الفهرس 
Literature ReviewOnly 14 pages are availabe for public view
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Abstract
Strategic planning is a method that many organizations use to drive
processes that defines the whole company. Strategic planning allows organizations
to make fundamental decisions that guide them to a better future vision. The result
of this effort, the strategic plan, serves as the basis for a road map that directs all
resources toward an ideal future. On the other side, the tools for achieving the
future goals of any construction company should include the company mix of
assets and the company’s capital structure. Other factors shown rather clearly are
the effect of the macro-economic factors on the strategic planning decision for any
construction company. These factors mainly include the expected inflation, interest
rate and cost of capital. One should also consider the effect of the construction
market conditions. Among these conditions is the expected growth in the
construction market and the degree of competition in such market.
The objective of this study is modeling the strategic planning decisions that can
help construction companies to identify their future goals regarding the expected
annual volume of work and the expected level of profitability by developing two
models utilizing the Artificial Neural Networks and Statistical Regression analysis
for predicting (Turnover of Total Assets (Revenue/Total Assets), Revenue / Working
Capital, Return on Revenues (Profit/Revenues) and (Profit / Total Assets). To
achieve the objective of this research, a comprehensive literature survey was
performed in the area of construction industry to identify the most significantly
effective factors upon the models in-question. The results were the identification of
nine factors (Inflation Rate, Interest Rate, Gross Domestic Product (GDP) at
market price, Total Investment, Construction Investment, Total Assets, Current
Ratio (Current Assets/Current Liabilities), Total Debt/Total Assets, and Total
Debt/Net Worth).Strategic planning is a method that many organizations use to drive
processes that defines the whole company. Strategic planning allows organizations
to make fundamental decisions that guide them to a better future vision. The result
of this effort, the strategic plan, serves as the basis for a road map that directs all
resources toward an ideal future. On the other side, the tools for achieving the
future goals of any construction company should include the company mix of
assets and the company’s capital structure. Other factors shown rather clearly are
the effect of the macro-economic factors on the strategic planning decision for any
construction company. These factors mainly include the expected inflation, interest
rate and cost of capital. One should also consider the effect of the construction
market conditions. Among these conditions is the expected growth in the
construction market and the degree of competition in such market.
The objective of this study is modeling the strategic planning decisions that can
help construction companies to identify their future goals regarding the expected
annual volume of work and the expected level of profitability by developing two
models utilizing the Artificial Neural Networks and Statistical Regression analysis
for predicting (Turnover of Total Assets (Revenue/Total Assets), Revenue / Working
Capital, Return on Revenues (Profit/Revenues) and (Profit / Total Assets). To
achieve the objective of this research, a comprehensive literature survey was
performed in the area of construction industry to identify the most significantly
effective factors upon the models in-question. The results were the identification of
nine factors (Inflation Rate, Interest Rate, Gross Domestic Product (GDP) at
market price, Total Investment, Construction Investment, Total Assets, Current
Ratio (Current Assets/Current Liabilities), Total Debt/Total Assets, and Total
Debt/Net Worth).Strategic planning is a method that many organizations use to drive
processes that defines the whole company. Strategic planning allows organizations
to make fundamental decisions that guide them to a better future vision. The result
of this effort, the strategic plan, serves as the basis for a road map that directs all
resources toward an ideal future. On the other side, the tools for achieving the
future goals of any construction company should include the company mix of
assets and the company’s capital structure. Other factors shown rather clearly are
the effect of the macro-economic factors on the strategic planning decision for any
construction company. These factors mainly include the expected inflation, interest
rate and cost of capital. One should also consider the effect of the construction
market conditions. Among these conditions is the expected growth in the
construction market and the degree of competition in such market.
The objective of this study is modeling the strategic planning decisions that can
help construction companies to identify their future goals regarding the expected
annual volume of work and the expected level of profitability by developing two
models utilizing the Artificial Neural Networks and Statistical Regression analysis
for predicting (Turnover of Total Assets (Revenue/Total Assets), Revenue / Working
Capital, Return on Revenues (Profit/Revenues) and (Profit / Total Assets). To
achieve the objective of this research, a comprehensive literature survey was
performed in the area of construction industry to identify the most significantly
effective factors upon the models in-question. The results were the identification of
nine factors (Inflation Rate, Interest Rate, Gross Domestic Product (GDP) at
market price, Total Investment, Construction Investment, Total Assets, Current
Ratio (Current Assets/Current Liabilities), Total Debt/Total Assets, and Total
Debt/Net Worth).This research studied the potential application of the Artificial Neural Networks
and Statistical Regression analysis for predicting (Turnover of Total Assets
(Revenue/Total Assets), Revenue / Working Capital, Return on Revenues
(Profit/Revenues) and Profit / Total Assets). Two models were developed. One model
was developed utilizing artificial neural networks and the second model developed
utilizing statistical regression analysis. For models development, 110 realistic data
were used while the remaining ten realistic data were used for models validation.
The research results indicated that the best neural network model was obtained
through 94 experiments for predicting (Turnover of Total Assets (Revenue/Total
Assets), Revenue / Working Capital, Return on Revenues (Profit/Revenues), Profit /
Total Assets). This model consists of input layer with 9 neurons, one hidden layer
with 15 neurons, and one output layer with 4 neurons. The learning rate of this model
is 0.50 and the training and testing tolerance is 0.10. The results of testing the best
model indicated a root mean square error (RMS) of value 0.0683 and average error =
0.0606. The research’s results were indicated that backward regression model is
preferable to other regression models because it has the advantage of looking at all the
available variables in the early stages of the model development process.
A comparison between the predictive capabilities of the neural network
model versus the predictive capabilities of the backward regression model
indicates that the neural network model was preferred.