QUANTITATIVE ANALYSIS OF TIME OVERRUNS IN TRANSPORT INFRASTRUCTURE PROJECTS

Transcription

1 QUANTITATIVE ANALYSIS OF TIME OVERRUNS IN TRANSPORT INFRASTRUCTURE PROJECTS João Guedes de Miranda Extended abstract Examination Committee Chairperson: Prof. Ana Paula Patrício Teixeira Ferreira Pinto França de Santana Supervisor: Prof. Vítor Faria e Sousa Supervisor: Prof. Nuno Gonçalo Cordeiro Marques de Almeida Members of the Committee: Eng. Sónia Cristina Simões Madeira Domingues May 2014

2 Quantitative Analysis of Time Overruns In transport Infrastructure Projects João Guedes de Miranda Instituto Superior Técnico, Technical University of Lisbon Abstract: Regardless the level of economic development of a country, time overruns are frequent and represent a major problem in construction projects. Regarding road projects, construction time overruns are among the most significant problems. In fact, timely completion of road projects is a priority due to the utility of this type of infrastructures and the sensibility of several of the construction activities involved to the weather conditions. Apart from the precision of project schedule estimation, delays are almost inevitable. Time overruns implications, such as heavy liquidated damages (LDs) for lateness to contractors, leads to an improvement in this investigation. This study intends to gather main causes related to construction projects and analyze if time overruns are explained by specific causes in each project instead of general variables. To provide basis for the hypotheses and to provide a methodology to support the quantitative risk management of time deviations this study is based on data from Virginia road projects, in U.S.A. Based on a sample of 739 transport infrastructure projects, this study aims to connect time overruns with several variables like contract proposals, original contract period, actual contract period, type of project and project size. The methodology fits time overrun data into an adjusted probability distribution. This approach concedes an opportunity to perform a risk management analysis of time delays following alternative practical ways: allows the owner to quantify a contingency fund for project duration, or assist contractors to prepare a proposal. Keywords: risk management; construction management; time overruns; forecasting; statistics; road construction projects. I Introduction For several companies, the main targets are minimizing schedule and costs (Wei, 2010). These targets are applied to transport infrastructure projects. In order to achieve schedule objective, it is essential to have a reliable forecast of project completion time in order to make an accurate forecast of project duration to set the pace of performance and able to revise it through time (Ahuja e Nandakumar, 1985). However, despite an accurate project management, usually there are time overruns in construction projects. Time overruns implications, such as heavy liquidated damages (LDs) for lateness to contractors, leads to an improvement in this investigation. Despite of this concern from stakeholders, time overruns still represent meaningful values. According to an audit by Tribunal de Contas in 2009, the five largest public works projects built between 1999 and 2008 resulted in an increased cost of approximately 241 M to the Portuguese State. In Portugal the deviations of time have been reflected in a general practice, and the delays in these projects were from 1.4 to 4.6 years representing an increase of 25% to 295% of the initial value of the contract. This study intends to analyze if time delays are related to general variables or based on specific causes to each project. Furthermore, it is set to establish a methodology and auxiliary process for risk management in transport infrastructure projects. 1

