SYNERGIC IMPACT ON SLIPPAGE,
COST AND TIME IN CONSTRUCTION
PROJECT
C. K .Georgekutty1
Civil Engineering, Cochin University of Science And Technnology Kalamaserry,Kochi,Kerala,India
ckgeorgekutty@hotmail.com
Phone 91-484-2310530, 4024865, Mobile 94470 31053
Dr .George Mathew 2
Associate Professor, Fire and Safety department ( Co-Author) Civil Engineering, Cochin University of Science And Technnology
Kalamaserry,Kochi,Kerala,India george_m@cusat.ac.in
Phone 91-484-257794
Abstract:
Construction Industry in Kerala has a physical environment which influence cost, time. This is a peculiar issue of a developing country, which leads to many setbacks in completion of projects. This approach has made many challenges in the growth of construction. Material management is an important aspect in project planning and control. It contributes a major portion of expense in construction projects. Controlling procurement and carrying cost can reduce total project cost. To identify the reason for setbacks and suggest an appropriate workable solution under the special circumstances is the aim of this study. A questionnaire survey has been conducted among hundred selected ongoing housing projects in Kochi and nearby districts. Based on the study, only 15% of the projects are expected to complete in time. Though all materials are equally important in construction, some of the key materials specifically control the project cost. A dynamic control over the materials can control project cost. Continues process will optimize the total project cost.
Key words- Construction- Material management- Project cost –Optimum solution.
Introduction
Literature Review
The major objective of this research is to find the synergic influence of slippage, cost, and time overrun in construction projects. Success of a project mainly depends on the ability to complete it in time within the budget and quality. Ibbs (2007) expressed that project delays are caused due to the direct and indirect cost. Delay of a project during its progress may lead to material loss, revenue loss, and improper utilization of machinery and tools.
Saka and Mudi (2007) observed that Nigerian construction industries, are collecting materials locally instead of collecting from suppliers. Whereas Indian construction industry is procuring materials from the point of origin [CMJ] ie. directly from the manufacturer. Iyer (2006) explains that Indian construction projects are facing cost and time overrun. The reasons are being studied by researchers to suggest possible remedial measures. Martin (2004) explains that most of the research has been taken up in developed countries, and their applicability in developing countries such as India, is yet to be explored.
Chan and Kumaraswamy (1994) expressed their views that similar issues of time over in Hong Kong Construction Projects. Projects taken up in India would be completed at a later stage, or would remain
incomplete, and in both cases the end result would be the same. Anderson (1983) expressed that a questionnaire survey is the bridge between researchers, and respondents. Conventional material purchasing has some problems related with solicitation process, such as searching for suppliers and obtaining product data information. E-trading portals may reduce the problems; however, new problems occur due to their approaches to build a closed system with their own customers and suppliers. In this research, a decentralized database system equipped with electronic agents for material procurement is proposed.
Iyer (2003) states that Construction project slippages in India are mainly due to the following sectors. Project planning and designing, implementation and material procurement and storage. Sturges,(2000) highlighted that several management techniques are available for early implementing a project. “Construction planners need to schedule and select appropriate resources, including crew sizes, materials, equipment and plant, to execute a construction project. These resources are essential for the successful completion of the project”
Ren (2008) explained that construction is delayed due to rapid growth of construction, tight construction schedule, involvement of International contractors, Unique architectural features, etc. Donyavi, (2009) has stated that-“The materials cost on a project ranges from 30% to 70% of the total project cost. Ofori [2000] explains that the construction industry is having poor co-ordination. Poor planning leads to inefficiency, low productivity, excessive waste, and health and safety problems. Faniran et.al. [1994] reported that improper planning is one of the major problems for construction. Seetharaman [2003] has stated that construction materials and components contribute around 50-60% of the total value of construction. He also comments that procurement of materials directly affects operations and profits.
Asnaashari et.al.[2009] focused on various reasons for project delay and concluded that the major delay is due to an unpredicted event which has least influence in the management system. Joy [2010] articulate that inventory control is exercised on stock carrying. The objective is to carry such quantity of physical stock as would enable an efficient execution of the construction.
