Global Advanced Research Journal of Engineering, Technology and Innovation (GARJETI) SSN: 2315-5124 June 2012 Vol. 1(3), pp 063-074

Copyright © 2015 Global Advanced Research Journals   

 

Original Research Articles

Assessment of the expected construction company's annual work volume using neural network and multiple regression models 

Mohamad H.H.1,  Ibrahim A. H.2  And  Massoud H.H.3 

1Associate Prof., Construction Engineering Dept., Faculty of Engineering, Zagazig University, Egypt .

2Assistant Prof., Construction Engineering Dept., Faculty of Engineering, Zagazig University, Egypt .

3Ph.D. Student, Construction Engineering Dept., Faculty of Engineering, Zagazig University, Egypt . 

Corresponding author Email: hazem_mm1969@yahoo.com 

Accepted 14 June 2012


Abstract

Annual work volume of any construction company can be considered as an important indicator for the company's financial performance. Business success heavily depends on the ability of financial executives to maximize the company's net profit and annual work volume. Consequently, the firm financial managers should continuously strive to maximize their company's annual work volume. Modelling the company's annual work volume can help financial management to investigate the serious effect that the different financial conditions can have on the expected annual work volume of their companies. Stated differently, financial managers can make sure that business operations of their companies are running in a successful manner. For example, inadequate working capital may interrupt the normal operations of the business which impairs the company's annual work volume and consequently its profitability. To elaborate more, excessive levels of current assets may have a negative effect on firm's work volume and profitability whereas a low level of current assets may lead to lower level of liquidity and stock outs which results in difficulties in maintaining smooth operations that leads to a corresponding decline in the annual work volume.  The objective of this research is to develop a mathematical model for the assessment of the expected construction companies' annual work volume. First, the main factors affecting firms' annual work volume were identified based on a comprehensive literature review. Next, pertinent data regarding these factors were collected. Such data are mainly concerned with the companies' financial statements as well as the economic environment. Then, two different annual work volume models were developed using the Multiple Regression (MR) and the Neural Network (NN) techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.   

Keywords: Construction Company's Annual Work Volume, Neural Network, Multiple Regressions.

Related Articles


Original Research Articles
Fereshteh Mehri, Maryam Adabi, Elaheh Talebi Ghane, Salman Khazaei
The concentration of toxic metals in teas: A global systematic review, meta-analysis and probabilistic health risk assessment
Glo. Adv. Res. J. Eng. Technol. Innov. May 2020 Vol: 9(5): - [Abstract] [Full Text - PDF] (709 KB)
Nwite
Implications of Small And Medium Enterprises on South East Development in Nigeria
Glo. Adv. Res. J. Eng. Technol. Innov. December 2014 Vol: 3(12): - [Abstract] [Full Text - PDF] (272 KB)
Owolabi A. Usman and Adegbite Tajudeen Adejare
The effects of foreign exchange regimes on industrial growth in Nigeria
Glo. Adv. Res. J. Eng. Technol. Innov. November 2012 Vol: 1(1): - [Abstract] [Full Text - PDF] (182 KB)
Anjana Bhardwaj, Manish, Arora AK
Classification of MUAP by using ANN pattern reorganization technique
Glo. Adv. Res. J. Eng. Technol. Innov. August 2012 Vol: 1(5): - [Abstract] [Full Text - PDF] (872 KB)
R. S. Morgan, I. S. Rahim, M. Abd El-Hady
A Comparison of Classification Techniques for the Land Use/ Land Cover Classification
Glo. Adv. Res. J. Eng. Technol. Innov. November 2015 Vol: 4(11): - [Abstract] [Full Text - PDF] (6,232 KB)
Charles O Ondoro, Patrick B Ojera and Moses N Oginda
Role of strategic purchasing and supply management practices in firm performance: A lesson from public bus transport firms in Kenya
Glo. Adv. Res. J. Eng. Technol. Innov. August 2013 Vol: 2(8): - [Abstract] [Full Text - PDF] (278 KB)

Current Issue

Viewing Options

View Full Article - PDF
Download Full Article - PDF

Search for Articles

Mohamad HH on Google Scholar
Mohamad HH on Pubmed
Ibrahim AH on Google Scholar
Ibrahim AH on Pubmed
Massoud HH on Google Scholar
Massoud HH on Pubmed

Statistics

Viewed 2170
Printed 704
Downloaded 880
Powered By iPortal Works