3 II Project time deviation According to Bordat et al. (2004), a project time delay is the difference between a project s original contract period and the time of bidding and its overall actual contract period at the end of construction. There are countless causes of time deviations, in Table 2 are shown some of the major causes gathered by several authors. When a contractor is responsible for project delays, it may result in liquidated damages, so it is relevant to identify sources and types of delays in order to determine the effect of a delay on the project. Those delays can be categorized in four types: Critical or noncritical; excusable or non-excusable; compensable or non-compensable; concurrent or non-concurrent. Transport infrastructures have their specific conditions and main causes vary from other project types. Based on Ellis e Thomas (2003), in Table 1 are summarized the most frequent causes in transport infrastructure projects, ranked by owners and contractors. Table 1 Most frequent reason for delays according to Ellis e Thomas (2003). Rank Owner Contractor 1º Utility Relocations Utility Relocations 2º 3º Different Site Conditions Errors in Plans and Specifications Errors in Plans and Specifications Different Site Conditions 4º Weather Weather 5º Permitting Issues Owner Requested Changes A contingency fund is the amount of resources beyond the initially estimated required to minimize the risk of overrun of a project. Although not as usual, a contingency period of time can be applied for time overruns. Ranasinghe (1994a) defines a contingency for the duration of a project as the amount of time to be included in a planning to cover unforeseen delays. Despite several authors estimates and quantifications, there isn t a unified quantification to account this contingency period. Assaf e Al-Hejji (2006) studied the causes of delays in construction projects in Saudi Arabia. Analyzed the different perspectives of the owners, contractors and designers and suggested measures to control and minimize delays in construction. As a conclusion of the study, the authors present the following recommendations: Contractors must ensure enough number of labors and keep them motivated to improve productivity; manage his financial resources and plan cash flow by utilizing progress payment; planning and scheduling must be seen as continuing processes during construction and match with the resources and time to develop the work; and site management and supervision have to be assigned as soon as project Is awarded; Owners should pay progress payment on the contractor on time; minimize change orders during construction; avoid delay in reviewing and approving of design documents; and check for resources and capabilities before awarding the contract to the lowest bidder. Designers must set a schedule to produce design documents on time and take special attention to mistakes and discrepancies to avoid redoing designs and drawings. 2

4 Al-Kh alil e Al-Gh afly, 1999, Alwi, 2003, Chan e K umaraswamy, 1997, D awood, 1998, Aibinu e Od eyinka, 2006, Al-Kh arash i e Skitmore, 2009, Arditi et al., 1985, Assaf et al., 1995, M D lakwa e F Culpin, 1990, Al T abtab ai, 2002, Abd El-R azek et al., 2008) Table 2 Time overrun causes in several type of projects. ( Cause Type of project Location Author Unforeseen ground conditions Financial difficulties (owners, contractors or subcontractors) Contractors difficulties in receiving plaments from owner Subcontractor issues Inadequate or incomplete designs Lack of resources Inexperienced contractor Fluctuation in labour productivity and price escalation Frequent change orders, calendarizacao inadequada Materials shortage Shortage of laborers Owner slow decision making Interference between client and contractor Poor risk management and supervision Inadequate construction planning Industrial water treatment 3 General Industrial water treatment General Industrial water treatment General South Arabia Al-Khalil e Al-Ghafly 1999 Hong Kong Alwi e Hampson 2003 Indonesia Chan e Kumaraswamy 1997 United Kingdom Dawood 1998 Aibinu e Odeyinka 2006 South Arabia Nigeria Turkey South Arabia Egypt Kuwait Nigeria Al-Karashi e Skitmore 2009 Al-Khalil e Al-Ghafly 1999 Arditi et al 1985 Assaf et al 1995 Aibinu e Odeyinka 2006 Al-Karashi e Skitmore 2009 Al-Khalil e Al-Ghafly 1999 Al-Tabtabai 2002 Assaf et al 1995 El-Razek et al 2008 Nigeria Aibinu e Odeyinka 2006 General United Kingdom Dawood 1998 South Arabia Aibinu e Odeyinka 2006 General General Industrial water treatment General Indonesia Al-Tabtabai 2002 Kuwait Alwi e Hampson 2003 Nigeria Assaf et al 1995 United Kingdom Dawood 1998 Nigeria Hong Kong Turkey South Arabia Kuwait Turkey Aibinu e Odeyinka 2006 Arditi et al 1985 Chan e Kumaraswamy 1997 Al-Khalil e Al-Ghafly 1999 Al-Tabtabai 2002 Arditi et al 1985 Assaf et al 1995 Edificios Nigeria Aibinu e Odeyinka 2006 General United Kingdom Dawood 1998 Al-Karashi e Skitmore 2009 Industrial water treatment General South Arabia Egypt Hong Kong Indonésia Nigeria Turkey Al-Khalil e Al-Ghafly 1999 Alwi e Hampson 2003 Arditi et al 1985 Chan e Kumaraswamy 1997 El-Razek et al 2008 Nigeria Aibinu e Odeyinka 2006 General United Kingdom Dawood 1998 Industrial water treatment General General General South Arabia Kuwait Nigeria Al-Khalil e Al-Ghafly 1999 Al-Tabtabai 2002 Assaf et al 1995 South Arabia Al-Tabtabai 2002 Egypt Alwi e Hampson 2003 Indonesia Arditi et al 1985 Kuwait Assaf et al 1995 Turkey El-Razek et al 2008 South Arabia Assaf et al 1995 Egypt Chan e Kumaraswamy 1997 Hong Kong Nigeria El-Razek et al 2008 Hong Kong Alwi e Hampson 2003 Indonesia Chan e Kumaraswamy 1997 Industrial water treatment South Arabia Al-Khalil e Al-Ghafly 1999 General Nigeria