Rasheed [1998] in his case research in procurement in Malaysian construction industry highlighted that there are constraints in procurement. Wamuziri. [2010] stated that in Scotland, most of the infrastructure projects signed has been delivered through PFI/PPP, procurement model. The basic objective of the resource management is to supply and support the field operations. The completion of a construction project at maximum efficiency of time and cost requires the judicious scheduling and allocation of available resources. Previous researches has not indicated the special physical conditions existing in Kerala. For estimating the synergic influence of cost and time overrun and slippages are the novelty of the current research.
Significance of the Research
Methodology of the Study
As a rule there is no hard and fast systems available for a construction implementation. A questionnaire survey and interaction study was conducted among the selected ongoing projects in different districts of Kerala with the technical heads and promoters. of the construction projects. The field results were tabulated and analyzed by statistical tool. The important tests carried out for the analysis …………
The following author’s books are refered for the field data analysis, Arbuckle, J. L. (2006)., Barclay, D., Higgins, C., & Thompson, R. (1995), Catell, R.B. (1966)., Hair, J. F. Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006), Kline, R. B. (2005).
Table-1 T-test for scale
QUESTION Group
Mean
Std.
Deviation t df
Sig. (2-tailed)
Q1 Lower 1.0000 .00000 -15.411 35
<.001
Higher 4.1000 .85224
Q2 Lower 1.0000 .00000 -28.707 35
<.001
Higher 4.4737 .51299
Q3 Lower 1.0000 .00000 -29.142 35
<.001
Higher 4.5263 .51299
Q4 Lower 1.0000 .00000 -29.911 35
<.001
Higher 4.5789 .50726
Q5 Lower 1.0000 .00000 -29.142 35
<.001
Higher 4.5263 .51299
Q6 Lower 1.0000 .00000 -29.911 35
<.001
Higher 4.5789 .50726
Q7 Lower 1.0000 .00000 -29.911 35
<.001
Higher 4.5789 .50726
Q8 Lower 1.0000 .00000 -29.911 35
<.001
Higher 4.5789 .50726
Q9 Lower 1.0000 .00000 -28.707 35
<.001
Higher 4.4737 .51299
Q10 Lower 1.0000 .00000 -15.041 35
<.001
Higher 4.1053 .87526
Q11 Lower 1.0000 .00000 -29.142 35
<.001
Higher 4.5263 .51299
Q12 Lower 1.0000 .00000 -28.707 35
<.001
Higher 4.4737 .51299
Q13 Lower 1.0000 .00000 -29.142 35
<.001
Higher 4.5263 .51299
Q14 Lower 1.0000 .00000 -28.592 35
<.001
Higher 4.4211 .50726
Q15 Lower 1.0000 .00000 -28.707 35
<.001
Higher 4.4737 .51299
FINDINGS
The statistical analysis comprised two stages. The first stage examined the descriptive statistics of the measurement items and assessed the reliability and validity of the measure used in this study. The second stage tested the proposed research model and this involved assessing the contributions and significance of the manifest variables path coefficients.
Exploratory Factor Analysis
value of 0.6 and the Barlett’s Test of Sphericity reached statistical significance, supporting the factorability of the correlation matrix.
Table 3 Factor loadings and rank
Variables Tech Rank Mat Rank Suc Rank
Q1 0.539 5 0.577 7
Q2 0.559 4 0.556 9
Q3 0.520 7 0.398 14
Q4 0.761 2 0.750 2
Q5 0.733 3 0.662 5
Q6 0.795 1 0.782 1
Q7 0.535 6 0.541 10
Q8 0.440 8 0.418 12
Q9 0.533 5 0.461 11
Q10 0.757 2 0.579 6
Q11 0.534 4 0.212 15
Q12 0.803 1 0.574 8
Q13 0.617 3 0.747 3
Q14 0.300 7 0.403 13
Q15 0.499 6 0.669 4
Total variance
explained 27.69% 22.86% 16.98%
PCA of responses with vari-max rotation.
