”. The study also suggested that the top three HRM practices should be implemented conjointly as there is no significant difference among their weights.

The output of this research can provide important information regarding the HRM practices in the Australian construction industry. Thus, international developers or contractors who want to do construction business in Australia can implement the essential HRM practices so that the productivity of their construction projects will not be affected negatively.

The purpose of this study is to present and compare the productive efficiency of China Mainland and China Hong Kong contractors, and to identify and investigate the components and sources of their efficiency under different economic and institutional environments.

Data envelopment analysis (DEA) is a non‐parametric approach to examine the relative efficiency among different firms. This study employs DEA based Malmquist Productivity Index (MPI) to compile the efficiency scores of 20 construction companies listed in the Hong Kong Exchange and Clearing Limited (HKEx) from 2004 to 2010.

A decomposition of MPI suggests that catch‐up effect has contributed more to contractor's efficiency than frontier‐shift effect. Compared to their Mainland counterparts, Hong Kong contractors have higher MPI mainly due to higher efficiency scores in catch‐up effect.

Hong Kong contractors have advantage over Mainland contractors in their managerial and strategic capabilities. Hence Hong Kong contractors should lever on their managerial expertise in accounting, financing and legal services when exporting their services. Meanwhile, Mainland contractors should improve their efficiency by making the most use of their technological and human resources, thus improving upon their international entrepreneurship.

This study is the first attempt to apply MPI to compare the productive efficiency of listed contractors in China Mainland and China Hong Kong. The findings contribute to the body of knowledge for productive efficiency measurement.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

This study aims to focus on exploring the construction productivity of building projects under the influence of potential factors. The three primary purposes are (1) determining critical factors affecting construction productivity; (2) identifying causal relationship and occurrence probability of these factors to develop a Bayesian network (BN) model; and (3) validating the accuracy of predictions from the proposed BN model via a case study.

A conceptual framework that includes three performance stages was used. Twenty-two possible factors were screened from a comprehensive literature review and evaluated through expert opinions. Data were collected using a structured questionnaire-based survey and case-study-based survey. The sampling methods were based on non-probability sampling.

Worker characteristic-related factors significantly affect labour productivity for a construction task. Construction productivity is dominated by the working frequency of workers (overtime), complexity of the task, level of technology application and accidents. Labour productivity is defined as nearly 50% of the baseline productivity using the BN model created by the caut 2sal relationship and probability of factors. The prediction error of the BN model was 6.6%, 10.0% and 9.3% for formwork (m²/h), reinforcing steel (ton/h) and concrete (m³/h), respectively.

The evaluation or prediction of productivity performance has become a necessary topic for research and practice.

Managers and practitioners in the construction sector can utilise the outcome of this study to create good productivity management policies for their prospective projects.

Worker-related characteristics are dominant among critical factors affecting labour productivity for a construction task; the proposed BN-based predictive model is built based on these critical factors. The BN approach is highly accurate for construction productivity prediction. The findings of this study can fill gaps in the construction management body of knowledge when modelling construction productivity under the effects of multiple factors and using a simple probabilistic graphic tool.

Spare parts management plays a relevant role for equipment-intensive companies. An important step of such process is the spare parts classification, enabling properly managing different items by taking into account their peculiarities. The purpose of this paper is to review the state of the art of classification of spare parts for manufacturing equipment by presenting an extensive literature analysis followed by an industrial assessment, with the final aim to identify eventual discrepancies.

Not only is the attention put on the literature about the subject, but also on an on-field analysis, that is presented comprehending an extensive survey and two in-depth exploratory case studies. The copper mining sector was chosen being representative for the case of capital intensive plants where the cost of maintenance has relevant weight on the total operating cost.

The paper highlights the status of the scientific literature on spare parts classification by showing the current situation in the real industrial world. The paper depicts the existing barriers that leave gaps between theory and real practice for the application of an effective multi-criteria spare parts classification.

The paper provides a review of the theory on spare parts classification methods and criteria, as well as empirical evidences especially for what concern current situation and barriers for an effective implementation in the industrial environment. The paper should be of interest to both academics and practitioners, since it provides original insights on the discrepancies between scientific and industrial world.

This study extends and refines the current knowledge on emerging supply chain designs (SCDs) for industrial additive manufacturing (AM) and manufacturing firms' rationales in selecting them.

Following an exploratory research design, a multiple-case study is conducted in the context of industrial AM. It focuses on two key dimensions of SCD, the geographic dispersion and governance structure. Four cohesive AM SCD configurations are characterized and form the basis for exploring the rationales for the SCD decision of manufacturing firms.

The findings indicate that manufacturing firms' SCD for industrial AM depends on the trade-off between economies of scale in a centralized setting and the market potential from customer proximity realized by decentral AM. Furthermore, the control of suppliers and the reevaluation of manufacturing firms' core competencies guide the governance choice. Many of the identified rationales currently drive manufacturing firms toward in-house AM at a centralized location or distributed AM in a secure, firm-owned network.

The arguments for the AM SCD choices are illustrated. They provide guidance for managers of manufacturing firms when implementing industrial AM.

The study reveals and enhances the understanding of why the extant academic expectation of decentralized and outsourced AM is not sufficiently reflected in current industry practice. Thereby, the study provides a basis for elaborative decision-support research on AM SCDs.

Examines the way that managers utilise equipment and facilities resources. Considers production management and productivity control. Stresses the importance of establishing control systems based on the collection of up‐to‐date information and effective routing procedures to ensure that production delays are minimized. Argues that a proper costing system is an important part of establishing a productivity measurement and control system and is the basis of effective planning and decision making.