5 III Data analysis The sample was obtained from Virginia Department of Transportation (VDOT) database. The projects were collected by date, throughout fiscal years of 1999 and 2000, totaling 739 projects. To analyze time delays, the general description is divided into categories such as: project type, road system, project location, chronological order, original contract period, actual contract period. In parallel to project type, it is also considered the number of contract proposals. Table 3 summarizes the descriptive statistics of time delay. Table 3 Descriptive statistics of time delay. Average In light of construction processes, in all those categories mentioned above, project type reveals to be outstanding from others and so, it is assumed that time delays should considered separately. Original Duration (days) Actual Duration (days) Sample 739 Time Delay (%) Minimum % Maximum % Sum Value % Std. Error % Standard Deviation Skewness Kurtosis Value Std. Error Value Erro Padrão Given the focus to project type, when appropriated all analysis are segregated by type. Those types are: new roads; new bridges and viaducts; roads rehabilitation; bridges and viaducts rehabilitation; shoulder and gardens rehabilitation; and others/mixed projects. This sample contains 49.8% of projects which had time overruns. Divided by type of work, the average time overrun for new roads (59 projects) is 13.9%, new bridges and viaducts (37) is 14.7%, rehabilitation of roads (416) is 10.3%, rehabilitation of bridges and viaducts (122) is 7.1% and for other types of roads (42) is 18.6%. Number of contract proposals has an average of 4.8 and 15 proposals as maximum. Projects are separetad in the following road systems: interstate; primary; secondary; other; and various systems. Figure 1 show clearly that majority of projects are primary and secondary roads. In terms of location, the sample is fairly distributed, as it can be seen in Figure 2. Finally to mention chronological order, the range of projects per month is between 14 and 43, with an average of 31 projects per month. Various 4% S 42% IS 14% P 31% O 9% Figure 1 Sample distribution by road system. 4

6 Actual Period (days) V Statistical analysis Bristol Culpeper IV.1 General factors analysis The previous sample had several statistical 13% 16% 14% 8% 15% 11% 1 8% 5% Fredericksburg Hampton Roads Lynchburg Northern Virginia Richmond Salem outliers, without those outliers our new sample has 657 projects to be analyzed. This chapter aims to verify if the assumption that time overruns can t be explained by independent variables common to all projects. Staunton Figure 2 Sample distribution by location. Original contract period and actual contract period are illustrated in Figure 3 sectioned by half a year, one, two, four and over four years. Approximately 87% of projects had an original contract time less than one year. In Figure 4 is illustrated time delay by duration intervals, it is possible to observe a project with 725% time delay as maximum > 1460 Contract Period (days) Figure 3 Distribution of contract period by intervals Original period Figure 5 displays the relation between original contract period and actual contract period. In this relation, a linear regression model is has the following equation: (4.1) In addition we obtained a coefficient of determination of Actual Period Original Period (days) Figure 5 Linear regression of actual contract period and original contract period. Figure 6 represents the histogram, in logarithmic scale, of number of projects in function of time delays. It can be seen clearly a positive skewness and there is a tendency for the actual contract period to be longer than original contract period. Figure 4 Boxplot of time delay by duration intervals. In Figure 7 is represented the influence of project size in time overrun. For better understanding, size is classified into 4 classes. There might be a slight indication that a higher class (larger project considering original contract time) has less time overruns, but a linear regression obtains a coefficient of 5

