A Critical Review of Forecasting Models to Predict Manpower Demand

Wong, Johnny; Chan, Apc; Chiang, Yat Hung

doi:10.5130/ajceb.v4i2.2930

Cited by 23 publications

(20 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They discussed challenges arising from rapid alterations in the duties and roles of military workforce and the civilian labor market. In a comprehensive review by Wong, Chan, and Chiang (2012), recent workforce planning methods were assessed to identify improvements for future workforce models developed in the construction industry. They elaborated the shortcomings associated with existing models such as ignoring complex fundamentals that affect human resources for health (HRH), including the effect of price competition on workforce participation and ignoring the influence of HRH decisions on public health.…”

Section: Previous Reviewsmentioning

confidence: 99%

“…They identified the vast majority of models as static models that lacked historic data to test their validity. In a comprehensive review by Wong, Chan, and Chiang (2012), recent workforce planning methods were assessed to identify improvements for future workforce models developed in the construction industry. They distinguished top-down forecasting as the chief approach used for dealing with workforce demand rather than bottom-up methodologies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Workforce forecasting models: A systematic review

Safarishahrbijari

2018

Journal of Forecasting

View full text Add to dashboard Cite

Workforce analytics involves using models that integrate internal and external data to predict future workforce and help organizations in any industry examine factors that have a prognostic effect. This paper assesses workforce modeling and prediction methods by examining their rationale, strengths, and constraints. It aims to identify enhancements for further development of workforce forecasting models and compares the capacity and reliability of different forecasting methods. Past and present modeling trends are described and critiqued based on their relevance to current requirements. Several approaches are reviewed, such as time series modeling and system dynamics simulation. Sensitivity analysis in models is assessed. The models are decomposed into three modes: supply‐based, demand‐based, and need‐based, which in some cases provide substantially different estimates of future workforce need. The chronological progression of models' development is analyzed. The articles are also classified based on the countries and the sectors that have paid great attention to workforce prediction research. Consideration of the use of workforce models and the inputs into such models is not within the scope of this paper.

show abstract

Section: Previous Reviewsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Workforce forecasting models: A systematic review

Safarishahrbijari

2018

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…For the estimation of labor supply and demand within the construction industry, five main classes of models including employer surveys, models of evolutionary comparison, mechanistic models, econometric modeling and cohort models [7] , are assessed by examining their rationale, strengths and constraints, and compared with their reliability, capacity and other aspects, respectively [8] . A number of quantitative econometric modeling techniques including multiple regression [9] , artificial neural networks (ANN) [10] , vector error correction (VEC) [11,12] , autoregressive integrated moving average (ARIMA) [13] and gray model (GM) [14] , have been utilized for simulation and prediction via a set of inter-related factors such as social variables, economic variables, industry variables and etc.…”

Section: Multivariate Time-series Analysis Of Microeconomics and Labomentioning

confidence: 99%

Comparative analysis of regional labor cost variations in China’s construction industry: Panel data modeling and analysis

Luo¹,

Fan²,

Liu³

2018

Creative Construction Conference 2018 - Proceedings

View full text Add to dashboard Cite

Labour cost estimation, control, and regulations are of great importance to the final success of construction project, and sustainable development of construction industry. Over the recent years, the affordability and availability of construction workforce, underpinned by rich labour resources across the Mainland China, has gradually become the history and exerted ripple effects on the construction manpower recruitment, cost management, and even industry development. However, little research has been found with focus on labour cost fluctuations at regional level in China. This study attempts to explore the major factors affecting regional construction labour cost variations over the past two decades, from 1995 to 2015. Panel data analysis, and time series econometric modelling, is thereby applied to identify critical determinants of construction labour cost fluctuations across three regions of different levels of economic development, i.e. underdeveloped west region, developing central region, and developed east region, within Mainland China's construction industry. Empirical results indicate that gross domestic product (GDP), unemployment rate, construction labour productivity, construction technical equipment ratio, and construction profit rate are five key factors determining the variations of unit labour cost in Mainland China's construction industry. GDP, construction labour productivity and unemployment rate are three common factors that affect regional construction labour cost; Besides, construction profit rate is found to be another dominant determinant of construction labour cost in west region, while construction technical equipment ratio acts as a significant but negative factor in central and east regions, with incremental effects towards construction unit labour cost from west region to central region, then east region. For the evolving construction market, these principal findings provide valuable insights for construction enterprises to formulate forward-looking market strategies, and for governments to fine tune economic policies.

show abstract

“…It aims to identify enhancements for further development of manpower forecasting model for the construction industry and compare the reliability and capacity of different forecasting methodologies 3. Given the improvement of the data available, advanced modeling techniques, and computer programs, manpower planning is likely to be more accessible with improved accuracy at every level of the society (Wong et al 2004;Bartholomew et al 1991;Agapiou et al 1995;Willems 1996).…”

Section: Forecasting Models To Predict Manpower Demandmentioning

confidence: 99%

“…Therefore, it is very important for construction companies to offer suitable prices for tenders for construction projects they are about to bid on based on the price of awards from previous tenders (Ng et al 2001;Wong et al 2004;Wong and Ng 2010).…”

Section: Forecasting Of Tenders For the Construction Of Programsmentioning

confidence: 99%

Mathematical Methods—Statistics and Forecasting

Haidar

2015

Construction Program Management – Decision Making and Optimization Techniques

View full text Add to dashboard Cite

Given the improvement of data available, advanced modeling techniques, and computer programs, the design, construction, and the delivery of programs are likely to be more accessible with improved accuracy at every level of the program. This chapter will provide an introduction for statistics applications and forecasting methods to familiarize program managers with their applications as they have been proven applicable and apprehendable for various aspects of a program delivery. Statistics with its wide scope is a very relevant tool for program management. It is concerned with scientific methods for collecting, organizing, summarizing, presenting, and analyzing data as well as with drawing valid conclusions and making reasonable decisions on the basis of such analysis. In a narrower sense, the term statistics is used to denote the data themselves or numbers derived from the data. A forecast is a prediction, projection, or estimate of some future activity, event, or occurrence. When historical data are available, some proven statistical forecasting methods have been developed for using these data to forecast future demand. Such a method assumes that historical trends will continue, so management then needs to make any adjustments to reflect current changes in the construction industry and the management of programs. Statistics AnalysisStatistics appertaining to program management involves finding out the best sample and populations required to solve a wide range of problems. Thus, we speak of recruitment and employment statistics, on-site accident statistics and statistics related to types of construction, leverage risk provisions, and forecasting statistics analysis.Programs are large and in many cases involve huge data. A program involving 400 schools in a developed country will involve mega studies related to the number of students per year, the age of the students, and the demographic distribution of the students which will involve the design, the size, and the type of schools. Then, it is

show abstract

A Critical Review of Forecasting Models to Predict Manpower Demand

Cited by 23 publications

References 17 publications

Workforce forecasting models: A systematic review

Workforce forecasting models: A systematic review

Comparative analysis of regional labor cost variations in China’s construction industry: Panel data modeling and analysis

Mathematical Methods—Statistics and Forecasting

Contact Info

Product

Resources

About