Confirmatory Factor Analysis
Table 4 Fit Indices of the Proposed Research Model
Recommended Level of Fit Tech (planning) Mat SUC
χ2
16.561 5.101 135.183
DF
16 11 80
P
>0.05
0.415 0.926 0
Normed χ2
<3
1.035 0.464 1.69
GFI
>0.90
0.946 0.98 0.82
AGFI
>0.91
0.879 0.949 0.729
NFI
>0.92
0.91 0.951 0.836
TLI
>0.93
0.994 1.137 0.899
CFI
a>0.94
0.996 1 0.923
RMR
<1
0.123 0.109 0.262
RMSEA
<0.05 0.022 0 0.099
Table 5 Standardized Regression
Variable Tech Rank Mat Rank Success Rank
Q1 0.332 7 0.373 9
Q2 0.341 6 0.417 8
Q3 0.370 5 0.265 11
Q4 0.702 3 0.575 4
Q5 0.786 2 0.652 3
Q6 0.836 1 0.993 1
Q7 0.415 4 0.433 6
Q8 0.257 8 0.175 14
Q9 0.222 6 0.254 12
Q10 0.454 3 0.327 10
Q11 0.526 2 0.111 15
Q12 1.049 1 0.426 7
Q13 0.39 4 0.916 2
Q14 0.234 5 0.178 13
Q15 0.198 7 0.502 5
Teh
Q4
Q5
Q3
.37
.70
.79
e3
e4
e5
Q6
e6
Q7
e7
Q2
e2
Q1
e1
.34 .33
.84
.41
Q8
e8
.26
.53 .37
.34
-.30
Fig.2 CFA Model for Technology
Mat
Q12
Q13
Q11
.53
1.05
.39
e3
e4
e5
Q14
e6
Q15
e7
Q10
e2
Q9
e1
.45 .22
.23
.20
.44
.32
.24
Suc
Q5 Q4
.57
.65
e4
e5
e6
Q8 e8
Q9 e9
Q3 e3
Q2 e2
.27 .42
.18
.25
e11 Q1
Q10
Q11
Q12
Q13
Q14 Q6
Q7
.37
.99
.43
.33
.11
.43
.92
.18
e1
e7
e10
e12
e13
14
Q15 15
.50 .92
.86
.86
.52 .36
-.15 -1.29
.29
.22 -1.04
Fig.4 Reliability Model for Success of Multi-story construction project.
Content validity
Content validity is a non-statistical type of validity that involves “systematic examination of the test content to determine whether it covers a representative sample of the behavior domain to be measured” or it the extent to which a measuring instrument provides adequate coverage of the topic understudy. If the instrument contains a representative sample of the universe, the content validity is good its determination is primarily judgmental and intuitive. It can also be determined by using a panel of persons, who shall judge how well the measuring instruments meets the standard, but there is no numerical way to express it. Accordingly the researcher consulted various safety experts and academic professionals in this field for this purpose and hence ensured that the questionnaire so prepared for the evaluation of work stress has sufficient content validity.
Face validity
Convergent validity
It is one of the approaches to the construct validity. Convergent validity refers to the degree to which a measure is correlated with other measures that is theoretically predicted correlated with in other words convergent validity is gauged by comparing it to measure of the same concept developed through other methods to assess how well the items are together. This involves empirical and theoretical support for the interpretation of the construct each item in the scale is treated as different approach to measure the construct. By using confirmatory factor analysis each item in the scale is checked with the help of coefficient called bentler-bonett fit index (NNFI or TLI). A scale with TLI values of 0.9 or above is an indication of strong convergent validity. It has been observed that TLI values of each construct as well as overall TLI values are more than 0.90, and this indicate strong convergent validity of the instrument.
