FORCASTING THE NUMBER OF BIDDERS IN THE IRAQI SCHOOL PROJECTS TENDERS

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 11, November 2018, pp , Article ID: IJCIET_09_11_115 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed FORCASTING THE NUMBER OF BIDDERS IN THE IRAQI SCHOOL PROJECTS TENDERS Gafel Kareem Aswed, Hussein Ali Mohammed, and Mohammed Neamah Ahmed College of Engineering, Karbala University, Karbala, Iraq ABSTRACT The lowest bid assignment approach is used in most infrastructure projects in the Iraqi public sector. The client announces the estimated cost of the project throughout bidding call. The successful bid depends, to a certain extent, on the contractors number sharing in the bid and ventures estimated cost. The contractor's decision to participate in the bid is based mainly on the competition s intensity and some other factors. In this paper, the bids prices of five contracting companies, which were submitted for auction in Karbala Governorate - Iraq for the period were comprehensively analyzed along with the estimated cost. Non-Linear Regression (NLR) and Artificial Neural Network analysis (ANN) were employed to create prediction models. It has been concluded that the contractor can predict the number of competitors using two factors: the degree of competition and the estimated cost of the project, whereas fruitless relationship in the case of client. Key words: Competitive bid, bidding price, school s projects, Tendering Cite this Article: Gafel Kareem Aswed, Hussein Ali Mohammed and Mohammed Neamah Ahmed, Forcasting the Number of Bidders in the Iraqi School Projects Tenders, International Journal of Civil Engineering and Technology (IJCIET) 9(11), 2018, pp INTRODUCTION There is a strong relationship between the large number of tenderers and the decline in bid prices, particularly, in school projects construction in Iraq. Underestimation bid price of this category of projects may be due to: 1. The traditional construction activities; 2. The large number of contractors hereafter 2003; 3. The announcement of the project estimated cost prior bidding step; 4. The severe competition between the contractors; and 5. Other minor factors. All the above-mentioned factors cause failure to the school projects. To avoid bidding in a highly competitive tender, it is advisable for the contractor to be aware of the applicants number who is going to compete with in the contract in order to mature his decision to participate in the tender or not. In this regard, a great deal of studies has been conducted to editor@iaeme.com

2 Forcasting the Number of Bidders in the Iraqi School Projects Tenders investigate the relationship between certain factors and the results of tenders in government projects. Skitmore et al., [1] carried out a study aiming in developing a bid spread analysis. They revealed that the bid is entirely random and there is no correlation between the contract value and the bidders number. The study also suggested implementing further research to shed a light on this case. Salem [2] proposed a method for bid selection other than the low bid price. It depends on the proportion of the bidder s price to the client cost estimate for the purpose of eliminating the effect of contract size. Carr [3] found that minimizing the competitive bidder s number can increase the tender price under specific conditions. Skitmore [4] generated a Monte Carlo simulation from a normal distribution of six bidders. He stated that bidders with relatively high CV levels and a larger number of involved bidders can bid higher in equilibrium, but they can expect little profit unless the number of involved bidders is small. Enshassi et al., [5] specified 78 factors from the previously published work that influence on contractor s decision to enter the bidding process. They concluded that it is necessary for the client and the contractor to improve their financial status and efficiency. Wong et al., [6] developed a vector error correction model to predict the tender price movements based on a group of associated financial and macroeconomic variables. They found that their model can successfully transcend the Box-Jenkins and other regression models. Oo et al., [7] examined the competitiveness between bids according to the project size, the work sector, the work nature, and the number of bidders in Hong Kong. They recommended that future bidding modelling should focus on individual models. Hu [8] found that in the first-price auctions when the seller and/or buyers are risk averse, the seller s optimal reserve price is a decreasing function of bidders number. Caovalitwongse et al., [9] proposed a regression and two neural network models to select the closest bid to the estimated cost of the project instead of the lowest bid price method. Ballesteros et al.[10] tried to prepare a Bid Tender Forecasting Models (BTFM) methodology to assess separately the future bidders attitude in which scoring and position effect on the bidding process been evaluated. Ballesteros et al., [11] compared the number of distributions by means of multiple chisquare tests for a number of bidders. They found that the log-normal distribution is the best one for a given contract size. Azman [12] stated that the cost, the state region, and the distance between the project site and the material source could affect the number of bidders. Schmidt [13] used the game theory to analyse the influence of expected opponent s decision in bid price that setting in public auction. He revealed that the tender price counts on the costs related to the performance of the contract and the foreseeable number of bidders. Tehran [14] evaluated engineers estimation based on bids examination after excluding dissonant bids. Aje et al., [15] found that the winning rate of Nigerian contractors in competitive bidding was influenced by some important factors such as material availability, labour productivity and profit level editor@iaeme.com