7 Time delay (%) Time delay (%) Time delay (%) Time Delay (%) Time delay (%) Time delay (%) Numberm of projects Time delay (%) determination of It can t be assumed that time delays are explained by project size y = 8E-08x + 0, R² = 0, $- $1,0 $2,0 $3,0 - Proposals standard deviation (millions) Figure 9 Delay by proposals standard deviation Time Delay (%) Figure 6 Histogram of time delays Figure 7 Distribution of time delay by project size. Contract proposals are analyzed considering the amount of proposals, standard deviation, maximum value, minimum value, medium value and range of values. In FIGURES 5 through 10, is illustrated the lack of relation between these variables and time delay, unable to explain any delay. y = 0,0276x + 0,0055 R² = 0, ,00 1,00 2,00 3,00 4, Classes of project size y = -0,0135x + 0,1091 R² = 0, Number of proposals Figure 8 Distribution of delay by number of proposals y = 4E-08x + 0,0093 R² = 0, $- $2 $4 $6 $8 Proposals maximum value (millons) Figure 10 Delay by proposals maximum value Figure 11 Delay by proposals minimum value. Figure 12 Delay by proposals average value. y = 6E-08x + 0,008 R² = 0, $- $5 $10 - Proposals minimum value (milions) y = 5E-08x R² = 0, $- $5 - Proposals average value (millions) y = 4E-08x + 0,0356 R² = 0, $- $2,0 $4,0 - Proposals value range (millions) Figure 13 Distribution of delay by proposals range values. 6

8 Previous analysis proves the main hypothesis. Time delays can t be explained or predicted by common factors to all projects, however, the actual contract time can be considered, approximately, 7% larger than original contract time. IV.2 Time overrun management Based on the assumption that time deviations may be caused by specific factors associated to each project, it is intended to provide a methodology and a basis to support the quantitative risk management of time deviations. It is admitted that, in similar projects, it is possible to forecast time overrun based on historical data. Standing on this hypothesis, this chapter pretends to fit time overrun into an adjusted probability distribution, using Oracle Crystal Ball software. In this analysis the focus is set relating current project with previous ones and knowledge of probable causes capable of leading to a time delay is useful. By fitting a probability distribution it is possible to estimate time overrun linked to a probability density value. The criteria to select a density probability value can be established based on risk management variables. Main causes for time delays can be used, for example, to set a risk level to current project by considering combinations between those causes. distribution, defined by average 1% and scale 18%. Figure 14 Statistical adjustment of time overruns of general contracts. For road rehabilitation projects, the distributions which gives the best fitting resutls is logistic distribution, defined by average 1% and scale 18%. Can be illustrated in Figure 15. Figure 15 Statistical adjustment of time overruns of road rehabilitation contracts. For bridges and viaducts rehabilitation projects, the distributions which gives the best fitting resutls is logistic distribution, defined by average -1% and scale 15%. Can be illustrated in Figure 16. Given the fact that project conditions may be different in each project type, it is important fit distributions to each type. Figure 14 displays adjusted fits of probability distributions and probability density distributions for general projects. For general projects, the distributions which gives the best fitting resutls is logistic Figure 16 Statistical adjustment of time overruns of bridges and viaducts rehabilitation contracts. 7