Unidimensionality analysis
Unidimensionality is a necessary condition for reliability analysis and construct validation. Items in a uni-dimensional scale estimate one single construct. In the absence of uni-uni-dimensionality a single member cannot be used to represent the value of the scale. One can reduce the problems associated with unidimensionality by carefully selecting the items in the scales This may warrant removing those items from the scales that reduce extent of uni-dimensionality. CFA can be used to access the uni-dimensionality of the scale. To use CFA a measurement model is specified for each construct. In this model, individual items constituting the construct are examined to see how closely they represent the same item. Comparative Fit Index (CFI) of 0.90 or higher for the model suggests that there is no evidence of lack of unidimensionality. The CFI for all the three constructs are computed by using AMOS software version-7 and the results are given in the table. It has been observed that all the CFI values for the individual constructs well above 0.90 and moreover the overall CFI value is 0.934, which indicates strong uni-dimensionality.
Grouping on the basis of One Sigma Limit
The respondents whose mean score is less than the lower limit is included in the satisfactory group, the mean score lies in between the lower and upper limit is classified as good and those having their score greater than the upper limit is classified as excellent.
Table 6 Statistical analysis of upper and lower limit of responses
Minimu
m Maximum Mean
Std. Deviation
Lower Limit
(Mean-SD)
Upper Limit
(Mean+SD)
Planning 8.00 36.00 18.7083 7.56835 11.14 26.28
Material 7.00 30.00 15.6667 6.12545 9.54 21.79
Planning
The survey indicated that, about 23.6 percent of the project has an excellent planning Score and another 51.4 percent has good planning Score and the remaining 25 percent are only satisfactory Score.
Table 7 Statistical analysis of planning
Frequency Percent Valid Percent Cumulative Percent
Valid Satisfactory 18 25.0 25.0 25.0
Good 37 51.4 51.4 76.4
Excellent 17 23.6 23.6 100.0
Figure.5 graphical Model of survey responses
Table 8 Statistical analysis of model
Frequency Percent Valid Percent Cumulative Percent
Valid Satisfactory 9 12.5 12.5 12.5
Good 48 66.7 66.7 79.2
Excellent 15 20.8 20.8 100.0
Total 72 100.0 100.0
Materials
In the case of material 20.8 percent has an excellent score, 66.7 percent has good score and the remaining 12.5 percent has satisfactory score.
Planning * Materials Cross tabulation
Table 9 Material tabulation
Among the projects having an excellent score in planning 70.6 percent has got excellent score in materials also and the remaining 29.4 percent got satisfactory score. In the case of projects having good planning score, 8.1 percent got excellent score in materials and 91.9 percent got good score for materials. In both the above cases no project has satisfactory score in materials. It is easy to verify that any projects having satisfactory score in planning got excellent score in materials. Also it is easy to verify that among the project who has satisfactory score planning 50 percent has got good score and another 50 percent got satisfactory.
From the study it can be inferred that
1. Less than 25 percent of the projects have excellent score in both planning and materials 2. The excellent in planning results in excellent material management.
Planning
Table 10 Material percent
Data Analysis.
The answers to question, depending on whether they lead to efficient planning/ procurement practices or not, have been grouped into Excellent (E), Good (G), Satisfactory (S), and Poor (P). Considering the entire questionnaire, (15Nos) the value of Crobach’s alpha has a value of 0.84. When the questions are grouped into two, namely planning (first eight) and procurement (remaining seven), the questions on planning gave an alpha value of 0.76 and those on procurement gave an alpha value of 0.67, which is close to the acceptance limit of 0.7. Hence as a whole, it can be said that the questions have relatively high internal consistency.
Table 11.
Correlation Coefficient of Question Nors.1 to 8
The degree of linear relationship between the questions has been checked with the help of Pearson’s Correlation Coefficient. The correlation matrix of the first eight questions (Planning) and that for the remaining seven questions (Procurement aspect) are presented separately in table12 and 13.
