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Manpower forecasting models in the construction industry: a systematic review

Yijie Zhao (School of Highway, Chang'an University, Xi'an, China) (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)
Kai Qi (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)
Albert P.C. Chan (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)
Yat Hung Chiang (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)
Ming Fung Francis Siu (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 14 July 2021

Issue publication date: 16 August 2022

1974

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Keywords

Acknowledgements

The authors gratefully acknowledge the Development Bureau of the Government (Contract No. WQ/088/17) of Hong Kong (SAR) and Research Grants Council GRF (F-PP6M) of Hong Kong (SAR) for providing the funding enabling the commissioning of this study.

Data availability statement: All data, models and code generated or used during the study appear in the submitted article.

Citation

Zhao, Y., Qi, K., Chan, A.P.C., Chiang, Y.H. and Siu, M.F.F. (2022), "Manpower forecasting models in the construction industry: a systematic review", Engineering, Construction and Architectural Management, Vol. 29 No. 8, pp. 3137-3156. https://doi.org/10.1108/ECAM-05-2020-0351

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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