3 Gafel Kareem Aswed, Hussein Ali Mohammed and Mohammed Neamah Ahmed Ballesteros et al., [16] developed an approach to dislodge the bias resulting from the distribution of contract size by assessing the probability of engagement of every possible bidder s in future tenders as a function of the tender price. Venkatesh et al., [17] presented a bid evaluation approach to select the best bid when excluding the unbalanced prices. They found that the advanced profit has an impact on the total project cost. Based on the suggestion of Oo et al., [7], this study aims at investigating the relationship among the bidders numbers (NB) (as a dependent variable) and the competitiveness percentage (C) as well as the estimated cost (EC) of school projects in Iraq as independent variables. This has been achieved by developing mathematical models based on a number of bidding contracting companies on school construction projects in Karbala province to be as a guide for them in taking a decision in their future bidding. 2. DATA DESCRIPTION Project information, such as the lowest bid, the contractor bid, the cost estimate, and the number of competitive bidders was collected from the contract department in the governorate of Karbala Iraq, and directorate of school buildings of Karbala Iraq. The span of the data was set over a period of five years ( ), which is belonging to (63) school projects. All school data was subjected in the predictive models. 3. COMPETITIVE PRICE IN BIDDING The closed secret tender method is used in school projects bidding in Iraq. The estimated cost of the school project is announced by the official employee resulting in very strong competitiveness. The opening and analysing tenders committee is hold and the contract was assigned to the lowest bid price. Equation (1) commonly used to calculate the competitiveness percentage for each contracting firm in the case study [7]: Where: X= bid value for the individual firm. X1=lowest bid value for the contract. The low value of (C) refers to a very high competition and vice versa. The (C) value could be within zero and infinity. 4. MODELS DEVELOPMENT Eleven models (Linear, Quadratic, Cubic, S-curve, Logarithmic, Inverse, Power, Logistic, Growth, Compound and Exponential) were explored. The best fit model between (NB) and every single factor (EC) in millions of Iraqi dinars (ID) and (C) for the five contracting firms was checked. The SPSS program version 23 was utilized to estimate the relation models as shown in Figure (1). Nonlinear regression model (NLR) test was used to develop the final models (see Figure (2)). (1) editor@iaeme.com

4 Forcasting the Number of Bidders in the Iraqi School Projects Tenders Figure 1 Model Estimation in SPSS program Figure 2 Final Models Development in SPSS program The resulting models are as in Equations (2, 3, 4, 5, and 6) for the companies numbered (1, 2, 3, 4 and 5) respectively. (2) With (R2=76.4%) (3) With (R2= 75.3%) With (R2= 76.3%) With (R2=68.9%) (4) (5) editor@iaeme.com

5 Gafel Kareem Aswed, Hussein Ali Mohammed and Mohammed Neamah Ahmed With (R2= 65.7%) (6) 5. CLIENT MODEL For the same data, a nonlinear regression model test was used to assess the client model as shown in Equation (7) and Table (1): With (R 2 = 21.7%) (7) Table 1 ANOVA analysis for the client data Source Sum of Squares df Mean Squares Regression Residual Uncorrected Total Corrected Total Dependent variable: No. of competitive bidders R 2 = 1 - (Residual Sum of Squares) / (Corrected Sum of Squares) = ARTIFICIAL NEURAL NETWORK MODEL The Radial Basis Function (RBF) neural network was used. The training set was 70% of the data set and the testing set was 30%. Input layer was the percentage of competitiveness (C) and the estimated cost (EC). The selected rescaling method for covariate was the standardized method. One hidden layer with (9) units and Softmax activation function were adopted. The output layer was the number of competitive bidders (NB) and the activation function was the identity as shown in Figure (3). The results showed that the testing relative error was 0.581, correlation coefficient (R) was 66.2% and R 2 was 43.8%. Figure 3 RBF neural network for Client Model editor@iaeme.com