Table 12 Correlation coefficient of question nos. 9 to 15
It can be visualized from these tables that there is no strong linear relationship between the questions in both the groups. Responses from first eight questions, which relate to planning area, grouped in to different category have been presented in table No,4
Table 13 Response of questions from planning area No.1 to 8
Q. No Excellent Good Satisfactory Poor
1. 7 21 21 51
2. 12 24 24 40
3. 14 24 21 42
4. 15 21 21 43
5. 14 15 20 51
6. 15 22 17 46
7. 15 15 27 46
8. 15 15 18 52
From table 4 it can be seen that only 7 percent of the projects adopted correct measures to control the time and cost overrun at the preparation of project estimation stage, whereas 51percent projects did not give importance
Frequency Percent
Valid Percent
Cumulative Percent
Valid Satisfactory 18 25.0 25.0 25.0
Q. Nos. Q.9 Q.10 Q.11 Q.12 Q.13 Q14 Q.15
Q.9 1.000 0.356 0.172 0.299 0.140 0.028 0.123
Q.10 0.356 1.000 0.298 0.554 0.233 0.094 0.391
Q.11 0.172 0.298 1.000 0.533 0.130 - 0.011 0.057
Q.12 0.299 0.554 0.533 1.000 0.333 0.258 0.225
Q.13 0.140 0.233 0.130 0.333 1.000 0.174 0.250
Q.14 0.028 0.094 -0.011 0.258 0.174 1.000 0.144
in the preparation of even the project estimate. Further, only 15percent projects carried out systematic scheduling methods, Responses from the last seven questions, on being procurement and carrying area, grouped into different category, have been presented in table 5. In general, it could be concluded that, about 7 to 15 percent projects applied appropriate methods to control cost and time overrun.35 percent to 40 percent of the projects come under good to satisfactory grading, even though they might lead to cost and time overrun, and could be improved by implementation of appropriate planning and scheduling methods. However, it must be noted that 40 to 50 percent of the projects did not have any measure to control time and cost over-run in their planning and scheduling stage. The responses for questions 1 to 8 are shown in percentages as a histogram to understand easily
Table No.1 4 Responses of Question from procurement and carrying area No.9 to 15
Q. No Excellent Good Satisfactory Poor
9. 12 21 21 46
10. 8 15 20 57
11. 14 21 22 43
12. 13 19 18 50
13. 14 17 22 47
14. 11 15 22 52
15. 13 18 23 46
On analysis of procurement and carrying, it may be seen that 12 percent of the projects were planned, for the procurement of materials in the Economic stage order level, where as 46 percent of the projects did not take any steps to control the ordering cost and carrying cost. It may be noted that there was a stretch between 21 to 42percent in the grading range of satisfactory to good which could be improved during implementation of the project to the optimum cost of the project, provided sufficient precautionary measures were taken to adopt Engineering and Management techniques.
In the case of carrying cost, only 14 percent of the projects were seen to have planned successfully, where as 43 percent of the projects not at all take any step to adopt any planning measures to achieve optimum cost level, and such projects were likely to incur excess cost and time, whereas 42 to 57 percent of the projects came under the grading of satisfactory to good which could be improved if sufficient precautionary measures would be taken during implementation. The responses for questions 9 to 15 are shown in percentages as a histogram to understand easily
Figure. 7 Percentage of Responses of Q9 to Q15.
From table 5, it could be observed that 12 percent of the projects used appropriate methods to reduce the cost and time overrun in the procurement of construction materials, whereas 46 percent of the projects did not consider the method of procurement as an important factor leading to cost and time over-run.
It may be noted that, while 14 percent of the projects were planned properly to replenish the materials without any buffer stock, 43 percent projects did plan any replenishment strategy and keep excess materials on store. From the analysis it could be visualized that 10 percent to 15 percent of the projects planned the material management whereas 30 percent to 40 percent projects, though they had insufficient planning, could be improved by taking proper measures during the time of procurement. 40 to 50 percent of the projects performed very poorly.
Based on the present study, the following conclusions could be arrived at. Since the performance in both planning and procurement of materials are more or less equal, it is clearly understood that well planned projects will yield better performance in material management.
Conclusion
References-
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