9 For shoulders and gardens rehabilitation projects, the distributions which gives the best fitting resutls is log-normal distribution, defined by average 16%, standard deviation 46% and localisation -58%. Can be illustrated in Figure 17. Figure 19 Statistical adjustment of time overruns of new bridges and viaducts construction contracts. Figure 17 Statistical adjustment of time overruns of shoulders and gardens rehabilitation contracts. For mixed/other projects, the distributions which gives the best fitting resutls is Gumbel distribution, defined by likeliest -8% and scale 33%. Can be observed in Figure 20. For road construction projects, the distributions which gives the best fitting resutls is Gumbel distribution, defined by likeliest -12% and scale 28%. As shown in Figure 18. Figure 20 Statistical adjustment of time overruns of mixed and other constructions contracts. Figure 18 Statistical adjustment of time overruns of new roads construction contracts. For bridges and viaducts construction projects, the distributions which gives the best fitting resutls is logistic distribution, defined by average 12% and scale 15%. As illustrated infigure 19. In every project type, the fitting revealed to be weak by applying Anderson Darling, Kolmogorov-Smirnov, and Chi-squared tests. Despite weak fittings, the adopted methodology is still useful. In case of future developments, the sample can be extended and fittings can slightly improve. But still, with the actual sample, it is possible to obtain decent results from the probability density distributions. 8

10 V Conclusions This dissertation aimed to contribute to a better understanding of time overrun causes and related factors. In addition is set to provide a methodology and a basis to support the quantitative risk management of time deviations. A correlation between time overruns and several factors common to all projects revealed to be null. As result, it is presumed that these variables aren t statistically relevant to time delay. As an exception, actual project duration appears to be approximately 7% larger than original project duration. Although there is no statistical relationship between the different factors and time delay, it is proposed that it is possible to use historical information of time delay from previous projects to forecast time delays in future projects. This approach concedes an opportunity to perform a risk management analysis of time delays following alternative practical ways: Converting qualitative risk analyses into statistically representative quantitative amounts for each type of project by matching each level of the qualitative scale used to a time overrun distribution probability As another practical example, it is able to verify results of quantitative risk analyses, by analyzing whether the percentile of the time overrun distribution is coherent with the specific characteristics of the contract. References ABD EL-RAZEK, M., BASSIONI, H. & MOBARAK, A Causes of Delay in Building Construction Projects in Egypt. Journal of Construction Engineering and Management, 134, AHUJA, H. N. & NANDAKUMAR, V Simulation model to forecast project completion time. Journal of construction engineering and management, 111, AIBINU, A. & ODEYINKA, H Construction Delays and Their Causative Factors in Nigeria. Journal of Construction Engineering and Management, 132, AL-KHALIL, M. I. & AL-GHAFLY, M. A Delay in public utility projects in Saudi Arabia. International Journal of Project Management, 17, AL-KHARASHI, A. & SKITMORE, M Causes of delays in Saudi Arabian public sector construction projects. Construction Management and Economics, 27, AL TABTABAI, H. M Causes for delays in construction projects in Kuwait. ALWI, S. H., K Identifying the important causes of delays in building construction projects. Proceedings The 9th East Asia- Pacific Conference on Structural Engineering and Construction, Bali, Indonesia. ARDITI, D., AKAN, G. T. & GURDAMAR, S Reasons for delays in public projects in Turkey. Construction Management and Economics, 3, ASSAF, S., AL-KHALIL, M. & AL-HAZMI, M Causes of Delay in Large Building Construction Projects. Journal of Management in Engineering, 11, BORDAT, C., SINHA, K. C. & LABI, S An Analysis of Cost Overruns and Time Delays of INDOT Projects, Joint Transportation Research Program. CHAN, D. W. M. & KUMARASWAMY, M. M A comparative study of causes of time overruns in Hong Kong construction projects. International Journal of Project Management, 15, DAWOOD, N Estimating project and activity duration: a risk management approach using network analysis. Construction Management and Economics, 16, ELLIS, R. D. & THOMAS, H. R. The root causes of delays in highway construction. 82nd Annual meeting of the transportation research board, M DLAKWA, M. & F CULPIN, M Reasons for overrun in public sector construction projects in Nigeria. International Journal of Project Management, 8, WEI, K. S Causes, Effects and Methods of Minimizing Delays in Construction Projects. Bachelor, Universiti Teknologi Malaysia, Skudai. 9