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The purpose of this paper is to develop a novel analytical approach for workface planning practice in industrial-construction sector such that the construction work package (CWP) resource budget can be sufficiently planned for delivering possible field installation work package (FIWP) schedules with work uncertainty.

The relationship between CWP resource budget and FIWP schedules is first elucidated based on workface planning practice. The literature of work packaging, workface planning and project scheduling is reviewed. A novel analytical approach is then developed to quantify CWP resource budget based on a probability theory, in consideration of the probability of occurrence of feasible FIWP schedules formulated based on a resource scheduling approach. The results of case studies given by the new approach are cross validated by using simulation and optimization techniques.

The new analytical approach can assist workface planning by quantifying the expected CWP resource budget to deliver the FIWP work scope with certain activities that are planned at project level and with uncertain activities that are found at workface level.

The new analytical approach helps project and workface planners to reliably deploy CWP resource budget for delivering FIWP schedules instead of guessing the budget based on experience. An industrial-construction project for upgrading oil-sands refinery facility is used to show the practical implications.

This research develops a new analytical approach for workface planning practice to determine sufficient CWP resource budget for delivering feasible FIWP schedules with work uncertainty.

For parts of the time on a typical construction site concrete pour, the site placing crew is idle waiting for the arrival of the next truckmixer delivery, whereas for other periods, truckmixers are idle on site waiting to be unloaded. Ideally, the work of the crew should be continuous, with successive truckmixers arriving on site just as the preceding truckmixer has been emptied, to provide perfect matching between site and concrete plant resources. However, in reality, sample benchmark data, representing 118 concrete pours of 69 m³ average volume, illustrate that significant wastage occurs of both crew and truckmixer time. The purpose of this paper is to present and explain the characteristics of the wastage pattern observed and provide further understanding of the effects of the factors affecting the productivity of this everyday routine site concreting system.

Analytical algebraic models have been developed applicable to both serial and circulating truckmixer dispatch policies. The models connect crew idle time, truckmixer waiting time, truckmixer round trip time, truckmixer unloading time and truckmixer numbers. The truckmixer dispatch interval is another parameter included in the serial dispatch model. The models illustrate that perfect resource matching cannot be expected in general, such is the sensitivity of the system to the values applying to those parameters. The models are directly derived from theoretical truckmixer and crew placing time-based flow charts, which graphically depict crew and truckmixer idle times as affected by truckmixer emptying times and other relevant parameters.

The models successfully represent the magnitudes of the resource wastage seen in real life but fail to mirror the wastage distribution of crew and truckmixer time for the 118 pour benchmark. When augmented to include the simulation of stochastic activity durations, however, the models produce pour combinations of crew and truckmixer wastage that do mirror those of the benchmark.

The basic contribution of the paper consists of the proposed analytical models themselves, and their augmented versions, which describe the site and truckmixer resource wastage characteristics actually observed in practice. A further contribution is the step this makes towards understanding why such an everyday construction process is so apparently wasteful of resources.

This paper aims to make a systematic review of the manpower prediction model of the construction industry. It aims to determine the forecasting model's development trend, analyse the use limitations and applicable conditions of each forecasting model and then identify the impact indicators of the human resource forecasting model from an economic point of view. It is hoped that this study will provide insights into the selection of forecasting models for governments and groups that are dealing with human resource forecasts.

The common search engine, Scopus, was used to retrieve construction manpower forecast-related articles for this review. Keywords such as “construction”, “building”, “labour”, “manpower” were searched. Papers that not related to the manpower prediction model of the construction industry were excluded. A total of 27 articles were obtained and rated according to the publication time, author and organisation of the article. The prediction model used in the selected paper was analysed.

The number of papers focussing on the prediction of manpower in the construction industry is on the rise. Hong Kong is the region with the largest number of published papers. Different methods have different requirements for the quality of historical data. Most forecasting methods are not suitable for sudden changes in the labour market. This paper also finds that the construction output is the economic indicator with the most significant influence on the forecasting model.