6 Forcasting the Number of Bidders in the Iraqi School Projects Tenders 7. DISSECTION AND CONCLUSIONS Based on the study results, each contractor could be able to expect the number of his competitor before bidding on school projects based on the estimated cost of the school project and the intensity of the competition between bidders. The built models interpret between 65.7% and 76.4% of the variance in the number of bidders. The rest percentage between 23.6% to 34.3% is explained by other random factors that have not been studied. Other possible factors, for example, the market conditions, the contractor need for work and bidding success rate that may affect the competitiveness, were not been considered. It could be said that there was a good relationship between the number of bidders and the two studied factors: estimated cost of the school project and degree of competitiveness. The small value of R2 =21.7 for client model indicated that there is no clear relation between the above factors according to ANOVA test. Artificial Neural Network (ANN) analysis also showed that the client would not be able to predict the number of bidders and the developed model interpret no more than 43.8% from the dependent variance (NB). 8. LIMITATIONS AND FUTURE RESEARCH The main limitation of this study is its assumption that the contractor will bid in the future contention as before regardless other random variable factors. The applicability of this research findings on projects other than school projects was not investigated. Future research could be carried out for other types of projects or new variables. ACKNOWLEDGEMENTS The authors would like to acknowledge the cooperation of the contract department in the governorate of Karbala Iraq, and the directorate of school buildings of Karbala Iraq, who provided the required data. REFERENCES [1] Skitmore, R.M. and Drew, D.S. and Ngai, S. (2001). Bid-spread. Journal of Construction Engineering Management, 127(2): pp [2] Salem, M.A. (2001). Construction bid price evaluation. Canadian Journal of Civil Engineering, 28(2): pp [3] Carr, P.G. (2005). Investigation of bid price competition measured through pre-bid project estimates, actual bid prices, and a number of bidders. Journal of Construction Engineering and Management, 131(11): pp [4] Skitmore, M. (2008). First and second price independent values sealed bid procurement auctions: some scalar equilibrium results. Construction Management and Economics, 26(8): pp [5] Enshassi, A., Mohamed, S. and El Karriri, A.A. (2010). Factors affecting the bid/no bid decision in the Palestinian construction industry. Journal of Financial Management of Property and Construction, 15(2): pp [6] Wong, J.M. and Ng, S.T. (2010). Forecasting construction tender price index in Hong Kong using the vector error correction model. Construction Management and Economics, 28(12): pp [7] Oo, B.L., Drew, D.S., and Runeson, G. (2010). Competitor analysis in construction bidding. Construction Management and Economics, 28(12): pp editor@iaeme.com

7 Gafel Kareem Aswed, Hussein Ali Mohammed and Mohammed Neamah Ahmed [8] Hu, A. (2011). How the bidder s number affects optimal reserve price in first-price auctions under risk aversion. Economics Letters, 113(1): pp [9] Art, W., Wang, W., Williams, T.P. and Chaovalitwongse, P. (2011). Data mining framework to optimize the bid selection policy for competitively bid highway construction projects. Journal of Construction Engineering and Management, 138(2): pp [10] Ballesteros-Pérez, P., González-Cruz, M.C., Fernández-Diego, M. and Pellicer, E. (2014). Estimating future bidding performance of competitor bidders in capped tenders. Journal of civil engineering and management, 20(5): pp [11] Ballesteros-Pérez, P. González-Cruz, M. Carmen, F. Jose L. and Skitmore, M. (2015). Analysis of the distribution of the number of bidders in construction contract auctions. Construction Management and Economics, 33(9): pp [12] Azman, M.A. (2014). Number of Bidders in Small and Medium Public Construction Procurement in Malaysia. Applied Mechanics & Materials, (567). [13] Schmidt, M. (2015). Price Determination in Public Procurement: A Game Theory Approach. European Financial and Accounting Journal, 10(1): pp [14] Tehrani, F.M. (2016). Engineer s estimate reliability and statistical characteristics of bids. Cogent Engineering,3(1): Pp.-12, Doi / [15] Aje, I.O., Oladinrin, T.O., and Nwaole, A.N. (2016). Factors influencing the success rate of contractors in competitive bidding for construction work in South-East, Nigeria. Journal of Construction in Developing Countries, 21(1): pp [16] Ballesteros-Pérez, P., Skitmore, M., Pellicer, E. and Gutiérrez-Bahamondes, J.H. (2016). Improving the estimation of the probability of bidder participation in procurement auctions. International journal of project management, 34(2): pp [17] Venkatesh, S., Rao, S.V. and Student, P.G. (2017). Evaluation Model for Unbalanced Bidding in Construction Industry. International Journal of Engineering Science, Volume 7 Issue No.5, Pp editor@iaeme.com