The research results discuss the problem that the prediction results are not accurate due to the sudden change of data in the current prediction model. Besides, the study results take stock of the published literature and can provide an overall understanding of the forecasting methods of human resources in the construction industry.

Through this study, decision-makers can choose a reasonable prediction model according to their situation. Decision-makers can make clear plans for future construction projects specifically when there are changes in the labour market caused by emergencies. Also, this study can help decision-makers understand the current research trend of human resources forecasting models.

Although the human resource prediction model's effectiveness in the construction industry is affected by the dynamic change of data, the research results show that it is expected to solve the problem using artificial intelligence. No one has researched this area, and it is expected to become the focus of research in the future.

Heavy industrial construction often relies on large mobile cranes to erect equipment and pre-assembled modules. Engineering calculations are required for the lifting analysis where lifting capacity is analyzed to ensure the feasibility of the lifting scenarios. Such engineering calculations are often presented in static formats, e.g. two-dimensional or three-dimensional models. However, it is difficult to help practitioners (e.g. lifting engineers, site crews and operators) understand the complexity of the lifting process and thus operational decisions are often made intuitively. Therefore, this paper aims to introduce a game-based simulation system to allow for interactive analysis of the lifting process to improve lifting efficiency and safety.

The proposed method treats the mobile crane as a robot with degree-of-freedoms, and the movements are simulated in the Unity game environment. The lifting capacity is calculated dynamically based on the lifting object weight, rigging weight and lifting radius.

Compared with the four-dimensional visualization, this development has added a dimension of real-time interactive simulation; this allows the users to understand the complexity and feasibility of the lifting process.

The developed prototype has been tested and validated using a real case study from a heavy industrial project with the possibility of generalizing crane lifting configurations.

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.

This study aims to investigate the effect of the health crisis, that is, coronavirus disease 2019 (COVID-19), on audit fees.

The authors use a sample of 5,008 international firms over the period 2014 to 2020. They use the ordinary least squares (OLS) regression to investigate the study hypotheses.

The results of OLS regression reveal a negative relationship between the COVID-19 pandemic and audit fees. This finding implies that the pandemic is associated with a reduction in audit fees.

This study contributes to the literature by providing the first comprehensive empirical evidence on the effect of the COVID-19 pandemic on audit fees. The results have implications for regulators and investors.

Despite the existing attempts on COVID-19 and audit fees, to the best of the authors’ knowledge, this study is the first that provides international insights into the economic consequences of COVID-19 on the accounting profession.

This study aims to investigate whether auditors compromise their independence for economically important clients in countries with a secrecy culture.

The authors empirically examine the research question based on a data set of 33 countries for the period from 1995 to 2018. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client size as a proxy for client importance. The authors adopt the Heckman (1979) two-stage model to mitigate the potential endogeneity issue involved in the selection of Big-N auditors.

Using a large sample of firms and controlling for the firm- and country/region-level factors, this study reveals that both Big-N and non-Big-N auditors are more likely to issue modified audit opinions to clients located in countries with a strong secrecy culture relative to those located in other countries. However, Big-N auditors are more likely to issue modified audit opinions for their economically important clients with a secrecy culture relative to their other clients, while no or weaker evidence is found for non-Big-N auditors. The results are consistent and robust to endogeneity tests and sensitivity analyses.

This study enriches the literature by providing a new perspective on auditor independence that an auditor’s reporting behavior can vary depending on the client’s importance and auditor type, even under the same secrecy culture.

Auditing is seasonal, with the majority of U.S. public companies having a December fiscal year-end. This results in an audit “busy season” and “off-season” with a non-trivial seasonal impact on the pricing of audit services. We apply an economic framework that explains how audit seasonality affects both the magnitude and the price elasticity of audit demand and audit supply. We find that the audit busy season is associated with an audit fee premium of approximately 10% based on a meta-analysis of 97 analyses from 18 audit fee studies of U.S public companies. A meta-regression of the contextual differences in research design between studies reveals that examining only Big N attenuates the busy season effect size but does not eliminate it, and that the busy season effect size may be larger post-SOX.